Patent Publication Number: US-2023155969-A1

Title: Context sensitive avatar captions

Description:
CLAIM OF PRIORITY 
     This application is a continuation of U.S. patent application Ser. No. 17/411,309, filed Aug. 25, 2021, which is a continuation of U.S. patent application Ser. No. 16/707,635, filed on Dec. 9, 2019, now issued as U.S. Pat. No. 11,128,586, each of which is hereby incorporated by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates generally to providing avatars with captions using a messaging application. 
     BACKGROUND 
     Users are always seeking new ways to connect with their friends on social media platforms. One way users try to connect with their friends is by sending customized messages with avatars. Many different types of avatars are available for users to choose from to include in the customized messages. 
    
    
     
       BRIEF DESCRIPTION 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 embodiments are illustrated by way of example, and not limitation, in the figures of the accompanying drawings in which: 
         FIG.  1    is a block diagram showing an example messaging system for exchanging data (e.g., messages and associated content) over a network, according to example embodiments. 
         FIG.  2    is a schematic diagram illustrating data which may be stored in the database of a messaging server system, according to example embodiments. 
         FIG.  3    is a schematic diagram illustrating a structure of a message generated by a messaging client application for communication, according to example embodiments. 
         FIG.  4    is a block diagram showing an example context sensitive avatar system, according to example embodiments. 
         FIG.  5    is a flowchart illustrating example operations of the context sensitive avatar system, according to example embodiments. 
         FIGS.  6 - 8    are illustrative inputs and outputs of the context sensitive avatar system, according to example embodiments. 
         FIG.  9    is a block diagram illustrating a representative software architecture, which may be used in conjunction with various hardware architectures herein described, according to example embodiments. 
         FIG.  10    is a block diagram illustrating components of a machine able to read instructions from a machine-readable medium (e.g., a machine-readable storage medium) and perform any one or more of the methodologies discussed herein, according to example embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     The description that follows includes systems, methods, techniques, instruction sequences, and computing machine program products that embody illustrative embodiments 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 embodiments. It will be evident, however, to those skilled in the art, that embodiments may be practiced without these specific details. In general, well-known instruction instances, protocols, structures, and techniques are not necessarily shown in detail. 
     Typical user devices allow users to communicate with each other using graphics. To do so, a user typically enters search parameters to find graphics that best represents a message the user is trying to convey. Specifically, many graphics may be available for a user to choose from. Finding the right graphics requires navigating through multiple pages of information and can be very tedious and time consuming. Given the complexity and amount of time it takes to find graphics of interest to include in a message being sent, users become discouraged from including graphics in their messages. This results in users losing interest in using advanced features of a messaging application which wastes resources. 
     The disclosed embodiments improve the efficiency of using the electronic device by providing a messaging application that intelligently and automatically selects an avatar having a particular expression based a message a user composes for transmission to a friend. This results in users spending less time composing messages that include avatars and allows users to more seamlessly create messages that are enriched with content for more accurately conveying a message. Specifically, according to the disclosed embodiments, a messaging application receives a user selection of an option to generate a message using an avatar with a caption. The messaging application presents the avatar and a caption entry region proximate to the avatar and populates the caption entry region with a text string provided by the user. As the user types in one or more words into the text string, the messaging application determines context of the caption and modifies an expression of the avatar based on the determined context. 
     In some cases, the caption is presented in a curved manner around the avatar. In some cases, the messaging application determines that the text string represents or identifies a second user. In response, the messaging application automatically retrieves a second avatar that represents the second user. The messaging application then presents both the avatar of the user composing the message and the retrieved avatar of the second user identified in the message together. The avatars are presented with expressions that correspond to the context of the message. In some cases, the message application determines that the user is engaged in a conversation with a second user. In response, the messaging application presents an avatar for each user engaged in the conversation with expressions that correspond to context of the conversation or text in the avatar caption. 
     In this way, the disclosed embodiments improve the efficiency of using the electronic device by reducing the number of screens and interfaces a user has to navigate through to select a suitable avatar to convey a message to other users. This is done by automatically determining context of a message a user is composing, and as the user types in words of the message, modifying the expression of the avatar that is presented and that will be included in the message being sent to other users. This also reduces the device resources (e.g., processor cycles, memory, and power usage) needed to accomplish a task with the device. 
       FIG.  1    is a block diagram showing an example messaging system  100  for exchanging data (e.g., messages and associated content) over a network  106 . The messaging system  100  includes multiple client devices  102 , each of which hosts a number of applications, including a messaging client application  104  and a third-party application  105 . Each messaging client application  104  is communicatively coupled to other instances of the messaging client application  104 , the third-party application  105 , and a messaging server system  108  via a network  106  (e.g., the Internet). 
     Accordingly, each messaging client application  104  and third-party application  105  is able to communicate and exchange data with another messaging client application  104  and third-party application(s)  105  and with the messaging server system  108  via the network  106 . The data exchanged between messaging client applications  104 , third-party applications  105 , and between a messaging client application  104  and the messaging server system  108  includes functions (e.g., commands to invoke functions) and payload data (e.g., text, audio, video, or other multimedia data). Any disclosed communications between the messaging client application  104  and the third-party application(s)  105  can be transmitted directly from the messaging client application  104  to the third-party application(s)  105  and/or indirectly (e.g., via one or more servers) from the messaging client application  104  to the third-party application(s)  105 . 
     The third-party application(s)  105  and the messaging client application  104  are applications that include a set of functions that allow the client device  102  to access a context sensitive avatar system  124 . The third-party application  105  is an application that is separate and distinct from the messaging client application  104 . The third-party application(s)  105  are downloaded and installed by the client device  102  separately from the messaging client application  104 . In some implementations, the third-party application(s)  105  are downloaded and installed by the client device  102  before or after the messaging client application  104  is downloaded and installed. The third-party application  105  is an application that is provided by an entity or organization that is different from the entity or organization that provides the messaging client application  104 . The third-party application  105  is an application that can be accessed by a client device  102  using separate login credentials than the messaging client application  104 . Namely, the third-party application  105  can maintain a first user account and the messaging client application  104  can maintain a second user account. In an embodiment, the third-party application  105  can be accessed by the client device  102  to perform various activities and interactions, such as listening to music, videos, track exercises, view graphical elements (e.g., stickers), communicate with other users, and so forth. 
