Snap Patent | Generative model for suggesting image modifications
Patent: Generative model for suggesting image modifications
Patent PDF: 20250131623
Publication Number: 20250131623
Publication Date: 2025-04-24
Assignee: Snap Inc
Abstract
Methods and systems are disclosed for suggesting modifications for an image using one or more machine learning models. The methods and systems select, by an interaction application, an individual content item from a plurality of previously captured content items that matches one or more criteria corresponding to sharable content and generate a prompt comprising the individual content item and a request for a plurality of suggested modifications to the individual content item. The methods and systems process the prompt by a large language model (LLM) to generate the plurality of suggested modifications to the individual content item and generate a modified individual content item corresponding to an individual suggested modification of the plurality of suggested modifications.
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Description
CLAIM OF PRIORITY
This application is a non-provisional of and claims priority to U.S. Provisional Application No. 63/592,397, filed Oct. 23, 2023, which is hereby incorporated by reference herein in its entirety.
TECHNICAL FIELD
The present disclosure relates generally to operating an interaction application for sharing content items.
BACKGROUND
Augmented reality (AR) is a virtual modification of a real environment. For example, in virtual reality (VR), a user is completely immersed in a virtual world, whereas in AR, the user is immersed in a world where virtual objects are combined or superimposed on the real world. An AR system aims to generate and present virtual objects that interact realistically with a real-world environment and with each other. Examples of AR applications can include single or multiple player video games, instant messaging systems, and the like. In general, these AR and/or VR systems are referred to as extended reality (XR) systems.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
In the drawings, which are not necessarily drawn to scale, like numerals may describe similar components in different views. To easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the figure number in which that element is first introduced. Some nonlimiting examples are illustrated in the figures of the accompanying drawings in which:
FIG. 1 is a diagrammatic representation of a networked environment in which the present disclosure may be deployed, according to some examples.
FIG. 2 is a diagrammatic representation of a messaging system that has both client-side and server-side functionality, according to some examples.
FIG. 3 is a diagrammatic representation of a data structure as maintained in a database, according to some examples.
FIG. 4 is a diagrammatic representation of a message, according to some examples.
FIG. 5 illustrates an example architecture for applying a personalized personal artificial intelligence (AI) agent to generate sharable content items, according to some examples.
FIGS. 6A, 6B, 7A, 7B, and 8 are diagrammatic representations of example inputs and outputs of the sharable content item feed system, in accordance with some examples.
FIGS. 9, 10A and 10B are flowcharts illustrating example operations and methods of the sharable content item feed system, in accordance with some examples.
FIG. 11 is a diagrammatic representation of a machine in the form of a computer system within which a set of instructions may be executed to cause the machine to perform any one or more of the methodologies discussed herein, in accordance with some examples.
FIG. 12 is a block diagram showing a software architecture within which examples may be implemented.
FIG. 13 illustrates a system in which a head-wearable apparatus may be implemented, in accordance with some examples.
DETAILED DESCRIPTION
The description that follows includes systems, methods, techniques, instruction sequences, and computing machine program products that embody illustrative examples of the disclosure. In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide an understanding of various examples. It will be evident, however, to those skilled in the art, that examples may be practiced without these specific details. In general, well-known instruction instances, protocols, structures, and techniques are not necessarily shown in detail.
Typically, various communication platforms allow users to share content and create images for transmission to other users. These images can be used to promote products or services and/or to simply represent different real-world objects in simulated or real environments. However, these systems require a user to use expensive equipment and technology to create high-quality, appealing images. Also, users may spend a great deal of effort meticulously finding images/videos to actually share. Once such images/videos are ultimately found, users spend even more time and effort placing objects in different environments and manually adjusting lighting and other image attributes to enhance the presentation of the objects in the images. All of these factors can add up to make the creation of high-quality images (e.g., for sharing with other users) a significant expense and detract from the overall use and enjoyment of the system. In addition, because users may not have the resources needed to create high-quality images, opportunities to share and present objects in ideal settings are missed.
As generative machine learning (ML) models become widespread, the ability to program new tasks using generative ML models becomes easier to do. Namely, generative ML models (e.g., AI that generates content) are currently popular methods for programming certain operations and actions and can potentially generate content from two-dimensional (2D) models, three-dimensional (3D) models, code, and more. However, in order to properly operate these generative ML models and obtain results that are satisfactory, specific prompts need to be provided along with specific types of inputs. Generating these prompts and selecting the inputs to drive the generative ML models is very time consuming and thus expensive, which further detracts from their overall use and enjoyment. As such, users typically avoid using these generative ML models to enhance content items to be shared with other users.
The disclosed techniques seek to improve the efficiency of using an electronic device by intelligently and automatically selecting content items that a user may be interested in sharing, and then automatically processing the selected content items by a generative ML model and/or a large language model (LLM) to modify and improve various visual aspects of the selected content items. This can reduce the overall time and expense incurred to identify and generate content items to share with other users. In addition, the disclosed techniques leverage learned information about users to dynamically generate context-sensitive prompts for the generative ML models to personalize the modifications that the generative ML models perform for each individual content item in the selected content items.
For example, the disclosed techniques select, by an interaction application, an individual content item, from a plurality of previously captured content items, that matches one or more criteria corresponding to sharable content and generate a prompt comprising the individual content item and a request for a plurality of suggested modifications to the individual content item. The disclosed techniques process the prompt by the LLM to generate the plurality of suggested modifications to the individual content item and generate a modified individual content item corresponding to an individual suggested modification of the plurality of suggested modifications. In this way, the disclosed techniques improve the overall experience of the user in using the electronic device and reduce the overall amount of resources needed to accomplish a task of producing high-quality and unique sharable content items.
Networked Computing Environment
FIG. 1 is a block diagram showing an example interaction system 100 for facilitating interactions (e.g., exchanging text messages, conducting text audio and video calls, or playing games) over a network. The interaction system 100 includes multiple user systems 102, each of which hosts multiple applications, including an interaction client 104 and other applications 106. Each interaction client 104 is communicatively coupled, via one or more communication networks including a network 108 (e.g., the Internet), to other instances of the interaction client 104 (e.g., hosted on respective other user systems 102), an interaction server system 110 and third-party servers 112). An interaction client 104 can also communicate with locally hosted applications 106 using Applications Program Interfaces (APIs).
Each user system 102 may include multiple user devices, such as a mobile device 114, head-wearable apparatus 116, and a computer client device 118 that are communicatively connected to exchange data and messages.
An interaction client 104 interacts with other interaction clients 104 and with the interaction server system 110 via the network 108. The data exchanged between the interaction clients 104 (e.g., interactions 120) and between the interaction clients 104 and the interaction server system 110 includes functions (e.g., commands to invoke functions) and payload data (e.g., text, audio, video, or other multimedia data).
The interaction server system 110 provides server-side functionality via the network 108 to the interaction clients 104. While certain functions of the interaction system 100 are described herein as being performed by either an interaction client 104 or by the interaction server system 110, the location of certain functionality either within the interaction client 104 or the interaction server system 110 may be a design choice. For example, it may be technically preferable to initially deploy particular technology and functionality within the interaction server system 110 but to later migrate this technology and functionality to the interaction client 104 where a user system 102 has sufficient processing capacity.
The interaction server system 110 supports various services and operations that are provided to the interaction clients 104. Such operations include transmitting data to, receiving data from, and processing data generated by the interaction clients 104. This data may include message content, client device information, geolocation information, media augmentation and overlays, message content persistence conditions, entity relationship information, and live event information. Data exchanges within the interaction system 100 are invoked and controlled through functions available via user interfaces (UIs) of the interaction clients 104.
Turning now specifically to the interaction server system 110, an API server 122 is coupled to and provides programmatic interfaces to interaction servers 124, making the functions of the interaction servers 124 accessible to interaction clients 104, other applications 106, and third-party server 112. The interaction servers 124 are communicatively coupled to a database server 126, facilitating access to a database 128 that stores data associated with interactions processed by the interaction servers 124. Similarly, a web server 130 is coupled to the interaction servers 124 and provides web-based interfaces to the interaction servers 124. To this end, the web server 130 processes incoming network requests over Hypertext Transfer Protocol (HTTP) and several other related protocols.
The API server 122 receives and transmits interaction data (e.g., commands and message payloads) between the interaction servers 124 and the user systems 102 (and, for example, interaction clients 104 and other applications 106) and the third-party servers 112. Specifically, the API server 122 provides a set of interfaces (e.g., routines and protocols) that can be called or queried by the interaction client 104 and other applications 106 to invoke functionality of the interaction servers 124. The API server 122 exposes various functions supported by the interaction servers 124, including account registration; login functionality; the sending of interaction data, via the interaction servers 124, from a particular interaction client 104 to another interaction client 104; the communication of media files (e.g., images or video) from an interaction client 104 to the interaction servers 124; the settings of a collection of media data (e.g., a story); the retrieval of a list of friends of a user of a user system 102; the retrieval of messages and content; the addition and deletion of entities (e.g., friends) to an entity relationship graph (e.g., the entity graph 310); the location of friends within an entity relationship graph; and opening an application event (e.g., relating to the interaction client 104).
The interaction servers 124 host multiple systems and subsystems, described below with reference to FIG. 2.
Linked Applications
Returning to the interaction client 104, features and functions of an external resource (e.g., a linked application 106 or applet) are made available to a user via an interface of the interaction client 104. In this context, “external” refers to the fact that the application 106 or applet is external to the interaction client 104. The external resource is often provided by a third party but may also be provided by the creator or provider of the interaction client 104. The interaction client 104 receives a user selection of an option to launch or access features of such an external resource. The external resource may be the application 106 installed on the user system 102 (e.g., a “native app”), or a small-scale version of the application (e.g., an “applet”) that is hosted on the user system 102 or remote of the user system 102 (e.g., on third-party servers 112). The small-scale version of the application includes a subset of features and functions of the application (e.g., the full-scale, native version of the application) and is implemented using a markup-language document. In some examples, the small-scale version of the application (e.g., an “applet”) is a web-based, markup-language version of the application and is embedded in the interaction client 104. In addition to using markup-language documents (e.g., a .*ml file), an applet may incorporate a scripting language (e.g., a .*js file or a .json file) and a style sheet (e.g., a .*ss file).
In response to receiving a user selection of the option to launch or access features of the external resource, the interaction client 104 determines whether the selected external resource is a web-based external resource or a locally-installed application 106. In some cases, applications 106 that are locally installed on the user system 102 can be launched independently of and separately from the interaction client 104, such as by selecting an icon corresponding to the application 106 on a home screen of the user system 102. Small-scale versions of such applications can be launched or accessed via the interaction client 104 and, in some examples, no or limited portions of the small-scale application can be accessed outside of the interaction client 104. The small-scale application can be launched by the interaction client 104 receiving, from a third-party server 112, for example, a markup-language document associated with the small-scale application and processing such a document.
In response to determining that the external resource is a locally-installed application 106, the interaction client 104 instructs the user system 102 to launch the external resource by executing locally-stored code corresponding to the external resource. In response to determining that the external resource is a web-based resource, the interaction client 104 communicates with the third-party servers 112 (for example) to obtain a markup-language document corresponding to the selected external resource. The interaction client 104 then processes the obtained markup-language document to present the web-based external resource within a UI of the interaction client 104.
The interaction client 104 can notify a user of the user system 102, or other users related to such a user (e.g., “friends”), of activity taking place in one or more external resources. For example, the interaction client 104 can provide participants in a conversation (e.g., a chat session) in the interaction client 104 with notifications relating to the current or recent use of an external resource by one or more members of a group of users. One or more users can be invited to join in an active external resource or to launch a recently used but currently inactive (in the group of friends) external resource. The external resource can provide participants in a conversation, each using respective interaction clients 104, with the ability to share an item, status, state, or location in an external resource in a chat session with one or more members of a group of users. The shared item may be an interactive chat card with which members of the chat can interact to, for example, launch the corresponding external resource, view specific information within the external resource, or take the member of the chat to a specific location or state within the external resource. Within a given external resource, response messages can be sent to users on the interaction client 104. The external resource can selectively include different media items in the responses, based on a current context of the external resource.
The interaction client 104 can present a list of the available external resources (e.g., applications 106 or applets) to a user to launch or access a given external resource. This list can be presented in a context-sensitive menu. For example, the icons representing different ones of the application 106 (or applets) can vary based on how the menu is launched by the user (e.g., from a conversation interface or from a non-conversation interface).
System Architecture
FIG. 2 is a block diagram illustrating further details regarding the interaction system 100, according to some examples. Specifically, the interaction system 100 is shown to comprise the interaction client 104 and the interaction servers 124. The interaction system 100 embodies multiple subsystems, which are supported on the client side by the interaction client 104 and on the server side by the interaction servers 124.
In some examples, these subsystems are implemented as microservices. A microservice subsystem (e.g., a microservice application) may have components that enable it to operate independently and communicate with other services. Example components of a microservice subsystem may include:
API interface: Microservices may communicate with other components through well-defined APIs or interfaces, using lightweight protocols such as REST or messaging. The API interface defines the inputs and outputs of the microservice subsystem and how it interacts with other microservice subsystems of the interaction system 100.
Data storage: A microservice subsystem may be responsible for its own data storage, which may be in the form of a database, cache, or other storage mechanism (e.g., using the database server 126 and database 128). This enables a microservice subsystem to operate independently of other microservices of the interaction system 100.
Service discovery: Microservice subsystems may find and communicate with other microservice subsystems of the interaction system 100. Service discovery mechanisms enable microservice subsystems to locate and communicate with other microservice subsystems in a scalable and efficient way.
Monitoring and logging: Microservice subsystems may need to be monitored and logged in order to ensure availability and performance. Monitoring and logging mechanisms enable the tracking of health and performance of a microservice subsystem.
In some examples, the interaction system 100 may employ a monolithic architecture, a service-oriented architecture (SOA), a function-as-a-service (FaaS) architecture, or a modular architecture:
An image processing system 202 provides various functions that enable a user to capture and augment (e.g., annotate or otherwise modify or edit) media content associated with a message.
A camera system 204 includes control software (e.g., in a camera application) that interacts with and controls camera hardware (e.g., directly or via operating system controls) of the user system 102 to modify and augment real-time images captured and displayed via the interaction client 104.
An augmentation system 206 provides functions related to the generation and publishing of augmentations (e.g., media overlays) for images captured in real-time by cameras of the user system 102 or retrieved from memory of the user system 102 (e.g., previously captured images). Certain examples are discussed with respect to images but similar techniques apply to any sort of content item, including animations, graphics, videos, audio files, image collages, and so forth. For example, the augmentation system 206 operatively selects, presents, and displays media overlays (e.g., an image filter or an image lens) to the interaction client 104 for the augmentation of real-time images received via the camera system 204 or stored images retrieved from memory 1208 (shown in FIG. 12) of a user system 102. These augmentations are selected by the augmentation system 206 and presented to a user of an interaction client 104, based on a number of inputs and data, such as, for example:
Entity relationship information of the user of the user system 102.
An augmentation may include audio and visual content and visual effects. Examples of audio and visual content include pictures, texts, logos, animations, and sound effects. An example of a visual effect includes color overlaying. The audio and visual content or the visual effects can be applied to a media content item (e.g., a photo and/or video) at user system 102 for communication in a message, or applied to video content, such as a video content stream or feed transmitted from an interaction client 104. As such, the image processing system 202 may interact with, and support, the various subsystems of a communication system 208, such as a messaging system 210 and a video communication system 212.
