Patent Publication Number: US-2021182499-A1

Title: Automatically Detecting and Storing Entity Information for Assistant Systems

Description:
PRIORITY 
     This application is a continuation under 35 U.S.C. § 120 of U.S. patent application Ser. No. 16/107847, filed 21 Aug. 2018, which is incorporated herein by reference. 
    
    
     TECHNICAL FIELD 
     This disclosure generally relates to databases and file management within network environments, and in particular relates to hardware and software for smart assistant systems. 
     BACKGROUND 
     An assistant system can provide information or services on behalf of a user based on a combination of user input, location awareness, and the ability to access information from a variety of online sources (such as weather conditions, traffic congestion, news, stock prices, user schedules, retail prices, etc.). The user input may include text (e.g., online chat), especially in an instant messaging application or other applications, voice, images, or a combination of them. The assistant system may perform concierge-type services (e.g., making dinner reservations, purchasing event tickets, making travel arrangements) or provide information based on the user input. The assistant system may also perform management or data-handling tasks based on online information and events without user initiation or interaction. Examples of those tasks that may be performed by an assistant system may include schedule management (e.g., sending an alert to a dinner date that a user is running late due to traffic conditions, update schedules for both parties, and change the restaurant reservation time). The assistant system may be enabled by the combination of computing devices, application programming interfaces (APIs), and the proliferation of applications on user devices. 
     A social-networking system, which may include a social-networking website, may enable its users (such as persons or organizations) to interact with it and with each other through it. The social-networking system may, with input from a user, create and store in the social-networking system a user profile associated with the user. The user profile may include demographic information, communication-channel information, and information on personal interests of the user. The social-networking system may also, with input from a user, create and store a record of relationships of the user with other users of the social-networking system, as well as provide services (e.g. profile/news feed posts, photo-sharing, event organization, messaging, games, or advertisements) to facilitate social interaction between or among users. 
     The social-networking system may send over one or more networks content or messages related to its services to a mobile or other computing device of a user. A user may also install software applications on a mobile or other computing device of the user for accessing a user profile of the user and other data within the social-networking system. The social-networking system may generate a personalized set of content objects to display to a user, such as a newsfeed of aggregated stories of other users connected to the user. 
     SUMMARY OF PARTICULAR EMBODIMENTS 
     In particular embodiments, the assistant system may assist a user to obtain information or services. The assistant system may enable the user to interact with it with multi-modal user input (such as voice, text, image, video) in stateful and multi-turn conversations to get assistance. The assistant system may create and store a user profile comprising both personal and contextual information associated with the user. In particular embodiments, the assistant system may analyze the user input using natural-language understanding. The analysis may be based on the user profile for more personalized and context-aware understanding. The assistant system may resolve entities associated with the user input based on the analysis. In particular embodiments, the assistant system may interact with different agents to obtain information or services that are associated with the resolved entities. The assistant system may generate a response for the user regarding the information or services by using natural-language generation. Through the interaction with the user, the assistant system may use dialog management techniques to manage and forward the conversation flow with the user. In particular embodiments, the assistant system may further assist the user to effectively and efficiently digest the obtained information by summarizing the information. The assistant system may also assist the user to be more engaging with an online social network by providing tools that help the user interact with the online social network (e.g., creating posts, comments, messages). The assistant system may additionally assist the user to manage different tasks such as keeping track of events. In particular embodiments, the assistant system may proactively execute tasks that are relevant to user interests and preferences based on the user profile without a user input. In particular embodiments, the assistant system may check privacy settings to ensure that accessing a user&#39;s profile or other user information and executing different tasks are permitted subject to the user&#39;s privacy settings. 
     In particular embodiments, the assistant system may identify information related to an entity when the information related to the entity is offered in a messaging conversation and store the information related to the entity. Later, the assistant system may provide back the information associated with the entity to the user. Users may share information associated with entities such as, for example, restaurant names, places, songs, movies, addresses, phone numbers, dates, and so on during their messaging conversations. The users may need to recall the shared information later, but the users may not be always able to remember the information or even remember when/where it was shared with them. A user may find going through the previous messaging conversations to find the information time consuming. The assistant system may be integrated with a messaging application. The assistant system may identify information associated with one or more entities in messaging conversations between users (or other suitable user content) and store the identified information to a data store in the system. The assistant system may then provide this stored information to the user proactively. As an example and not by way of limitation, Alice sets a meeting with Bob, one of her colleague, using a messaging application that is integrated with the assistant system. Alice and Bob agree on a location and time for the meeting on a messaging thread of the messaging application. The assistant system serving Alice may identify an event in the future (a meeting with Bob) and the location and time associated with the event. The assistant system may present a suggestion “Do you want to set a reminder for the meeting with Bob?” to Alice using a pop-up window on the messaging thread. If Alice accepts the suggestion, the assistant system may store the location, time for the meeting in the data store and set a timer for the reminder. When the timer expires, the assistant system may present a reminder for the meeting with the location and time information on the client device screen of Alice. The assistant system may also provide corresponding stored information when the user searches for an entity. As another example and not by way of limitation, Charles, a former colleague of Alice, has moved to a new job recently. Alice chats with Charles on a messaging thread of the messaging application. During the messaging conversation, Charles told Alice his new email address ‘charles@newcompany.com.’ The assistant system serving Alice may identify the new email address of Charles and may present a suggestion “Do you want to save Charles&#39;s email address ‘charles@newcompany.com’?” to Alice using a pop-up window on the messaging thread. If Alice accepts the suggestion, the assistant system may store the email address of Charles. When Alice searches the email address of Charles, the assistant system may provide the stored email address to Alice. To identify information associated with an entity from messages on a messaging thread and store the identified information, the natural-language understanding module of the assistant system may parse the messages on the messaging thread and identify one or more intents and one or more slots on each message. The dialog engine may pass off the identified intents and slots to a first-party agent, which may map the intents and slots to a suggestion and may present the mapped suggestion to Alice (e.g., with a pop-up, or inline with the message by displaying a “Save” button below the message). Although this disclosure describes identifying information related to an entity from a messaging thread and storing the information in a particular manner, this disclosure contemplates identifying information related to an entity from a messaging thread and storing the information in any suitable manner. 
     In particular embodiments, the assistant system may receive, from a first client system associated with a first user, a message sent from the first user to a second user. The assistant system may analyze the message from the first user to identify one or more intents and one or more slots of the received message. In particular embodiments, at least one of the identified intents is an intent to offer entity information. In particular embodiments, one or more of the slots comprise values for entity information of a particular information type associated with an entity. In particular embodiments, the entity belongs to a particular entity domain. The assistant system may compute a confidence score for the intent to offer entity information based on user behavior history records associated with the second user. The assistant system may send, to a second client system associated with the second user, if the confidence score exceeds a threshold score, instructions for presenting a suggestion to the second user to store the values for entity information in association with a profile record for the entity. The assistant system may receive, from the second client system associated with the second user, an indication from the second user confirming the values for entity information should be stored with the profile record for the entity. 
     The embodiments disclosed herein are only examples, and the scope of this disclosure is not limited to them. Particular embodiments may include all, some, or none of the components, elements, features, functions, operations, or steps of the embodiments disclosed herein. Embodiments according to the invention are in particular disclosed in the attached claims directed to a method, a storage medium, a system and a computer program product, wherein any feature mentioned in one claim category, e.g. method, can be claimed in another claim category, e.g. system, as well. The dependencies or references back in the attached claims are chosen for formal reasons only. However any subject matter resulting from a deliberate reference back to any previous claims (in particular multiple dependencies) can be claimed as well, so that any combination of claims and the features thereof are disclosed and can be claimed regardless of the dependencies chosen in the attached claims. The subject-matter which can be claimed comprises not only the combinations of features as set out in the attached claims but also any other combination of features in the claims, wherein each feature mentioned in the claims can be combined with any other feature or combination of other features in the claims. Furthermore, any of the embodiments and features described or depicted herein can be claimed in a separate claim and/or in any combination with any embodiment or feature described or depicted herein or with any of the features of the attached claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates an example network environment associated with an assistant system. 