     As an example, the third-party application  105  can be a social networking application, a dating application, a ride or car sharing application, a shopping application, a trading application, a gaming application, an imaging application, a music application, a video browsing application, an exercise tracking application, a health monitoring application, a graphical element or sticker browsing application, or any other suitable application. 
     The messaging client application  104  allows a user to access a camera feature. The camera feature of the messaging client application  104  activates a front-facing camera of the client device  102  and presents video or images captured or received by the front-facing camera on a display screen of the client device  102  as the video or images are being captured. In an implementation, the front-facing camera is integrated or placed on a same side of the client device  102  as the screen that presents the content captured by the front-facing camera. 
     After the user presses a suitable button of the messaging client application  104  to store the image or video captured by the front-facing camera, the messaging client application  104  allows the user to review or edit the images that were captured. In some cases, one or more editing tools may be presented to modify or edit the stored images. Such editing tools may include text tools allowing the user to add text to the images or videos. Such text tools include a big text option, an avatar with caption option, a rainbow text option, a scripted text option, and so forth. 
     In response to receiving a user selection of the avatar with caption option, the messaging client application  104  retrieves an avatar of the user. The avatar is presented on top of the image or video that was captured and includes a text entry region. In some cases, the avatar is initially presented with a neutral expression (e.g., not smiling or sad). In some embodiments, the avatar is presented with a default expression which may be selected by the user. The text entry region may be proximate to the avatar, such as above or below the avatar. The text entry region allows the user to input a text string. As the user types in the words of the text string the string wraps around the avatar in a circular manner to surround the avatar. 
     In some embodiments, the messaging client application  104  processes and analyzes one or more words of the text string input by the user. The messaging client application  104  determines context of the string based on the one or more words of the text string. For example, if the string includes positive or happy words (e.g., glad, excited, ecstatic, impressed, and so forth), the messaging client application  104  determines the context to be positive or happy. In response, the messaging client application  104  retrieves an avatar expression that is associated or that represents a positive or happy mood. The messaging client application  104  modifies the avatar expression immediately once the context is determined to represent the retrieved avatar expression. In this way, as the user types in words of the text string in the caption, the messaging client application  104  dynamically adjusts and modifies the expression of the avatar to represent the mood or context of the message in the text string. For example, if the user inputs additional words after the positive or happy words and after the avatar expression is modified, the avatar expression may change based on the context of the additional words. Namely, the additional words may be determined to be associated with an excited context. In response, the messaging client application  104  modifies the expression of the avatar from representing a positive or happy expression to representing an excited expression. 
     In some embodiments, the messaging client application  104  may determine that one or more words in the text string represent another user of the messaging application. For example, the one or more words may specifically identify a username of another user or may include attributes that are uniquely associated with the other user. In response, the messaging client application  104  retrieves a second avatar of the other user and presents the second avatar together with a first avatar of the user composing the message. In such cases, the expressions of both avatars may be modified to represent context of the message composed by the user. In some cases, the expression of the first avatar corresponding to the user composing the text string may be modified to represent a first context in the message associated with the user and the expression of the second avatar corresponding to the other user mentioned in the text string may be modified to represent a second context. As an example, the user may type in the string “I am happy but John Smith is sad”. In such cases, the expression of the user&#39;s avatar may be modified to represent a happy context and the avatar of the other user, John Smith, may be modified to represent a sad context. 
     In some cases, the messaging client application  104  determines that the user has launched the camera to capture the image or video from within a conversation with one or more other users. In such cases, any image or video and message composed with the image or video may be automatically directed to the one or more other users with whom the user is engaged in a conversation. In such cases, the messaging client application  104  may automatically retrieve avatars of each user involved in the conversation when the user selects the avatar with caption option. The messaging client application  104  may modify the expressions of all the avatars being presented based on the context of the caption or text string input by the user. 
     After the user completes composing the caption with the avatar, the messaging client application  104  may receive input from the user that selects a send option. In response, the messaging client application  104  sends the image or video that was captured by the user with the avatar with the caption that augments the image or video to one or more designated recipients. The designated recipients may be manually input by the user after composing the message. Alternatively, the designated recipients may be automatically populated to include all members of a conversation when the camera to capture the image or video that includes the avatar with caption was launched from within a conversation with the members of the conversation. 
     The messaging server system  108  provides server-side functionality via the network  106  to a particular messaging client application  104 . While certain functions of the messaging system  100  are described herein as being performed by either a messaging client application  104  or by the messaging server system  108 , it will be appreciated that the location of certain functionality either within the messaging client application  104  or the messaging server system  108  is a design choice. For example, it may be technically preferable to initially deploy certain technology and functionality within the messaging server system  108 , but to later migrate this technology and functionality to the messaging client application  104  where a client device  102  has a sufficient processing capacity. 
     The messaging server system  108  supports various services and operations that are provided to the messaging client application  104 . Such operations include transmitting data to, receiving data from, and processing data generated by the messaging client application  104 . This data may include message content, client device information, graphical elements, geolocation information, media annotation and overlays, virtual objects, message content persistence conditions, social network information, and live event information, as examples. Data exchanges within the messaging system  100  are invoked and controlled through functions available via user interfaces (UIs) (e.g., graphical user interfaces) of the messaging client application  104 . 
     Turning now specifically to the messaging server system  108 , an API server  110  is coupled to, and provides a programmatic interface to, an application server  112 . The application server  112  is communicatively coupled to a database server  118 , which facilitates access to a database  120  in which is stored data associated with messages processed by the application server  112 . 
     Dealing specifically with the API server  110 , this server  110  receives and transmits message data (e.g., commands and message payloads) between the client device  102  and the application server  112 . Specifically, the API server  110  provides a set of interfaces (e.g., routines and protocols) that can be called or queried by the messaging client application  104  and the third-party application  105  in order to invoke functionality of the application server  112 . The API server  110  exposes various functions supported by the application server  112 , including account registration; login functionality; the sending of messages, via the application server  112 , from a particular messaging client application  104  to another messaging client application  104  or third-party application  105 ; the sending of media files (e.g., graphical elements, images or video) from a messaging client application  104  to a messaging server application  114 , and for possible access by another messaging client application  104  or third-party application  105 ; a graphical element list; the setting of a collection of media data (e.g., story); the retrieval of such collections; the retrieval of a list of friends of a user of a client device  102 ; the retrieval of messages and content; the adding and deleting of friends to a social graph; the location of friends within a social graph; access to user conversation data; access to avatar information stored on messaging server system  108 ; and opening an application event (e.g., relating to the messaging client application  104 ). 
