Patent Publication Number: US-2023137440-A1

Title: Point and clean

Description:
TECHNICAL FIELD 
     The present disclosure relates generally to controlling connected devices, such as Internet of Things (IoT) devices, using a messaging application. 
     BACKGROUND 
     As the popularity of social networking grows, social networks are expanding their capabilities. To improve ease of use, social networks are integrating more and more functions such that a user may accomplish many or even most of their computer-based tasks within the social network itself. 
    
    
     
       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, in accordance with some examples. 
         FIG.  2    is a diagrammatic representation of a messaging client application, in accordance with some examples. 
         FIG.  3    is a diagrammatic representation of a data structure as maintained in a database, in accordance with some examples. 
         FIG.  4    is a diagrammatic representation of a message, in accordance with some examples. 
         FIG.  5    is a block diagram showing an example remote control system, according to example examples. 
         FIGS.  6 ,  7 , and  8    are diagrammatic representations of outputs of the remote control system, in accordance with some examples. 
         FIG.  9    is a flowchart illustrating example operations of the remote control system, in accordance with some examples. 
         FIG.  10    is a diagrammatic representation of a machine in the form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein, in accordance with some examples. 
         FIG.  11    is a block diagram showing a software architecture within which examples may be implemented. 
     
    
    
     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, a mobile phone can be used to control various IoT devices in a user&#39;s home. To do so, a user has to preconfigure each of the IoT devices on the phone using complex setup procedures. For example, the mobile device can present a setup screen in which serial numbers, IP addresses and various other unique identifying information is input by a user for each IoT device they desire to control. Such information that is input is then used to establish an authenticated connection between the mobile device and the specified IoT device. In order to then control the specified IoT device, the user has to navigate through several screens and pages of information to find the particular icon or identifier of the IoT device. For example, the mobile device can present a folder structure of different IoT devices, such as robotic cleaning devices, and the user can navigate through the hierarchy of folders to find the desired device to control. This complex procedure to setup and control IoT devices can be very time consuming and onerous on the end users which takes away from the overall appeal of remotely controlling such devices using a mobile device. Also, the need to navigate a complex menu structure to control such devices can consume a great deal of resources. 
     Certain robotic cleaning device systems allow a user to manually select a robotic cleaning device to control over the Internet. The user can even instruct the robotic cleaning device to start a cleaning process. The robotic cleaning device can then map out a room and begin automated cleaning. While such systems generally work well, these systems sometimes miss certain soiled areas. These systems provide no mechanism for a user to instruct the cleaning device to clean a particular area of interest in a simple and efficient manner. To clean a desired area, the user can be presented with a predetermined map of an area and the user can then select a region to clean. However, such a map is not intuitive to interact with and can be difficult to use to identify and select a particular area that may be soiled. In many cases, the users have to wait for the robotic cleaning device to detect the soiled area of interest by itself through routine operations, and even then the robotic cleaning device can miss the soiled area of interest. This can result in the robotic cleaning device wasting battery power and resources cleaning areas that need not be cleaned or that are not soiled. This can also frustrate users, which can lead to lack of use and waste of resources. 
     The disclosed techniques improve the efficiency of using the electronic device to control connected devices, such as IoT devices including robotic cleaning devices, by using a messaging application to automatically recognize real-world objects associated with connected devices in one or more images captured by the electronic device and then enabling control of the connected devices. Namely, the disclosed examples provide a messaging application implemented on a client device that detects a real-world object depicted in a received image and determines a current location of the client device. The messaging application identifies a plurality of robotic cleaning devices associated with an account of the messaging application. The messaging application transmits a message comprising the current location of the client device to a first robotic cleaning device of the plurality of robotic cleaning devices and causes the first robotic cleaning device to clean the real-world object depicted in the received image based on the message transmitted by the messaging application. 
     In this way, the disclosed techniques provide a simple and intuitive way for users to interact with robotic cleaning devices to clean a desired area. Namely, the disclosed techniques allow a user to capture an image of a soiled area using a messaging application and automatically communicate with a suitable robotic cleaning device to clean the specific soiled area depicted in the image. As a result, a specific area of interest can be identified (manually or automatically) in an image captured by a camera of the client device and used to instruct the robotic cleaning device to clean only that specific area. This reduces the overall amount of resources required to control connected devices and increases the overall appeal of using the messaging application. 
     Networked Computing Environment 
       FIG.  1    is a block diagram showing an example messaging system  100  for exchanging data (e.g., messages and associated content) over a network. The messaging system  100  includes multiple instances of a client device  102 , each of which hosts a number of applications, including a messaging client  104  and other external applications  109  (e.g., third-party applications). Each messaging client  104  is communicatively coupled to other instances of the messaging client  104  (e.g., hosted on respective other client devices  102 ), a messaging server system  108  and external app(s) servers  110  via a network  112  (e.g., the Internet). A messaging client  104  can also communicate with locally-hosted third-party applications, such as external apps  109  using Application Programming Interfaces (APIs). 
     A messaging client  104  is able to communicate and exchange data with other messaging clients  104  and with the messaging server system  108  via the network  112 . The data exchanged between messaging clients  104 , and between a messaging client  104  and the messaging server system  108 , includes functions (e.g., commands to invoke functions) as well as payload data (e.g., text, audio, video or other multimedia data). 
     The messaging server system  108  provides server-side functionality via the network  112  to a particular messaging client  104 . While certain functions of the messaging system  100  are described herein as being performed by either a messaging client  104  or by the messaging server system  108 , the location of certain functionality either within the messaging client  104  or the messaging server system  108  may be 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  104  where a client device  102  has sufficient processing capacity. 
     The messaging server system  108  supports various services and operations that are provided to the messaging client  104 . Such operations include transmitting data to, receiving data from, and processing data generated by the messaging client  104 . This data may include message content, client device information, geolocation information, media augmentation and overlays, 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 of the messaging client  104 . 
     Turning now specifically to the messaging server system  108 , an API server  116  is coupled to, and provides a programmatic interface to, application servers  114 . The application servers  114  are communicatively coupled to a database server  120 , which facilitates access to a database  126  that stores data associated with messages processed by the application servers  114 . Similarly, a web server  128  is coupled to the application servers  114  and provides web-based interfaces to the application servers  114 . To this end, the web server  128  processes incoming network requests over the Hypertext Transfer Protocol (HTTP) and several other related protocols. 
     The API server  116  receives and transmits message data (e.g., commands and message payloads) between the client device  102  and the application servers  114 . Specifically, the API server  116  provides a set of interfaces (e.g., routines and protocols) that can be called or queried by the messaging client  104  in order to invoke functionality of the application servers  114 . The API server  116  exposes various functions supported by the application servers  114 , including account registration; login functionality; the sending of messages, via the application servers  114 , from a particular messaging client  104  to another messaging client  104 ; the sending of media files (e.g., images or video) from a messaging client  104  to a messaging server  118 , and for possible access by another messaging client  104 ; the settings of a collection of media data (e.g., story); the retrieval of a list of friends of a user of a client device  102 ; the retrieval of such collections; the retrieval of messages and content; the addition and deletion of entities (e.g., friends) to an entity graph (e.g., a social graph); the location of friends within a social graph; and opening an application event (e.g., relating to the messaging client  104 ). 
     The application servers  114  host a number of server applications and subsystems, including, for example, a messaging server  118 , an image processing server  122 , and a social network server  124 . The messaging server  118  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  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 to the messaging client  104 . Other processor- and memory-intensive processing of data may also be performed server-side by the messaging server  118 , in view of the hardware requirements for such processing. 