         FIG. 2  illustrates an example architecture of the assistant system. 
         FIG. 3  illustrates an example diagram flow of responding to a user request by the assistant system. 
         FIG. 4  illustrates an example scenario for presenting a suggestion to store values for entity information associated with an entity to a user. 
         FIG. 5  illustrates an example scenario for presenting a suggestion to set a reminder for a future event. 
         FIG. 6  illustrates an example method for presenting a suggestion to store values of entity information identified in exchanged messages. 
         FIG. 7  illustrates an example social graph. 
         FIG. 8  illustrates an example view of an embedding space. 
         FIG. 9  illustrates an example artificial neural network. 
         FIG. 10  illustrates an example computer system. 
     
    
    
     DESCRIPTION OF EXAMPLE EMBODIMENTS 
     System Overview 
       FIG. 1  illustrates an example network environment  100  associated with an assistant system. Network environment  100  includes a client system  130 , an assistant system  140 , a social-networking system  160 , and a third-party system  170  connected to each other by a network  110 . Although  FIG. 1  illustrates a particular arrangement of a client system  130 , an assistant system  140 , a social-networking system  160 , a third-party system  170 , and a network  110 , this disclosure contemplates any suitable arrangement of a client system  130 , an assistant system  140 , a social-networking system  160 , a third-party system  170 , and a network  110 . As an example and not by way of limitation, two or more of a client system  130 , a social-networking system  160 , an assistant system  140 , and a third-party system  170  may be connected to each other directly, bypassing a network  110 . As another example, two or more of a client system  130 , an assistant system  140 , a social-networking system  160 , and a third-party system  170  may be physically or logically co-located with each other in whole or in part. Moreover, although  FIG. 1  illustrates a particular number of client systems  130 , assistant systems  140 , social-networking systems  160 , third-party systems  170 , and networks  110 , this disclosure contemplates any suitable number of client systems  130 , assistant systems  140 , social-networking systems  160 , third-party systems  170 , and networks  110 . As an example and not by way of limitation, network environment  100  may include multiple client systems  130 , assistant systems  140 , social-networking systems  160 , third-party systems  170 , and networks  110 . 
     This disclosure contemplates any suitable network  110 . As an example and not by way of limitation, one or more portions of a network  110  may include 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), a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a cellular telephone network, or a combination of two or more of these. A network  110  may include one or more networks  110 . 
     Links  150  may connect a client system  130 , an assistant system  140 , a social-networking system  160 , and a third-party system  170  to a communication network  110  or to each other. This disclosure contemplates any suitable links  150 . In particular embodiments, one or more links  150  include one or more wireline (such as for example Digital Subscriber Line (DSL) or Data Over Cable Service Interface Specification (DOCSIS)), wireless (such as for example Wi-Fi or Worldwide Interoperability for Microwave Access (WiMAX)), or optical (such as for example Synchronous Optical Network (SONET) or Synchronous Digital Hierarchy (SDH)) links. In particular embodiments, one or more links  150  each include an ad hoc network, an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, a portion of the Internet, a portion of the PSTN, a cellular technology-based network, a satellite communications technology-based network, another link  150 , or a combination of two or more such links  150 . Links  150  need not necessarily be the same throughout a network environment  100 . One or more first links  150  may differ in one or more respects from one or more second links  150 . 
     In particular embodiments, a client system  130  may be an electronic device including hardware, software, or embedded logic components or a combination of two or more such components and capable of carrying out the appropriate functionalities implemented or supported by a client system  130 . As an example and not by way of limitation, a client system  130  may include a computer system such as a desktop computer, notebook or laptop computer, netbook, a tablet computer, e-book reader, GPS device, camera, personal digital assistant (PDA), handheld electronic device, cellular telephone, smartphone, smart speaker, other suitable electronic device, or any suitable combination thereof. In particular embodiments, the client system  130  may be a smart assistant device. More information on smart assistant devices may be found in U.S. patent application Ser. No. 15/949,011, filed 9 Apr. 2018, U.S. Patent Application No. 62/655,751, filed 10 Apr. 2018, U.S. patent application Ser. No. 29/631,910, filed 3 Jan. 2018, U.S. patent application Ser. No. 29/631,747, filed 2 Jan. 2018, U.S. patent application Ser. No. 29/631,913, filed 3 Jan. 2018, and U.S. patent application Ser. No. 29/631,914, filed 3 Jan. 2018, which are incorporated by reference. This disclosure contemplates any suitable client systems  130 . A client system  130  may enable a network user at a client system  130  to access a network  110 . A client system  130  may enable its user to communicate with other users at other client systems  130 . 
     In particular embodiments, a client system  130  may include a web browser  132  and may have one or more add-ons, plug-ins, or other extensions. A user at a client system  130  may enter a Uniform Resource Locator (URL) or other address directing a web browser  132  to a particular server (such as server  162 , or a server associated with a third-party system  170 ), and the web browser  132  may generate a Hyper Text Transfer Protocol (HTTP) request and communicate the HTTP request to server. The server may accept the HTTP request and communicate to a client system  130  one or more Hyper Text Markup Language (HTML) files responsive to the HTTP request. The client system  130  may render a web interface (e.g. a webpage) based on the HTML files from the server for presentation to the user. This disclosure contemplates any suitable source files. As an example and not by way of limitation, a web interface may be rendered from HTML files, Extensible Hyper Text Markup Language (XHTML) files, or Extensible Markup Language (XML) files, according to particular needs. Such interfaces may also execute scripts such as, for example and without limitation, those written in JAVASCRIPT, JAVA, MICROSOFT SILVERLIGHT, combinations of markup language and scripts such as AJAX (Asynchronous JAVASCRIPT and XML), and the like. Herein, reference to a web interface encompasses one or more corresponding source files (which a browser may use to render the web interface) and vice versa, where appropriate. 
     In particular embodiments, a client system  130  may include a social-networking application  134  installed on the client system  130 . A user at a client system  130  may use the social-networking application  134  to access on online social network. The user at the client system  130  may use the social-networking application  134  to communicate with the user&#39;s social connections (e.g., friends, followers, followed accounts, contacts, etc.). The user at the client system  130  may also use the social-networking application  134  to interact with a plurality of content objects (e.g., posts, news articles, ephemeral content, etc.) on the online social network. As an example and not by way of limitation, the user may browse trending topics and breaking news using the social-networking application  134 . 
     In particular embodiments, a client system  130  may include an assistant application  136 . A user at a client system  130  may use the assistant application  136  to interact with the assistant system  140 . In particular embodiments, the assistant application  136  may comprise a stand-alone application. In particular embodiments, the assistant application  136  may be integrated into the social-networking application  134  or another suitable application (e.g., a messaging application). In particular embodiments, the assistant application  136  may be also integrated into the client system  130 , an assistant hardware device, or any other suitable hardware devices. In particular embodiments, the assistant application  136  may be accessed via the web browser  132 . In particular embodiments, the user may provide input via different modalities. As an example and not by way of limitation, the modalities may include audio, text, image, video, etc. The assistant application  136  may communicate the user input to the assistant system  140 . Based on the user input, the assistant system  140  may generate responses. The assistant system  140  may send the generated responses to the assistant application  136 . The assistant application  136  may then present the responses to the user at the client system  130 . The presented responses may be based on different modalities such as audio, text, image, and video. As an example and not by way of limitation, the user may verbally ask the assistant application  136  about the traffic information (i.e., via an audio modality). The assistant application  136  may then communicate the request to the assistant system  140 . The assistant system  140  may accordingly generate the result and send it back to the assistant application  136 . The assistant application  136  may further present the result to the user in text. 