A media overlay may include text or image data that can be overlaid on top of a photograph taken by the user system 102 or a video stream produced by the user system 102. In some examples, the media overlay may be a location overlay (e.g., Venice beach), a name of a live event, or a name of a merchant overlay (e.g., Beach Coffee House). In further examples, the image processing system 202 uses the geolocation of the user system 102 to identify a media overlay that includes the name of a merchant at the geolocation of the user system 102. The media overlay may include other indicia associated with the merchant. The media overlays may be stored in the databases 128 and accessed through the database server 126.
The image processing system 202 provides a user-based publication platform that enables users to select a geolocation on a map and upload content associated with the selected geolocation. The user may also specify circumstances under which a particular media overlay should be offered to other users. The image processing system 202 generates a media overlay that includes the uploaded content and associates the uploaded content with the selected geolocation.
An augmentation creation system 214 supports AR developer platforms and includes an application for content creators (e.g., artists and developers) to create and publish augmentations (e.g., AR experiences) of the interaction client 104. The augmentation creation system 214 provides a library of built-in features and tools to content creators including, for example, custom shaders, tracking technology, and templates.
In some examples, the augmentation creation system 214 provides a merchant-based publication platform that enables merchants to select a particular augmentation associated with a geolocation via a bidding process. For example, the augmentation creation system 214 associates a media overlay of the highest bidding merchant with a corresponding geolocation for a predefined amount of time.
A communication system 208 is responsible for enabling and processing multiple forms of communication and interaction within the interaction system 100 and includes a messaging system 210, an audio communication system 216, and a video communication system 212. The messaging system 210 is responsible for enforcing the temporary or time-limited access to content by the interaction clients 104. The messaging system 210 incorporates multiple timers (e.g., within a user management system 218) that, based on duration and display parameters associated with a message or collection of messages (e.g., a story), selectively enable access (e.g., for presentation and display) to messages and associated content via the interaction client 104. The audio communication system 216 enables and supports audio communications (e.g., real-time audio chat) between multiple interaction clients 104. Similarly, the video communication system 212 enables and supports video communications (e.g., real-time video chat) between multiple interaction clients 104.
A user management system 218 is operationally responsible for the management of user data and profiles, and maintains entity information (e.g., stored in entity tables 308, entity graphs 310, and profile data 302 of FIG. 3) regarding users and relationships between users of the interaction system 100.
A collection management system 220 is operationally responsible for managing sets or collections of media (e.g., collections of text, image video, and audio data). A collection of content (e.g., messages, including images, video, text, and audio) may be organized into an “event gallery” or an “event story.” Such a collection may be made available for a specified time period, such as the duration of an event to which the content relates. For example, content relating to a music concert may be made available as a “story” for the duration of that music concert. The collection management system 220 may also be responsible for publishing an icon that provides notification of a particular collection to the UI of the interaction client 104. The collection management system 220 includes a curation function that allows a collection manager to manage and curate a particular collection of content. For example, the curation interface enables an event organizer to curate a collection of content relating to a specific event (e.g., to delete inappropriate content or redundant messages). Additionally, the collection management system 220 employs machine vision (or image recognition technology) and content rules to curate a content collection automatically. In certain examples, compensation may be paid to a user to include user-generated content into a collection. In such cases, the collection management system 220 operates to automatically make payments to such users to use their content. Any content item in the collection of content is sometimes referred to as a “memory.”
A map system 222 provides various geographic location (e.g., geolocation) functions and supports the presentation of map-based media content and messages by the interaction client 104. For example, the map system 222 enables the display of user icons or avatars (e.g., stored in profile data 302 of FIG. 3) on a map to indicate a current or past location of “friends” of a user, as well as media content (e.g., collections of messages including photographs and videos) generated by such friends, within the context of a map. For example, a message posted by a user to the interaction system 100 from a specific geographic location may be displayed within the context of a map at that particular location to “friends” of a specific user on a map interface of the interaction client 104. A user can furthermore share his or her location and status information (e.g., using an appropriate status avatar) with other users of the interaction system 100 via the interaction client 104, with this location and status information being similarly displayed within the context of a map interface of the interaction client 104 to selected users.
A game system 224 provides various gaming functions within the context of the interaction client 104. The interaction client 104 provides a game interface providing a list of available games that can be launched by a user within the context of the interaction client 104 and played with other users of the interaction system 100. The interaction system 100 further enables a particular user to invite other users to participate in the play of a specific game by issuing invitations to such other users from the interaction client 104. The interaction client 104 also supports audio, video, and text messaging (e.g., chats) within the context of gameplay, provides a leaderboard for the games, and also supports the provision of in-game rewards (e.g., coins and items).
An external resource system 226 provides an interface for the interaction client 104 to communicate with remote servers (e.g., third-party servers 112) to launch or access external resources, i.e., applications or applets. Each third-party server 112 hosts, for example, a markup language (e.g., HTML5) based application or a small-scale version of an application (e.g., game, utility, payment, or ride-sharing application). The interaction client 104 may launch a web-based resource (e.g., application) by accessing the HTML5 file from the third-party servers 112 associated with the web-based resource. Applications hosted by third-party servers 112 are programmed in JavaScript leveraging a Software Development Kit (SDK) provided by the interaction servers 124. The SDK includes APIs with functions that can be called or invoked by the web-based application. The interaction servers 124 host a JavaScript library that provides a given external resource access to specific user data of the interaction client 104. HTML5 is an example of technology for programming games, but applications and resources programmed based on other technologies can be used.
To integrate the functions of the SDK into the web-based resource, the SDK is downloaded by the third-party server 112 from the interaction servers 124 or is otherwise received by the third-party server 112. Once downloaded or received, the SDK is included as part of the application code of a web-based external resource. The code of the web-based resource can then call or invoke certain functions of the SDK to integrate features of the interaction client 104 into the web-based resource.
The SDK stored on the interaction server system 110 effectively provides the bridge between an external resource (e.g., applications 106 or applets) and the interaction client 104. This gives the user a seamless experience of communicating with other users on the interaction client 104 while also preserving the look and feel of the interaction client 104. To bridge communications between an external resource and an interaction client 104, the SDK facilitates communication between third-party servers 112 and the interaction client 104. A bridge script running on a user system 102 establishes two one-way communication channels between an external resource and the interaction client 104. Messages are sent between the external resource and the interaction client 104 via these communication channels asynchronously. Each SDK function invocation is sent as a message and callback. Each SDK function is implemented by constructing a unique callback identifier and sending a message with that callback identifier.
By using the SDK, not all information from the interaction client 104 is shared with third-party servers 112. The SDK limits which information is shared based on the needs of the external resource. Each third-party server 112 provides an HTML5 file corresponding to the web-based external resource to interaction servers 124. The interaction servers 124 can add a visual representation (such as a box art or other graphic) of the web-based external resource in the interaction client 104. Once the user selects the visual representation or instructs the interaction client 104 through a graphical user interface (GUI) of the interaction client 104 to access features of the web-based external resource, the interaction client 104 obtains the HTML5 file and instantiates the resources to access the features of the web-based external resource.
The interaction client 104 presents a GUI (e.g., a landing page or title screen) for an external resource. During, before, or after presenting the landing page or title screen, the interaction client 104 determines whether the launched external resource has been previously authorized to access user data of the interaction client 104. In response to determining that the launched external resource has been previously authorized to access user data of the interaction client 104, the interaction client 104 presents another GUI of the external resource that includes functions and features of the external resource. In response to determining that the launched external resource has not been previously authorized to access user data of the interaction client 104, after a threshold period of time (e.g., 3 seconds) of displaying the landing page or title screen of the external resource, the interaction client 104 slides up (e.g., animates a menu as surfacing from a bottom of the screen to a middle or other portion of the screen) a menu for authorizing the external resource to access the user data. The menu identifies the type of user data that the external resource will be authorized to use. In response to receiving a user selection of an accept option, the interaction client 104 adds the external resource to a list of authorized external resources and allows the external resource to access user data from the interaction client 104. The external resource is authorized by the interaction client 104 to access the user data under an OAuth 2 framework.
The interaction client 104 controls the type of user data that is shared with external resources based on the type of external resource being authorized. For example, external resources that include full-scale applications (e.g., an application 106) are provided with access to a first type of user data (e.g., 2D avatars of users with or without different avatar characteristics). As another example, external resources that include small-scale versions of applications (e.g., web-based versions of applications) are provided with access to a second type of user data (e.g., payment information, 2D avatars of users, 3D avatars of users, and avatars with various avatar characteristics). Avatar characteristics include different ways to customize a look and feel of an avatar, such as different poses, facial features, clothing, and so forth.
An advertisement system 228 operationally enables the purchasing of advertisements by third parties for presentation to end-users via the interaction clients 104 and also handles the delivery and presentation of these advertisements.
An artificial intelligence and machine learning system 230 provides a variety of services to different subsystems within the interaction system 100. For example, the artificial intelligence and machine learning system 230 operates with the image processing system 202 and the camera system 204 to analyze images and extract information such as objects, text, or faces. This information can then be used by the image processing system 202 to enhance, filter, or manipulate images. The artificial intelligence and machine learning system 230 may be used by the augmentation system 206 to generate augmented content, XR experiences, and AR experiences, such as adding virtual objects or animations to real-world images.
The communication system 208 and messaging system 210 may use the artificial intelligence and machine learning system 230 to analyze communication patterns and provide insights into how users interact with each other and provide intelligent message classification and tagging, such as categorizing messages based on sentiment or topic. The artificial intelligence and machine learning system 230 may also provide chatbot functionality to message interactions 120 between user systems 102 and between a user system 102 and the interaction server system 110. The artificial intelligence and machine learning system 230 may also work with the audio communication system 216 to provide speech recognition and natural language processing capabilities, allowing users to interact with the interaction system 100 using voice commands. The artificial intelligence and machine learning system 230 may also provide personalized AI agent system 232 functionality to message interactions 120 between user systems 102 and between a user system 102 and the interaction server system 110.
In some cases, the artificial intelligence and machine learning system 230 can implement one or more machine learning models that process a profile of a user to determine sharing patterns or criteria for the user. The artificial intelligence and machine learning system 230 uses the sharing patterns to process a collection of previously captured content items (e.g., memories) to select a subset of content items that are most likely to be interesting for the user to share. Then, the artificial intelligence and machine learning system 230 automatically analyze each content items in the subset to generate one or more augmentations or modifications unique for each content item. The modifications and/or augmentations can be selected based on derived preferences of the user and/or based on augments and modifications that the user is determined to have made to other content items that were previously shared with other users. In some cases, the modifications are made based on suggested modifications generated by one or more LLMs. After modifying the content items, the artificial intelligence and machine learning system 230 can generate a shareable content items feed for presentation to the user (automatically or in response to a specific request). Each content item in the shareable content item feed can be presented individually in full screen or partial screen sequentially in an automated or semi-automated manner. Once a particular content item of interest is found in the shareable content item feed that is presented to the user, input can be received requesting that the particular content item of interest be shared with one or more other users.
In some cases, content items that are included in the content item feed can include an indicator that is overlaid persistently or temporarily over the content item. The indicator can represent to a user or consumer that the content item being viewed or consumed was at least partially generated automatically by the artificial intelligence and machine learning system 230. The indicator can be a specific graphical element or text.
Broadly, machine learning may involve using computer algorithms to automatically learn patterns and relationships in data, potentially without the need for explicit programming to do so after the algorithm is trained. Examples of machine learning algorithms can be divided into three main categories: supervised learning, unsupervised learning, and reinforcement learning.
Unsupervised learning involves training a model on unlabeled data to find hidden patterns and relationships in the data. Examples of unsupervised learning algorithms include clustering, principal component analysis, and generative models like autoencoders.
Reinforcement learning involves training a model to make decisions in a dynamic environment by receiving feedback in the form of rewards or penalties. Examples of reinforcement learning algorithms include Q-learning and policy gradient methods.
Examples of specific machine learning algorithms that may be deployed, according to some examples, include logistic regression, which is a type of supervised learning algorithm used for binary classification tasks. Logistic regression models the probability of a binary response variable based on one or more predictor variables. Another example type of machine learning algorithm is Naïve Bayes, which is another supervised learning algorithm used for classification tasks. Naïve Bayes is based on Bayes' theorem and assumes that the predictor variables are independent of each other. Random Forest is another type of supervised learning algorithm used for classification, regression, and other tasks. Random Forest builds a collection of decision trees and combines their outputs to make predictions. Further examples include neural networks which consist of interconnected layers of nodes (or neurons) that process information and make predictions based on the input data. Matrix factorization is another type of machine learning algorithm used for recommender systems and other tasks. Matrix factorization decomposes a matrix into two or more matrices to uncover hidden patterns or relationships in the data. Support Vector Machines (SVM) are a type of supervised learning algorithm used for classification, regression, and other tasks. SVM finds a hyperplane that separates the different classes in the data. Other types of machine learning algorithms include decision trees, k-nearest neighbors, clustering algorithms, and deep learning algorithms such as convolutional neural networks (CNN), recurrent neural networks (RNN), and transformer models. The choice of algorithm depends on the nature of the data, the complexity of the problem, and the performance requirements of the application.
Although several specific examples of machine learning algorithms are discussed herein, the principles discussed herein can be applied to other machine learning algorithms as well. Deep learning algorithms such as convolutional neural networks, recurrent neural networks, and transformers, as well as more traditional machine learning algorithms like decision trees, random forests, and gradient boosting may be used in various machine learning applications. Generating a trained machine-learning program, such as artificial intelligence and machine learning system 230, may include multiple types of phases that form part of the machine-learning pipeline, including for example the following phases:
Feature engineering: This may include selecting and transforming the training data to create features that are useful for predicting the target variable. Feature engineering may include (1) receiving features (e.g., as structured or labeled data in supervised learning) and/or (2) identifying features (e.g., unstructured or unlabeled data for unsupervised learning) in training data.
Model selection and training: This may include specifying a particular problem or desired response from input data, selecting an appropriate machine learning algorithm, and training it on the preprocessed data. This may further involve splitting the data into training and testing sets, using cross-validation to evaluate the model, and tuning hyperparameters to improve performance. Model selection can be based on factors such as the type of data, problem complexity, computational resources, or desired performance.
Model evaluation: This may include evaluating the performance of a trained model (e.g., the trained machine-learning program) on a separate testing dataset. This can help determine if the model is overfitting or underfitting and if it is suitable for deployment.
Prediction: This involves using a trained model (e.g., trained machine-learning program) to generate predictions on new, unseen data.
Validation, refinement or retraining: This may include updating a model based on feedback generated from the prediction phase, such as new data or user feedback.
Deployment: This may include integrating the trained model (e.g., the trained machine-learning program) into a larger system or application, such as a web service, mobile app, or Internet of Things (IoT) device. This can involve setting up APIs, building a UI, and ensuring that the model is scalable and can handle large volumes of data.
Prior to the training phase, feature engineering is used to identify features. This may include identifying informative, discriminating, and independent features for the effective operation of the trained machine-learning program in pattern recognition, classification, and regression. In some examples, the training data includes labeled data, which is known data for pre-identified features and one or more outcomes.