     The application servers  114  also include an image processing server  122  that is dedicated to performing various image processing operations, typically with respect to images or video within the payload of a message sent from or received at the messaging server  118 . 
     Image processing server  122  is used to implement scan functionality of the augmentation system  208  (shown in  FIG.  2   ). Scan functionality includes activating and providing one or more augmented reality (AR) experiences on a client device  102  when an image is captured by the client device  102 . Specifically, the messaging client  104  on the client device  102  can be used to activate a camera. The camera displays one or more real-time images or a video to a user along with one or more icons or identifiers of one or more AR experiences. The user can select a given one of the identifiers to launch the corresponding AR experience or perform a desired image modification. The image processing server  122  can receive a video and/or one or more images captured by the client device  102 . The image processing server  122  can perform feature analysis and object recognition on the received video and/or one or more images to identify and detect one or more real-world objects that are depicted in the received video and/or images. The image processing server  122  can access features and/or attributes of each real-world object that is detected from a database or by searching the Internet. In response to detecting the real-world objects, the image processing server  122  can generate a list of identifiers of real-world objects being depicted in the video and/or images and can associate the one or more attributes or features with each object in the list. 
     In some cases, the image processing server  122  can process features depicted in an image or video to identify a soiled area of a physical real-world environment. The image processing server  122  can identify the soiled area by applying a trained neural network to the image or video. The image processing server  122  can display a virtual element or augmented reality element on top of the image or video to identify each soiled area. In another implementation, the image processing server  122  can automatically control a robotic cleaning device to approach the soiled area that is identified and apply a suitable cleaning process to clean the soiled area. 
     The social network server  124  supports various social networking functions and services and makes these functions and services available to the messaging server  118 . To this end, the social network server  124  maintains and accesses an entity graph  308  (as shown in  FIG.  3   ) within the database  126 . Examples of functions and services supported by the social network server  124  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. 
     Returning to the messaging client  104 , features and functions of an external resource (e.g., a third-party application  109  or applet) are made available to a user via an interface of the messaging client  104 . The messaging client  104  receives a user selection of an option to launch or access features of an external resource (e.g., a third-party resource), such as external apps  109 . The external resource may be a third-party application (external apps  109 ) installed on the client device  102  (e.g., a “native app”) or a small-scale version of the third-party application (e.g., an “applet”) that is hosted on the client device  102  or remote of the client device  102  (e.g., on third-party servers  110 ). The small-scale version of the third-party application includes a subset of features and functions of the third-party application (e.g., the full-scale, native version of the third-party standalone application) and is implemented using a markup-language document. In one example, the small-scale version of the third-party application (e.g., an “applet”) is a web-based, markup-language version of the third-party application and is embedded in the messaging 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 (external app  109 ), the messaging client  104  determines whether the selected external resource is a web-based external resource or a locally-installed external application. In some cases, external applications  109  that are locally installed on the client device  102  can be launched independently of and separately from the messaging client  104 , such as by selecting an icon, corresponding to the external application  109 , on a home screen of the client device  102 . Small-scale versions of such external applications can be launched or accessed via the messaging client  104  and, in some examples, no or limited portions of the small-scale external application can be accessed outside of the messaging client  104 . The small-scale external application can be launched by the messaging client  104  receiving, from an external app(s) server  110 , a markup-language document associated with the small-scale external application and processing such a document. 
     In response to determining that the external resource is a locally-installed external application  109 , the messaging client  104  instructs the client device  102  to launch the external application  109  by executing locally-stored code corresponding to the external application  109 . In response to determining that the external resource is a web-based resource, the messaging client  104  communicates with the external app(s) servers  110  to obtain a markup-language document corresponding to the selected resource. The messaging client  104  then processes the obtained markup-language document to present the web-based external resource within a user interface of the messaging client  104 . 
     The messaging client  104  can notify a user of the client device  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 messaging client  104  can provide participants in a conversation (e.g., a chat session) in the messaging 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 a respective messaging client  104 , with the ability to share an item, status, state, or location in an external resource with one or more members of a group of users into a chat session. The shared item may be an interactive chat card with which members of the chat can interact, for example, to 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 messaging client  104 . The external resource can selectively include different media items in the responses based on a current context of the external resource. 
     The messaging client  104  can present a list of the available external resources (e.g., third-party or external applications  109  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 external application  109  (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). 
     In some examples, the messaging client  104  can present a remote control setup interface. The remote control setup interface allows the user to scan and capture images and/or videos of various rooms in a home, household, office, restaurant, or any other physical location. In one example, the messaging client  104  can receive input that specifies a current physical location of the client device  102 . The current physical location can identify an entire home or a particular room in the home, such as the living room. In response to receiving the input that specifies the current physical location (e.g., including a textual or visual identifier of the physical location, such as a room name or type), the messaging client  104  processes the images and/or videos to identify a set of real-world objects depicted in the images and/or videos. For example, the messaging client  104  can instruct the user to walk around the physical location to capture a video of the entire location, such as a 360-degree video. This way, any real-world object that is at the current physical location can be represented in the captured video and identified by the messaging client  104 , such as by performing one or more object recognition techniques or processes. The messaging client  104  can automatically associate each of the real-world objects with the current physical location (e.g., the room name or type) and with their respective previously registered IoT device. 
     In some implementations, the messaging client  104  obtains from a remote server, such as a particular product manufacturer, a list of IoT devices (e.g., robotic cleaning devices, such as robotic vacuum cleaners, robotic pet hair removal devices, robotic mops, robotic window cleaners, robotic pool cleaner, robotic litter box cleaner, robotic gutter cleaner, and so forth) that have previously been registered to an account associated with the client device  102 . Namely, the messaging client  104  can receive input from the user that provides an account identifier (e.g., username and password) for IoT devices of one or more product manufacturers. The messaging client  104  accesses a website or database or manufacturer server associated with the one or more product manufacturers based on the account identifier. The messaging client  104  obtains, from the website or database or manufacturer server, IoT devices that are associated with the account identifier and indicated to be currently active in the user&#39;s account. 
     Example IoT devices can include a list of connected robotic cleaning devices. A connected robotic cleaning device includes a stationary or non-stationary device that can include wheels or other movement mechanism and is configured to detect soiled areas and clean, disinfect, and/or remove or repair the soiled portion of the area. The connected robotic cleaning device can include an on-board camera which captures a video feed of a target area as the robotic cleaning device moves around. When the connected robotic cleaning device approaches an area captured by the video feed, the connected robotic cleaning device can apply one or more mechanisms to clean, disinfect, and/or remove or repair the soiled portion of the area. In some cases, the robotic cleaning devices operate on a schedule and follow a path to clean a portion of a household. The robotic cleaning devices can include one or more sensors to avoid obstacles. As referred to herein, a “soiled” area includes a physical region that has wet and/or dry particles or substances that are undesirable or unclean, such as dirt, bacteria, virus, or spillage. For example, a soiled area is a wet or dirty area. 
     The messaging client  104  can obtain unique features or attributes of each of the IoT devices that are obtained from the website or database. In one example, the messaging client  104  can search the unique features or attributes of each IoT device in the list received from the website or database based on the features or attributes of the real-world objects identified in the video or images of the current physical location. For example, the IoT device can include a television object with certain visual attributes, such as a size, dimensions, and color. The messaging client  104  can compare the visual attributes of the television real-world object detected in the video or images captured at the current physical location to the visual attributes of the television object on the list. The messaging client  104  can determine that the video or images captured at the current physical location include visual attributes that match a set of visual attributes of a respective set of IoT devices in the list. In response, the messaging client  104  can determine that the current location includes the set of IoT devices in the list. The messaging client  104  can associate each IoT device of the set with the current physical location (e.g., a GPS coordinate or range of GPS coordinates or the room name or type input by the user). 