     The application server  112  hosts a number of applications and subsystems, including a messaging server application  114 , an image processing system  116 , a social network system  122 , and the context sensitive avatar system  124 . The messaging server application  114  implements a number of message processing technologies and functions, particularly related to the aggregation and other processing of content (e.g., textual and multimedia content) included in messages received from multiple instances of the messaging client application  104 . As will be described in further detail, the text and media content from multiple sources may be aggregated into collections of content (e.g., called stories or galleries). These collections are then made available, by the messaging server application  114 , to the messaging client application  104 . Other processor- and memory-intensive processing of data may also be performed server-side by the messaging server application  114 , in view of the hardware requirements for such processing. 
     The application server  112  also includes an image processing system  116  that is dedicated to performing various image processing operations, typically with respect to images or video received within the payload of a message at the messaging server application  114 . A portion of the image processing system  116  may also be implemented by the context sensitive avatar system  124 . 
     The social network system  122  supports various social networking functions and services and makes these functions and services available to the messaging server application  114 . To this end, the social network system  122  maintains and accesses an entity graph within the database  120 . Examples of functions and services supported by the social network system  122  include the identification of other users of the messaging system  100  with which a particular user has relationships or is “following” and also the identification of other entities and interests of a particular user. Such other users may be referred to as the user&#39;s friends. Social network system  122  may access location information associated with each of the user&#39;s friends to determine where they live or are currently located geographically. Social network system  122  may maintain a location profile for each of the user&#39;s friends indicating the geographical location where the user&#39;s friends live. 
     The context sensitive avatar system  124  dynamically modifies an expression of an avatar based on context of a caption associated with the avatar. For example, the context sensitive avatar system  124  receives a user selection of an avatar with caption option. The context sensitive avatar system  124  presents an avatar of the user and a text entry region. As the user types in words in the text entry region, the context sensitive avatar system  124  determines context of one or more words in the text entry region. In some cases, the context sensitive avatar system  124  determines multiple contexts associated with the one or more words in the text entry region. The context sensitive avatar system  124  ranks the multiple contexts based on relevance and selects a given context associated with a highest rank. The context sensitive avatar system  124  retrieves an avatar expression associated with the given context and modifies the expression of the avatar to represent the retrieved avatar expression. In some cases, the context sensitive avatar system  124  presents multiple avatars with the caption and modifies the expressions of all the avatars based on the context of the caption. 
     The application server  112  is communicatively coupled to a database server  118 , which facilitates access to a database  120 , in which is stored data associated with messages processed by the messaging server application  114 . Database  120  may be a third-party database. For example, the application server  112  may be associated with a first entity, and the database  120  or a portion of the database  120  may be associated and hosted by a second different entity. In some implementations, database  120  stores user data that the first entity collects about various each of the users of a service provided by the first entity. For example, the user data includes user names, phone numbers, passwords, addresses, friends, activity information, preferences, videos or content consumed by the user, and so forth. 
       FIG.  2    is a schematic diagram  200  illustrating data, which may be stored in the database  120  of the messaging server system  108 , according to certain example embodiments. While the content of the database  120  is shown to comprise a number of 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  120  includes message data stored within a message table  214 . An entity table  202  stores entity data, including an entity graph  204 . Entities for which records are maintained within the entity table  202  may include individuals, corporate entities, organizations, objects, places, events, and so forth. Regardless of type, any entity regarding which the messaging server system  108  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  204  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. 
     Message table  214  may store a collection of conversations between a user and one or more friends or entities. Message table  214  may include various attributes of each conversation, such as the list of participants, the size of the conversation (e.g., number of users and/or number of messages), the chat color of the conversation, a unique identifier for the conversation, and any other conversation related feature(s). 
     The database  120  also stores annotation data, in the example form of filters, in an annotation table  212 . Database  120  also stores annotated content received in the annotation table  212 . Filters for which data is stored within the annotation table  212  are associated with and applied to videos (for which data is stored in a video table  210 ) and/or images (for which data is stored in an image table  208 ). Filters, in one example, 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 gallery of filters presented to a sending user by the messaging client application  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 UI by the messaging client application  104 , based on geolocation information determined by a Global Positioning System (GPS) unit of the client device  102 . Another type of filter is a data filter, which may be selectively presented to a sending user by the messaging client application  104 , based on other inputs or information gathered by the client device  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 client device  102 , or the current time. 
     Other annotation data that may be stored within the image table  208  is augmented reality data or LENSES. Augmented reality data may be a real-time special effect and sound that may be added to an image or a video. 
     As described above, LENSES, overlays, image transformations, AR images and similar terms refer to modifications that may be made to videos or images. This includes real-time modification which modifies an image as it is captured using a device sensor and then displayed on a screen of the device with the modifications. This also includes modifications to stored content, such as video clips in a gallery that may be modified. For example, in a device with access to multiple LENSES, a user can use a single video clip with multiple LENSES to see how the different LENSES will modify the stored clip. For example, multiple LENSES that apply different pseudorandom movement models can be applied to the same content by selecting different LENSES for the content. Similarly, real-time video capture may be used with an illustrated modification to show how video images currently being captured by sensors of a device would modify the captured data. Such data may simply be displayed on the screen and not stored in memory, or the content captured by the device sensors may be recorded and stored in memory with or without the modifications (or both). In some systems, a preview feature can show how different LENSES will look within different windows in a display at the same time. This can, for example, enable multiple windows with different pseudorandom animations to be viewed on a display at the same time. 
     Data and various systems to use LENSES or other such transform systems to modify content using this data can thus involve detection of objects (e.g. faces, hands, bodies, cats, dogs, surfaces, objects, etc.), tracking of such objects as they leave, enter, and move around the field of view in video frames, and the modification or transformation of such objects as they are tracked. In various embodiments, different methods for achieving such transformations may be used. For example, some embodiments may involve generating a three-dimensional mesh model of the object or objects, and using transformations and animated textures of the model within the video to achieve the transformation. In other embodiments, tracking of points on an object may be used to place an image or texture (which may be two dimensional or three dimensional) at the tracked position. In still further embodiments, neural network analysis of video frames may be used to place images, models, or textures in content (e.g. images or frames of video). LENS data thus refers both to the images, models, and textures used to create transformations in content, as well as to additional modeling and analysis information needed to achieve such transformations with object detection, tracking, and placement. 