Each of the features may be a variable or attribute, such as individual measurable property of a process, article, system, or phenomenon represented by a data set (e.g., the training data). Features may also be of different types, such as numeric features, strings, vectors, matrices, encodings, and graphs, and may include the data obtained from the multimodal memory 508 shown in FIG. 5. Concept features can include abstract relationships or patterns in data, such as determining a topic of a document or discussion in a chat window between users. Content features include determining a context based on input information, such as determining a context of a user based on user interactions or surrounding environmental factors. Context features can include text features, such as frequency or preference of words or phrases, image features, such as pixels, textures, or pattern recognition, audio classification, such as spectrograms; and/or the like. Attribute features include intrinsic attributes (directly observable) or extrinsic features (derived), such as identifying square footage, location, or age of a real estate property identified in a camera feed. User data features include data pertaining to a particular individual or to a group of individuals, such as in a geographical location or that share demographic characteristics. User data can include demographic data (such as age, gender, location, or occupation), user behavior (such as browsing history, purchase history, conversion rates, click-through rates, or engagement metrics), or user preferences (such as preferences to certain video, text, or digital content items). Historical data includes past events or trends that can help identify patterns or relationships over time.
In training phases, the machine-learning pipeline uses the training data to find correlations among the features that affect a predicted outcome or prediction/inference data. With the training data and the identified features, the trained machine-learning program is trained during the training phase during machine-learning program training. The machine-learning program training appraises values of the features as they correlate to the training data. The result of the training is the trained machine-learning program (e.g., a trained or learned model).
Further, the training phase may involve machine learning, in which the training data is structured (e.g., labeled during preprocessing operations), and the trained machine-learning program implements a relatively simple neural network capable of performing, for example, classification and clustering operations. In other examples, the training phase may involve deep learning, in which the training data is unstructured, and the trained machine-learning program implements a deep neural network that is able to perform both feature extraction and classification/clustering operations. The neural network includes a hierarchical (e.g., layered) organization of neurons, with each layer including multiple neurons or nodes. Neurons in the input layer receive the input data, while neurons in the output layer produce the final output of the network. Between the input and output layers, there may be one or more hidden layers, each including multiple neurons.
Each neuron in the neural network operationally computes a small function, such as an activation function, that takes as input the weighted sum of the outputs of the neurons in the previous layer, as well as a bias term. The output of this function is then passed as input to the neurons in the next layer. If the output of the activation function exceeds a certain threshold, an output is communicated from that neuron (e.g., transmitting neuron) to a connected neuron (e.g., receiving neuron) in successive layers. The connections between neurons have associated weights, which define the influence of the input from a transmitting neuron to a receiving neuron. During the training phase, these weights are adjusted by the learning algorithm to optimize the performance of the network. Different types of neural networks may use different activation functions and learning algorithms, which can affect their performance on different tasks. Overall, the layered organization of neurons and the use of activation functions and weights enable neural networks to model complex relationships between inputs and outputs, and to generalize to new inputs that were not seen during training.
In some examples, the neural network may also be one of a number of different types of neural networks or a combination thereof, such as a single-layer feed-forward network, a Multilayer Perceptron (MLP), an Artificial Neural Network (ANN), a Recurrent Neural Network (RNN), a Long Short-Term Memory Network (LSTM), a Bidirectional Neural Network, a symmetrically connected neural network, a Deep Belief Network (DBN), a Convolutional Neural Network (CNN), a Generative Adversarial Network (GAN), an Autoencoder Neural Network (AE), a Restricted Boltzmann Machine (RBM), a Hopfield Network, a Self-Organizing Map (SOM), a Radial Basis Function Network (RBFN), a Spiking Neural Network (SNN), a Liquid State Machine (LSM), an Echo State Network (ESN), a Neural Turing Machine (NTM), or a Transformer Network, merely for example.
The neural network can be iteratively trained by adjusting model parameters to minimize a specific loss function or maximize a certain objective. The system can continue to train the neural network by adjusting parameters based on the output of the validation, refinement, or retraining block, and rerun the prediction on new or already run training data. The system can employ optimization techniques for these adjustments such as gradient descent algorithms, momentum algorithms, Nesterov Accelerated Gradient (NAG) algorithm, and/or the like. The system can continue to iteratively train the neural network even after deployment of the neural network. The neural network can be continuously trained as new data emerges, such as based on user creation or system-generated training data.
In some examples the trained machine-learning program, such as personalized AI agent system 232 may be a generative AI model. Generative AI is a term that may refer to any type of artificial intelligence that can create new content from training data. For example, generative AI can produce text, images, video, audio, code or synthetic data that are similar to the original data but not identical. In some cases, generative AI can include or implement large language models (LLMs). The generative AI and/or LLMs receive a prompt (including instructions) along with a set of data to process based on the prompt. The generative AI and/or LLMs process the data in accordance with the instructions of the prompt and generate an output that includes modifications of the set of data based on prior knowledge of the generative AI and/or LLMs.
Some of the techniques that may be used in generative AI are:
Recurrent Neural Networks (RNNs): RNNs are designed for processing sequential data, such as speech, text, and time series data. They have feedback loops that allow them to capture temporal dependencies and remember past inputs. RNNs may be used in applications such as speech recognition, machine translation, and sentiment analysis.
Generative adversarial networks (GANs): These are models that consist of two neural networks: a generator and a discriminator. The generator tries to create realistic content that can fool the discriminator, while the discriminator tries to distinguish between real and fake content. The two networks compete with each other and improve over time. GANs may be used in applications such as image synthesis, video prediction, and style transfer.
Variational autoencoders (VAEs): These are models that encode input data into a latent space (a compressed representation) and then decode it back into output data. The latent space can be manipulated to generate new variations of the output data. They may use self-attention mechanisms to process input data, allowing them to handle long sequences of text and capture complex dependencies.
Transformer models: These are models that use attention mechanisms to learn the relationships between different parts of input data (such as words or pixels) and generate output data based on these relationships. Transformer models can handle sequential data such as text or speech as well as non-sequential data such as images or code.
In generative AI examples, the prediction/inference data that is output include trend assessment and predictions, translations, summaries, image or video recognition and categorization, natural language processing, face recognition, user sentiment assessments, advertisement targeting and optimization, voice recognition, or media content generation, recommendation, and personalization.
A personalized AI agent system 232 provides personalized features to a user of an interaction client 104 by analyzing user data and behavior to understand their preferences and interests. By utilizing ML algorithms and data analytics, the personalized AI agent system 232 can learn and adapt to inferences of the user and then generatively suggest content relevant, specific, and custom tailored to the user. For example, the personalized AI agent system 232 can use the learned preferences and inferences of the user to identify which content items in a collection of content items the user may be most interested in sharing. The personalized AI agent system 232 can also select and/or generate augmentations to enhance the identified content items to create unique experiences and sharable content items. The personalized AI agent system 232 can analyze data from multiple sources, such as various user systems 102, messages, profile information, external data sources, image data captured in real-time by a camera of the user system 102, and/or any combination thereof to generate content items (e.g., code and/or prompts) in real time and provide such content items to the user and to base the selection of the content items to be shared and modifications to the content items to be shared.
The personalized AI agent system 232 tracks user activity, such as the posts (content items) the users like, share, or comment on; the topics the users follow; the people the users connect with; and the time the users spend on the platform. Tracking performed by the personalized AI agent system 232 is only enabled if the user opts into the experience of receiving real-time generated content item modifications. The personalized AI agent system 232 can present to the user a full list of all activity and information that will be tracked and used to generate real-time content item modifications. Only after receiving confirmation from the user that the user approves having such activity and information tracked does the personalized AI agent system 232 begin collecting such data and using such data to provide and generate the real-time and on-the-fly content item modifications for presentation to the user.
The personalized AI agent system 232 can retrieve data from multiple data sources, such as activity on a user's mobile phone, an AR/VR device, a smart watch, a laptop, or other user device. Based on this information, the personalized AI agent system 232 can identify patterns and predict users' interests to generate a multimodal memory for a particular user. The personalized AI agent system 232 analyzes the user's profile information, such as their age, gender, location, messages exchanged, and/or interactions performed on the user system 102 to provide personalized features. In some examples, the personalized AI agent system 232 suggests events and groups that are nearby, or recommend job opportunities that match the user's qualifications. The personalized AI agent system 232 can generate real-time AR experiences and/or message content that is/are relevant to current circumstances and/or a real-world environment perceived by the user.
Moreover, the personalized AI agent system 232 analyzes the content that the user creates and suggests the best time to post, the optimal hashtags to use, and the type of content that receives the most engagement. By doing so, the personalized AI agent system 232 helps the user increase their visibility and reach a wider audience. In this way, the personalized AI agent system 232 can assess data from different devices to provide personalized features through a variety of different devices. The personalized AI agent system 232 provides such personalized features automatically in real time based on the multimodal memory associated with the user and in the communication channel for the particular content that is preferred by the user. Analyzing user data and behavior to understand their preferences and interests, and then suggesting and generating content items that are relevant to the users, not only enhances the user experience but also increases engagement and retention rates on the platform.
In some examples, the personalized AI agent system 232 selects an individual content item from a plurality of previously captured content items that matches one or more criteria corresponding to sharable content and generates a prompt comprising the individual content item and a request for a plurality of suggested modifications to the individual content item. The personalized AI agent system 232 processes the prompt, such as by the LLM, to generate the plurality of suggested modifications to the individual content item and generates a modified individual content item corresponding to an individual suggested modification of the plurality of suggested modifications, such as for inclusion in a content item feed.
Data Architecture
FIG. 3 is a schematic diagram illustrating data structures 300, which may be stored in a database 304 of the interaction server system 110, according to certain examples. While the content of the database 304 is shown to comprise multiple tables, it will be appreciated that the data could be stored in other types of data structures (e.g., as an object-oriented database).
The database 304 includes message data stored within a message table 306. This message data includes, for any particular message, at least message sender data, message recipient (or receiver) data, and a payload. Further details regarding information that may be included in a message, and included within the message data stored in the message table 306, are described below with reference to FIG. 4.
An entity table 308 stores entity data, and is linked (e.g., referentially) to an entity graph 310 and profile data 302. Entities for which records are maintained within the entity table 308 may include individuals, corporate entities, organizations, objects, places, events, and so forth. Regardless of entity type, any entity regarding which the interaction server system 110 stores data may be a recognized entity. Each entity is provided with a unique identifier, as well as an entity type identifier (not shown).
The entity graph 310 stores information regarding relationships and associations between entities. Such relationships may be social, professional (e.g., work at a common corporation or organization), interest-based, or activity-based, merely for example. Certain relationships between entities may be unidirectional, such as a subscription by an individual user to digital content of a commercial or publishing user (e.g., a newspaper or other digital media outlet, or a brand). Other relationships may be bidirectional, such as a “friend” relationship between individual users of the interaction system 100.
Certain permissions and relationships may be attached to each relationship, and also to each direction of a relationship. For example, a bidirectional relationship (e.g., a friend relationship between individual users) may include authorization for the publication of digital content items between the individual users, but may impose certain restrictions or filters on the publication of such digital content items (e.g., based on content characteristics, location data or time of day data). Similarly, a subscription relationship between an individual user and a commercial user may impose different degrees of restrictions on the publication of digital content from the commercial user to the individual user, and may significantly restrict or block the publication of digital content from the individual user to the commercial user. A particular user, as an example of an entity, may record certain restrictions (e.g., by way of privacy settings) in a record for that entity within the entity table 308. Such privacy settings may be applied to all types of relationships within the context of the interaction system 100 or may selectively be applied to certain types of relationships.
The profile data 302 stores multiple types of profile data about a particular entity. The profile data 302 may be selectively used and presented to other users of the interaction system 100 based on privacy settings specified by a particular entity. Where the entity is an individual, the profile data 302 includes, for example, a username, telephone number, address, settings (e.g., notification and privacy settings), and a user-selected avatar representation (or collection of such avatar representations). A particular user may then selectively include one or more of these avatar representations within the content of messages communicated via the interaction system 100 and on map interfaces displayed by interaction clients 104 to other users. The collection of avatar representations may include “status avatars,” which present a graphical representation of a status or activity that the user may select to communicate at a particular time.
Where the entity is a group, the profile data 302 for the group may similarly include one or more avatar representations associated with the group, in addition to the group name, members, and various settings (e.g., notifications) for the relevant group.
The database 304 also stores augmentation data, such as overlays or filters, in an augmentation table 312. The augmentation data is associated with and applied to videos (for which data is stored in a video table 314) and images (for which data is stored in an image table 316).
Filters, in some examples, are overlays that are displayed as overlaid on an image or video during presentation to a recipient user. Filters may be of various types, including user-selected filters from a set of filters presented to a sending user by the interaction client 104 when the sending user is composing a message. Other types of filters include geolocation filters (also known as geo-filters), which may be presented to a sending user based on geographic location. For example, geolocation filters specific to a neighborhood or special location may be presented within a user interface by the interaction client 104, based on geolocation information determined by a Global Positioning System (GPS) unit of the user system 102.
Another type of filter is a data filter, which may be selectively presented to a sending user by the interaction client 104 based on other inputs or information gathered by the user system 102 during the message creation process. Examples of data filters include current temperature at a specific location, a current speed at which a sending user is traveling, battery life for a user system 102, or the current time. Each filter can be associated with various metadata that describes the filter. Such metadata can include one or more keywords descriptive of the filter.
Other augmentation data that may be stored within the image table 316 includes AR content items (e.g., corresponding to applying “lenses” or XR experiences). An XR content item (e.g., XR object) may be a real-time special effect and sound that may be added to an image or a video. Any discussion with respect to XR content and/or XR experiences and XR application can be similarly applied to AR and VR content, experiences and/or applications.
A collections table 318 stores data regarding collections of messages and associated image, video, or audio data, which are compiled into a collection (e.g., a story or a gallery). The creation of a particular collection may be initiated by a particular user (e.g., each user for which a record is maintained in the entity table 308). A user may create a “personal story” in the form of a collection of content that has been created and sent/broadcast by that user. To this end, the user interface of the interaction client 104 may include an icon that is user-selectable to enable a sending user to add specific content to his or her personal story.
A collection may also constitute a “live story,” which is a collection of content from multiple users that is created manually, automatically, or using a combination of manual and automatic techniques. For example, a “live story” may constitute a curated stream of user-submitted content from various locations and events. Users whose client devices have location services enabled and are at a common location event at a particular time may, for example, be presented with an option, via a user interface of the interaction client 104, to contribute content to a particular live story. The live story may be identified to the user by the interaction client 104, based on his or her location. The end result is a “live story” told from a community perspective.
A further type of content collection is known as a “location story,” which enables a user whose user system 102 is located within a specific geographic location (e.g., on a college or university campus) to contribute to a particular collection. In some examples, a contribution to a location story may employ a second degree of authentication to verify that the end-user belongs to a specific organization or other entity (e.g., is a student on the university campus).
As mentioned above, the video table 314 stores video data that, in some examples, is associated with messages for which records are maintained within the message table 306. Similarly, the image table 316 stores image data associated with messages for which message data is stored in the entity table 308. The entity table 308 may associate various augmentations from the augmentation table 312 with various images and videos stored in the image table 316 and the video table 314.
The databases 304 also include trained machine learning techniques 307 that stores parameters of one or more machine learning models that have been trained during training of the shareable content item feed system 590 (FIG. 5) and/or the personalized AI agent system 232 (FIG. 2). For example, trained machine learning techniques 307 stores the trained parameters of one or more artificial neural network machine learning models or techniques.