     In one example, the messaging client  104  can present a list of robotic cleaning devices that are obtained from the website or database or manufacturer server to the user. The messaging client  104  can receive input from the user that associates each robotic cleaning device with a set of visual attributes (e.g., corresponding to different soiled portion types) and/or locations. For example, the messaging client  104  can receive input that associates a vacuum robotic cleaning device with a first portion of a home (e.g., a living room) and with dry particles as the soiled portion. As another example, the messaging client  104  can receive input that associates a wet vacuum or mop robotic cleaning device with the first portion of a home and with wet particles as the soiled portion. As another example, the messaging client  104  can receive input that associates a second vacuum robotic cleaning device with a second portion of a home (e.g., one or more bedrooms) and with dry particles and pet hair as the soiled portion. The messaging client  104  can store in the database an association between features or attributes of the soiled portion types and GPS or location information of each specified physical location with each respective robotic cleaning device. 
     In an example, the messaging client  104  can detect a soiled portion of a real-world object in an image or video and obtain location information associated with the image or video. In response, the messaging client  104  can select a robotic cleaning device that is associated with the location information and that is associated with the soiled portion type that is identified in the image or video. The messaging client  104  can communicate automatically with the selected robotic cleaning device to provide a message that specifies the location of the soiled portion of the real-world object depicted in the image or video and instructs the robotic cleaning device to move to the location to clean the soiled portion of the real-world object. The messaging client  104  can include visual attributes that represent the real-world object depicted in the image or video and/or visual attributes of the soiled portion of the image. For example, the messaging client  104  can receive an image that depicts a rug with dirt or a stain. The messaging client  104  can determine that the image was captured in a location corresponding to a living room. The messaging client  104  can determine that the depicted rug includes a soiled portion (e.g., the dirt or stain) and can select a dry robotic vacuum cleaning device that is associated with the living room and is associated with the type of soiled portion corresponding to the dirt or stain. The messaging client  104  can instruct the dry robotic vacuum cleaning device to approach or move to the living room location and clean the dirt or stain from the rug. 
     In an example, the messaging client  104  can receive input from a user that selects a soiled portion of an image or video captured by a camera of the client device  102 . In response, the messaging client  104  obtains location information associated with the image or video and generates one or more visual attributes of the selected soiled portion of the image or video. The messaging client  104  can select a robotic cleaning device that is associated with the location information and that is associated with the soiled portion type that is identified in the image or video based on processing the visual attributes of the selected soiled portion. The messaging client  104  can communicate automatically with the selected robotic cleaning device to provide a message that specifies the location of the soiled portion selected by the user from the image or video and instructs the robotic cleaning device to move to the location to clean the soiled portion. The messaging client  104  can include the visual attributes of the selected soiled portion of the image or video. 
     In one example, the dry robotic vacuum cleaning device receives the message and moves to the location automatically. Once the dry robotic vacuum cleaning device reaches the location (e.g., the living room), the dry robotic vacuum cleaning device turns on a video camera to search for a real-world object that matches one or more visual attributes of the real-world object depicted in the image or video (e.g., the rug). The dry robotic vacuum cleaning device can move about the location in 360 degrees until the visual attributes of real-world objects captured by the camera of the dry robotic vacuum cleaning device match the visual attributes of the real-world object depicted in the image captured by the messaging client  104  or the visual attributes of the soiled portion selected by the user from the image or video. In response to determining that the video includes attributes that match the visual attributes of the real-world object received from the messaging client  104  or the soiled portion selected by the user, the dry robotic vacuum cleaning device automatically navigates to and moves to the location corresponding to the video and cleans the area corresponding to the video captured by the camera of the dry robotic vacuum cleaning device. 
     In some implementations, the messaging client  104  can obtain an API from the one or more manufacturers that enables remote control of the set IoT devices (e.g., connected video cameras) associated with the one or more manufacturers. The messaging client  104  can use the API to enable remote control of the set of IoT devices, such as to instruct robotic cleaning devices to move to and clean a particular area. Specifically, the messaging client  104  can associate the soiled portion types and/or locations with a given set of the connected robotic cleaning devices and can also associate the API of the given set of connected robotic cleaning devices with the specified locations and/or soiled portion types. In this way, whenever an image of a soiled portion of a real-world object is captured at the current physical location, the messaging client  104  can automatically obtain the API associated with the IoT device (e.g., the robotic cleaning device) associated with the depicted soiled portion of the real-world object to instruct the robotic cleaning device to clean the area corresponding to the soiled portion, such as by transmitting one or more commands to the IoT device over a local shared or common wireless network (e g., a local area network or wide area network). For example, whenever an image of the real-world object is captured at the current physical location, the messaging client  104  can automatically obtain the API associated with the robotic cleaning device corresponding to a soiled portion type and the current physical location and instruct the robotic cleaning device to clean the current physical location. 
     The process of associating such robotic cleaning devices with specific locations and/or soiled portion types with their respective APIs to enable remote control of robotic cleaning devices can be repeated for multiple physical locations. For example, the messaging client  104  can instruct the user to move to a new physical location, such as a new room in a home, and repeat the process of scanning a video or images that depict one or more real-world objects at that new physical location. The messaging client  104  can associate each of the real-world objects with the new physical location. In this way, when an image of such real-world objects is captured and received in the future, the messaging client  104  can determine the current location (e.g., without accessing GPS information). The messaging client  104  can search a list of previously registered robotic cleaning devices to enable the user to associate the robotic cleaning devices or a subset thereof with the new physical location and/or with a soiled portion type. 
     In some examples, the messaging client  104  can receive a user request to clean an area at a particular physical location. In response, the messaging client  104  can activate a rear-facing or front-facing camera of the client device  102 . The messaging client  104  can instruct the user to point the camera at the real-world object of interest the user would like to clean or which includes a soiled portion. The messaging client  104  can capture an image or images of the real-world object of interest, such as when the user selects an option indicating that the real-world object of interest with the soiled portion is currently depicted in the image or images. The messaging client  104  can perform object recognition on the received image or images to generate a list of identified objects and their respective features. 
     The messaging client  104  can access a database based on the particular physical location. For example, the messaging client  104  can obtain a current set of GPS coordinates. The messaging client  104  can identify in the database a set of GPS coordinates that are within a certain threshold proximity or range (e.g., within 10 meters) of the current set of GPS coordinates. In response to identifying the set of GPS coordinates that are within the certain threshold proximity to the current set of GPS coordinates, the messaging client  104  obtains the list of robotic cleaning devices associated with the set of GPS coordinates. The messaging client  104  compares one or more attributes of the real-world object currently depicted in the image or images with the attributes associated with the soiled portion types stored in the database. For example, the messaging client  104  can compare attributes of a real-world rug having dirt with the attributes associated with a dirt soiled portion type to determine the soiled portion type of the rug depicted in the image. The messaging client  104  can then select, from the list of robotic cleaning devices associated with the set of GPS coordinates, a given robotic cleaning device that is associated with the determined soiled portion type. In response to identifying the given robotic cleaning device, the messaging client  104  can access the API of the given robotic cleaning device. 