     In particular embodiments, an assistant system  140  may assist users to retrieve information from different sources. The assistant system  140  may also assist user to request services from different service providers. In particular embodiments, the assist system  140  may receive a user request for information or services via the assistant application  136  in the client system  130 . The assist system  140  may use natural-language understanding to analyze the user request based on user&#39;s profile and other relevant information. The result of the analysis may comprise different entities associated with an online social network. The assistant system  140  may then retrieve information or request services associated with these entities. In particular embodiments, the assistant system  140  may interact with the social-networking system  160  and/or third-party system  170  when retrieving information or requesting services for the user. In particular embodiments, the assistant system  140  may generate a personalized communication content for the user using natural-language generating techniques. The personalized communication content may comprise, for example, the retrieved information or the status of the requested services. In particular embodiments, the assistant system  140  may enable the user to interact with it regarding the information or services in a stateful and multi-turn conversation by using dialog-management techniques. The functionality of the assistant system  140  is described in more detail in the discussion of  FIG. 2  below. 
     In particular embodiments, the social-networking system  160  may be a network-addressable computing system that can host an online social network. The social-networking system  160  may generate, store, receive, and send social-networking data, such as, for example, user-profile data, concept-profile data, social-graph information, or other suitable data related to the online social network. The social-networking system  160  may be accessed by the other components of network environment  100  either directly or via a network  110 . As an example and not by way of limitation, a client system  130  may access the social-networking system  160  using a web browser  132 , or a native application associated with the social-networking system  160  (e.g., a mobile social-networking application, a messaging application, another suitable application, or any combination thereof) either directly or via a network  110 . In particular embodiments, the social-networking system  160  may include one or more servers  162 . Each server  162  may be a unitary server or a distributed server spanning multiple computers or multiple datacenters. Servers  162  may be of various types, such as, for example and without limitation, web server, news server, mail server, message server, advertising server, file server, application server, exchange server, database server, proxy server, another server suitable for performing functions or processes described herein, or any combination thereof. In particular embodiments, each server  162  may include hardware, software, or embedded logic components or a combination of two or more such components for carrying out the appropriate functionalities implemented or supported by server  162 . In particular embodiments, the social-networking system  160  may include one or more data stores  164 . Data stores  164  may be used to store various types of information. In particular embodiments, the information stored in data stores  164  may be organized according to specific data structures. In particular embodiments, each data store  164  may be a relational, columnar, correlation, or other suitable database. Although this disclosure describes or illustrates particular types of databases, this disclosure contemplates any suitable types of databases. Particular embodiments may provide interfaces that enable a client system  130 , a social-networking system  160 , or a third-party system  170  to manage, retrieve, modify, add, or delete, the information stored in data store  164 . 
     In particular embodiments, the social-networking system  160  may store one or more social graphs in one or more data stores  164 . In particular embodiments, a social graph may include multiple nodes—which may include multiple user nodes (each corresponding to a particular user) or multiple concept nodes (each corresponding to a particular concept)—and multiple edges connecting the nodes. The social-networking system  160  may provide users of the online social network the ability to communicate and interact with other users. In particular embodiments, users may join the online social network via the social-networking system  160  and then add connections (e.g., relationships) to a number of other users of the social-networking system  160  whom they want to be connected to. Herein, the term “friend” may refer to any other user of the social-networking system  160  with whom a user has formed a connection, association, or relationship via the social-networking system  160 . 
     In particular embodiments, the social-networking system  160  may provide users with the ability to take actions on various types of items or objects, supported by the social-networking system  160 . As an example and not by way of limitation, the items and objects may include groups or social networks to which users of the social-networking system  160  may belong, events or calendar entries in which a user might be interested, computer-based applications that a user may use, transactions that allow users to buy or sell items via the service, interactions with advertisements that a user may perform, or other suitable items or objects. A user may interact with anything that is capable of being represented in the social-networking system  160  or by an external system of a third-party system  170 , which is separate from the social-networking system  160  and coupled to the social-networking system  160  via a network  110 . 
     In particular embodiments, the social-networking system  160  may be capable of linking a variety of entities. As an example and not by way of limitation, the social-networking system  160  may enable users to interact with each other as well as receive content from third-party systems  170  or other entities, or to allow users to interact with these entities through an application programming interfaces (API) or other communication channels. 
     In particular embodiments, a third-party system  170  may include one or more types of servers, one or more data stores, one or more interfaces, including but not limited to APIs, one or more web services, one or more content sources, one or more networks, or any other suitable components, e.g., that servers may communicate with. A third-party system  170  may be operated by a different entity from an entity operating the social-networking system  160 . In particular embodiments, however, the social-networking system  160  and third-party systems  170  may operate in conjunction with each other to provide social-networking services to users of the social-networking system  160  or third-party systems  170 . In this sense, the social-networking system  160  may provide a platform, or backbone, which other systems, such as third-party systems  170 , may use to provide social-networking services and functionality to users across the Internet. 
     In particular embodiments, a third-party system  170  may include a third-party content object provider. A third-party content object provider may include one or more sources of content objects, which may be communicated to a client system  130 . As an example and not by way of limitation, content objects may include information regarding things or activities of interest to the user, such as, for example, movie show times, movie reviews, restaurant reviews, restaurant menus, product information and reviews, or other suitable information. As another example and not by way of limitation, content objects may include incentive content objects, such as coupons, discount tickets, gift certificates, or other suitable incentive objects. 
     In particular embodiments, the social-networking system  160  also includes user-generated content objects, which may enhance a user&#39;s interactions with the social-networking system  160 . User-generated content may include anything a user can add, upload, send, or “post” to the social-networking system  160 . As an example and not by way of limitation, a user communicates posts to the social-networking system  160  from a client system  130 . Posts may include data such as status updates or other textual data, location information, photos, videos, links, music or other similar data or media. Content may also be added to the social-networking system  160  by a third-party through a “communication channel,” such as a newsfeed or stream. 