Data Communications Architecture
FIG. 4 is a schematic diagram illustrating a structure of a message 400, according to some examples, generated by an interaction client 104 for communication to a further interaction client 104 via the interaction servers 124. The content of a particular message 400 is used to populate the message table 306 stored within the database 304, accessible by the interaction servers 124. Similarly, the content of a message 400 is stored in memory as “in-transit” or “in-flight” data of the user system 102 or the interaction servers 124. A message 400 is shown to include the following example components:
Message text payload 404: text, to be generated by a user via a user interface of the user system 102, and that is included in the message 400.
Message image payload 406: image data, captured by a camera component of a user system 102 or retrieved from a memory component of a user system 102, and that is included in the message 400. Image data for a sent or received message 400 may be stored in the image table 316.
Message video payload 408: video data, captured by a camera component or retrieved from a memory component of the user system 102, and that is included in the message 400. Video data for a sent or received message 400 may be stored in the image table 316.
Message audio payload 410: audio data, captured by a microphone or retrieved from a memory component of the user system 102, and that is included in the message 400.
Message augmentation data 412: augmentation data (e.g., filters, stickers, or other annotations or enhancements) that represents augmentations to be applied to message image payload 406, message video payload 408, or message audio payload 410 of the message 400. Augmentation data for a sent or received message 400 may be stored in the augmentation table 312.
Message duration parameter 414: parameter value indicating, in seconds, the amount of time for which content of the message (e.g., the message image payload 406, message video payload 408, message audio payload 410) is to be presented or made accessible to a user via the interaction client 104.
Message geolocation parameter 416: geolocation data (e.g., latitudinal and longitudinal coordinates) associated with the content payload of the message. Multiple message geolocation parameter 416 values may be included in the payload, each of these parameter values being associated with respect to content items included in the content (e.g., a specific image within the message image payload 406, or a specific video in the message video payload 408).
Message story identifier 418: identifier values identifying one or more content collections (e.g., “stories” identified in the collections table 318) with which a particular content item in the message image payload 406 of the message 400 is associated. For example, multiple images within the message image payload 406 may each be associated with multiple content collections using identifier values.
Message tag 420: each message 400 may be tagged with multiple tags, each of which is indicative of the subject matter of content included in the message payload. For example, where a particular image included in the message image payload 406 depicts an animal (e.g., a lion), a tag value may be included within the message tag 420 that is indicative of the relevant animal. Tag values may be generated manually, based on user input, or may be automatically generated using, for example, image recognition.
Message sender identifier 422: an identifier (e.g., a messaging system identifier, email address, or device identifier) indicative of a user of the user system 102 on which the message 400 was generated and from which the message 400 was sent.
Message receiver identifier 424: an identifier (e.g., a messaging system identifier, email address, or device identifier) indicative of a user of the user system 102 to which the message 400 is addressed.
The contents (e.g., values) of the various components of message 400 may be pointers to locations in tables within which content data values are stored. For example, an image value in the message image payload 406 may be a pointer to (or address of) a location within an image table 316. Similarly, values within the message video payload 408 may point to data stored within an image table 316, values stored within the message augmentation data 412 may point to data stored in an augmentation table 312, values stored within the message story identifier 418 may point to data stored in a collections table 318, and values stored within the message sender identifier 422 and the message receiver identifier 424 may point to user records stored within an entity table 308.
Personal AI Agent System
FIG. 5 illustrates an example architecture 500 for applying a personal AI agent 502 to identify relevant features that are personalized for a user. The example architecture 500 can include a personal AI agent 502, a user database 504, tool components 512, a shareable content item feed system 590, and UI components 520. The personal AI agent 502 implements one or more machine learning models that communicate with user database 504, UI components 520, and the tool components 512 to selectively and intelligently generate modified content items for a user to share or for inclusion in a sharable content item feed.
The user database 504 includes user customizations database 506, a multimodal memory 508, and user specific models 510. In some cases, the personal AI agent 502 collects data from various sources and generates a multimodal memory 508 specific to a particular user. The personal AI agent 502 then provides personalized features to the user based on the identity model captured in the multimodal memory 508.
The multimodal memory 508 stores information pertinent to a user. Any of the data collected and stored in the multimodal memory 508 is collected and stored with express permission from the user on an opt-in basis. Non-limiting examples of multimodal memory types include:
Behavioral data: information about an individual's actions and interactions with a website, application, VR device, or other digital touchpoints. This data can include website visits, clicks, downloads, purchases, user interaction with other users, and content items the user shares with other users.
Psychographic data: information about an individual's personality.
Contextual data: information about the time, location, and device used by an individual when interacting with digital touchpoints. This data can help the personal AI agent 502 to understand the context of the interaction and personalize the experience accordingly.
Purchase history data: information about an individual's past purchases, such as products bought, frequency of purchases, and purchase amounts. This data can be used by the personal AI agent 502 to create personalized recommendations and offers. Data can also include advertisement interaction data that includes user's interactions with advertisements such as clicks, impressions, and conversions.
Interests data: information about an individual's hobbies, interests, and passions, which can be collected through online posts and activities.
Communication data: information about how an individual prefers to be contacted and their communication history with other users. This data can be used to personalize communication channels and messaging (e.g., SMS message on phone or pop-up message on AR device).
Data from augmentation devices (such as AR/VR devices): information from a camera feed that the user uses to capture images or videos of the user's surroundings; selections of digital content items, such as augmentations or overlays used on the camera feeds; biometric data such as heart rate, body temperature, facial expressions, and/or the like; user's preferred interaction methods on the AR device; types/duration of AR interactions; eye tracing, eye focus, and gaze direction for where users are looking and for how long; body movements such as head or body motions, gestures, or interactions with the virtual environment; hand and finger movements using controllers or tracking devices; audio data from a microphone; user emotional responses such as heart rate, skin conductance, or facial expressions; user cognitive performance such as attention, memory, or problem-solving skills; and/or the like. Types of augmentations applied to content items that the user shares with other users include the following examples.
Contacts and connections data: data about a user's contacts and connections, including their friends, followers, and groups.
Device data: information about the user's device, including the type of device, operating system, and browser, which can be used to optimize the platform's performance and to provide a seamless experience across different devices.
The personal AI agent 502 links different aspects of a user profile into a multimodal memory 508. The multimodal memory 508 stores various aspects of a user profile (e.g., users' preferences, life styles, interest, friends) and can use this contextual information later on to create custom tailored content. The value of such custom-tailored content increases over time and across other form factors as the personal AI agent 502 collects more data related to the user. With more and more digital touchpoints from the user as time progresses, the personal AI agent 502 gains a much deeper understanding of the user and can use historical context to find relevancy in any current user activity.
Multimodal memory 508 includes entity-based memory, which refers to memory that is focused on specific things, such as objects, people, places, events, and experiences. This aspect of multimodal memory 508 stores information about the attributes, features, and characteristics of these specific objects or people, such as remembering the name of a person, their appearance, their occupation, or their interests. Multimodal memory 508 also includes a knowledge graph memory, which refers to memory that is focused on how things are related or connected to each other. This aspect of the multimodal memory 508 organizes information in a structured way, where entities are linked together based on their relationships and attributes in a weighted manner. Knowledge graph memory helps the personal AI agent 502 understand the context and meaning of information by showing how different entities and concepts are related. For example, the knowledge graph can store associations indicating what objects (e.g., animals) a user likes and what objects (e.g., food items) a user dislikes. The knowledge graph can store associations indicating what words/phrases/augmentations a user applies to images that contain or depict items a user likes and what words/phrases/augmentations a user applies to images that contain or depict items a user dislikes.
Each entity in the multimodal memory 508 can be linked to each other entity. The links between these entities can be weighted in different manners to represent how closely related the entities are to each other. For example, an entity representing a dog name can be linked to an entity for the user and to an entity representing animals, whereas another entity representing a human with the same name can be linked to the entity for the user and another entity representing contacts of the user. The entity representing the dog name can be linked with a greater weight to the entity for the animal than the link between the entity representing the human with the same name and the entity for the animal. Similarly, the entity representing the human with the same name as the dog can be linked with a greater weight to the entity for the contacts than the link between the entity representing the dog name and the entity for the human. In this way, a variety of information about a given user or individual or organization can be collected and related to establish links between the information.
In some cases, the entity-based memory is focused on specific things, while the knowledge graph memory is focused on how things are related. Entity-based memory stores information about individual entities, while knowledge graph memory organizes information based on the relationships and connections between entities. Both types of memory can be used to learn and understand the current context associated with one or more users.
The personal AI agent 502 uses embeddings for the multimodal memory 508, which refers to a technique used in machine learning to represent and store data from multiple modalities (such as images, text, and audio) in a common vector space. The purpose of embeddings is to capture the semantic meaning and relationships between different modalities, allowing for more efficient and accurate processing of multimodal data. In some examples, the personal AI agent 502 feeds the knowledge graph to an external or internal process to generate the latent embeddings. The personal AI agent 502 stores the latent embeddings in the multimodal memory 508 for use in generating the on-the-fly content recommendations and analysis. In some cases, the content recommendations are provided to the user system 102 without the user issuing a specific request for the content recommendations. For example, the user system 102 is used to capture or access an image and the personal AI agent 502 detects the image being viewed by the user system 102. The personal AI agent 502 leverages the user database 504 to generate a specific AR experience and can automatically apply the generated AR experience on the image currently being accessed or viewed by the user system 102. In some cases, the personal AI agent 502 uses the tool components 512 (discussed below) to generate the unique AR experience to provide and activate the AR experience on the user system 102.
The personal AI agent 502 uses these embeddings for cross-modal comparisons and analysis. In some examples, an embedding of an image is compared to the embedding of a corresponding text description to identify semantic relationships between the two. In the context of the multimodal memory 508, embeddings are used to store and retrieve information from different modalities in a more efficient and effective manner. In some examples, if a user has stored an entity (e.g., a memory object) that includes an image, text description, and audio recording, embeddings can be used to represent each of these modalities in a common vector space. This allows for the efficient retrieval and integration of information from multiple modalities when accessing the memory object.
The personal AI agent 502 generates embeddings using one or more techniques, such as neural networks. These embeddings can be fine-tuned and optimized for specific applications and tasks. The personal AI agent creates embeddings for the multimodal memory that include information about individuals and their relationships with other individuals, entities, and devices and are generated using various data sources as described herein by identifying patterns and connections between entities. As such, the personal AI agent 502 can apply the multimodal memory for the user in a variety of different ways to provide personalized features for the user.
Users in the past may have selected certain customization options, such as content augmentations, graphics, or features within an application or AR device. The interaction system 100 stores such customizations of the user in a user customizations database 506. The personal AI agent 502 uses such customizations in providing relevant content, such as by identifying preferences of customizations of the user. In some examples, the user may have used a certain type of augmentation (e.g., adding pizza to camera feeds) or stickers that the user sends to friends.
The user customizations database 506 includes customizations made by the user within the interaction system 100. The user customizations database 506 stores profile customizations (e.g., profile picture, cover photo, and introduction), news feed preferences (e.g., prioritizing certain friends, pages, and groups), and privacy settings (e.g., who can see posts, profile information, and activity). The user customizations database 506 stores personalized avatar and sticker selection, customized content augmentations and how these were applied to a camera feed, sound and music preferences for content creation, subscription preferences for channels and creators, and other types of user customizations based on user's viewing history and engagement.
The user specific models 510 include generative models for generating graphics, text, and images for the user. For example, the user specific models 510 can generate stickers that include photographs, graphics, or animations. The user specific models 510 generate avatars that represent users on the interaction system platform. Users can customize avatars to reflect the user's appearance, personality, and interests. The user specific models 510 generate filters and content augmentations, such as XR effects that can be applied in real-time, to enhance photos and videos. The user specific models 510 generate memes to share humorous images, videos, and captions that convey a specific cultural idea or trend. The user specific models 510 generate hashtags to categorize and organize user posts based on a particular theme or topic, which allow users to connect with other users who share similar interests or to participate in trending conversations. The user specific models 510 generate short-lived photos and videos that can be viewed by their followers for a limited time, which include filters, stickers, and text overlays to make them more engaging.
The tool components 512 includes one or more neural network engines 514, one or more external data sources 516, and one or more feature APIs 518. The neural network engine 514 includes one or more generative machine learning models. The generative machine learning models can be trained to generate a variety of different content. For example, the generative machine learning models are trained to receive a prompt as input (which can include any combination of text, images, audio, and/or videos) and to generate an output that responds to the prompt. In some cases, the generative machine learning models generate an artificial image/video, code segments, and/or text that are responsive to the prompt. In some cases, the generative machine learning model generates content augmentations, such as filters that can overlay, modify, or augment a real-world camera feed with digital content items or content items that were previously captured. The tools components 512 can include a filter tool, a caption tool, and/or an image-to-image tool. The caption tool (or add caption tool) can be used for adding a caption to an individual content item, the image-to-image tool can be used for processing an individual content item by a generative model to generate a new content item according to a description, and the filter tool can be used for selecting a predefined filter matching one or more keywords to apply to the individual content item.
In some cases, the personal AI agent 502 provides, as input to the neural network engine 514, a prompt that is generated by the personal AI agent 502 based on information gathered from the UI components 520 (representing a current real-world environment) and user database 504. Namely, the personal AI agent 502 generates a prompt that includes an image captured by the user system 102 and one or more vectors derived from the multimodal memory 508. This prompt can be provided as input to the neural network engine 514. The neural network engine 514 then accesses additional sources of data, such as external data sources 516 and/or feature APIs 518 to generate content that matches the inputs of the prompt. In some examples, the personal AI agent 502 collects contextual data of a conversation, hears audio from the user, and/or receives some other input from the user or the user's environment and generates a request for the neural network engine 514.
The external data sources 516 can include various search engines, chat bots, email applications, calendar applications, messaging applications, social network applications, news sources, live media sources, and/or any combination thereof. The personal AI agent 502 accesses external data from external data sources 516 to generate and/or apply multimodal memory 508 for a user. In some examples, the personal AI agent 502 collects user data in different media types and creates embeddings for the user's multimodal memory 508. The personal AI agent 502 can retrieve data from external data sources 516 to apply to multimodal memory 508 and/or to use in generating the input for the neural network engine. The external data sources 516 can include any combination of a repository of scientific papers that include content information about the papers, title, author, abstract, keywords, and citations; data from emails, such as email addresses, contact lists, email content, attachments, and metadata (such as timestamps and IP addresses); search engine data, such as search queries, search history, location data, device information, and web activity; and/or communication data, such as messages, voice and video calls, roles in group messages, posts, comments, votes, user subscriptions, likes, followers, and hashtags.
The neural network engine 514 can intelligently select one or more of the external data sources 516 to populate data to respond to the prompt received from the personal AI agent 502. The feature APIs 518 can provide access to a variety of different additional tools which may be proprietary. Using a given one of the APIs from the feature APIs 518, the neural network engine 514 can access additional machine learning tools to generate additional content. For example, one of the proprietary tools can include an AR experience generation tool. The neural network engine 514 can access the API of this tool to prompt the tool to generate an AR experience having specific features generated by the neural network engine 514 based on the prompt received from the personal AI agent 502. The neural network engine 514 can receive the specific AR experience and can further apply various effects and modifications to the AR experience in order to generate a response to the personal AI agent 502 that includes the AR experience matching the prompt. The personal AI agent 502 can then automatically augment and/or modify the image currently being presented on the user system 102 using the unique AR experience generated by the neural network engine 514.