     In one example, the messaging client  104  uses the accessed API to instruct the given robotic cleaning device to move to and clean the area associated with the GPS coordinates. In an implementation, the messaging client  104  generates a graphical user interface that includes a command to clean the area depicted in the image and instructs the robotic cleaning device in response to receiving input that selects the command. In another example, the messaging client  104  communicates an identifier of the IoT device to a manufacturer server of the robotic cleaning device. The manufacturer server then selects a cleaning command to transmit to the robotic cleaning device. The cleaning command issued to the robotic cleaning device can include the GPS coordinates and a set of visual attributes of a soiled portion of the image (e.g., an automatically selected soiled portion or soiled portion specified by the user, such as by dragging a cleaning visual element over a given region of the image). 
     System Architecture 
       FIG.  2    is a block diagram illustrating further details regarding the messaging system  100 , according to some examples. Specifically, the messaging system  100  is shown to comprise the messaging client  104  and the application servers  114 . The messaging system  100  embodies a number of subsystems, which are supported on the client side by the messaging client  104  and on the sever side by the application servers  114 . These subsystems include, for example, an ephemeral timer system  202 , a collection management system  204 , an augmentation system  208 , a map system  210 , a game system  212 , and an external resource system  220 . 
     The ephemeral timer system  202  is responsible for enforcing the temporary or time-limited access to content by the messaging client  104  and the messaging server  118 . The ephemeral timer system  202  incorporates a number of timers 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 messaging client  104 . Further details regarding the operation of the ephemeral timer system  202  are provided below. 
     The collection management system  204  is 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  204  may also be responsible for publishing an icon that provides notification of the existence of a particular collection to the user interface of the messaging client  104 . 
     The collection management system  204  further includes a curation interface  206  that allows a collection manager to manage and curate a particular collection of content. For example, the curation interface  206  enables an event organizer to curate a collection of content relating to a specific event (e.g., delete inappropriate content or redundant messages). Additionally, the collection management system  204  employs machine vision (or image recognition technology) and content rules to automatically curate a content collection. In certain examples, compensation may be paid to a user for the inclusion of user-generated content into a collection. In such cases, the collection management system  204  operates to automatically make payments to such users for the use of their content. 
     The augmentation system  208  provides various functions that enable a user to augment (e.g., annotate or otherwise modify or edit) media content associated with a message. For example, the augmentation system  208  provides functions related to the generation and publishing of media overlays for messages processed by the messaging system  100 . The augmentation system  208  operatively supplies a media overlay or augmentation (e.g., an image filter) to the messaging client  104  based on a geolocation of the client device  102 . In another example, the augmentation system  208  operatively supplies a media overlay to the messaging client  104  based on other information, such as social network information of the user of the client device  102 . A media overlay 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) at the client device  102 . For example, the media overlay may include text, a graphical element, or image that can be overlaid on top of a photograph taken by the client device  102 . In another example, the media overlay includes an identification of 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 another example, the augmentation system  208  uses the geolocation of the client device  102  to identify a media overlay that includes the name of a merchant at the geolocation of the client device  102 . The media overlay may include other indicia associated with the merchant. The media overlays may be stored in the database  126  and accessed through the database server  120 . 
     In some examples, the augmentation system  208  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 augmentation system  208  generates a media overlay that includes the uploaded content and associates the uploaded content with the selected geolocation. 
     In other examples, the augmentation system  208  provides a merchant-based publication platform that enables merchants to select a particular media overlay associated with a geolocation via a bidding process. For example, the augmentation system  208  associates the media overlay of the highest bidding merchant with a corresponding geolocation for a predefined amount of time. The augmentation system  208  communicates with the image processing server  122  to obtain AR experiences and presents identifiers of such experiences in one or more user interfaces (e.g., as icons over a real-time image or video or as thumbnails or icons in interfaces dedicated for presented identifiers of AR experiences). Once an AR experience is selected, one or more images, videos, or AR graphical elements are retrieved and presented as an overlay on top of the images or video captured by the client device  102 . In some cases, the camera is switched to a front-facing view (e.g., the front-facing camera of the client device  102  is activated in response to activation of a particular AR experience) and the images from the front-facing camera of the client device  102  start being displayed on the client device  102  instead of the rear-facing camera of the client device  102 . The one or more images, videos, or AR graphical elements are retrieved and presented as an overlay on top of the images that are captured and displayed by the front-facing camera of the client device  102 . 
     In other examples, the augmentation system  208  is able to communicate and exchange data with another augmentation system  208  on another client device  102  and with the server via the network  112 . The data exchanged can include a session identifier that identifies the shared AR session, a transformation between a first client device  102  and a second client device  102  (e.g., a plurality of client devices  102  include the first and second devices) that is used to align the shared AR session to a common point of origin, a common coordinate frame, functions (e.g., commands to invoke functions), and other payload data (e.g., text, audio, video, or other multimedia data). 
     The augmentation system  208  sends the transformation to the second client device  102  so that the second client device  102  can adjust the AR coordinate system based on the transformation. In this way, the first and second client devices  102  synch up their coordinate systems and frames for displaying content in the AR session. Specifically, the augmentation system  208  computes the point of origin of the second client device  102  in the coordinate system of the first client device  102 . The augmentation system  208  can then determine an offset in the coordinate system of the second client device  102  based on the position of the point of origin from the perspective of the second client device  102  in the coordinate system of the second client device  102 . This offset is used to generate the transformation so that the second client device  102  generates AR content according to a common coordinate system or frame as the first client device  102 . 
     The augmentation system  208  can communicate with the client device  102  to establish individual or shared AR sessions. The augmentation system  208  can also be coupled to the messaging server  118  to establish an electronic group communication session (e.g., group chat, instant messaging) for the client devices  102  in a shared AR session. The electronic group communication session can be associated with a session identifier provided by the client devices  102  to gain access to the electronic group communication session and to the shared AR session. In one example, the client devices  102  first gain access to the electronic group communication session and then obtain the session identifier in the electronic group communication session that allows the client devices  102  to access the shared AR session. In some examples, the client devices  102  are able to access the shared AR session without aid or communication with the augmentation system  208  in the application servers  114 . 
     The map system  210  provides various geographic location functions and supports the presentation of map-based media content and messages by the messaging client  104 . For example, the map system  210  enables the display of user icons or avatars (e.g., stored in profile data  316 ) 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 messaging 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 messaging 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 messaging system  100  via the messaging client  104 , with this location and status information being similarly displayed within the context of a map interface of the messaging client  104  to selected users. 
     The game system  212  provides various gaming functions within the context of the messaging client  104 . The messaging client  104  provides a game interface providing a list of available games (e.g., web-based games or web-based applications) that can be launched by a user within the context of the messaging client  104  and played with other users of the messaging system  100 . The messaging 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 messaging client  104 . The messaging client  104  also supports both voice 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). 
     The external resource system  220  provides an interface for the messaging client  104  to communicate with external app(s) servers  110  to launch or access external resources. Each external resource (apps) server  110  hosts, for example, a markup language (e.g., HTML5) based application or small-scale version of an external application (e.g., game, utility, payment, or ride-sharing application that is external to the messaging client  104 ). The messaging client  104  may launch a web-based resource (e.g., application) by accessing the HTML5 file from the external resource (apps) servers  110  associated with the web-based resource. In certain examples, applications hosted by external resource servers  110  are programmed in JavaScript leveraging a Software Development Kit (SDK) provided by the messaging server  118 . The SDK includes APIs with functions that can be called or invoked by the web-based application. In certain examples, the messaging server  118  includes a JavaScript library that provides a given third-party resource access to certain user data of the messaging client  104 . HTML5 is used as an example technology for programming games, but applications and resources programmed based on other technologies can be used. 