     In particular embodiments, the social-networking system  160  may include a variety of servers, sub-systems, programs, modules, logs, and data stores. In particular embodiments, the social-networking system  160  may include one or more of the following: a web server, action logger, API-request server, relevance-and-ranking engine, content-object classifier, notification controller, action log, third-party-content-object-exposure log, inference module, authorization/privacy server, search module, advertisement-targeting module, user-interface module, user-profile store, connection store, third-party content store, or location store. The social-networking system  160  may also include suitable components such as network interfaces, security mechanisms, load balancers, failover servers, management-and-network-operations consoles, other suitable components, or any suitable combination thereof. In particular embodiments, the social-networking system  160  may include one or more user-profile stores for storing user profiles. A user profile may include, for example, biographic information, demographic information, behavioral information, social information, or other types of descriptive information, such as work experience, educational history, hobbies or preferences, interests, affinities, or location. Interest information may include interests related to one or more categories. Categories may be general or specific. As an example and not by way of limitation, if a user “likes” an article about a brand of shoes the category may be the brand, or the general category of “shoes” or “clothing.” A connection store may be used for storing connection information about users. The connection information may indicate users who have similar or common work experience, group memberships, hobbies, educational history, or are in any way related or share common attributes. The connection information may also include user-defined connections between different users and content (both internal and external). A web server may be used for linking the social-networking system  160  to one or more client systems  130  or one or more third-party systems  170  via a network  110 . The web server may include a mail server or other messaging functionality for receiving and routing messages between the social-networking system  160  and one or more client systems  130 . An API-request server may allow a third-party system  170  to access information from the social-networking system  160  by calling one or more APIs. An action logger may be used to receive communications from a web server about a user&#39;s actions on or off the social-networking system  160 . In conjunction with the action log, a third-party-content-object log may be maintained of user exposures to third-party-content objects. A notification controller may provide information regarding content objects to a client system  130 . Information may be pushed to a client system  130  as notifications, or information may be pulled from a client system  130  responsive to a request received from a client system  130 . Authorization servers may be used to enforce one or more privacy settings of the users of the social-networking system  160 . A privacy setting of a user determines how particular information associated with a user can be shared. The authorization server may allow users to opt in to or opt out of having their actions logged by the social-networking system  160  or shared with other systems (e.g., a third-party system  170 ), such as, for example, by setting appropriate privacy settings. Third-party-content-object stores may be used to store content objects received from third parties, such as a third-party system  170 . Location stores may be used for storing location information received from client systems  130  associated with users. Advertisement-pricing modules may combine social information, the current time, location information, or other suitable information to provide relevant advertisements, in the form of notifications, to a user. 
     Assistant Systems 
       FIG. 2  illustrates an example architecture  200  of the assistant system  140 . In particular embodiments, the assistant system  140  may assist a user to obtain information or services. The assistant system  140  may enable the user to interact with it with multi-modal user input (such as voice, text, image, video) in stateful and multi-turn conversations to get assistance. The assistant system  140  may create and store a user profile comprising both personal and contextual information associated with the user. In particular embodiments, the assistant system  140  may analyze the user input using natural-language understanding. The analysis may be based on the user profile for more personalized and context-aware understanding. The assistant system  140  may resolve entities associated with the user input based on the analysis. In particular embodiments, the assistant system  140  may interact with different agents to obtain information or services that are associated with the resolved entities. The assistant system  140  may generate a response for the user regarding the information or services by using natural-language generation. Through the interaction with the user, the assistant system  140  may use dialog management techniques to manage and forward the conversation flow with the user. In particular embodiments, the assistant system  140  may further assist the user to effectively and efficiently digest the obtained information by summarizing the information. The assistant system  140  may also assist the user to be more engaging with an online social network by providing tools that help the user interact with the online social network (e.g., creating posts, comments, messages). The assistant system  140  may additionally assist the user to manage different tasks such as keeping track of events. In particular embodiments, the assistant system  140  may proactively execute pre-authorized tasks that are relevant to user interests and preferences based on the user profile, at a time relevant for the user, without a user input. In particular embodiments, the assistant system  140  may check privacy settings to ensure that accessing a user&#39;s profile or other user information and executing different tasks are permitted subject to the user&#39;s privacy settings. More information on assisting users subject to privacy settings may be found in U.S. Patent Application No. 62/675,090, filed 22 May 2018, which is incorporated by reference. 
     In particular embodiments, the assistant system  140  may receive a user input from the assistant application  136  in the client system  130  associated with the user. In particular embodiments, the user input may be a user generated input that is sent to the assistant system  140  in a single turn. If the user input is based on a text modality, the assistant system  140  may receive it at a messaging platform  205 . If the user input is based on an audio modality (e.g., the user may speak to the assistant application  136  or send a video including speech to the assistant application  136 ), the assistant system  140  may process it using an audio speech recognition (ASR) module  210  to convert the user input into text. If the user input is based on an image or video modality, the assistant system  140  may process it using optical character recognition techniques within the messaging platform  205  to convert the user input into text. The output of the messaging platform  205  or the ASR module  210  may be received at an assistant xbot  215 . More information on handling user input based on different modalities may be found in U.S. patent application Ser. No. 16/053,600, filed 2 Aug. 2018, which is incorporated by reference. 
     In particular embodiments, the assistant xbot  215  may be a type of chat bot. The assistant xbot  215  may comprise a programmable service channel, which may be a software code, logic, or routine that functions as a personal assistant to the user. The assistant xbot  215  may work as the user&#39;s portal to the assistant system  140 . The assistant xbot  215  may therefore be considered as a type of conversational agent. In particular embodiments, the assistant xbot  215  may send the textual user input to a natural-language understanding (NLU) module  220  to interpret the user input. In particular embodiments, the NLU module  220  may get information from a user context engine  225  and a semantic information aggregator  230  to accurately understand the user input. The user context engine  225  may store the user profile of the user. The user profile of the user may comprise user-profile data including demographic information, social information, and contextual information associated with the user. The user-profile data may also include user interests and preferences on a plurality of topics, aggregated through conversations on news feed, search logs, messaging platform  205 , etc. The usage of a user profile may be protected behind a privacy check module  245  to ensure that a user&#39;s information can be used only for his/her benefit, and not shared with anyone else. More information on user profiles may be found in U.S. patent application Ser. No. 15/967,239, filed 30 Apr. 2018, which is incorporated by reference. The semantic information aggregator  230  may provide ontology data associated with a plurality of predefined domains, intents, and slots to the NLU module  220 . In particular embodiments, a domain may denote a social context of interaction, e.g., education. An intent may be an element in a pre-defined taxonomy of semantic intentions, which may indicate a purpose of a user interacting with the assistant system  140 . In particular embodiments, an intent may be an output of the NLU module  220  if the user input comprises a text/speech input. The NLU module  220  may classify the text/speech input into a member of the pre-defined taxonomy, e.g., for the input “Play Beethoven&#39;s 5th,” the NLU module  220  may classify the input as having the intent [intent:play_music]. In particular embodiments, a domain may be conceptually a namespace for a set of intents, e.g., music. A slot may be a named sub-string with the user input, representing a basic semantic entity. For example, a slot for “pizza” may be [slot:dish]. In particular embodiments, a set of valid or expected named slots may be conditioned on the classified intent. As an example and not by way of limitation, for [intent:play_music], a slot may be [slot:song_name]. The semantic information aggregator  230  may additionally extract information from a social graph, a knowledge graph, and a concept graph, and retrieve a user&#39;s profile from the user context engine  225 . The semantic information aggregator  230  may further process information from these different sources by determining what information to aggregate, annotating n-grams of the user input, ranking the n-grams with confidence scores based on the aggregated information, formulating the ranked n-grams into features that can be used by the NLU module  220  for understanding the user input. More information on aggregating semantic information may be found in U.S. patent application Ser. No. 15/967,342, filed 30 Apr. 2018, which is incorporated by reference. Based on the output of the user context engine  225  and the semantic information aggregator  230 , the NLU module  220  may identify a domain, an intent, and one or more slots from the user input in a personalized and context-aware manner. In particular embodiments, the NLU module  220  may comprise a lexicon of language and a parser and grammar rules to partition sentences into an internal representation. The NLU module  220  may also comprise one or more programs that perform naive semantics or stochastic semantic analysis to the use of pragmatics to understand a user input. In particular embodiments, the parser may be based on a deep learning architecture comprising multiple long-short term memory (LSTM) networks. As an example and not by way of limitation, the parser may be based on a recurrent neural network grammar (RNNG) model, which is a type of recurrent and recursive LSTM algorithm. More information on natural-language understanding may be found in U.S. patent application Ser. No. 16/011,062, filed 18 Jun. 2018, U.S. patent application Ser. No. 16/025,317, filed 2 Jul. 2018, and U.S. patent application Ser. No. 16/038,120, filed 17 Jul. 2018, each of which is incorporated by reference. 