     Real time video processing can be performed with any kind of video data, (e.g. video streams, video files, etc.) saved in a memory of a computerized system of any kind. For example, a user can load video files and save them in a memory of a device, or can generate a video stream using sensors of the device. Additionally, any objects can be processed using a computer animation model, such as a human&#39;s face and parts of a human body, animals, or non-living things such as chairs, cars, or other objects. 
     In some embodiments, when a particular modification is selected along with content to be transformed, elements to be transformed are identified by the computing device, and then detected and tracked if they are present in the frames of the video. The elements of the object are modified according to the request for modification, thus transforming the frames of the video stream. Transformation of frames of a video stream can be performed by different methods for different kinds of transformation. For example, for transformations of frames mostly referring to changing forms of object&#39;s elements characteristic points for each of element of an object are calculated (e.g. using an Active Shape Model (ASM) or other known methods). Then, a mesh based on the characteristic points is generated for each of the at least one element of the object. This mesh used in the following stage of tracking the elements of the object in the video stream. In the process of tracking, the mentioned mesh for each element is aligned with a position of each element. Then, additional points are generated on the mesh. A first set of first points is generated for each element based on a request for modification, and a set of second points is generated for each element based on the set of first points and the request for modification. Then, the frames of the video stream can be transformed by modifying the elements of the object on the basis of the sets of first and second points and the mesh. In such method a background of the modified object can be changed or distorted as well by tracking and modifying the background. 
     In one or more embodiments, transformations changing some areas of an object using its elements can be performed by calculating of characteristic points for each element of an object and generating a mesh based on the calculated characteristic points. Points are generated on the mesh, and then various areas based on the points are generated. The elements of the object are then tracked by aligning the area for each element with a position for each of the at least one element, and properties of the areas can be modified based on the request for modification, thus transforming the frames of the video stream. Depending on the specific request for modification properties of the mentioned areas can be transformed in different ways. Such modifications may involve: changing color of areas; removing at least some part of areas from the frames of the video stream; including one or more new objects into areas which are based on a request for modification; and modifying or distorting the elements of an area or object. In various embodiments, any combination of such modifications or other similar modifications may be used. For certain models to be animated, some characteristic points can be selected as control points to be used in determining the entire state-space of options for the model animation. 
     In some embodiments of a computer animation model to transform image data using face detection, the face is detected on an image with use of a specific face detection algorithm (e.g. Viola-Jones). Then, an Active Shape Model (ASM) algorithm is applied to the face region of an image to detect facial feature reference points. 
     In other embodiments, other methods and algorithms suitable for face detection can be used. For example, in some embodiments, features are located using a landmark which represents a distinguishable point present in most of the images under consideration. For facial landmarks, for example, the location of the left eye pupil may be used. In an initial landmark is not identifiable (e.g. if a person has an eyepatch), secondary landmarks may be used. Such landmark identification procedures may be used for any such objects. In some embodiments, a set of landmarks forms a shape. Shapes can be represented as vectors using the coordinates of the points in the shape. One shape is aligned to another with a similarity transform (allowing translation, scaling, and rotation) that minimizes the average Euclidean distance between shape points. The mean shape is the mean of the aligned training shapes. 
     In some embodiments, a search for landmarks from the mean shape aligned to the position and size of the face determined by a global face detector is started. Such a search then repeats the steps of suggesting a tentative shape by adjusting the locations of shape points by template matching of the image texture around each point and then conforming the tentative shape to a global shape model until convergence occurs. In some systems, individual template matches are unreliable and the shape model pools the results of the weak template matchers to form a stronger overall classifier. The entire search is repeated at each level in an image pyramid, from coarse to fine resolution. 
     Embodiments of a transformation system can capture an image or video stream on a client device and perform complex image manipulations locally on a client device such as client device  102  while maintaining a suitable user experience, computation time, and power consumption. The complex image manipulations may include size and shape changes, emotion transfers (e.g., changing a face from a frown to a smile), state transfers (e.g., aging a subject, reducing apparent age, changing gender), style transfers, graphical element application, and any other suitable image or video manipulation implemented by a convolutional neural network that has been configured to execute efficiently on a client device. 
     In some example embodiments, a computer animation model to transform image data can be used by a system where a user may capture an image or video stream of the user (e.g., a selfie) using a client device  102  having a neural network operating as part of a messaging client application  104  operating on the client device  102 . The transform system operating within the messaging client application  104  determines the presence of a face within the image or video stream and provides modification icons associated with a computer animation model to transform image data, or the computer animation model can be present as associated with an interface described herein. The modification icons include changes which may be the basis for modifying the user&#39;s face within the image or video stream as part of the modification operation. Once a modification icon is selected, the transform system initiates a process to convert the image of the user to reflect the selected modification icon (e.g., generate a smiling face on the user). In some embodiments, a modified image or video stream may be presented in a graphical user interface displayed on the mobile client device as soon as the image or video stream is captured and a specified modification is selected. The transform system may implement a complex convolutional neural network on a portion of the image or video stream to generate and apply the selected modification. That is, the user may capture the image or video stream and be presented with a modified result in real time or near real time once a modification icon has been selected. Further, the modification may be persistent while the video stream is being captured and the selected modification icon remains toggled. Machine taught neural networks may be used to enable such modifications. 
     In some embodiments, the graphical user interface, presenting the modification performed by the transform system, may supply the user with additional interaction options. Such options may be based on the interface used to initiate the content capture and selection of a particular computer animation model (e.g. initiation from a content creator user interface). In various embodiments, a modification may be persistent after an initial selection of a modification icon. The user may toggle the modification on or off by tapping or otherwise selecting the face being modified by the transformation system. and store it for later viewing or browse to other areas of the imaging application. Where multiple faces are modified by the transformation system, the user may toggle the modification on or off globally by tapping or selecting a single face modified and displayed within a graphical user interface. In some embodiments, individual faces, among a group of multiple faces, may be individually modified or such modifications may be individually toggled by tapping or selecting the individual face or a series of individual faces displayed within the graphical user interface. 
     As mentioned above, the video table  210  stores video data which, in one embodiment, is associated with messages for which records are maintained within the message table  214 . Similarly, the image table  208  stores image data associated with messages for which message data is stored in the entity table  202 . The entity table  202  may associate various annotations from the annotation table  212  with various images and videos stored in the image table  208  and the video table  210 . 
     Avatar expressions list  207  stores a list of different avatar expressions that are associated with different contexts. For example, the avatar expressions list  207  may store different avatar textures, each associated with a different context. The avatar texture may be retrieved and used to modify an avatar for one or more users to represent the context associated with the avatar texture. 