     In order to integrate the functions of the SDK into the web-based resource, the SDK is downloaded by an external resource (apps) server  110  from the messaging server  118  or is otherwise received by the external resource (apps) server  110 . 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 messaging client  104  into the web-based resource. 
     The SDK stored on the messaging server  118  effectively provides the bridge between an external resource (e.g., third-party or external applications  109  or applets) and the messaging client  104 . This provides the user with a seamless experience of communicating with other users on the messaging client  104 , while also preserving the look and feel of the messaging client  104 . To bridge communications between an external resource and a messaging client  104 , in certain examples, the SDK facilitates communication between external resource servers  110  and the messaging client  104 . In certain examples, a WebViewJavaScriptBridge running on a client device  102  establishes two one-way communication channels between an external resource and the messaging client  104 . Messages are sent between the external resource and the messaging 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 messaging client  104  is shared with external resource servers  110 . The SDK limits which information is shared based on the needs of the external resource. In certain examples, each external resource server  110  provides an HTML5 file corresponding to the web-based external resource to the messaging server  118 . The messaging server  118  can add a visual representation (such as a box art or other graphic) of the web-based external resource in the messaging client  104 . Once the user selects the visual representation or instructs the messaging client  104  through a graphical user interface of the messaging client  104  to access features of the web-based external resource, the messaging client  104  obtains the HTML5 file and instantiates the resources necessary to access the features of the web-based external resource. 
     The messaging client  104  presents a graphical user interface (e.g., a landing page or title screen) for an external resource. During, before, or after presenting the landing page or title screen, the messaging client  104  determines whether the launched external resource has been previously authorized to access user data of the messaging client  104 . In response to determining that the launched external resource has been previously authorized to access user data of the messaging client  104 , the messaging client  104  presents another graphical user interface 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 messaging 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 messaging client  104  slides up (e.g., animates a menu as surfacing from a bottom of the screen to a middle of 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 messaging client  104  adds the external resource to a list of authorized external resources and allows the external resource to access user data from the messaging client  104 . In some examples, the external resource is authorized by the messaging client  104  to access the user data in accordance with an OAuth  2  framework. 
     The messaging 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 external applications (e.g., a third-party or external application  109 ) are provided with access to a first type of user data (e.g., only two-dimensional (2D) avatars of users with or without different avatar characteristics). As another example, external resources that include small-scale versions of external applications (e.g., web-based versions of third-party applications) are provided with access to a second type of user data (e.g., payment information, 2D avatars of users, three-dimensional (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. 
     A remote control system  224  provides an interface for detecting real-world objects in a captured image that are soiled and identifying one or more robotic cleaning devices corresponding to the soiled portions of the real-world objects. The remote control system  224  presents a user interface to select a soiled portion of an image or video. The remote control system  224  communicates with a selected one of the identified robotic cleaning devices to instruct the identified robotic cleaning device to move to an clean the soiled portion of the real-world object depicted in the image. An illustrative implementation of the remote control system  224  is shown and described in connection with  FIG.  5    below. 
     Data Architecture 
       FIG.  3    is a schematic diagram illustrating data structures  300 , which may be stored in the database  126  of the messaging server system  108 , according to certain examples. While the content of the database  126  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  126  includes message data stored within a message table  302 . This message data includes, for any particular one 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  302 , are described below with reference to  FIG.  4   . 
     An entity table  306  stores entity data, and is linked (e.g., referentially) to an entity graph  308  and profile data  316 . Entities for which records are maintained within the entity table  306  may include individuals, corporate entities, organizations, objects, places, events, and so forth. Regardless of entity 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  308  stores information regarding relationships and associations between entities. Such relationships may be social, professional (e.g., work at a common corporation or organization), interested-based, or activity-based, merely for example. 
     The profile data  316  stores multiple types of profile data about a particular entity. The profile data  316  may be selectively used and presented to other users of the messaging system  100 , based on privacy settings specified by a particular entity. Where the entity is an individual, the profile data  316  includes, for example, a user name, telephone number, address, and settings (e.g., notification and privacy settings), as well as 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 messaging system  100  and on map interfaces displayed by messaging 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  316  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  126  also stores augmentation data, such as overlays or filters, in an augmentation table  310 . The augmentation data is associated with and applied to videos (for which data is stored in a video table  304 ) and images (for which data is stored in an image table  312 ). 
     The database  126  can also store data pertaining to individual and shared AR sessions. This data can include data communicated between an AR session client controller of a first client device  102  and another AR session client controller of a second client device  102 , and data communicated between the AR session client controller and the augmentation system  208 . Data can include data used to establish the common coordinate frame of the shared AR scene, the transformation between the devices, the session identifier, images depicting a body, skeletal joint positions, wrist joint positions, feet, and so forth. 
     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 set of filters presented to a sending user by the messaging 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 messaging client  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  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 augmentation data that may be stored within the image table  312  includes AR content items (e.g., corresponding to applying AR experiences). An AR content item or AR item may be a real-time special effect and sound that may be added to an image or a video. 
     As described above, augmentation data includes AR content items, overlays, image transformations, AR images, AR logos or emblems, and similar terms that refer to modifications that may be applied to image data (e.g., videos or images). This includes real-time modifications, which modify an image as it is captured using device sensors (e.g., one or multiple cameras) of a client device  102  and then displayed on a screen of the client device  102  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 client device  102  with access to multiple AR content items, a user can use a single video clip with multiple AR content items to see how the different AR content items will modify the stored clip. For example, multiple AR content items that apply different pseudorandom movement models can be applied to the same content by selecting different AR content items 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 client device  102  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 AR content items 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. 
     In some examples, the AR content items include a cleaning indicator that can be interacted with by a user to move to a portion of an image that a user desires to have cleaned. The remote control system  224  determines a real-world location associated with the cleaning indicator and instructs a suitable robotic cleaning device to move to and clean a real-world environment corresponding to the real-world location. 
     Data and various systems using AR content items 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 examples, different methods for achieving such transformations may be used. Some examples may involve generating a 3D 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 examples, tracking of points on an object may be used to place an image or texture (which may be 2D or 3D) at the tracked position. In still further examples, neural network analysis of video frames may be used to place images, models, or textures in content (e.g., images or frames of video). AR content items thus refer 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 examples, 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 an object&#39;s elements, characteristic points for each 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 is 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 some examples, transformations changing some areas of an object using its elements can be performed by calculating 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 elements, 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 examples, 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 examples 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 ASM algorithm is applied to the face region of an image to detect facial feature reference points. 
     Other methods and algorithms suitable for face detection can be used. For example, in some examples, 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. If 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 examples, 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 examples, a search is started for landmarks from the mean shape aligned to the position and size of the face determined by a global face detector. 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 matches to form a stronger overall classifier. The entire search is repeated at each level in an image pyramid, from coarse to fine resolution. 
     A transformation system can capture an image or video stream on a client device (e.g., the client device  102 ) and perform complex image manipulations locally on the 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 the client device  102 . 
     In some examples, 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  104  operating on the client device  102 . The transformation system operating within the messaging client  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 that 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 transformation 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). A modified image or video stream may be presented in a graphical user interface displayed on the client device  102  as soon as the image or video stream is captured and a specified modification is selected. The transformation 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. 