The personal AI agent 502 retrieves the personalized content for the user and applies the content through various feature APIs 518. The personal AI agent 502 applies the personalized and/or recommended content to interaction client features via the feature APIs 518 including a photo, video, or podcast that a user captures and shares with friends. The personal AI agent 502 recommends filters, stickers, text overlays, or content augmentation effects applied to a camera feed that can be then be recorded and sent to individual users. The personal AI agent 502 applies the personalized and/or recommended content to interaction client features via the feature APIs 518 including messages, photos, and videos to individual friends or to groups; collection of photos or videos, such as a collection that can be viewed by friends for up to a certain time period (e.g., 24 hours); content from media partners, such as news articles, videos, and shows that are custom-curated for the user; map-related features, such as when and who to share locations with, how the system shares and what information is displayed on the map UI; filters and/or XR experiences that include visual overlays applied to photos or video, such as adding location-based information, temperature, time, and other graphics; and customized avatars that can be used in photos/video, chat messages, and in other features.
In many cases, multiple input devices can be accessed by the personal AI agent 502 to generate the prompt for the neural network engine 514. These input devices or combination of input devices can include a wearable device 522 such as an AR/VR device 526, a device with a monitor 524 such as a user system 102, and/or a smart watch 528. The outputs generated by the personal AI agent 502 can be provided to any one of the same or different combinations of the AR/VR device 526, a user system 102, and/or a smart watch 528. The personal AI agent 502 identifies the preferred method of communication using the multimodal memory 508. In some examples, the personal AI agent 502 identifies that the user likes to be reminded via the smart watch 528, given directions on the AR/VR device 526, and receive text messages on the user's mobile phone.
The process for training the neural network engine 514 can be the same as previously discussed. The neural network engine 514 can receive a collection of training data that includes a variety of different prompts and ground truth responses. These ground truth responses can include identification of sources of data that can be used to respond to a prompt and/or generated image content or text content that responds to the prompt. The neural network engine 514 can iterate over the training data until a loss function satisfies a stopping criterion, where at each iteration parameters of the neural network engine 514 are updated. In a similar manner, the personal AI agent 502 can be trained based on training data to generate a prompt to provide to the neural network engine 514. The training data can include a variety of latent vectors, images, and information obtained from the user database 504 and corresponding ground truth prompts. The personal AI agent 502 can iterate over the training data until a loss function satisfies a stopping criterion, where parameters of the personal AI agent 502 are updated at each iteration. In this way, the personal AI agent 502 can generate real-time prompts based on current information accessed or obtained by the UI components 520 to provide to the tool components 512 for generating content to provide back to the UI components 520.
The shareable content item feed system 590 can continuously or periodically analyze images captured by one or more interaction clients 104, such as the UI components 520. The shareable content item feed system 590 can apply one or more ML models to a collection of previously captured content items to select content items that match criteria indicating that the content items are likely to be of interest in sharing. In response to selecting the content items that match the criteria, the shareable content item feed system 590 can generate a prompt for modifying each of the selected content items individually using a different augmentation that is selected based on preferences provided by the personal AI agent 502 and/or based on objects depicted in the respective images. In some cases, the prompt is dynamically generated based on contextual cues gathered from the images and/or information obtained from the multimodal memory 508.
The shareable content item feed system 590 accesses, by the interaction client 104, an XR application. The shareable content item feed system 590 can together and/or separate from the personal AI agent 502 provide the prompt to the tool components 512 to process the prompt using the generative ML model (e.g., the neural network engine 514) to generate revisions or augmentations for each content item that has been selected. The modified content items are then ranked, such as based on recency of capture, and placed in a shareable content item feed.
The shareable content item feed system 590 can be part of the personal AI agent 502 and perform similar functions as the personal AI agent 502. The shareable content item feed system 590 can then present the shareable content item feed automatically and/or in response to input received from the user. For example, a user request to share content can be received. In response, a first content item from the shareable content item feed is presented in full screen. A gesture (such as a swipe up gesture) can be detected and, in response, the shareable content item feed system 590 retrieves and presents a second content item from the shareable content item feed in full screen. The shareable content item feed system 590 monitors interactions, such as dwell time, of the user for each content item presented in the shareable content item feed. The shareable content item feed system 590 can dynamically reprioritize and re-rank the content items in the shareable content item feed and/or retrieve and generate a new modified content for inclusion in the shareable content item feed based on the monitored interactions. Once a content item of interest is selected by the user, the shareable content item feed system 590 detects input that selects an option to share the content item. In such cases, the shareable content item feed system 590 sends the content item from the shareable content item feed to one or more designated recipients.
The personalized AI agent system 232 and the shareable content item feed system 590 provide users with opt in/opt out options to opt out of the use of user information. The personalized AI agent system 232 and the shareable content item feed system 590 can operate by default as opt-in systems. Opt-in and opt-out options are mechanisms used by the system to give users control over the use of their personal data. These options are especially significant in the era of data privacy regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Below are some examples of opt-in mechanisms:
2. Opt-out: In an opt-out approach, the system assumes that users consent to data collection and processing by default. However, users are provided with options to withdraw their consent at any time. The system applies the opt-out approach in a limited number of circumstances.
The system provides privacy policy or settings, where users can access an application or website's privacy policy, which includes information about how to opt out of data collection and processing, or preference options that allow users to manage their privacy settings and disable specific data collection and sharing practices. To promote transparency and user control, the systems described herein clearly communicate data collection and processing practices, and provide easy-to-use opt-in and opt-out options.
Shareable Content Item Feed System
In some examples, the shareable content item feed system 590 accesses a list of previously captured content items, such as images, pictures, videos, audio files, and so forth. The shareable content item feed system 590 may search for only those previously captured content items that are associated with timestamps that precede a current time by a threshold period. For example, the shareable content item feed system 590 can access only those content items that were captured in the last week or in the previous two days. In some cases, the shareable content item feed system 590 can access all of the previously captured content items that are associated with a user and stored on the user database 504 and/or user system 102.
The shareable content item feed system 590 processes the retrieved or accessed previously captured content items using one or more machine learning models, such as using the personal AI agent 502. The shareable content item feed system 590 can determine which of the previously captured content items match or are associated with attributes that correspond to one or more shareable criteria. For example, the shareable content item feed system 590 can access criteria that defines content items that are good candidates to be shared with other users. The criteria can be general to the population of users of the interaction client 104 and/or specific to the user of the interaction client 104.
For example, in some cases, the shareable criteria can be initially defined to include a certain category or theme of content and certain depictions of items or objects in the images. In such cases, the shareable content item feed system 590 extracts features from each of the previously captured content items that have been retrieved or accessed and determines whether the features correspond to the initially defined shareable criteria. Any content item that matches the initially defined sharable criteria can be included in a set of content items to be modified. This results in a collection of content items selected from the previously captured content items for inclusion in an initial set of content items to be modified. The initial set of content items can then be processed by the shareable content item feed system 590, as discussed below, to apply one or more different modifications to each respective content item for generating a shareable content item feed.
In some cases, the shareable content item feed system 590 generates an embedding for each content item that was previously captured by the user system 102 (e.g., within a certain past time interval, such as captured in the past week). The embedding can represent tags, visual cues, and/or descriptions of each content item. In some cases, these cues, tags, and descriptions are generated by processing each content item by an LLM with a prompt requesting generation of descriptive tags for each content item. The shareable content item feed system 590 can then access a list of predetermined descriptions. The shareable content item feed system 590 can compare the visual cues or tags and descriptions to the list of predetermined descriptions. If a content item is associated with cues or tags and descriptions that are on the list of the predetermined descriptions, the shareable content item feed system 590 adds the content item to a list of candidate content items that are sharable. The shareable content item feed system 590 can also determine whether any content item in the list of candidate content items has been designated by a user as private. The shareable content item feed system 590 can remove such content items designated as private from the list of candidate content items.
The shareable content item feed system 590 can then determine, based on the tags and visual cues of the candidate content items, whether lighting conditions meet a certain darkness threshold. If so, the shareable content item feed system 590 determines that the content items have poor lighting and removes such content items from the list of candidate content items. The filtered list of candidate content items can then be used to select individual content items for automated modifications by the shareable content item feed system 590.
In some examples, the personal AI agent 502 can process the previously captured content items based on preferences of the user to identify those content items that are most likely of interest to the user to share, such as based on a history of interactions associated with the user. Specifically, after initially generating the shareable content item feed, over time, such as over the course of days, weeks, and/or months, the personal AI agent 502 can build a profile that represents content items shared by a user of the interaction client 104. For example, after a certain quantity or number of content items have been shared by the user, the personal AI agent 502 can process the content items to extract features that represent the content items that were shared by the user. The personal AI agent 502 can then update the initially defined criteria to be more user specific. The shareable content item feed system 590 can then use the updated criteria to select a new group or collection of content items from the previously captured content items (which may now include new or additional previously captured content items). The shareable content item feed system 590 can add the selected new group or collection of content items in an updated set of content items to be modified automatically. The updated set of content items can then be processed by the shareable content item feed system 590, as discussed below, to apply one or more different modifications to each respective content item for generating a new or updated shareable content item feed.
In some examples, the criteria for selecting content items to include in a set of content items to modify can be derived or determined automatically by the personal AI agent 502. Namely, the personal AI agent 502 can use one or more generative machine learning models along with a prompt to generate the criteria. The prompt can instruct the generative machine learning models to process data including the entire collection of content items a given user has previously captured and/or accessed and obtain interests or preferences of the user. The prompt can further instruct the generative machine learning models to derive criteria based on the data that represents the most likely attributes of content items the user likes to share with other users. This criteria can then be used by the shareable content item feed system 590 to select a set of content items from previously captured content items to modify.
After the set of content items to be modified is generated, the shareable content item feed system 590 can rank the content items in the set of content items. The ranking can be performed based on preferences of the user and/or based on a likelihood that the content item is of interest for the user to share. Namely, the shareable content item feed system 590 can generate a score for each content item in the set of content items. The score can represent a likelihood that the content item is interesting for the user to share. The content items with the highest score can be presented at the top of the list of the set of content items. In some cases, the set of content items are ranked based on a capture timestamp associated with each content item. In such cases, content items that were more recently captured can be placed ahead of other content items that were captured less recently. This causes the content items that were more recently captured to be modified and presented first to a user in a shareable content item feed.
The shareable content item feed system 590 processes the set of content items and analyzes the set of content items with the help of the personal AI agent 502 to apply one or more modifications to each content item. For example, the shareable content item feed system 590 can retrieve a first content item in the list. The shareable content item feed system 590 can obtain one or more attributes of the first content item. The one or more attributes can indicate what real-world or virtual objects are depicted in the first content item, a location at which the first content item was captured, a timestamp indicating when the first content item was captured, and various other information associated with the first content item. The personal AI agent 502 can analyze the first content item and, based on preferences of the user (e.g., data stored in the user database 504), predict one or more modifications to apply to the first content item. In some cases, the personal AI agent 502 can receive a prompt from the shareable content item feed system 590 with instructions to analyze the first content item based on knowledge about the user to predict or identify one or more modifications (e.g., captions, augmented reality elements or experiences, filters, annotations, and so forth) to apply or associate with the first content item.
The personal AI agent 502 provides to the shareable content item feed system 590 the one or more modifications that have been identified or predicted. The shareable content item feed system 590 then modifies the first content item in accordance with the one or more modifications and adds the modified first content item to a shareable content item feed. For example, the shareable content item feed system 590 overlays a first set of augmented reality elements on the first content item to generate the modified first content item. This process is repeated for each other content item in the set of content items.
For example, the shareable content item feed system 590 can access a prompt 600, as shown in FIGS. 6A-B. The prompt 600 can include visual cues or descriptive tags associated with the selected individual content item and a request for a plurality of suggested modifications to perform for the individual content item. Specifically, the prompt 600 can include an instruction 610 to inform or request the LLM to create share-worthy modifications (edits) given a current date, time and location along with information about an item that is a good candidate for modification. The instruction 610 can specify the existence of various tools, such as a filter tool, an image-to-image modification tool, and/or a caption tool that can be utilized to perform the suggested modifications. In some cases, the instruction 610 can specify that the image-to-image modification tool should always be selected or that it can optionally be selected. The instruction 610 can instruct the LLM to avoid generating content that fails to meet safety criteria (e.g., prevent generating suggested modifications that are not safe for work (NSFW).
In some cases, the prompt 600 includes a description section 620 that defines the operation of each of the available tools. For example, the description section 620 can include a description for the add caption tool. The description can specify that for the add caption tool, the LLM is instructed to provide a plaintext caption to add to the content item that is clever, funny, satirical, sarcastic, and/or resonates with someone who is within a certain age range (which can correspond to the age of the user of the user system 102). The description can indicate that the caption should be kept short and funny and should be written in English. The instructions for the description of the add caption tool can instruct the LLM to add a specified graphical element to a front of the text of the caption and at the end of the text. The specified graphical element represents to a user that the caption was generated automatically.
As another example, the description section 620 includes an image-to-image tool description. The description for the image-to-image tool can instruct the LLM to generate a suggested modification that reimagines the content item in a different style with callouts to relevant details to consider. The description section 620 includes a filter tool description. The description for the filter tool can instruct the LLM to generate a suggested modification that adds color or other adjustment to change the look and feel of the input content item and to provide two or three keywords that describe the filter that is being suggested. Such filters change the vibe or visual style without transforming the scene being depicted in the content item.
The prompt 600 can include an instruction 630 to the LLM to return a predetermined number of suggested modifications (e.g., three suggested modifications) using any combination of the available tools described in description section 620. The instruction 630 can instruct the LLM to structure each suggested modification according to a specified format 640. The specified format 640 can be in a JSON or HTML or other markup language format and can include field for a name of the suggested modification, a field for a summary of the suggested modification, a field for a vibe including a single word describing the suggested modification, a field for filter keywords that include the keywords of the selected filter if the filter tool is selected, a field for image-to-image modification descriptions if the image-to-image tool is selected, and a caption field for text of a caption if the caption tool is selected.
For example, the shareable content item feed system 590 can retrieve a second content item in the list. The shareable content item feed system 590 can obtain one or more attributes of the second content item. The one or more attributes can indicate what real-world or virtual objects are depicted in the second content item, a location at which the second content item was captured, a timestamp indicating when the second content item was captured, and various other information associated with the second content item. The personal AI agent 502 can analyze the second content item and, based on preferences of the user (e.g., data stored in the user database 504), predict an additional set of one or more modifications to apply to the second content item. In some cases, the personal AI agent 502 can receive a prompt from the shareable content item feed system 590 with instructions to analyze the second content item based on knowledge about the user to predict or identify one or more modifications (e.g., captions, augmented reality elements or experiences, filters, annotations, and so forth) to apply or associate with the second content item. The personal AI agent 502 provides to the shareable content item feed system 590 the additional set of the one or more modifications that have been identified or predicted. For example, the shareable content item feed system 590 overlays a second set of augmented reality elements on the first content item and adds a specific caption to generate the modified second content item. The shareable content item feed system 590 then modifies the second content item in accordance with the additional set of the one or more modifications and adds the modified second content item to a shareable content item feed. In this way, each content item in the shareable content item feed can be modified by a different augmentation.