     Contexts list  209  stores a list of different contexts associated with different words or combinations of words. Avatar expressions lists  207  stores a list of rules that the context sensitive avatar system  124  uses to process a text string to derive or determine a context of the text string. 
     A story table  206  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  202 ). 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 UI of the messaging client application  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 UI of the messaging client application  104 , to contribute content to a particular live story. The live story may be identified to the user by the messaging client application  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 client device  102  is located within a specific geographic location (e.g., on a college or university campus) to contribute to a particular collection. In some embodiments, a contribution to a location story may require 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). 
       FIG.  3    is a schematic diagram illustrating a structure of a message  300 , according to some embodiments, generated by a messaging client application  104  for communication to a further messaging client application  104  or the messaging server application  114 . The content of a particular message  300  is used to populate the message table  214  stored within the database  120 , accessible by the messaging server application  114 . Similarly, the content of a message  300  is stored in memory as “in-transit” or “in-flight” data of the client device  102  or the application server  112 . The message  300  is shown to include the following components:
         A message identifier  302 : a unique identifier that identifies the message  300 .   A message text payload  304 : text, to be generated by a user via a UI of the client device  102  and that is included in the message  300 .   A message image payload  306 : image data, captured by a camera component of a client device  102  or retrieved from memory of a client device  102 , and that is included in the message  300 .   A message video payload  308 : video data, captured by a camera component or retrieved from a memory component of the client device  102  and that is included in the message  300 .   A message audio payload  310 : audio data, captured by a microphone or retrieved from the memory component of the client device  102 , and that is included in the message  300 .   Message annotations  312 : annotation data (e.g., filters, stickers, or other enhancements) that represents annotations to be applied to message image payload  306 , message video payload  308 , or message audio payload  310  of the message  300 .   A message duration parameter  314 : parameter value indicating, in seconds, the amount of time for which content of the message (e.g., the message image payload  306 , message video payload  308 , message audio payload  310 ) is to be presented or made accessible to a user via the messaging client application  104 .   A message geolocation parameter  316 : geolocation data (e.g., latitudinal and longitudinal coordinates) associated with the content payload of the message. Multiple message geolocation parameter  316  values may be included in the payload, with 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  306 , or a specific video in the message video payload  308 ).   A message story identifier  318 : identifier value identifying one or more content collections (e.g., “stories”) with which a particular content item in the message image payload  306  of the message  300  is associated. For example, multiple images within the message image payload  306  may each be associated with multiple content collections using identifier values.   A message tag  320 : each message  300  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  306  depicts an animal (e.g., a lion), a tag value may be included within the message tag  320  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.   A message sender identifier  322 : an identifier (e.g., a messaging system identifier, email address, or device identifier) indicative of a user of the client device  102  on which the message  300  was generated and from which the message  300  was sent.   A message receiver identifier  324 : an identifier (e.g., a messaging system identifier, email address, or device identifier) indicative of user(s) of the client device  102  to which the message  300  is addressed. In the case of a conversation between multiple users, the identifier may indicate each user involved in the conversation.       

     The contents (e.g., values) of the various components of message  300  may be pointers to locations in tables within which content data values are stored. For example, an image value in the message image payload  306  may be a pointer to (or address of) a location within an image table  208 . Similarly, values within the message video payload  308  may point to data stored within a video table  210 , values stored within the message annotations  312  may point to data stored in an annotation table  212 , values stored within the message story identifier  318  may point to data stored in a story table  206 , and values stored within the message sender identifier  322  and the message receiver identifier  324  may point to user records stored within an entity table  202 . 
       FIG.  4    is a block diagram showing an example context sensitive avatar system  124 , according to example embodiments. Context sensitive avatar system  124  includes an avatar caption module  414 , a context analysis module  416 , and an avatar expression modification module  418 . 
     The user launches an image capture component of the messaging client application  104 . In response, the avatar caption module  414  presents on a display screen of the client device  102  an image or video captured by a front-facing or rear-facing camera of the client device  102 . 
     The avatar caption module  414  presents one or more editing tools to the user to modify the image or video is being presented to the user on the screen of the client device  102 . The editing tools allow the user to select one or more graphical elements (e.g., avatars, text, emojis, images, videos, and so forth) to add or augment the image or video being presented to the user. For example, the user can add text to the image or video presented on the display screen at a user selected position. In some cases, the editing tools allow the user to select an avatar with caption option. 
     In response to receiving the user selection of the avatar with caption option, the avatar caption module  414  determines whether the user is engaged in a conversation with one or more other users. Namely, the avatar caption module  414  determines whether the image or video was captured in response to the user selecting a reply with camera option from within a conversation with another user. In response to determining that the reply with camera option was selected from within a conversation to capture the image, the avatar caption module  414  presents avatars for each user with whom the user is engaged in the conversation. The avatars are presented with a caption entry region above or below the avatars. In response to determining that the user is not currently engaged with another member in a conversation (e.g., the reply with camera option was not selected to capture the image), the avatar caption module  414  presents one avatar for the user with the caption entry region above or below the avatar. In both cases (single or multiple avatar presentation), text that is input by the user into the caption entry region wraps around the avatars in a circular manner as the user types in words of a text string for the caption entry region. 
     Context analysis module  416  processes one or more words in the text string input by the user. The context analysis module  416  analysis the one or more words as the user types in the words with reference to the words and/or rules stored in the contexts list  209 . The context analysis module  416  generates a list of contexts for the words the user types in and ranks the generated list of contexts. The context analysis module  416  selects a given context from the generated list that is associated with a highest rank. 
     In some cases, the context analysis module  416  determines which words in the string are associated with which avatar that is presented. The context analysis module  416  may perform semantic analysis and processing to determine, based on the sentence structure, which words are associated with a particular avatar. For example, if the text string is “I am happy and John Smith is sad,” the context analysis module  416  determines that happy is associated with the user who is typing in the expression and that sad is associated with the avatar for John Smith. In such cases, the context analysis module  416  retrieves and selects a context for each avatar that is associated with a particular word. Alternatively, the context analysis module  416  associates all the avatars with the same highest ranked context from the list of contexts. 
     The avatar expression modification module  418  receives the context or contexts selected by the context analysis module  416 . The avatar expression modification module  418  retrieves a texture or textures from the avatar expressions list  207  that is associated with the selected context or contexts. For example, if the selected context is sad, the avatar expression modification module  418  retrieves an avatar texture associated with a sad context. For example, if the selected contexts are sad and happy, the avatar expression modification module  418  retrieves avatar textures associated with sad and happy contexts. 