     The graphical user interface, presenting the modification performed by the transformation 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 examples, 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 examples, 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. 
     A story table  314  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  306 ). 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 messaging 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 messaging client  104 , to contribute content to a particular live story. The live story may be identified to the user by the messaging 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 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 examples, 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). 
     As mentioned above, the video table  304  stores video data that, in one example, is associated with messages for which records are maintained within the message table  302 . Similarly, the image table  312  stores image data associated with messages for which message data is stored in the entity table  306 . The entity table  306  may associate various augmentations from the augmentation table  310  with various images and videos stored in the image table  312  and the video table  304 . 
     Data Communications Architecture 
       FIG.  4    is a schematic diagram illustrating a structure of a message  400 , according to some examples, generated by a messaging client  104  for communication to a further messaging client  104  or the messaging server  118 . The content of a particular message  400  is used to populate the message table  302  stored within the database  126 , accessible by the messaging server  118 . Similarly, the content of a message  400  is stored in memory as “in-transit” or “in-flight” data of the client device  102  or the application servers  114 . A message  400  is shown to include the following example components:
         message identifier  402 : a unique identifier that identifies the message  400 .   message text payload  404 : text, to be generated by a user via a user interface of the client device  102 , and that is included in the message  400 .   message image payload  406 : image data, captured by a camera component of a client device  102  or retrieved from a memory component of a client device  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  312 .   message video payload  408 : 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  400 . Video data for a sent or received message  400  may be stored in the video table  304 .   message audio payload  410 : audio data, captured by a microphone or retrieved from a memory component of the client device  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  310 .   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 messaging 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 story table  314 ) 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 client device  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 client device  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  312 . Similarly, values within the message video payload  408  may point to data stored within a video table  304 , values stored within the message augmentation data  412  may point to data stored in an augmentation table  310 , values stored within the message story identifier  418  may point to data stored in a story table  314 , 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  306 . 
     Remote Control System 
       FIG.  5    is a block diagram showing an example remote control system  224 , according to some examples. Remote control system  224  includes a set of components that operate on a set of input data. The remote control system  224  includes an object recognition module  510 , a location module  520 , a robotic cleaning device selection module  530 , and a command transmission module  540 . 
     In one example, the object recognition module  510  receives an image or video that depicts one or more real-world objects. The object recognition module  510  performs object recognition on the received image or video to obtain one or more features or attributes of the real-world objects. The object recognition module  510  uses the features or attributes to generate a list of unique real-world objects, such as a television, front door, side of a wall, ceiling, refrigerator, rug, electronic door lock, a table, a thermostat, a sound system, a light bulb, an oven, a kitchen appliance, or any other suitable real-world object. For example, the object recognition module  510  can apply one or more trained neural networks to identify particular objects or regions of interest that depict real-world objects. The trained neural network can be trained using training images that depict various types of real-world objects along with ground-truth information that details the portions of the images that include the real-world objects and the types of the objects. 
     In one example, the object recognition module  510  can detect a soiled portion of the real-world object depicted in the image. Specifically, the location module  520  can apply a trained neural network to the real-world object depicted in the image to identify particular regions of interest that depict soiled portions and that provide identification of the soiled portion types. The trained neural network can be trained using training images that depict various types of soiled portions of real-world objects along with ground-truth information that details the portions of the images that include the soiled portions and the types of the soiled portions. The neural network can be trained using regression and back propagation. Namely, the neural network can perform a first prediction of a soiled portion of a training image and soiled portion type. The first prediction can be compared with the ground truth soiled portion region and soiled portion type. Based on a deviation between the first prediction and the ground truth information, parameters of the neural network can be updated. The neural network with the updated parameters is again applied to another training image to generate a second prediction. The parameters are again updated based on a deviation associated with the second prediction and the respective ground truth information. The process of training in this way continues until a stopping criterion, such as maximum number of interactions is reached or the deviation is within a specified threshold. 
     In another example, the object recognition module  510  can receive input from a user that drags a visual identifier (e.g., an augmented reality element) over a region of an image or video of interest. The region of the image or video of interest can be a portion of a real-world environment the user has an interest in having cleaned. The object recognition module  510  can recognize the soiled portion type of the region selected by the user. In another implementation, the object recognition module  510  can receive input from a user that selects a soiled attribute corresponding to the selected portion of the image. In another implementation, the object recognition module  510  presents a list of possible soiled portion types and the user provides input that selects one or more soiled attribute types from the list. 
     The object recognition module  510  provides the list of real-world objects to the location module  520 . The location module  520  can access current physical location information of a client device  102  that captured the image or video. For example, the location module  520  can obtain the current GPS coordinates of the client device  102 . The location module  520  provides the current GPS coordinates of the client device  102  along with the list of real-world objects depicted in the image or video to the robotic cleaning device selection module  530 . In some cases, rather than using GPS coordinates of the client device  102 , the location module  520  can access a database that previously associated the detected real-world objects with a given location. The location module  520  can retrieve or determine the given location that is associated with the real-world objects detected in the received image or video, such as by matching one or more visual attributes of the real-world objects with visual attributes associated with one or more physical locations. The location module  520  can then provide that given location to the robotic cleaning device selection module along with one or more visual features of the soiled portion of the image that depicts the real-world object. The location module  520  can also provide the identification of the soiled portion type depicted in the received image. 
     The robotic cleaning device selection module  530  accesses a database of previously registered IoT devices (e.g., robotic cleaning devices) associated with the client device  102 . For example, the robotic cleaning device selection module  530  can be preconfigured by the user to associate different robotic cleaning devices with their respective physical locations, types of soiled portions, and corresponding APIs for controlling the robotic cleaning devices. For example, the robotic cleaning device selection module  530  can present a setup screen  600 , as shown in  FIG.  6   . The setup screen  600  allows a user to associate different robotic cleaning devices with respective location(s) and soiled attribute(s). Specifically, the setup screen  600  includes a list of various robotic cleaning devices determined to be previously registered to an account associated with the user. The setup screen  600  includes a first association region  610  and a second association region  620 . Each of the first association region  610  and the second association region  620  defines parameters for automatically controlling respective robotic cleaning devices. 
     For example, the first association region  610  includes an identifier of a first robotic cleaning device  614  (e.g., a vacuum robotic cleaning device). The first robotic cleaning device  614  can be selected by a user from an interactive list of available robotic cleaning devices associated with the user&#39;s account. The first association region  610  includes a soiled attribute selection region  612 . The soiled attribute selection region  612  is configured to receive input from a user that selects one or more soiled attributes (soiled types of regions, such as dry dirt, wet dirt, pet hair, and so forth). The first association region  610  is also configured to receive input from the user that selects one or more locations  616  to associate with the first robotic cleaning device  614 . Namely, the first association region  610  can access various locations previously stored by the location module  520  and can present such locations in a menu to a user. The first association region  610  receives a user selection of one or more of the locations. In response, the first association region  610  associates the first robotic cleaning device  614  with the soiled attributes specified in the soiled attribute selection region  612  and the one or more locations  616  specified by the user. 
     The setup screen  600  is configured to receive input in a similar manner in connection with the second association region  620 . Namely, the second association region  620  associates a second robotic cleaning device with respective soiled attributes specified in a soiled attribute selection region of the second association region  620  and one or more locations specified in the second association region  620 . In some cases, multiple different types of robotic cleaning devices are associated with a same set of locations or overlapping set of locations but with different soiled attributes. For example, a living room location can be associated with a dry vacuum robotic cleaning device and a mop cleaning device. The dry vacuum robotic cleaning device can be associated with a dry soiled attribute and the mop cleaning device can be associated with a wet soiled attribute. The parameters defined in the setup screen  600  are stored in a database, locally or remotely. 