     In particular embodiments, the identified domain, intent, and one or more slots from the NLU module  220  may be sent to a dialog engine  235 . In particular embodiments, the dialog engine  235  may manage the dialog state and flow of the conversation between the user and the assistant xbot  215 . The dialog engine  235  may additionally store previous conversations between the user and the assistant xbot  215 . In particular embodiments, the dialog engine  235  may communicate with an entity resolution module  240  to resolve entities associated with the one or more slots, which supports the dialog engine  235  to forward the flow of the conversation between the user and the assistant xbot  215 . In particular embodiments, the entity resolution module  240  may access the social graph, the knowledge graph, and the concept graph when resolving the entities. Entities may include, for example, unique users or concepts, each of which may have a unique identifier (ID). As an example and not by way of limitation, the knowledge graph may comprise a plurality of entities. Each entity may comprise a single record associated with one or more attribute values. The particular record may be associated with a unique entity identifier. Each record may have diverse values for an attribute of the entity. Each attribute value may be associated with a confidence probability. A confidence probability for an attribute value represents a probability that the value is accurate for the given attribute. Each attribute value may be also associated with a semantic weight. A semantic weight for an attribute value may represent how the value semantically appropriate for the given attribute considering all the available information. For example, the knowledge graph may comprise an entity of a movie “The Martian” (2015), which includes information that has been extracted from multiple content sources and then deduped, resolved, and fused to generate the single unique record for the knowledge graph. The entity may be associated with a space attribute value which indicates the genre of the movie “The Martian” (2015). More information on the knowledge graph may be found in U.S. patent application Ser. No. 16/048,049, filed 27 Jul. 2018, and U.S. patent application Ser. No. 16/048,101, filed 27 Jul. 2018, each of which is incorporated by reference. The entity resolution module  240  may additionally request a user profile of the user associated with the user input from the user context engine  225 . In particular embodiments, the entity resolution module  240  may communicate with a privacy check module  245  to guarantee that the resolving of the entities does not violate privacy policies. In particular embodiments, the privacy check module  245  may use an authorization/privacy server to enforce privacy policies. As an example and not by way of limitation, an entity to be resolved may be another user who specifies in his/her privacy settings that his/her identity should not be searchable on the online social network, and thus the entity resolution module  240  may not return that user&#39;s identifier in response to a request. Based on the information obtained from the social graph, knowledge graph, concept graph, and user profile, and subject to applicable privacy policies, the entity resolution module  240  may therefore accurately resolve the entities associated with the user input in a personalized and context-aware manner. In particular embodiments, each of the resolved entities may be associated with one or more identifiers hosted by the social-networking system  160 . As an example and not by way of limitation, an identifier may comprise a unique user identifier (ID). In particular embodiments, each of the resolved entities may be also associated with a confidence score. More information on resolving entities may be found in U.S. patent application Ser. No. 16/048,049, filed 27 Jul. 2018, and U.S. patent application Ser. No. 16/048,072, filed 27 Jul. 2018, each of which is incorporated by reference. 
     In particular embodiments, the dialog engine  235  may communicate with different agents based on the identified intent and domain, and the resolved entities. In particular embodiments, an agent may be an implementation that serves as a broker across a plurality of content providers for one domain. A content provider may be an entity responsible for carrying out an action associated with an intent or completing a task associated with the intent. As an example and not by way of limitation, multiple device-specific implementations (e.g., real-time calls for a client system  130  or a messaging application on the client system  130 ) may be handled internally by a single agent. Alternatively, these device-specific implementations may be handled by multiple agents associated with multiple domains. In particular embodiments, the agents may comprise first-party agents  250  and third-party agents  255 . In particular embodiments, first-party agents  250  may comprise internal agents that are accessible and controllable by the assistant system  140  (e.g. agents associated with services provided by the online social network). In particular embodiments, third-party agents  255  may comprise external agents that the assistant system  140  has no control over (e.g., music streams agents, ticket sales agents). The first-party agents  250  may be associated with first-party providers  260  that provide content objects and/or services hosted by the social-networking system  160 . The third-party agents  255  may be associated with third-party providers  265  that provide content objects and/or services hosted by the third-party system  170 . 
     In particular embodiments, the communication from the dialog engine  235  to the first-party agents  250  may comprise requesting particular content objects and/or services provided by the first-party providers  260 . As a result, the first-party agents  250  may retrieve the requested content objects from the first-party providers  260  and/or execute tasks that command the first-party providers  260  to perform the requested services. In particular embodiments, the communication from the dialog engine  235  to the third-party agents  255  may comprise requesting particular content objects and/or services provided by the third-party providers  265 . As a result, the third-party agents  255  may retrieve the requested content objects from the third-party providers  265  and/or execute tasks that command the third-party providers  265  to perform the requested services. The third-party agents  255  may access the privacy check module  245  to guarantee no privacy violations before interacting with the third-party providers  265 . As an example and not by way of limitation, the user associated with the user input may specify in his/her privacy settings that his/her profile information is invisible to any third-party content providers. Therefore, when retrieving content objects associated with the user input from the third-party providers  265 , the third-party agents  255  may complete the retrieval without revealing to the third-party providers  265  which user is requesting the content objects. 
     In particular embodiments, each of the first-party agents  250  or third-party agents  255  may be designated for a particular domain. As an example and not by way of limitation, the domain may comprise weather, transportation, music, etc. In particular embodiments, the assistant system  140  may use a plurality of agents collaboratively to respond to a user input. As an example and not by way of limitation, the user input may comprise “direct me to my next meeting.” The assistant system  140  may use a calendar agent to retrieve the location of the next meeting. The assistant system  140  may then use a navigation agent to direct the user to the next meeting. 
     In particular embodiments, each of the first-party agents  250  or third-party agents  255  may retrieve a user profile from the user context engine  225  to execute tasks in a personalized and context-aware manner. As an example and not by way of limitation, a user input may comprise “book me a ride to the airport.” A transportation agent may execute the task of booking the ride. The transportation agent may retrieve the user profile of the user from the user context engine  225  before booking the ride. For example, the user profile may indicate that the user prefers taxis, so the transportation agent may book a taxi for the user. As another example, the contextual information associated with the user profile may indicate that the user is in a hurry so the transportation agent may book a ride from a ride-sharing service for the user since it may be faster to get a car from a ride-sharing service than a taxi company. In particular embodiment, each of the first-party agents  250  or third-party agents  255  may take into account other factors when executing tasks. As an example and not by way of limitation, other factors may comprise price, rating, efficiency, partnerships with the online social network, etc. 