     The avatar expression modification module  418  uses the retrieved textures to modify the expression of the avatar or avatars as the user types in the text string in the caption entry region. The avatar expression modification module  418  may receive an indication from the context analysis module  416  that identifies which avatar of multiple avatars that are presented is associated with which context. For example, when the context analysis module  416  determines that a first avatar is associated with words corresponding to a first context and a second avatar is associated with words corresponding to a second context, the context analysis module  416  provides this information to the avatar expression modification module  418 . The avatar expression modification module  418  then uses a first texture associated with the first context to modify the expression of the first avatar and uses a second texture associated with the second context to modify the expression of the second avatar. 
     The context analysis module  416  continuously and dynamically processes words of the caption entry region being input by the user to continuously and dynamically rank and select the most suitable context for the caption. When a different context is selected by the context analysis module  416 , the context analysis module  416  notifies the avatar expression modification module  418  to modify the expression of the avatar based on the different or newly selected context. This way, as the user types in words of a string into the caption entry region, the expressions of the avatar or avatars change to represent the context or the string. 
       FIG.  5    is a flowchart illustrating example operations of the context sensitive avatar system  124  in performing process  500 , according to example embodiments. The process  500  may be embodied in computer-readable instructions for execution by one or more processors such that the operations of the process  500  may be performed in part or in whole by the functional components of the messaging server system  108  and/or third-party application  105 ; accordingly, the process  500  is described below by way of example with reference thereto. However, in other embodiments, at least some of the operations of the process  500  may be deployed on various other hardware configurations. The process  500  is therefore not intended to be limited to the messaging server system  108  and can be implemented in whole, or in part, by any other component. Some or all of the operations of process  500  can be in parallel, out of order, or entirely omitted. 
     At operation  501 , the context sensitive avatar system  124  receives input that selects an option to generate a message using an avatar with a caption. For example, the context sensitive avatar system  124  determines that a user has captured an image using a camera of a mobile device and has input a request to add text to the image. The context sensitive avatar system  124 , in response, presents a menu of text insertion options including an avatar with caption option. The context sensitive avatar system  124  receives a user selection of the avatar with caption option from the menu. 
     At operation  502 , the context sensitive avatar system  124  presents the avatar and a caption entry region proximate to the avatar. For example, the context sensitive avatar system  124  retrieves an avatar associated with the user with a neutral expression. The context sensitive avatar system  124  presents a cursor above or below the avatar that enables the user to input a text string that includes one or more words that surround the avatar in a circular manner. 
     At operation  503 , the context sensitive avatar system  124  populates the caption entry region with a text string comprising one or more words. For example, the context sensitive avatar system  124  receives one or more words from the user typing in characters on a keypad. 
     At operation  504 , the context sensitive avatar system  124  determines context based on the one or more words in the text string. For example, the context sensitive avatar system  124  processes the words, as the words are entered by the user, to determine whether the words are associated with a positive or negative context or a happy or sad context. 
     At operation  505 , the context sensitive avatar system  124  modifies an expression of the avatar based on the determined context. For example, the context sensitive avatar system  124  retrieves a texture for the avatar associated with the determined context and modifies the expression of the avatar using the retrieved texture. Namely, if the context is determined to be sad, the context sensitive avatar system  124  modifies the avatar&#39;s expression from neutral to sad. 
       FIGS.  6 - 8    are illustrative inputs and outputs of the context sensitive avatar system  124 , according to example embodiments. For example, as shown in  FIG.  6   , after the user captures an image with a camera of the user device, the user selects an option to add text to the image. In response, the context sensitive avatar system  124  presents a text options menu  630  with a plurality of text entry types. For example, a first text entry type is an “@” type which allows a user to add text with a reference to another user or a subject. A second text entry type is a big text entry type which allows a user to input text and add text to the image with very large font or style. A third text entry type is an avatar with caption option  632  that allows the user to add an avatar and input a text in caption entry region proximate to the avatar. A fourth text entry type is a rainbow text entry type which allows the user to input text with colorful attributes. A fifth text entry type is a script type with allows the user to input text with a script attribute. 
     As shown in screen  601 , the user selects the avatar with caption option  632 . In response, the context sensitive avatar system  124  retrieves an avatar  610  associated with the user and presents the avatar  610  together with a caption entry region  620 . The context sensitive avatar system  124  allows the user to type in text and, as shown in screen  602 , the string  622  input by the user wraps around in a circular manner around the avatar  612 . Also, the context sensitive avatar system  124  determines that the context in the string input by the user is happy. For example, the context sensitive avatar system  124  determines that the word “glad” is in the string  622  and that the word has a happy context. In response, the context sensitive avatar system  124  modifies the expression of the avatar  610  to have a happy expression as shown by avatar  612 . Namely, avatar  610  in screen  601  may have a neutral expression and avatar  612  may have a happy expression to represent the context of the string  622  input by the user. 
     Screen  603  shows another example in which the string input by the user is determined to have a sad context. For example, the string may include the word “worst” which is determined by the context sensitive avatar system  124  to have a sad context. In response, the expression of avatar  610  is modified to have a sad expression as shown in screen  603 . The user can select a send option to designate recipients of a message that includes the image captured by the user augmented with the avatar with the caption having the modified expression. 
     As shown in  FIG.  7   , the user types in a string that includes a symbol  710  (e.g., “@”) that references a subject or user. Screen  701  includes a string with the symbol  710  that references a username of another user. In response, the context sensitive avatar system  124  retrieves a second avatar associated with the referenced username of the other user. The context sensitive avatar system  124  presents both avatars  720  (one for the user composing the string and another for the second user). The avatars  720  are surrounded by the string with the symbol  710  that wraps around the avatars  720  in a circular manner. The avatars  720  are modified to have an expression that corresponds to the context of the string input by the user. The user can select a send option to designate recipients of a message that includes the image captured by the user augmented with the avatars  720  with the caption that includes the symbol  710 . 