     Referring back to  FIG.  5   , the robotic cleaning device selection module  530  searches the database of previously registered robotic cleaning devices using the current GPS coordinates or location information received from the location module  520  (e.g., the living room or bedroom). Namely, the robotic cleaning device selection module  530  can search for GPS coordinates stored in the database that are within a threshold proximity (e.g., 10 meters) of the current GPS coordinates. In response to identifying such GPS coordinates stored in the database, the robotic cleaning device selection module  530  can retrieve a list of previously registered robotic cleaning devices associated with the identified GPS coordinates or with the location information. 
     The robotic cleaning device selection module  530  can access the soiled attributes associated with each previously registered robotic cleaning device from the list. The robotic cleaning device selection module  530  can compare the soiled attributes associated with each identified robotic cleaning device corresponding to the current location with the soiled type of the region of the image determined by the object recognition module  510 . In response, the robotic cleaning device selection module  530  can select a given robotic cleaning device that is associated with soiled attribute type(s) that match the soiled type of the region of the captured image or video determined by the object recognition module  510  and/or specified by the user. 
     For example, as shown in  FIG.  7   , a user interface  700  is presented on a client device  102  that includes an image depicting a real-world environment. The object recognition module  510  detects a rug real-world object  710  that includes a soiled portion  722 . In one example, the object recognition module  510  can present an augmented reality element (e.g., a circle region) that a user can drag over to identify the soiled portion  722 . The object recognition module  510  can present a notification  720  with an option to specify a type of the soiled portion  722  and that identifies the current location (as determined by the location module  520 ). The notification  720  can be presented in response to detecting a soiled portion  722  of the rug real-world object  710  depicted in the image. The soiled portion type presented in the notification  720  can be automatically determined (e.g., via the trained neural network) or specified by the user. 
     The robotic cleaning device selection module  530  can determine that multiple robotic cleaning devices are associated with the current location of the client device  102  or associated with the location corresponding to the rug real-world object  710  depicted in the image. In such cases, the robotic cleaning device selection module  530  can determine that the soiled attributes of the soiled region of the rug real-world object  710  matches one or more soiled attributes associated with a given one of the robotic cleaning devices associated with the current location. For example, the robotic cleaning device selection module  530  can determine that a dry vacuum robotic cleaning device that has previously been registered at the current location (e.g., a entryway or living room) is associated with dry soiled attributes of the soiled rug real-world object  710  and that a wet vacuum robotic cleaning device is not associated with the those dry soiled attributes but is associated with the current location. 
     In response to identifying the given robotic cleaning device, the robotic cleaning device selection module  530  can present in the user interface  700  a representation of the given robotic cleaning device (e.g., a picture, name, image, video or animation representing the given robotic cleaning device). The robotic cleaning device selection module  530  can obtain a command for controlling the given robotic cleaning device (e.g., start cleaning command  730 ) and present the command in the user interface  700 . In response to receiving a user selection of the command  730 , the robotic cleaning device selection module  530  can communicate with the command transmission module  540  to transmit a message with an instruction to clean the current location associated with the image that depicts the rug real-world object  710 . The message can include visual attributes of the soiled portion  722  and location information (e.g., GPS coordinates or an identifier of a room in a home). 
     The robotic cleaning device selection module  530  can communicate with the given robotic cleaning device to obtain a current state or status information. The robotic cleaning device selection module  530  can render a notification in the user interface  700  that indicates the current status or state. 
     In one example, the command transmission module  540  accesses the API associated with the given robotic cleaning device. The command transmission module  540  obtains one or more instructions from the API corresponding to the clean command. The command transmission module  540  uses the instructions to send a message including the instructions over a local area network to the robotic cleaning device to clean the area corresponding to the soiled portion  722 . In an example, the command transmission module  540  obtains the IP address associated with the robotic cleaning device from the list of previously registered robotic cleaning devices. The command transmission module  540  generates a data packet for transmission to the IP address including the one or more instructions obtained from the API. The robotic cleaning device receives the data packet and obtains the instructions stored in the data packet. The robotic cleaning device then executes the instructions, such as to physically move to the location and clean the soiled area, in response to obtaining the instructions stored in the data packet. 
     In some examples, to enhance security, rather than sending a packet directly to the robotic cleaning device with the instructions, the command transmission module  540  can send an identifier of the robotic cleaning device (e.g., a serial number or IP address) to a manufacturer server associated with the robotic cleaning device. The command transmission module  540  also provides the particular command that has been selected and the location and the visual features of the soiled portion  722 . The manufacturer server associated with the robotic cleaning device then processes the command to generate an instruction. The manufacturer server associated with the robotic cleaning device sends the instruction over the Internet to the robotic cleaning device to cause the robotic cleaning device to execute the instruction, such as to clean the area. 
     In this way, the user is able to point a camera of the client device  102  towards a target real-world object. In response, the remote control system  224  can identify the target real-world object and search for a previously registered robotic cleaning device that can clean a soiled portion (e.g., soiled portion  722 ) corresponding to the target real-world object. The remote control system  224  can then seamlessly send a command to control a robotic cleaning device to clean the soiled portion. 
     As shown in  FIG.  8   , a video or an image  810  is presented in a user interface  800  depicting the given robotic cleaning device  840  which has received the command to clean the soiled portion of the real-world object. Specifically, the video or image  810  depicts a soiled portion  822 . The given robotic cleaning device  840  navigates to and moves to the location corresponding to the soiled portion  822  based on the location information received in the message from the command transmission module  540 . In order to specifically identify the region that needs to be cleaned, the given robotic cleaning device  840  activates an on-board camera when the given robotic cleaning device  840  reaches a threshold proximity of the location received in the message. In one example, when the given robotic cleaning device  840  reaches the threshold proximity of the location received in the message, a confirmation notification  830  is presented to the user. The confirmation notification  830  includes a start cleaning option. In response to receiving input that selects the start cleaning option, the given robotic cleaning device  840  begins searching for areas to clean that have soiled portions. 
     In one example, in response to receiving input that selects the start clean option, the command transmission module  540  transmits (directly or indirectly) a further message to the given robotic cleaning device  840  to begin capturing images from the camera and detect soiled portions of a real-world environment. In another example, when the given robotic cleaning device  840  reaches the threshold proximity of the location received in the message, the given robotic cleaning device  840  automatically begins capturing images from the camera and detects soiled portions of a real-world environment. The given robotic cleaning device  840  moves to each soiled portion of the real-world environment as the soiled portion is detected to clean the area. 
     In one example, the given robotic cleaning device  840  compares visual attributes of a soiled portion of an image captured by the on-board camera with visual features of the soiled portion  822  received in the message from the command transmission module  540 . Namely, the given robotic cleaning device  840  searches for visual features that match visual features of the soiled portion  822  selected by the user using the messaging client  104  and/or automatically detected by the object recognition module  510 . In response to detecting a match between visual features of the real-world environment captured by the on-board camera of the given robotic cleaning device  840  and the visual features received in the message from the command transmission module  540 , the given robotic cleaning device  840  moves to and navigates to the corresponding location of the real-world environment depicted in the image captured by the on-board camera of the given robotic cleaning device  840 . The given robotic cleaning device  840  then applies a cleaning process to clean the soiled portion  822 . 