     In particular embodiments, the dialog engine  235  may communicate with a conversational understanding composer (CU composer)  270 . The dialog engine  235  may send the requested content objects and/or the statuses of the requested services to the CU composer  270 . In particular embodiments, the dialog engine  235  may send the requested content objects and/or the statuses of the requested services as a &lt;k, c, u, d&gt; tuple, in which k indicates a knowledge source, c indicates a communicative goal, u indicates a user model, and d indicates a discourse model. In particular embodiments, the CU composer  270  may comprise a natural-language generator (NLG)  271  and a user interface (UI) payload generator  272 . The natural-language generator  271  may generate a communication content based on the output of the dialog engine  235 . In particular embodiments, the NLG  271  may comprise a content determination component, a sentence planner, and a surface realization component. The content determination component may determine the communication content based on the knowledge source, communicative goal, and the user&#39;s expectations. As an example and not by way of limitation, the determining may be based on a description logic. The description logic may comprise, for example, three fundamental notions which are individuals (representing objects in the domain), concepts (describing sets of individuals), and roles (representing binary relations between individuals or concepts). The description logic may be characterized by a set of constructors that allow the natural-language generator  271  to build complex concepts/roles from atomic ones. In particular embodiments, the content determination component may perform the following tasks to determine the communication content. The first task may comprise a translation task, in which the input to the natural-language generator  271  may be translated to concepts. The second task may comprise a selection task, in which relevant concepts may be selected among those resulted from the translation task based on the user model. The third task may comprise a verification task, in which the coherence of the selected concepts may be verified. The fourth task may comprise an instantiation task, in which the verified concepts may be instantiated as an executable file that can be processed by the natural-language generator  271 . The sentence planner may determine the organization of the communication content to make it human understandable. The surface realization component may determine specific words to use, the sequence of the sentences, and the style of the communication content. The UI payload generator  272  may determine a preferred modality of the communication content to be presented to the user. In particular embodiments, the CU composer  270  may communicate with the privacy check module  245  to make sure the generation of the communication content follows the privacy policies. In particular embodiments, the CU composer  270  may retrieve a user profile from the user context engine  225  when generating the communication content and determining the modality of the communication content. As a result, the communication content may be more natural, personalized, and context-aware for the user. As an example and not by way of limitation, the user profile may indicate that the user likes short sentences in conversations so the generated communication content may be based on short sentences. As another example and not by way of limitation, the contextual information associated with the user profile may indicated that the user is using a device that only outputs audio signals so the UI payload generator  272  may determine the modality of the communication content as audio. More information on natural-language generation may be found in U.S. patent application Ser. No. 15/967,279, filed 30 Apr. 2018, and U.S. patent application Ser. No. 15/966,455, filed 30 Apr. 2018, each of which is incorporated by reference. 
     In particular embodiments, the CU composer  270  may send the generated communication content to the assistant xbot  215 . In particular embodiments, the assistant xbot  215  may send the communication content to the messaging platform  205 . The messaging platform  205  may further send the communication content to the client system  130  via the assistant application  136 . In alternative embodiments, the assistant xbot  215  may send the communication content to a text-to-speech (TTS) module  275 . The TTS module  275  may convert the communication content to an audio clip. The TTS module  275  may further send the audio clip to the client system  130  via the assistant application  136 . 
     In particular embodiments, the assistant xbot  215  may interact with a proactive inference layer  280  without receiving a user input. The proactive inference layer  280  may infer user interests and preferences based on the user profile that is retrieved from the user context engine  225 . In particular embodiments, the proactive inference layer  280  may further communicate with proactive agents  285  regarding the inference. The proactive agents  285  may execute proactive tasks based on the inference. As an example and not by way of limitation, the proactive tasks may comprise sending content objects or providing services to the user. In particular embodiments, each proactive task may be associated with an agenda item. The agenda item may comprise a recurring item such as a daily digest. The agenda item may also comprise a one-time item. In particular embodiments, a proactive agent  285  may retrieve the user profile from the user context engine  225  when executing the proactive task. Therefore, the proactive agent  285  may execute the proactive task in a personalized and context-aware manner. As an example and not by way of limitation, the proactive inference layer may infer that the user likes the band Maroon 5 and the proactive agent  285  may generate a recommendation of Maroon 5&#39;s new song/album to the user. 
     In particular embodiments, the proactive agent  285  may generate candidate entities associated with the proactive task based on a user profile. The generation may be based on a straightforward backend query using deterministic filters to retrieve the candidate entities from a structured data store. The generation may be alternatively based on a machine-learning model that is trained based on the user profile, entity attributes, and relevance between users and entities. As an example and not by way of limitation, the machine-learning model may be based on support vector machines (SVM). As another example and not by way of limitation, the machine-learning model may be based on a regression model. As another example and not by way of limitation, the machine-learning model may be based on a deep convolutional neural network (DCNN). In particular embodiments, the proactive agent  285  may also rank the generated candidate entities based on the user profile and the content associated with the candidate entities. The ranking may be based on the similarities between a user&#39;s interests and the candidate entities. As an example and not by way of limitation, the assistant system  140  may generate a feature vector representing a user&#39;s interest and feature vectors representing the candidate entities. The assistant system  140  may then calculate similarity scores (e.g., based on cosine similarity) between the feature vector representing the user&#39;s interest and the feature vectors representing the candidate entities. The ranking may be alternatively based on a ranking model that is trained based on user feedback data. 
     In particular embodiments, the proactive task may comprise recommending the candidate entities to a user. The proactive agent  285  may schedule the recommendation, thereby associating a recommendation time with the recommended candidate entities. The recommended candidate entities may be also associated with a priority and an expiration time. In particular embodiments, the recommended candidate entities may be sent to a proactive scheduler. The proactive scheduler may determine an actual time to send the recommended candidate entities to the user based on the priority associated with the task and other relevant factors (e.g., clicks and impressions of the recommended candidate entities). In particular embodiments, the proactive scheduler may then send the recommended candidate entities with the determined actual time to an asynchronous tier. The asynchronous tier may temporarily store the recommended candidate entities as a job. In particular embodiments, the asynchronous tier may send the job to the dialog engine  235  at the determined actual time for execution. In alternative embodiments, the asynchronous tier may execute the job by sending it to other surfaces (e.g., other notification services associated with the social-networking system  160 ). In particular embodiments, the dialog engine  235  may identify the dialog intent, state, and history associated with the user. Based on the dialog intent, the dialog engine  235  may select some candidate entities among the recommended candidate entities to send to the client system  130 . In particular embodiments, the dialog state and history may indicate if the user is engaged in an ongoing conversation with the assistant xbot  215 . If the user is engaged in an ongoing conversation and the priority of the task of recommendation is low, the dialog engine  235  may communicate with the proactive scheduler to reschedule a time to send the selected candidate entities to the client system  130 . If the user is engaged in an ongoing conversation and the priority of the task of recommendation is high, the dialog engine  235  may initiate a new dialog session with the user in which the selected candidate entities may be presented. As a result, the interruption of the ongoing conversation may be prevented. When it is determined that sending the selected candidate entities is not interruptive to the user, the dialog engine  235  may send the selected candidate entities to the CU composer  270  to generate a personalized and context-aware communication content comprising the selected candidate entities, subject to the user&#39;s privacy settings. In particular embodiments, the CU composer  270  may send the communication content to the assistant xbot  215  which may then send it to the client system  130  via the messaging platform  205  or the TTS module  275 . More information on proactively assisting users may be found in U.S. patent application Ser. No. 15/967,193, filed 30 Apr. 2018, and U.S. patent application Ser. No. 16/036,827, filed 16 Jul. 2018, each of which is incorporated by reference. 
     In particular embodiments, the assistant xbot  215  may communicate with a proactive agent  285  in response to a user input. As an example and not by way of limitation, the user may ask the assistant xbot  215  to set up a reminder. The assistant xbot  215  may request a proactive agent  285  to set up such reminder and the proactive agent  285  may proactively execute the task of reminding the user at a later time. 