In some examples, the shareable content item feed system 590 generates a notification that indicates to the user of the interaction client 104 that the shareable content item feed includes new shareable content items. In response to receiving input that selects the notification, the shareable content item feed system 590 presents the shareable content item feed. Namely, the shareable content item feed system 590 retrieves the first content item that is first in the shareable content item feed and presents that content item in full screen or in partial screen to the user. The shareable content item feed system 590 presents an option to share the first content item with one or more designated recipients. The shareable content item feed system 590 receives a gesture (e.g., a swipe up gesture). In response, the shareable content item feed system 590 retrieves a second content item that is second in the shareable content item feed and presents the second content item instead of the first content item. This process of navigating through the content items in the shareable content item feed can continue indefinitely or until no more content items exist in the shareable content item feed.
In some cases, the shareable content item feed system 590 receives input that activates a live or real-time camera feed of the user system 102. In response, the shareable content item feed system 590 presents in full screen images of the live or real-time camera feed that is being received from a front-facing or rear-facing camera of the user system 102. The shareable content item feed system 590 can detect a gesture performed by the user. The gesture can include swiping up along the screen on which the live or real-time camera feed is being presented. In response, the shareable content item feed system 590 retrieves the first content item that is first in the shareable content item feed and presents that content item in full screen or in partial screen to the user instead of the live or real-time camera feed. The shareable content item feed system 590 presents an option to share the first content item with one or more designated recipients. The shareable content item feed system 590 receives another instance of the gesture (e.g., a swipe up gesture). In response, the shareable content item feed system 590 retrieves a second content item that is second in the shareable content item feed and presents the second content item instead of the first content item. This process of navigating through the content items in the shareable content item feed can continue indefinitely or until there exist no more content items in the shareable content item feed.
In some examples, the shareable content item feed system 590 receives input to access the shareable content item feed from a conversation or chat interface. Namely, the interaction client 104 can present a UI in which one or more messages are exchanged between a plurality of participants. The interaction client 104 can receive input from a user to share content with the participants in the conversation. In response, the interaction client 104 can retrieve identities of the plurality of participants and provide identifiers of the participants to the shareable content item feed system 590. The shareable content item feed system 590 can generate a prompt that includes instructions for the personal AI agent 502 to process the set of previously captured content items and retrieve or select content items that match preferences of the identifiers of the participants in the conversation and/or depict faces associated with the identifiers. The prompt can further instruct the personal AI agent 502 to further base the retrieval or selection of the content items on those content items that are of greatest interest to the user to share. Once that selection of content items is retrieved, the personal AI agent 502 can be instructed by the shareable content item feed system 590 to modify the selection of content items and add the modified selection of content items to a shareable content item feed that is specific to the participants of the conversation. The shareable content item feed system 590 can present in full screen the first content item that is first in the shareable content item feed. The shareable content item feed system 590 presents an option to share the first content item with the conversation participants. The shareable content item feed system 590 receives another instance of the gesture (e.g., a swipe up gesture). In response, the shareable content item feed system 590 retrieves a second content item that is second in the shareable content item feed and presents the second content item instead of the first content item. This process of navigating through the content items in the shareable content item feed can continue indefinitely or until no more content items exist in the shareable content item feed.
In some examples, the shareable content item feed system 590 updates the content items that are included in the shareable content item feed periodically, continuously, and/or in response to detecting a specified quantity of new content items being added to the list of previously captured content items. In such cases, the shareable content item feed system 590 re-processes the updated list of previously captured content items to generate new modified content items for inclusion in the shareable content item feed. Namely, the shareable content item feed system 590 can modify one or more content items that have been newly added to the list of previously captured content items and that satisfy (match) shareable criteria and add the modified one or more content items to the shareable content item feed as new items. The shareable content item feed system 590 can place such new modified content items at the top of the shareable content item feed so they are presented first to the user when the shareable content item feed is accessed.
In some examples, the shareable content item feed system 590 analyzes user interactions with the shareable content item feed while the shareable content item feed is being presented to the user. The shareable content item feed system 590 can determine a dwell time (amount of time spent by the user viewing each content item before navigating away to a different content item) for each content item in the shareable content item feed that is presented. The shareable content item feed system 590 can determine that a particular content item is associated with a dwell time that transgresses a threshold or that is greater than dwell times determined for other content items. In response, the shareable content item feed system 590 can extract one or more features from the particular content item. The shareable content item feed system 590 can search the set of previously captured content items (or the shareable content item feed) for content items that have similar features as the extracted one or more features. For example, the shareable content item feed system 590 can determine that the particular content item depicts two faces of two people. The shareable content item feed system 590 can search the set of previously captured content items for additional content items that exclusively or inclusively depict the same two faces of the two people. The shareable content item feed system 590 can then modify the additional content items (in a similar manner as discussed previously) to augment the additional content items with one or more modifications. The shareable content item feed system 590 can then place the augmented/modified additional content items in the shareable content item feed so they are presented ahead of all other content items that have not yet been presented to the user.
The shareable content item feed system 590 can process the prompt 600 and a selected content item, such as by an LLM. The shareable content item feed system 590 can generate an output 700 based on the prompt 600. For example, the output 700 can include multiple suggested modifications for the content item. The suggested modifications can include a first suggested modification 710, a second suggested modification 720, and a third suggested modification 730. Each suggested modification can include a different combination of the available tools. For example, the second suggested modification 720 can include a field for the image-to-image tool 722, whereas the third suggested modification 730 excludes the field for the image-to-image tool 722. The third suggested modification 730 can include a filter tool field 732 with keywords that can be used to selected a filter to apply to the content item, whereas the second suggested modification 720 excludes the filter tool field. The first suggested modification 710, the second suggested modification 720, and the third suggested modification 730 can each include a caption field 734 including a text string for a suggested caption generated by the LLM. In some cases, less than all of the suggested modifications in the output 700 include the caption field.
The shareable content item feed system 590 can process the output 700 to select a particular suggested modification to apply to the content item. For example, the shareable content item feed system 590 can randomly select one of the suggested modifications, such as second suggested modification 720.
The shareable content item feed system 590 can process fields of the selected suggested modification to automatically or semi-automatically modify the content item. For example, the shareable content item feed system 590 can determine that the selected suggested modification includes a caption field 734. In such cases, the shareable content item feed system 590 retrieves the text string specified in the caption field 734 and overlays the text string on the content item. For example, as shown in FIG. 8, the shareable content item feed system 590 modifies the content item 800 by overlaying the text of the caption 810 15% below a center of the content item. The shareable content item feed system 590 can append a graphical indicator 812 (representing to a consumer that the caption was automatically generated by the LLM) at the beginning of the text and the same graphical indicator 812 at the end of the text.
In some cases, the shareable content item feed system 590 can determine that the selected suggested modification includes a caption field 732 that includes keywords that describe a filter to apply to the content item. In response, the shareable content item feed system 590 accesses a list of predetermined filters available in the interaction server system 110. In some cases, the predetermined filters can be provided by third-party servers 112 in some cases. The shareable content item feed system 590 obtains metadata for the predetermined filters and searches the metadata to identify a set of filters that have metadata that match the keywords provided in the caption field 734. The shareable content item feed system 590 can then rank the set of filters and select a filter that is associated with a top rank. The shareable content item feed system 590 modifies the content item automatically using the selected filter.
In some cases, the shareable content item feed system 590 can determine that the selected suggested modification includes a field for the image-to-image tool 722. In such cases, the shareable content item feed system 590 accesses an image-to-image prompt template 701 (FIG. 7) for improving the description for the image modification provided by the field for the image-to-image tool 722. The template can include a set of instructions of a prompt that indicate to the LLM that it is being given a prompt for modifying or augmenting an existing content item and instruct the LLM to rewrite the prompt to be more useful and safer by expanding on the concepts that are given by adding more useful details of outfits or accessories or circumstances. Specifically, the shareable content item feed system 590 can process the template 701 with the description for the image modification to generate an improved or new description for modifying the image by generating a new image. The shareable content item feed system 590 processes the new prompt by the LLM to generate a revised image modification description. In some cases, the new prompt includes one or more negative revisions indicating modifications that are disallowed to be performed. For example, the LLM is instructed to generate a new prompt that is not a complete sentence and is a series of 5-15 comma separated keywords in English.
After the LLM processes the content of the field for the image-to-image tool 722 using the image-to-image prompt template 701, a new prompt is generated. The new prompt is processed together with the content item by the shareable content item feed system 590 to generate a new content item that matches the description provided by the new prompt that was generated by the LLM. For example, as shown in FIG. 8, a set of images 801 including a first image 811 and a second image 813 can be processed together with various prompts 830, 840, and 850 to render or generate new images 832, 842, and 852. One of these new images 832, 842, and 852 can be provided in a content item feed to a user with an indicator overlaid on the images 832, 842, and 852 informing a consumer that the images 832, 842, and 852 were automatically generated. In some cases, if the selected suggested modification included a caption field, the text of the caption is overlaid on the newly generated image. In some cases, prior to making the automatically modified content item available to a consumer, one or more safety checks can be performed by processing the automatically modified image by a safety check model. The content item that was automatically modified is only made available if the modified content item passes the safety check model.
For example, the first image 811 can be processed by the generative model with the prompt 830 to generate the new image 832. The second image 813 can be processed by the generative model with the prompt 840 to generate the new image 842. The first image 811 can be processed by the generative model with the prompt 850 to generate the new image 852, which differs from the image 832 because of the differences in description of the prompt 850 from the description of the prompt 830.
FIG. 9 is a flowchart of a process or method 900 performed by the shareable content item feed system 590, in accordance with some examples. Although the flowchart can describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed. A process may correspond to a method, a procedure, and the like. The steps of methods may be performed in whole or in part, may be performed in conjunction with some or all of the steps in other methods, and may be performed by any number of different systems or any portion thereof, such as a processor included in any of the systems.
At operation 901, the shareable content item feed system 590 (e.g., a user system 102 or a server) selects, by an interaction application, an individual content item from a plurality of previously captured content items that matches one or more criteria corresponding to sharable content, as discussed above.
At operation 902, the shareable content item feed system 590 generates a prompt comprising the individual content item and a request for a plurality of suggested modifications to the individual content item, as discussed above.
At operation 903, the shareable content item feed system 590 processes the prompt by an LLM to generate the plurality of suggested modifications to the individual content item, as discussed above.
At operation 904, the shareable content item feed system 590 generates a modified individual content item corresponding to an individual suggested modification of the plurality of suggested modifications, as discussed above.
FIGS. 10A-B is a flowchart of a process or method 1000 performed by the shareable content item feed system 590, in accordance with some examples. Although the flowchart can describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed. A process may correspond to a method, a procedure, and the like. The steps of methods may be performed in whole or in part, may be performed in conjunction with some or all of the steps in other methods, and may be performed by any number of different systems or any portion thereof, such as a processor included in any of the systems. The process or method 1000 repeats for every additional content item that is included in a candidate set of content items that is generated by the shareable content item feed system 590.
At operation 1001, the shareable content item feed system 590 (e.g., a user system 102 or a server) detects that a user has saved or created a new content item, as discussed above. In response, the shareable content item feed system 590 generates an embedding, cue, or descriptive tags that describes visual attributes of the content item at operation 1002.
At operation 1010, the shareable content item feed system 590 retrieves a set of criteria, such as a set of visual tags that are preselected for identifying shareable content. The shareable content item feed system 590 then, at operation 1011, compares the set of criteria to the embeddings of the content item to determine whether the content item satisfies shareable criteria. If so, the shareable content item feed system 590 determines whether the user who captured the content item has authorized the shareable content item feed system 590 to perform edits automatically at operation 1012.
The shareable content item feed system 590, at operation 1020, determines whether the credentials of the user entitle or authorize the user to have shareable content item feed system 590 automatically modify the content item. In response to determining that the user is not authorized for having automatically generated modifications, the shareable content item feed system 590 proceeds to operation 1022 where another content item is analyzed at operations 1001 and/or 1002. In response to determining that the user is authorized for having automatically generated modifications, the shareable content item feed system 590 performs operation 1024.
At operation 1024, the content item is scanned for safety using a safety model. If the content item is determined to satisfy safety standards at operation 1030, the shareable content item feed system 590 performs operation 1040. Otherwise, the shareable content item feed system 590 proceeds to operation 1022. At operation 1040, a description of the content item is provided to the LLM along with the prompt 600. The LLM, at operation 1042, processes the content item and/or description of the content item to generate a plurality of suggested modifications. The LLM can receive one or more positive and negative prompts 1043 to ensure the generated suggested modification satisfy safety standards and pass the safety model/check. In some cases, the shareable content item feed system 590 stores the prompt in cold storage 1044.
At operation 1050, the shareable content item feed system 590 selects a random one of the plurality of suggested modifications. The shareable content item feed system 590 determines if the selected suggested modification includes a caption edit (e.g., includes a field that provides description for the caption tool). If so, the shareable content item feed system 590 performs operation 1052 to check the text of the caption for safety using the safety model. If it is determined in operation 1054 that the suggested modification excludes the caption edit, the shareable content item feed system 590 performs operation 1056. The shareable content item feed system 590 performs operation 1082 where no edits are performed or allowed if the text of the caption fails the safety check and the shareable content item feed system 590 selects another candidate content item at operation 1001 and/or 1002.
In response to determining that the text of the caption passes the safety check, the shareable content item feed system 590 performs operation 1056 to determine if the suggested modification includes an image-to-image modification. If not, the shareable content item feed system 590 proceeds to operation 1090 where the content item is modified according to the suggested modification (e.g., a caption is added, a filter is added, and/or a new image with the filter/caption is generated) and presented to a consumer/user.
In response to determining that the suggested modification includes the image-to-image modification in the field specifying the modification tool, the shareable content item feed system 590 performs operation 1060. At operation 1060, the shareable content item feed system 590 processes the text of the description in the field for the image-to-image modification using the LLM according to the prompt template 701. The LLM revises the prompt and generates a new prompt which is then analyzed for safety at operation 1062. If the new prompt passes the safety at operation 1064, a generative model is applied at operation 1070 according to the new prompt to generate a new content item. The new image is analyzed for safety at operation 1080 and if the new content item passes the safety check, the shareable content item feed system 590 proceeds to operation 1090 where the new content item is presented and/or modified according to the suggested modification that is selected. If the new content item fails the safety check, the shareable content item feed system 590 performs operation 1082 where the content item is discarded and another content item is selected for analysis.
EXAMPLES
Example 1. A method comprising: selecting, by an interaction application, an individual content item from a plurality of previously captured content items that matches one or more criteria corresponding to sharable content; generating a prompt comprising the individual content item and a request for a plurality of suggested modifications to the individual content item; processing the prompt by a large language model (LLM) to generate the plurality of suggested modifications to the individual content item; and generating a modified individual content item corresponding to an individual suggested modification of the plurality of suggested modifications.
Example 2. The method of Example 1, wherein the one or more criteria comprise a list of predetermined descriptions, wherein the one or more criteria exclude content items designated as private by a user of the interaction application, and wherein the one or more criteria excludes content items with lighting that meets a darkness threshold.
Example 3. The method of any one of Examples 1-2, wherein each of the previously captured content items is processed to generate visual tags and to compare the visual tags to the one or more criteria to identify sharable content.
Example 4. The method of Example 3, wherein the prompt comprises the visual tags associated with the individual content item, a timestamp representing when the individual content item was captured, a location where the individual content item was captured, a current time and date, and a language associated with a user.
Example 5. The method of Example 4, wherein the prompt identifies a plurality of creative tools available to the LLM to use in generating the plurality of suggested modifications.
Example 6. The method of Example 5, wherein the plurality of creative tools comprise an add caption tool for adding a caption to the individual content item, an image-to-image tool for processing the individual content item by a generative model to generate a new content item according to a description, and a filter tool for selecting a predefined filter matching one or more keywords.