       FIG.  9    is a flowchart of a process  900  performed by the remote control system  224 , in accordance with some example 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 remote control system  224  (e.g., a client device  102  or a server) detects, by a messaging application implemented on a client device, a real-world object depicted in a received image, as discussed above. For example, as shown in  FIG.  7   , a real-world object  710  is detected in a living room location. 
     At operation  902 , the remote control system  224  determines a current location of the client device, as discussed above. For example, as shown in  FIG.  7   , the remote control system  224  determines that a current location (e.g., GPS coordinates) of the client device  102  is a living room location. 
     At operation  903 , the remote control system  224  identifies a plurality of robotic cleaning devices associated with an account of the messaging application, as discussed above. For example, the remote control system  224  identifies a dry robotic vacuum device and a mop robotic vacuum device as corresponding to the current living room location. 
     At operation  904 , the remote control system  224  transmits, by the messaging application, a message comprising the current location of the client device to a first robotic cleaning device of the plurality of robotic cleaning devices, as discussed above. For example, the remote control system  224  selects a dry robotic vacuum device based on an association of soiled attributes of the real-world object depicted in the image with the dry robotic vacuum device and not with the wet robotic vacuum device. 
     At operation  905 , the remote control system  224  causes, by the messaging application, the first robotic cleaning device to clean the real-world object depicted in the received image based on the message transmitted by the messaging application, as discussed above. For example, as shown in  FIG.  8   , the given robotic cleaning device  840  is depicted in a camera feed of the messaging client  104  as reaching the location and cleaning the soiled portion  822  of the real-world object. This can be performed in response to the given robotic cleaning device  840  receiving the message from the command transmission module  540 . 
     Machine Architecture 
       FIG.  10    is a diagrammatic representation of the machine  1000  within which instructions  1008  (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. For example, the instructions  1008  may cause the machine  1000  to execute any one or more of the methods described herein. The instructions  1008  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. The machine  1000  may operate 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 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  1008 , sequentially or otherwise, that specify actions to be taken by the 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  1008  to perform any one or more of the methodologies discussed herein. The machine  1000 , for example, may comprise the client device  102  or any one of a number of server devices forming part of the messaging server system  108 . In some examples, the machine  1000  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  1000  may include processors  1002 , memory  1004 , and input/output (I/O) components  1038 , which may be configured to communicate with each other via a bus  1040 . In an example, the processors  1002  (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  1006  and a processor  1010  that execute the instructions  1008 . 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.  10    shows multiple processors  1002 , 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 multiples cores, or any combination thereof. 
     The memory  1004  includes a main memory  1012 , a static memory  1014 , and a storage unit  1016 , all accessible to the processors  1002  via the bus  1040 . The main memory  1004 , the static memory  1014 , and the storage unit  1016  store the instructions  1008  embodying any one or more of the methodologies or functions described herein. The instructions  1008  may also reside, completely or partially, within the main memory  1012 , within the static memory  1014 , within machine-readable medium within the storage unit  1016 , within at least one of the processors  1002  (e.g., within the processor&#39;s cache memory), or any suitable combination thereof, during execution thereof by the machine  1000 . 
     The I/O components  1038  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  1038  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  1038  may include many other components that are not shown in  FIG.  10   . In various examples, the I/O components  1038  may include user output components  1024  and user input components  1026 . The user output components  1024  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  1026  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. 
     In further examples, the I/O components  1038  may include biometric components  1028 , motion components  1030 , environmental components  1032 , or position components  1034 , among a wide array of other components. For example, the biometric components  1028  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  1030  include acceleration sensor components (e.g., accelerometer), gravitation sensor components, and rotation sensor components (e.g., gyroscope). 
     The environmental components  1032  include, for example, one or more 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 client device  102  may have a camera system comprising, for example, front cameras on a front surface of the client device  102  and rear cameras on a rear surface of the client device  102 . The front cameras may, for example, be used to capture still images and video of a user of the client device  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 client device  102  may also include a  3600  camera for capturing 360° photographs and videos. 
     Further, the camera system of a client device  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 client device  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  1034  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  1038  further include communication components  1036  operable to couple the machine  1000  to a network  1020  or devices  1022  via respective coupling or connections. For example, the communication components  1036  may include a network interface component or another suitable device to interface with the network  1020 . In further examples, the communication components  1036  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  1022  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  1036  may detect identifiers or include components operable to detect identifiers. For example, the communication components  1036  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  1036 , 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  1012 , static memory  1014 , and memory of the processors  1002 ) and storage unit  1016  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  1008 ), when executed by processors  1002 , cause various operations to implement the disclosed examples. 
     The instructions  1008  may be transmitted or received over the network  1020 , using a transmission medium, via a network interface device (e.g., a network interface component included in the communication components  1036 ) and using any one of several well-known transfer protocols (e.g., HTTP). Similarly, the instructions  1008  may be transmitted or received using a transmission medium via a coupling (e.g., a peer-to-peer coupling) to the devices  1022 . 
     Software Architecture 
       FIG.  11    is a block diagram  1100  illustrating a software architecture  1104 , which can be installed on any one or more of the devices described herein. The software architecture  1104  is supported by hardware such as a machine  1102  that includes processors  1120 , memory  1126 , and I/O components  1138 . In this example, the software architecture  1104  can be conceptualized as a stack of layers, where each layer provides a particular functionality. The software architecture  1104  includes layers such as an operating system  1112 , libraries  1110 , frameworks  1108 , and applications  1106 . Operationally, the applications  1106  invoke API calls  1150  through the software stack and receive messages  1152  in response to the API calls  1150 . The operating system  1112  manages hardware resources and provides common services. The operating system  1112  includes, for example, a kernel  1114 , services  1116 , and drivers  1122 . The kernel  1114  acts as an abstraction layer between the hardware and the other software layers. For example, the kernel  1114  provides memory management, processor management (e.g., scheduling), component management, networking, and security settings, among other functionalities. The services  1116  can provide other common services for the other software layers. The drivers  1122  are responsible for controlling or interfacing with the underlying hardware. For instance, the drivers  1122  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  1110  provide a common low-level infrastructure used by applications  1106 . The libraries  1110  can include system libraries  1118  (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  1110  can include API libraries  1124  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  1110  can also include a wide variety of other libraries  1128  to provide many other APIs to the applications  1106 . 
     The frameworks  1108  provide a common high-level infrastructure that is used by the applications  1106 . For example, the frameworks  1108  provide various graphical user interface functions, high-level resource management, and high-level location services. The frameworks  1108  can provide a broad spectrum of other APIs that can be used by the applications  1106 , some of which may be specific to a particular operating system or platform. 
     In an example, the applications  1106  may include a home application  1136 , a contacts application  1130 , a browser application  1132 , a book reader application  1134 , a location application  1142 , a media application  1144 , a messaging application  1146 , a game application  1148 , and a broad assortment of other applications such as an external application  1140 . The applications  1106  are programs that execute functions defined in the programs. Various programming languages can be employed to create one or more of the applications  1106 , 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 external application  1140  (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 external application  1140  can invoke the API calls  1150  provided by the operating system  1112  to facilitate functionality described herein. 
     Glossary 
     “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, portable digital assistant (PDA), smartphone, tablet, ultrabook, 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. 
     “Communication network” 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 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 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 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 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  1002  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 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 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 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,” and “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 to a tangible medium that is capable of storing, encoding, or carrying the instructions for execution by a machine. 
     “Signal medium” refers 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. 
     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.