     In particular embodiments, the assistant system  140  may comprise a summarizer  290 . The summarizer  290  may provide customized news feed summaries to a user. In particular embodiments, the summarizer  290  may comprise a plurality of meta agents. The plurality of meta agents may use the first-party agents  250 , third-party agents  255 , or proactive agents  285  to generated news feed summaries. In particular embodiments, the summarizer  290  may retrieve user interests and preferences from the proactive inference layer  280 . The summarizer  290  may then retrieve entities associated with the user interests and preferences from the entity resolution module  240 . The summarizer  290  may further retrieve a user profile from the user context engine  225 . Based on the information from the proactive inference layer  280 , the entity resolution module  240 , and the user context engine  225 , the summarizer  290  may generate personalized and context-aware summaries for the user. In particular embodiments, the summarizer  290  may send the summaries to the CU composer  270 . The CU composer  270  may process the summaries and send the processing results to the assistant xbot  215 . The assistant xbot  215  may then send the processed summaries to the client system  130  via the messaging platform  205  or the TTS module  275 . More information on summarization may be found in U.S. patent application Ser. No. 15/967,290, filed 30 Apr. 2018, which is incorporated by reference. 
       FIG. 3  illustrates an example diagram flow  300  of responding to a user request by the assistant system  140 . In particular embodiments, the assistant xbot  215  may access a request manager  305  upon receiving the user request. The request manager  305  may comprise a context extractor  306  and a conversational understanding object generator (CU object generator)  307 . The context extractor  306  may extract contextual information associated with the user request. The context extractor  306  may also update contextual information based on the assistant application  136  executing on the client system  130 . As an example and not by way of limitation, the update of contextual information may comprise content items are displayed on the client system  130 . As another example and not by way of limitation, the update of contextual information may comprise alarm is set on the client system  130 . As another example and not by way of limitation, the update of contextual information may comprise a song is playing on the client system  130 . The CU object generator  307  may generate particular content objects relevant to the user request. The content objects may comprise dialog-session data and features associated with the user request, which may be shared with all the modules of the assistant system  140 . In particular embodiments, the request manager  305  may store the contextual information and the generated content objects in data store  310  which is a particular data store implemented in the assistant system  140 . 
     In particular embodiments, the request manger  305  may send the generated content objects to the NLU module  220 . The NLU module  220  may perform a plurality of steps to process the content objects. At step  221 , the NLU module  220  may generate a whitelist for the content objects. In particular embodiments, the whitelist may comprise interpretation data matching the user request. At step  222 , the NLU module  220  may perform a featurization based on the whitelist. At step  223 , the NLU module  220  may perform domain classification/selection on user request based on the features resulted from the featurization to classify the user request into predefined domains. The domain classification/selection results may be further processed based on two related procedures. At step  224   a , the NLU module  220  may process the domain classification/selection result using an intent classifier. The intent classifier may determine the user&#39;s intent associated with the user request. In particular embodiments, there may be one intent classifier for each domain to determine the most possible intents in a given domain. As an example and not by way of limitation, the intent classifier may be based on a machine-learning model that may take the domain classification/selection result as input and calculate a probability of the input being associated with a particular predefined intent. At step  224   b , the NLU module may process the domain classification/selection result using a meta-intent classifier. The meta-intent classifier may determine categories that describe the user&#39;s intent. In particular embodiments, intents that are common to multiple domains may be processed by the meta-intent classifier. As an example and not by way of limitation, the meta-intent classifier may be based on a machine-learning model that may take the domain classification/selection result as input and calculate a probability of the input being associated with a particular predefined meta-intent. At step  225   a , the NLU module  220  may use a slot tagger to annotate one or more slots associated with the user request. In particular embodiments, the slot tagger may annotate the one or more slots for the n-grams of the user request. At step  225   b , the NLU module  220  may use a meta slot tagger to annotate one or more slots for the classification result from the meta-intent classifier. In particular embodiments, the meta slot tagger may tag generic slots such as references to items (e.g., the first), the type of slot, the value of the slot, etc. As an example and not by way of limitation, a user request may comprise “change 500 dollars in my account to Japanese yen.” The intent classifier may take the user request as input and formulate it into a vector. The intent classifier may then calculate probabilities of the user request being associated with different predefined intents based on a vector comparison between the vector representing the user request and the vectors representing different predefined intents. In a similar manner, the slot tagger may take the user request as input and formulate each word into a vector. The intent classifier may then calculate probabilities of each word being associated with different predefined slots based on a vector comparison between the vector representing the word and the vectors representing different predefined slots. The intent of the user may be classified as “changing money”. The slots of the user request may comprise “500”, “dollars”, “account”, and “Japanese yen”. The meta-intent of the user may be classified as “financial service”. The meta slot may comprise “finance”. 
     In particular embodiments, the NLU module  220  may improve the domain classification/selection of the content objects by extracting semantic information from the semantic information aggregator  230 . In particular embodiments, the semantic information aggregator  230  may aggregate semantic information in the following way. The semantic information aggregator  230  may first retrieve information from the user context engine  225 . In particular embodiments, the user context engine  225  may comprise offline aggregators  226  and an online inference service  227 . The offline aggregators  226  may process a plurality of data associated with the user that are collected from a prior time window. As an example and not by way of limitation, the data may include news feed posts/comments, interactions with news feed posts/comments, search history, etc. that are collected from a prior 90-day window. The processing result may be stored in the user context engine  225  as part of the user profile. The online inference service  227  may analyze the conversational data associated with the user that are received by the assistant system  140  at a current time. The analysis result may be stored in the user context engine  225  also as part of the user profile. In particular embodiments, both the offline aggregators  226  and online inference service  227  may extract personalization features from the plurality of data. The extracted personalization features may be used by other modules of the assistant system  140  to better understand user input. In particular embodiments, the semantic information aggregator  230  may then process the retrieved information, i.e., a user profile, from the user context engine  225  in the following steps. At step  231 , the semantic information aggregator  230  may process the retrieved information from the user context engine  225  based on natural-language processing (NLP). In particular embodiments, the semantic information aggregator  230  may tokenize text by text normalization, extract syntax features from text, and extract semantic features from text based on NLP. The semantic information aggregator  230  may additionally extract features from contextual information, which is accessed from dialog history between a user and the assistant system  140 . The semantic information aggregator  230  may further conduct global word embedding, domain-specific embedding, and/or dynamic embedding based on the contextual information. At step  232 , the processing result may be annotated with entities by an entity tagger. Based on the annotations, the semantic information aggregator  230  may generate dictionaries for the retrieved information at step  233 . In particular embodiments, the dictionaries may comprise global dictionary features which can be updated dynamically offline. At step  234 , the semantic information aggregator  230  may rank the entities tagged by the entity tagger. In particular embodiments, the semantic information aggregator  230  may communicate with different graphs  330  including social graph, knowledge graph, and concept graph to extract ontology data that is relevant to the retrieved information from the user context engine  225 . In particular embodiments, the semantic information aggregator  230  may aggregate the user profile, the ranked entities, and the information from the graphs  330 . The semantic information aggregator  230  may then send the aggregated information to the NLU module  220  to facilitate the domain classification/selection. 
     In particular embodiments, the output of the NLU module  220  may be sent to a co-reference module  315  to interpret references of the content objects associated with the user request. In particular embodiments, the co-reference module  315  may be used to identify an item the user request refers to. The co-reference module  315  may comprise reference creation  316  and reference resolution  317 . In particular embodiments, the reference creation  316  may create references for entities determined by the NLU module  220 . The reference resolution  317  may resolve these references accurately. In particular embodiments, the co-reference module  315  may access the user context engine  225  and the dialog engine  235  when necessary to interpret references with improved accuracy. 