Example 7. The method of Example 6, wherein the plurality of suggested modifications comprise: a first suggested modification that includes a first description of the first suggested modification, a first vibe representing the first suggested modification, and a first combination of one or more of the plurality of creative tools; and a second suggested modification that includes a second description of the second suggested modification, a second vibe representing the second suggested modification, and a second combination of one or more of the plurality of creative tools, the second combination including a different subset of the plurality of creative tools from the first combination.
Example 8. The method of Example 7, wherein the first combination of the one or more of the plurality of creative tools comprises the add caption tool comprising a first caption and the filter tool comprising a first set of keywords.
Example 9. The method of any one of Examples 7-8, further comprising: randomly selecting the first suggested modification from the plurality of suggested modifications.
Example 10. The method of Example 9, further comprising: determining that the first suggested modification comprises the add caption tool comprising a first caption; and in response to determining that the first suggested modification comprises the add caption tool comprising the first caption, overlaying text on the individual content item comprising the first caption at a predetermined position.
Example 11. The method of Example 10, further comprising: appending to a front portion of the text a graphical element that indicates that the text was generated by the LLM; and appending to an end portion of the text the graphical element that indicates that the text was generated by the LLM.
Example 12. The method of any one of Examples 10-11, wherein the text is removable from being overlaid on the individual content item in response to user input.
Example 13. The method of any one of Examples 9-12, further comprising: determining that the first suggested modification comprises the filter tool comprising a first set of keywords; and in response to determining that the first suggested modification comprises the filter tool comprising the first set of keywords, searching a plurality of predetermined filters for an individual filter that matches the first set of keywords.
Example 14. The method of Example 13, further comprising: ranking the plurality of predetermined filters based on comparing metadata associated with each of the plurality of predetermined filters with the first set of keywords; and selecting the individual filter that is associated with a highest rank in response to ranking of the plurality of predetermined filters.
Example 15. The method of any one of Examples 7-14, wherein the second combination of the one or more of the plurality of creative tools comprises the image-to-image tool comprising a first descriptive modification.
Example 16. The method of Example 15, further comprising: generating a new prompt comprising the first descriptive modification and a request to expand and refine the first descriptive modification; and processing the new prompt by the LLM to generate a revised image modification description, the new prompt comprising one or more negative revisions indicating modifications that are disallowed to be performed.
Example 17. The method of Example 16, further comprising: processing the individual content item and the new prompt by a generative model to generate a new content item that corresponds to the revised image modification description, the modified individual content item comprising a portion of the new content item.
Example 18. The method of any one of Examples 1-17, further comprising: presenting the modified individual content item by the interaction application with an indicator that specifies that the modified individual content item was automatically generated.
Example 19. A system comprising: at least one processor; and at least one memory component having instructions stored thereon that, when executed by the at least one processor, cause the at least one processor to perform operations comprising: selecting, by an interaction application, an individual content item from a plurality of previously captured content items that matches one or more criteria corresponding to sharable content; generating a prompt comprising the individual content item and a request for a plurality of suggested modifications to the individual content item; processing the prompt by a large language model (LLM) to generate the plurality of suggested modifications to the individual content item; and generating a modified individual content item corresponding to an individual suggested modification of the plurality of suggested modifications.
Example 20. A non-transitory computer-readable storage medium having stored thereon instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising: selecting, by an interaction application, an individual content item from a plurality of previously captured content items that matches one or more criteria corresponding to sharable content; generating a prompt comprising the individual content item and a request for a plurality of suggested modifications to the individual content item; processing the prompt by a large language model (LLM) to generate the plurality of suggested modifications to the individual content item; and generating a modified individual content item corresponding to an individual suggested modification of the plurality of suggested modifications.
Machine Architecture
FIG. 11 is a diagrammatic representation of a machine 1100 within which instructions 1102 (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine 1100 to perform any one or more of the methodologies discussed herein may be executed. For example, the instructions 1102 may cause the machine 1100 to execute any one or more of the methods described herein. The instructions 1102 transform the general, non-programmed machine 1100 into a particular machine 1100 programmed to carry out the described and illustrated functions in the manner described. The machine 1100 may operate as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine 1100 may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine 1100 may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a set-top box (STB), a personal digital assistant (PDA), an entertainment media system, a cellular telephone, a smartphone, a mobile device, a wearable device (e.g., a smartwatch), a smart home device (e.g., a smart appliance), other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions 1102, sequentially or otherwise, that specify actions to be taken by the machine 1100. Further, while a single machine 1100 is illustrated, the term “machine” shall also be taken to include a collection of machines that individually or jointly execute the instructions 1102 to perform any one or more of the methodologies discussed herein. The machine 1100, for example, may comprise the user system 102 or any one of multiple server devices forming part of the interaction server system 110. In some examples, the machine 1100 may also comprise both client and server systems, with certain operations of a particular method or algorithm being performed on the server-side and with certain operations of the particular method or algorithm being performed on the client-side.
The machine 1100 may include processors 1104, memory 1106, and input/output (I/O) components 1108, which may be configured to communicate with each other via a bus 1110. In an example, the processors 1104 (e.g., a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) Processor, a Complex Instruction Set Computing (CISC) Processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Radio-Frequency Integrated Circuit (RFIC), another processor, or any suitable combination thereof) may include, for example, a processor 1112 and a processor 1114 that execute the instructions 1102. The term “processor” is intended to include multi-core processors that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions contemporaneously. Although FIG. 11 shows multiple processors 1104, the machine 1100 may include a single processor with a single-core, a single processor with multiple cores (e.g., a multi-core processor), multiple processors with a single core, multiple processors with multiples cores, or any combination thereof.
The memory 1106 includes a main memory 1116, a static memory 1118, and a storage unit 1120, all accessible to the processors 1104 via the bus 1110. The main memory 1106, the static memory 1118, and storage unit 1120 store the instructions 1102 embodying any one or more of the methodologies or functions described herein. The instructions 1102 may also reside, completely or partially, within the main memory 1116, within the static memory 1118, within machine-readable medium 1122 within the storage unit 1120, within at least one of the processors 1104 (e.g., within the processor's cache memory), or any suitable combination thereof, during execution thereof by the machine 1100.
The I/O components 1108 may include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components 1108 that are included in a particular machine will depend on the type of machine. For example, portable machines such as mobile phones may include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components 1108 may include many other components that are not shown in FIG. 11. In various examples, the I/O components 1108 may include user output components 1124 and user input components 1126. The user output components 1124 may include visual components (e.g., a display such as a plasma display panel (PDP), a light-emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor, resistance mechanisms), other signal generators, and so forth. The user input components 1126 may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components), point-based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or another pointing instrument), tactile input components (e.g., a physical button, a touch screen that provides location and force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like. Any biometric collected by the biometric components is captured and stored with user approval and deleted on user request.
Further, such biometric data may be used for very limited purposes, such as identification verification. To ensure limited and authorized use of biometric information and other personally identifiable information (PII), access to this data is restricted to authorized personnel only, if allowed at all. Any use of biometric data may strictly be limited to identification verification purposes, and the data is not shared or sold to any third party without the explicit consent of the user. In addition, appropriate technical and organizational measures are implemented to ensure the security and confidentiality of this sensitive information.
In further examples, the I/O components 1108 may include biometric components 1128, motion components 1130, environmental components 1132, or position components 1134, among a wide array of other components. For example, the biometric components 1128 include components to detect expressions (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye-tracking), measure biosignals (e.g., blood pressure, heart rate, body temperature, perspiration, or brain waves), identify a person (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram-based identification), and the like. The biometric components may include a brain-machine interface (BMI) system that allows communication between the brain and an external device or machine. This may be achieved by recording brain activity data, translating this data into a format that can be understood by a computer, and then using the resulting signals to control the device or machine.
Example types of BMI technologies include:
Electroencephalography (EEG) based BMIs, which record electrical activity in the brain using electrodes placed on the scalp.
Invasive BMIs, which use electrodes that are surgically implanted into the brain.
Optogenetics BMIs, which use light to control the activity of specific nerve cells in the brain
The motion components 1130 include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope).
The environmental components 1132 include, for example, one or cameras (with still image/photograph and video capabilities), illumination sensor components (e.g., photometer), temperature sensor components (e.g., one or more thermometers that detect ambient temperature), humidity sensor components, pressure sensor components (e.g., barometer), acoustic sensor components (e.g., one or more microphones that detect background noise), proximity sensor components (e.g., infrared sensors that detect nearby objects), gas sensors (e.g., gas detection sensors to detection concentrations of hazardous gases for safety or to measure pollutants in the atmosphere), or other components that may provide indications, measurements, or signals corresponding to a surrounding physical environment.
With respect to cameras, the user system 102 may have a camera system comprising, for example, front cameras on a front surface of the user system 102 and rear cameras on a rear surface of the user system 102. The front cameras may, for example, be used to capture still images and video of a user of the user system 102 (e.g., “selfies”), which may then be augmented with augmentation data (e.g., filters) described above. The rear cameras may, for example, be used to capture still images and videos in a more traditional camera mode, with these images similarly being augmented with augmentation data. In addition to front and rear cameras, the user system 102 may also include a 360° camera for capturing 360° photographs and videos.
Further, the camera system of the user system 102 may include dual rear cameras (e.g., a primary camera as well as a depth-sensing camera), or even triple, quad, or penta rear camera configurations on the front and rear sides of the user system 102. These multiple cameras systems may include a wide camera, an ultra-wide camera, a telephoto camera, a macro camera, and a depth sensor, for example.
The position components 1134 include location sensor components (e.g., a GPS receiver component), altitude sensor components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensor components (e.g., magnetometers), and the like.
Communication may be implemented using a wide variety of technologies. The I/O components 1108 further include communication components 1136 operable to couple the machine 1100 to a network 1138 or devices 1140 via respective coupling or connections. For example, the communication components 1136 may include a network interface component or another suitable device to interface with the network 1138. In further examples, the communication components 1136 may include wired communication components, wireless communication components, cellular communication components, Near Field Communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components to provide communication via other modalities. The devices 1140 may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a universal serial bus (USB)).
Moreover, the communication components 1136 may detect identifiers or include components operable to detect identifiers. For example, the communication components 1136 may include Radio Frequency Identification (RFID) tag reader components, NFC smart tag detection components, optical reader components (e.g., an optical sensor to detect one-dimensional bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph™, MaxiCode, PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes), or acoustic detection components (e.g., microphones to identify tagged audio signals). In addition, a variety of information may be derived via the communication components 1136, such as location via Internet Protocol (IP) geolocation, location via Wi-Fi® signal triangulation, location via detecting an NFC beacon signal that may indicate a particular location, and so forth.
The various memories (e.g., main memory 1116, static memory 1118, and memory of the processors 1104) and storage unit 1120 may store one or more sets of instructions and data structures (e.g., software) embodying or used by any one or more of the methodologies or functions described herein. These instructions (e.g., the instructions 1102), when executed by processors 1104, cause various operations to implement the disclosed examples.
The instructions 1102 may be transmitted or received over the network 1138, using a transmission medium, via a network interface device (e.g., a network interface component included in the communication components 1136) and using any one of several well-known transfer protocols (e.g., HTTP). Similarly, the instructions 1102 may be transmitted or received using a transmission medium via a coupling (e.g., a peer-to-peer coupling) to the devices 1140.
Software Architecture
FIG. 12 is a block diagram 1200 illustrating a software architecture 1202, which can be installed on any one or more of the devices described herein. The software architecture 1202 is supported by hardware such as a machine 1204 that includes processors 1206, memory 1208, and I/O components 1210. In this example, the software architecture 1202 can be conceptualized as a stack of layers, where each layer provides a particular functionality. The software architecture 1202 includes layers such as an operating system 1212, libraries 1214, frameworks 1216, and applications 1218. Operationally, the applications 1218 invoke API calls 1220 through the software stack and receive messages 1222 in response to the API calls 1220.
The operating system 1212 manages hardware resources and provides common services. The operating system 1212 includes, for example, a kernel 1224, services 1226, and drivers 1228. The kernel 1224 acts as an abstraction layer between the hardware and the other software layers. For example, the kernel 1224 provides memory management, processor management (e.g., scheduling), component management, networking, and security settings, among other functionalities. The services 1226 can provide other common services for the other software layers. The drivers 1228 are responsible for controlling or interfacing with the underlying hardware. For instance, the drivers 1228 can include display drivers, camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flash memory drivers, serial communication drivers (e.g., USB drivers), WI-FI® drivers, audio drivers, power management drivers, and so forth.
The libraries 1214 provide a common low-level infrastructure used by the applications 1218. The libraries 1214 can include system libraries 1230 (e.g., C standard library) that provide functions such as memory allocation functions, string manipulation functions, mathematic functions, and the like. In addition, the libraries 1214 can include API libraries 1232 such as media libraries (e.g., libraries to support presentation and manipulation of various media formats such as Moving Picture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC), Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC), Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group (JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries (e.g., an OpenGL framework used to render in 2D and 3D in a graphic content on a display), database libraries (e.g., SQLite to provide various relational database functions), web libraries (e.g., WebKit to provide web browsing functionality), and the like. The libraries 1214 can also include a wide variety of other libraries 1234 to provide many other APIs to the applications 1218.
The frameworks 1216 provide a common high-level infrastructure that is used by the applications 1218. For example, the frameworks 1216 provide various GUI functions, high-level resource management, and high-level location services. The frameworks 1216 can provide a broad spectrum of other APIs that can be used by the applications 1218, some of which may be specific to a particular operating system or platform.
In an example, the applications 1218 may include a home application 1236, a contacts application 1238, a browser application 1240, a book reader application 1242, a location application 1244, a media application 1246, a messaging application 1248, a game application 1250, and a broad assortment of other applications such as a third-party application 1252. The applications 1218 are programs that execute functions defined in the programs. Various programming languages can be employed to create one or more of the applications 1218, structured in a variety of manners, such as object-oriented programming languages (e.g., Objective-C, Java, or C++) or procedural programming languages (e.g., C or assembly language). In a specific example, the third-party application 1252 (e.g., an application developed using the ANDROID™ or IOS™ SDK by an entity other than the vendor of the particular platform) may be mobile software running on a mobile operating system such as IOS™, ANDROID™, WINDOWS® Phone, or another mobile operating system. In this example, the third-party application 1252 can invoke the API calls 1220 provided by the operating system 1212 to facilitate functionalities described herein.
System with Head-Wearable Apparatus
FIG. 13 illustrates a system 1300 including a head-wearable apparatus 116 with a selector input device, according to some examples. FIG. 13 is a high-level functional block diagram of an example head-wearable apparatus 116 communicatively coupled to a mobile device 114 and various server systems 1304 (e.g., the interaction server system 110) via various networks 1316.
The head-wearable apparatus 116 includes one or more cameras, each of which may be, for example, a visible light camera 1306, an infrared emitter 1308, and an infrared camera 1310.
The mobile device 114 connects with head-wearable apparatus 116 using both a low-power wireless connection 1312 and a high-speed wireless connection 1314. The mobile device 114 is also connected to the server system 1304 and the network 1316.
The head-wearable apparatus 116 further includes two image displays of optical assembly 1318. The two image displays of optical assembly 1318 include one associated with the left lateral side and one associated with the right lateral side of the head-wearable apparatus 116. The head-wearable apparatus 116 also includes an image display driver 1320, an image processor 1322, low-power circuitry 1324, and high-speed circuitry 1326. The image display of optical assembly 1318 is for presenting images and videos, including an image that can include a GUI, to a user of the head-wearable apparatus 116.