     In some embodiments, the user selects a reply with camera option from within a conversation with another user. In response to the user selecting the reply with camera option, the messaging client application  104  activates the camera and allows the user to capture an image or video. After the user captures the image or video, the user can augment the image or video with text by selecting a text entry option from a text tools menu. In response to the user selecting the avatar with caption option, the context sensitive avatar system  124  determines that the user captured the image when the user selected the reply with camera option and that the user is engaged in a conversation with one or more other users. In such cases, the context sensitive avatar system  124  retrieves avatars for each user with whom the user is engaged in the conversation and presents all the avatars on the screen  801  together with the caption entry region. The user can enter a string into the caption entry region and the string wraps around the avatars in a circular manner. The expressions of one or more of the avatars are modified based on context of the string input by the user. In some cases, the context sensitive avatar system  124  modifies the expressions of the avatars by analyzing one or more words in the conversation (e.g., words previously exchanged between the users) and/or by analyzing one or more words in the string being input by the user for the cation (e.g., words not previously exchanged between the users). 
     In some embodiments, the initial expressions of the avatars that are presented when the user selects the avatar with caption option may be determined based on one or more words in a last message exchanged between the users. The initial expressions of the avatars are then modified based on the one or more words of the string input by the user in the caption entry region. For example, the user may be engaged in a conversation with another user, John. The last message sent or received by the user to/from John, may be “I am happy today”. The context sensitive avatar system  124  may determine that the context of this message is happy. In response to receiving a user selection of the avatar with caption option (after selecting a reply with camera option and capturing an image), the context sensitive avatar system  124  may present avatars for the user and for John with happy expressions. The user may then input the string “today is not a good day” into the caption entry region. The context sensitive avatar system  124  may determine that the string is associated with a sad context and, in response, may modify the expressions of the avatars for the user and for John from being happy to be sad. The user can then select a send option to send a message to all the users involved in the conversation. The message includes the image captured by the user, the avatars with the modified expressions, and the caption with the string input by the user wrapping around in a circular manner around the avatars. The recipients of the message are automatically selected based on the identities of the members of the conversation. 
       FIG.  9    is a block diagram illustrating an example software architecture  906 , which may be used in conjunction with various hardware architectures herein described.  FIG.  9    is a non-limiting example of a software architecture and it will be appreciated that many other architectures may be implemented to facilitate the functionality described herein. The software architecture  906  may execute on hardware such as machine  1000  of  FIG.  10    that includes, among other things, processors  1004 , memory  1014 , and input/output (I/O) components  1018 . A representative hardware layer  952  is illustrated and can represent, for example, the machine  1000  of  FIG.  10   . The representative hardware layer  952  includes a processing unit  954  having associated executable instructions  904 . Executable instructions  904  represent the executable instructions of the software architecture  906 , including implementation of the methods, components, and so forth described herein. The hardware layer  952  also includes memory and/or storage modules memory/storage  956 , which also have executable instructions  904 . The hardware layer  952  may also comprise other hardware  958 . 
     In the example architecture of  FIG.  9   , the software architecture  906  may be conceptualized as a stack of layers where each layer provides particular functionality. For example, the software architecture  906  may include layers such as an operating system  902 , libraries  920 , frameworks/middleware  918 , applications  916 , and a presentation layer  914 . Operationally, the applications  916  and/or other components within the layers may invoke API calls  908  through the software stack and receive messages  912  in response to the API calls  908 . The layers illustrated are representative in nature and not all software architectures have all layers. For example, some mobile or special purpose operating systems may not provide a frameworks/middleware  918 , while others may provide such a layer. Other software architectures may include additional or different layers. 
     The operating system  902  may manage hardware resources and provide common services. The operating system  902  may include, for example, a kernel  922 , services  924 , and drivers  926 . The kernel  922  may act as an abstraction layer between the hardware and the other software layers. For example, the kernel  922  may be responsible for memory management, processor management (e.g., scheduling), component management, networking, security settings, and so on. The services  924  may provide other common services for the other software layers. The drivers  926  are responsible for controlling or interfacing with the underlying hardware. For instance, the drivers  926  include display drivers, camera drivers, Bluetooth® drivers, flash memory drivers, serial communication drivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers, audio drivers, power management drivers, and so forth depending on the hardware configuration. 
     The libraries  920  provide a common infrastructure that is used by the applications  916  and/or other components and/or layers. The libraries  920  provide functionality that allows other software components to perform tasks in an easier fashion than to interface directly with the underlying operating system  902  functionality (e.g., kernel  922 , services  924  and/or drivers  926 ). The libraries  920  may include system libraries  944  (e.g., C standard library) that may provide functions such as memory allocation functions, string manipulation functions, mathematical functions, and the like. In addition, the libraries  920  may include API libraries  946  such as media libraries (e.g., libraries to support presentation and manipulation of various media format such as MPREG4, H.264, MP3, AAC, AMR, JPG, PNG), graphics libraries (e.g., an OpenGL framework that may be used to render two-dimensional and three-dimensional in a graphic content on a display), database libraries (e.g., SQLite that may provide various relational database functions), web libraries (e.g., WebKit that may provide web browsing functionality), and the like. The libraries  920  may also include a wide variety of other libraries  948  to provide many other APIs to the applications  916  and other software components/modules. 
     The frameworks/middleware  918  (also sometimes referred to as middleware) provide a higher-level common infrastructure that may be used by the applications  916  and/or other software components/modules. For example, the frameworks/middleware  918  may provide various graphic user interface functions, high-level resource management, high-level location services, and so forth. The frameworks/middleware  918  may provide a broad spectrum of other APIs that may be utilized by the applications  916  and/or other software components/modules, some of which may be specific to a particular operating system  902  or platform. 
     The applications  916  include built-in applications  938  and/or third-party applications  940 . Examples of representative built-in applications  938  may include, but are not limited to, a contacts application, a browser application, a book reader application, a location application, a media application, a messaging application, and/or a game application. Third-party applications  940  may include an application developed using the ANDROID™ or IOS™ software development kit (SDK) by an entity other than the vendor of the particular platform, and may be mobile software running on a mobile operating system such as IOS™, ANDROID™, WINDOWS® Phone, or other mobile operating systems. The third-party applications  940  may invoke the API calls  908  provided by the mobile operating system (such as operating system  902 ) to facilitate functionality described herein. 
     The applications  916  may use built-in operating system functions (e.g., kernel  922 , services  924 , and/or drivers  926 ), libraries  920 , and frameworks/middleware  918  to create UIs to interact with users of the system. Alternatively, or additionally, in some systems, interactions with a user may occur through a presentation layer, such as presentation layer  914 . In these systems, the application/component “logic” can be separated from the aspects of the application/component that interact with a user. 