The image display driver 1320 commands and controls the image display of optical assembly 1318. The image display driver 1320 may deliver image data directly to the image display of optical assembly 1318 for presentation or may convert the image data into a signal or data format suitable for delivery to the image display device. For example, the image data may be video data formatted according to compression formats, such as H.264 (MPEG-4 Part 10), HEVC, Theora, Dirac, RealVideo RV40, VP8, VP9, or the like, and still image data may be formatted according to compression formats such as PNG, JPEG, Tagged Image File Format (TIFF) or exchangeable image file format (EXIF) or the like.
The head-wearable apparatus 116 includes a frame and stems (or temples) extending from a lateral side of the frame. The head-wearable apparatus 116 further includes a user input device 1328 (e.g., touch sensor or push button), including an input surface on the head-wearable apparatus 116. The user input device 1328 (e.g., touch sensor or push button) is to receive from the user an input selection to manipulate the GUI of the presented image.
The components shown in FIG. 13 for the head-wearable apparatus 116 are located on one or more circuit boards, for example a PCB or flexible PCB, in the rims or temples. Alternatively, or additionally, the depicted components can be located in the chunks, frames, hinges, or bridge of the head-wearable apparatus 116. Left and right visible light cameras 1306 can include digital camera elements such as a complementary metal oxide-semiconductor (CMOS) image sensor, charge-coupled device, camera lenses, or any other respective visible or light-capturing elements that may be used to capture data, including images of scenes with unknown objects.
The head-wearable apparatus 116 includes a memory 1302, which stores instructions to perform a subset or all of the functions described herein. The memory 1302 can also include a storage device.
As shown in FIG. 13, the high-speed circuitry 1326 includes a high-speed processor 1330, a memory 1302, and high-speed wireless circuitry 1332. In some examples, the image display driver 1320 is coupled to the high-speed circuitry 1326 and operated by the high-speed processor 1330 in order to drive the left and right image displays of the image display of optical assembly 1318. The high-speed processor 1330 may be any processor capable of managing high-speed communications and operation of any general computing system needed for the head-wearable apparatus 116. The high-speed processor 1330 includes processing resources needed for managing high-speed data transfers on a high-speed wireless connection 1314 to a wireless local area network (WLAN) using the high-speed wireless circuitry 1332. In certain examples, the high-speed processor 1330 executes an operating system such as a LINUX operating system or other such operating system of the head-wearable apparatus 116, and the operating system is stored in the memory 1302 for execution. In addition to any other responsibilities, the high-speed processor 1330 executing a software architecture for the head-wearable apparatus 116 is used to manage data transfers with high-speed wireless circuitry 1332. In certain examples, the high-speed wireless circuitry 1332 is configured to implement Institute of Electrical and Electronic Engineers (IEEE) 802.11 communication standards, also referred to herein as WiFi. In some examples, other high-speed communications standards may be implemented by the high-speed wireless circuitry 1332.
Low-power wireless circuitry 1334 and the high-speed wireless circuitry 1332 of the head-wearable apparatus 116 can include short-range transceivers (Bluetooth™) and wireless wide, local, or wide area network transceivers (e.g., cellular or WiFi). Mobile device 114, including the transceivers communicating via the low-power wireless connection 1312 and the high-speed wireless connection 1314, may be implemented using details of the architecture of the head-wearable apparatus 116, as can other elements of the network 1316.
As shown in FIG. 13, the low-power processor 1336 or high-speed processor 1330 of the head-wearable apparatus 116 can be coupled to the camera (visible light camera 1306, infrared emitter 1308, or infrared camera 1310), the image display driver 1320, the user input device 1328 (e.g., touch sensor or push button), and the memory 1302.
The head-wearable apparatus 116 is connected to a host computer. For example, the head-wearable apparatus 116 is paired with the mobile device 114 via the high-speed wireless connection 1314 or connected to the server system 1304 via the network 1316. The server system 1304 may be one or more computing devices as part of a service or network computing system, for example, that includes a processor, a memory, and network communication interface to communicate over the network 1316 with the mobile device 114 and the head-wearable apparatus 116.
The mobile device 114 includes a processor and a network communication interface coupled to the processor. The network communication interface allows for communication over the network 1316, low-power wireless connection 1312, or high-speed wireless connection 1314. Mobile device 114 can further store at least portions of the instructions for generating binaural audio content in the memory of mobile device 114 to implement the functionality described herein.
Output components of the head-wearable apparatus 116 include visual components, such as a display such as a LCD, a PDP, a LED display, a projector, or a waveguide. The image displays of the optical assembly are driven by the image display driver 1320. The output components of the head-wearable apparatus 116 further include acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor), other signal generators, and so forth. The input components of the head-wearable apparatus 116, the mobile device 114, and server system 1304, such as the user input device 1328, may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard, or other alphanumeric input components), point-based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or other pointing instruments), tactile input components (e.g., a physical button, a touch screen that provides location and force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like.
The head-wearable apparatus 116 may also include additional peripheral device elements. Such peripheral device elements may include biometric sensors, additional sensors, or display elements integrated with the head-wearable apparatus 116. For example, peripheral device elements may include any I/O components including output components, motion components, position components, or any other such elements described herein.
For example, the biometric components include components to detect expressions (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye-tracking), measure biosignals (e.g., blood pressure, heart rate, body temperature, perspiration, or brain waves), identify a person (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram-based identification), and the like. The biometric components may include a BMI system that allows communication between the brain and an external device or machine. This may be achieved by recording brain activity data, translating this data into a format that can be understood by a computer, and then using the resulting signals to control the device or machine.
The motion components include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope), and so forth. The position components include location sensor components to generate location coordinates (e.g., a GPS receiver component), Wi-Fi or Bluetooth™ transceivers to generate positioning system coordinates, altitude sensor components (e.g., altimeters or barometers that detect air pressure from which altitude may be derived), orientation sensor components (e.g., magnetometers), and the like. Such positioning system coordinates can also be received over low-power wireless connections 1312 and high-speed wireless connection 1314 from the mobile device 114 via the low-power wireless circuitry 1334 or high-speed wireless circuitry 1332.
Glossary
“Carrier signal” refers, for example, to any intangible medium that is capable of storing, encoding, or carrying instructions for execution by the machine and includes digital or analog communications signals or other intangible media to facilitate communication of such instructions. Instructions may be transmitted or received over a network using a transmission medium via a network interface device.
“Client device” refers, for example, to any machine that interfaces to a communications network to obtain resources from one or more server systems or other client devices. A client device may be, but is not limited to, a mobile phone, desktop computer, laptop, portable digital assistant (PDA), smartphone, tablet, ultrabook, netbook, laptop, multi-processor system, microprocessor-based or programmable consumer electronics, game console, STB, or any other communication device that a user may use to access a network.
“Communication network” refers, for example, to one or more portions of a network that may be an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a WLAN, a wide area network (WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), the Internet, a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a plain old telephone service (POTS) network, a cellular telephone network, a wireless network, a Wi-Fi® network, another type of network, or a combination of two or more such networks. For example, a network or a portion of a network may include a wireless or cellular network, and the coupling may be a Code Division Multiple Access (CDMA) connection, a Global System for Mobile communications (GSM) connection, or other types of cellular or wireless coupling. In this example, the coupling may implement any of a variety of types of data transfer technology, such as Single Carrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized (EVDO) technology, General Packet Radio Service (GPRS) technology, Enhanced Data rates for GSM Evolution (EDGE) technology, third Generation Partnership Project (3GPP) including 3G, fourth-generation wireless (4G) networks, Universal Mobile Telecommunications System (UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability for Microwave Access (WiMAX), Long Term Evolution (LTE) standard, others defined by various standard-setting organizations, other long-range protocols, or other data transfer technology.
“Component” refers, for example, to a device, physical entity, or logic having boundaries defined by function or subroutine calls, branch points, APIs, or other technologies that provide for the partitioning or modularization of particular processing or control functions. Components may be combined via their interfaces with other components to carry out a machine process. A component may be a packaged functional hardware unit designed for use with other components and a part of a program that usually performs a particular function of related functions. Components may constitute either software components (e.g., code embodied on a machine-readable medium) or hardware components.
A “hardware component” is a tangible unit capable of performing certain operations and may be configured or arranged in a certain physical manner. In various examples, one or more computer systems (e.g., a standalone computer system, a client computer system, or a server computer system) or one or more hardware components of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a hardware component that operates to perform certain operations as described herein.
A hardware component may also be implemented mechanically, electronically, or any suitable combination thereof. For example, a hardware component may include dedicated circuitry or logic that is permanently configured to perform certain operations. A hardware component may be a special-purpose processor, such as a field-programmable gate array (FPGA) or an ASIC. A hardware component may also include programmable logic or circuitry that is temporarily configured by software to perform certain operations. For example, a hardware component may include software executed by a general-purpose processor or other programmable processors. Once configured by such software, hardware components become specific machines (or specific components of a machine) uniquely tailored to perform the configured functions and are no longer general-purpose processors. It will be appreciated that the decision to implement a hardware component mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software), may be driven by cost and time considerations. Accordingly, the phrase “hardware component” (or “hardware-implemented component”) should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein.
Considering examples in which hardware components are temporarily configured (e.g., programmed), each of the hardware components need not be configured or instantiated at any one instance in time. For example, where a hardware component comprises a general-purpose processor configured by software to become a special-purpose processor, the general-purpose processor may be configured as respectively different special-purpose processors (e.g., comprising different hardware components) at different times. Software accordingly configures a particular processor or processors, for example, to constitute a particular hardware component at one instance of time and to constitute a different hardware component at a different instance of time. Hardware components can provide information to, and receive information from, other hardware components. Accordingly, the described hardware components may be regarded as being communicatively coupled. Where multiple hardware components exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) between or among two or more of the hardware components. In examples in which multiple hardware components are configured or instantiated at different times, communications between such hardware components may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware components have access. For example, one hardware component may perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. A further hardware component may then, at a later time, access the memory device to retrieve and process the stored output. Hardware components may also initiate communications with input or output devices, and can operate on a resource (e.g., a collection of information). The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented components that operate to perform one or more operations or functions described herein.
As used herein, “processor-implemented component” refers to a hardware component implemented using one or more processors. Similarly, the methods described herein may be at least partially processor-implemented, with a particular processor or processors being an example of hardware. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented components. Moreover, the one or more processors may also operate to support performance of the relevant operations in a “cloud computing” environment or as a “software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), with these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., an API). The performance of certain of the operations may be distributed among the processors, not only residing within a single machine, but deployed across a number of machines. In some examples, the processors or processor-implemented components may be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other examples, the processors or processor-implemented components may be distributed across a number of geographic locations.
“Computer-readable storage medium” refers, for example, to both machine-storage media and transmission media. Thus, the terms include both storage devices/media and carrier waves/modulated data signals. The terms “machine-readable medium,” “computer-readable medium,” and “device-readable medium” mean the same thing and may be used interchangeably in this disclosure. “Ephemeral message” refers, for example, to a message that is accessible for a time-limited duration. An ephemeral message may be a text, an image, a video and the like. The access time for the ephemeral message may be set by the message sender. Alternatively, the access time may be a default setting or a setting specified by the recipient. Regardless of the setting technique, the message is transitory.
“Machine storage medium” refers, for example, to a single or multiple storage devices and media (e.g., a centralized or distributed database, and associated caches and servers) that store executable instructions, routines and data. The term shall accordingly be taken to include, but not be limited to, solid-state memories, and optical and magnetic media, including memory internal or external to processors. Specific examples of machine-storage media, computer-storage media and device-storage media include non-volatile memory, including by way of example semiconductor memory devices, e.g., erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), FPGA, and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks The terms “machine-storage medium,” “device-storage medium,” “computer-storage medium” mean the same thing and may be used interchangeably in this disclosure.
The terms “machine-storage media,” “computer-storage media,” and “device-storage media” specifically exclude carrier waves, modulated data signals, and other such media, at least some of which are covered under the term “signal medium.” “Non-transitory computer-readable storage medium” refers, for example, to a tangible medium that is capable of storing, encoding, or carrying the instructions for execution by a machine. “Signal medium” refers, for example, to any intangible medium that is capable of storing, encoding, or carrying the instructions for execution by a machine and includes digital or analog communications signals or other intangible media to facilitate communication of software or data. The term “signal medium” shall be taken to include any form of a modulated data signal, carrier wave, and so forth. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a matter as to encode information in the signal. The terms “transmission medium” and “signal medium” mean the same thing and may be used interchangeably in this disclosure.
“User device” refers, for example, to a device accessed, controlled or owned by a user and with which the user interacts perform an action, or interaction on the user device, including interaction with other users or computer systems. “Carrier signal” refers to any intangible medium that is capable of storing, encoding, or carrying instructions for execution by the machine and includes digital or analog communications signals or other intangible media to facilitate communication of such instructions. Instructions may be transmitted or received over a network using a transmission medium via a network interface device. “Client device” refers to any machine that interfaces to a communications network to obtain resources from one or more server systems or other client devices. A client device may be, but is not limited to, a mobile phone, desktop computer, laptop, PDA, smartphone, tablet, ultrabook, netbook, laptop, multi-processor system, microprocessor-based or programmable consumer electronics, game console, STB, or any other communication device that a user may use to access a network.
“Communication network” refers to one or more portions of a network that may be an ad hoc network, an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, the Internet, a portion of the Internet, a portion of the PSTN, a POTS network, a cellular telephone network, a wireless network, a Wi-Fi® network, another type of network, or a combination of two or more such networks. For example, a network or a portion of a network may include a wireless or cellular network, and the coupling may be a CDMA connection, a GSM connection, or other types of cellular or wireless coupling. In this example, the coupling may implement any of a variety of types of data transfer technology, such as 1×RTT, EVDO technology, GPRS technology, EDGE technology, 3GPP including 3G, 4G networks, UMTS, HSPA, WiMAX, LTE standard, others defined by various standard-setting organizations, other long-range protocols, or other data transfer technology.
Components may constitute either software components (e.g., code embodied on a machine-readable medium) or hardware components. A “hardware component” is a tangible unit capable of performing certain operations and may be configured or arranged in a certain physical manner. In various examples, one or more computer systems (e.g., a standalone computer system, a client computer system, or a server computer system) or one or more hardware components of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a hardware component that operates to perform certain operations as described herein.
A hardware component may also be implemented mechanically, electronically, or any suitable combination thereof. For example, a hardware component may include dedicated circuitry or logic that is permanently configured to perform certain operations. A hardware component may be a special-purpose processor, such as a FPGA or an ASIC. A hardware component may also include programmable logic or circuitry that is temporarily configured by software to perform certain operations. For example, a hardware component may include software executed by a general-purpose processor or other programmable processor. Once configured by such software, hardware components become specific machines (or specific components of a machine) uniquely tailored to perform the configured functions and are no longer general-purpose processors. It will be appreciated that the decision to implement a hardware component mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software), may be driven by cost and time considerations. Accordingly, the phrase “hardware component” (or “hardware-implemented component”) should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein.
The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented components that operate to perform one or more operations or functions described herein.
Changes and modifications may be made to the disclosed examples without departing from the scope of the present disclosure. These and other changes or modifications are intended to be included within the scope of the present disclosure, as expressed in the following claims.