       FIG.  10    is a block diagram illustrating components of a machine  1000 , according to some example embodiments, able to read instructions from a machine-readable medium (e.g., a machine-readable storage medium) and perform any one or more of the methodologies discussed herein. Specifically,  FIG.  10    shows a diagrammatic representation of the machine  1000  in the example form of a computer system, within which instructions  1010  (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine  1000  to perform any one or more of the methodologies discussed herein may be executed. As such, the instructions  1010  may be used to implement modules or components described herein. The instructions  1010  transform the general, non-programmed machine  1000  into a particular machine  1000  programmed to carry out the described and illustrated functions in the manner described. In alternative embodiments, the machine  1000  operates as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the machine  1000  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  1000  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 smart phone, a mobile device, a wearable device (e.g., a smart watch), 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  1010 , sequentially or otherwise, that specify actions to be taken by machine  1000 . Further, while only a single machine  1000  is illustrated, the term “machine” shall also be taken to include a collection of machines that individually or jointly execute the instructions  1010  to perform any one or more of the methodologies discussed herein. 
     The machine  1000  may include processors  1004 , memory/storage  1006 , and I/O components  1018 , which may be configured to communicate with each other such as via a bus  1002 . In an example embodiment, the processors  1004  (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  1008  and a processor  1012  that may execute the instructions  1010 . The term “processor” is intended to include multi-core processors  1004  that may comprise two or more independent processors (sometimes referred to as “cores”) that may execute instructions contemporaneously. Although  FIG.  10    shows multiple processors  1004 , the machine  1000  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 multiple cores, or any combination thereof. 
     The memory/storage  1006  may include a memory  1014 , such as a main memory, or other memory storage, and a storage unit  1016 , both accessible to the processors  1004  such as via the bus  1002 . The storage unit  1016  and memory  1014  store the instructions  1010  embodying any one or more of the methodologies or functions described herein. The instructions  1010  may also reside, completely or partially, within the memory  1014 , within the storage unit  1016 , within at least one of the processors  1004  (e.g., within the processor&#39;s cache memory), or any suitable combination thereof, during execution thereof by the machine  1000 . Accordingly, the memory  1014 , the storage unit  1016 , and the memory of processors  1004  are examples of machine-readable media. 
     The I/O components  1018  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  1018  that are included in a particular machine  1000  will depend on the type of machine. For example, portable machines such as mobile phones will likely 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  1018  may include many other components that are not shown in  FIG.  10   . The I/O components  1018  are grouped according to functionality merely for simplifying the following discussion and the grouping is in no way limiting. In various example embodiments, the I/O components  1018  may include output components  1026  and input components  1028 . The output components  1026  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 input components  1028  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 instrument), tactile input components (e.g., a physical button, a touch screen that provides location and/or force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like. 
     In further example embodiments, the I/O components  1018  may include biometric components  1039 , motion components  1034 , environmental components  1036 , or position components  1038  among a wide array of other components. For example, the biometric components  1039  may 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 motion components  1034  may include acceleration sensor components (e.g., accelerometer), gravitation sensor components, rotation sensor components (e.g., gyroscope), and so forth. The environmental components  1036  may include, for example, illumination sensor components (e.g., photometer), temperature sensor components (e.g., one or more thermometer 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. The position components  1038  may 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  1018  may include communication components  1040  operable to couple the machine  1000  to a network  1037  or devices  1029  via coupling  1024  and coupling  1022 , respectively. For example, the communication components  1040  may include a network interface component or other suitable device to interface with the network  1037 . In further examples, communication components  1040  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  1029  may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB). 
     Moreover, the communication components  1040  may detect identifiers or include components operable to detect identifiers. For example, the communication components  1040  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  1040 , such as location via Internet Protocol (IP) geo-location, location via Wi-Fi® signal triangulation, location via detecting a NFC beacon signal that may indicate a particular location, and so forth. 
     Glossary: 
     “CARRIER SIGNAL” in this context refers to any intangible medium that is capable of storing, encoding, or carrying transitory or non-transitory instructions for execution by the machine, and includes digital or analog communications signals or other intangible medium to facilitate communication of such instructions. Instructions may be transmitted or received over the network using a transitory or non-transitory transmission medium via a network interface device and using any one of a number of well-known transfer protocols. 
     “CLIENT DEVICE” in this context 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, smart phone, tablet, ultra book, netbook, laptop, multi-processor system, microprocessor-based or programmable consumer electronics, game console, set-top box, or any other communication device that a user may use to access a network. 
     “COMMUNICATIONS NETWORK” in this context refers 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 wireless LAN (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 type 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 (1xRTT), 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. 
     “EPHEMERAL MESSAGE” in this context refers 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-READABLE MEDIUM” in this context refers to a component, device, or other tangible media able to store instructions and data temporarily or permanently and may include, but is not limited to, random-access memory (RAM), read-only memory (ROM), buffer memory, flash memory, optical media, magnetic media, cache memory, other types of storage (e.g., Erasable Programmable Read-Only Memory (EEPROM)) and/or any suitable combination thereof. The term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) able to store instructions. The term “machine-readable medium” shall also be taken to include any medium, or combination of multiple media, that is capable of storing instructions (e.g., code) for execution by a machine, such that the instructions, when executed by one or more processors of the machine, cause the machine to perform any one or more of the methodologies described herein. Accordingly, a “machine-readable medium” refers to a single storage apparatus or device, as well as “cloud-based” storage systems or storage networks that include multiple storage apparatus or devices. The term “machine-readable medium” excludes signals per se. 
     “COMPONENT” in this context refers 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 example embodiments, 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 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. Considering embodiments 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 embodiments 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-implanted 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 example embodiments, 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 example embodiments, the processors or processor-implemented components may be distributed across a number of geographic locations. 
     “PROCESSOR” in this context refers to any circuit or virtual circuit (a physical circuit emulated by logic executing on an actual processor) that manipulates data values according to control signals (e.g., “commands,” “op codes,” “machine code,” etc.) and which produces corresponding output signals that are applied to operate a machine. A processor may, for example, be a CPU, a RISC processor, a CISC processor, a GPU, a DSP, an ASIC, a RFIC or any combination thereof. A processor may further be a multi-core processor having two or more independent processors (sometimes referred to as “cores”) that may execute instructions contemporaneously. 
     “TIMESTAMP” in this context refers to a sequence of characters or encoded information identifying when a certain event occurred, for example giving date and time of day, sometimes accurate to a small fraction of a second. 
     Changes and modifications may be made to the disclosed embodiments 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.