     In particular embodiments, the identified domains, intents, meta-intents, slots, and meta slots, along with the resolved references may be sent to the entity resolution module  240  to resolve relevant entities. The entity resolution module  240  may execute generic and domain-specific entity resolution. In particular embodiments, the entity resolution module  240  may comprise domain entity resolution  241  and generic entity resolution  242 . The domain entity resolution  241  may resolve the entities by categorizing the slots and meta slots into different domains. In particular embodiments, entities may be resolved based on the ontology data extracted from the graphs  330 . The ontology data may comprise the structural relationship between different slots/meta-slots and domains. The ontology may also comprise information of how the slots/meta-slots may be grouped, related within a hierarchy where the higher level comprises the domain, and subdivided according to similarities and differences. The generic entity resolution  242  may resolve the entities by categorizing the slots and meta slots into different generic topics. In particular embodiments, the resolving may be also based on the ontology data extracted from the graphs  330 . The ontology data may comprise the structural relationship between different slots/meta-slots and generic topics. The ontology may also comprise information of how the slots/meta-slots may be grouped, related within a hierarchy where the higher level comprises the topic, and subdivided according to similarities and differences. As an example and not by way of limitation, in response to the input of an inquiry of the advantages of a car, the generic entity resolution  242  may resolve the car as vehicle and the domain entity resolution  241  may resolve the car as electric car. 
     In particular embodiments, the output of the entity resolution module  240  may be sent to the dialog engine  235  to forward the flow of the conversation with the user. The dialog engine  235  may comprise dialog intent resolution  236  and dialog state update/ranker  237 . In particular embodiments, the dialog intent resolution  236  may resolve the user intent associated with the current dialog session based on dialog history between the user and the assistant system  140 . The dialog intent resolution  236  may map intents determined by the NLU module  220  to different dialog intents. The dialog intent resolution  236  may further rank dialog intents based on signals from the NLU module  220 , the entity resolution module  240 , and dialog history between the user and the assistant system  140 . In particular embodiments, the dialog state update/ranker  237  may update/rank the dialog state of the current dialog session. As an example and not by way of limitation, the dialog state update/ranker  237  may update the dialog state as “completed” if the dialog session is over. As another example and not by way of limitation, the dialog state update/ranker  237  may rank the dialog state based on a priority associated with it. 
     In particular embodiments, the dialog engine  235  may communicate with a task completion module  335  about the dialog intent and associated content objects. In particular embodiments, the task completion module  335  may rank different dialog hypotheses for different dialog intents. The task completion module  335  may comprise an action selection component  336 . In particular embodiments, the dialog engine  235  may additionally check against dialog policies  320  regarding the dialog state. In particular embodiments, a dialog policy  320  may comprise a data structure that describes an execution plan of an action by an agent  340 . An agent  340  may select among registered content providers to complete the action. The data structure may be constructed by the dialog engine  235  based on an intent and one or more slots associated with the intent. A dialog policy  320  may further comprise multiple goals related to each other through logical operators. In particular embodiments, a goal may be an outcome of a portion of the dialog policy and it may be constructed by the dialog engine  235 . A goal may be represented by an identifier (e.g., string) with one or more named arguments, which parameterize the goal. As an example and not by way of limitation, a goal with its associated goal argument may be represented as {confirm_artist, args:{artist: “Madonna”}}. In particular embodiments, a dialog policy may be based on a tree-structured representation, in which goals are mapped to leaves of the tree. In particular embodiments, the dialog engine  235  may execute a dialog policy  320  to determine the next action to carry out. The dialog policies  320  may comprise generic policy  321  and domain specific policies  322 , both of which may guide how to select the next system action based on the dialog state. In particular embodiments, the task completion module  335  may communicate with dialog policies  320  to obtain the guidance of the next system action. In particular embodiments, the action selection component  336  may therefore select an action based on the dialog intent, the associated content objects, and the guidance from dialog policies  320 . 
     In particular embodiments, the output of the task completion module  335  may be sent to the CU composer  270 . In alternative embodiments, the selected action may require one or more agents  340  to be involved. As a result, the task completion module  335  may inform the agents  340  about the selected action. Meanwhile, the dialog engine  235  may receive an instruction to update the dialog state. As an example and not by way of limitation, the update may comprise awaiting agents&#39; response. In particular embodiments, the CU composer  270  may generate a communication content for the user using the NLG  271  based on the output of the task completion module  335 . In particular embodiments, the NLG  271  may use different language models and/or language templates to generate natural language outputs. The generation of natural language outputs may be application specific. The generation of natural language outputs may be also personalized for each user. The CU composer  270  may also determine a modality of the generated communication content using the UI payload generator  272 . Since the generated communication content may be considered as a response to the user request, the CU composer  270  may additionally rank the generated communication content using a response ranker  273 . As an example and not by way of limitation, the ranking may indicate the priority of the response. 
     In particular embodiments, the output of the CU composer  270  may be sent to a response manager  325 . The response manager  325  may perform different tasks including storing/updating the dialog state  326  retrieved from data store  310  and generating responses  327 . In particular embodiments, the output of CU composer  270  may comprise one or more of natural-language strings, speech, or actions with parameters. As a result, the response manager  325  may determine what tasks to perform based on the output of CU composer  270 . In particular embodiments, the generated response and the communication content may be sent to the assistant xbot  215 . In alternative embodiments, the output of the CU composer  270  may be additionally sent to the TTS module  275  if the determined modality of the communication content is audio. The speech generated by the TTS module  275  and the response generated by the response manager  325  may be then sent to the assistant xbot  215 . 
     Automatically Detecting and Storing Entity Information 
     In particular embodiments, the assistant system  140  may identify information related to an entity when the information related to the entity is offered in a messaging conversation and store the information related to the entity. Later, the assistant system  140  may provide back the information associated with the entity to the user. Users may share information associated with entities such as, for example, restaurant names, places, songs, movies, addresses, phone numbers, dates, and so on during their messaging conversations. The users may need to recall the shared information later, but the users may not be always able to remember the information or even remember when/where it was shared with them. A user may find going through the previous messaging conversations to find the information time consuming. The assistant system  140  may be integrated with a messaging application. The assistant system  140  may identify information associated with one or more entities in messaging conversations between users (or other suitable user content) and store the identified information to a data store in the system. The assistant system  140  may then provide this stored information to the user proactively. As an example and not by way of limitation, Alice sets a meeting with Bob, one of her colleague, using a messaging application that is integrated with the assistant system  140 . Alice and Bob agree on a location and time for the meeting on a messaging thread of the messaging application. The assistant system  140  serving Alice may identify an event in the future (a meeting with Bob) and the location and time associated with the event. The assistant system  140  may present a suggestion “Do you want to set a reminder for the meeting with Bob?” to Alice using a pop-up window on the messaging thread. If Alice accepts the suggestion, the assistant system  140  may store the location, time for the meeting in the data store and set a timer for the reminder. When the timer expires, the assistant system  140  may present a reminder for the meeting with the location and time information on the client device screen of Alice. The assistant system  140  may also provide corresponding stored information when the user searches for an entity. As another example and not by way of limitation, Charles, a former colleague of Alice, has moved to a new job recently. Alice chats with Charles on a messaging thread of the messaging application. During the messaging conversation, Charles told Alice his new email address ‘charles@newcompany.com.’ The assistant system  140  serving Alice may identify the new email address of Charles and may present a suggestion “Do you want to save Charles&#39;s email address ‘charles@newcompany.com’?” to Alice using a pop-up window on the messaging thread. If Alice accepts the suggestion, the assistant system  140  may store the email address of Charles. When Alice searches the email address of Charles, the assistant system  140  may provide the stored email address to Alice. To identify information associated with an entity from messages on a messaging thread and store the identified information, the natural-language understanding (NLU) module  220  of the assistant system  140  may parse the messages on the messaging thread and identify one or more intents and one or more slots on each message. The dialog engine  235  may pass off the identified intents and slots to a first-party agent  250 , which may map the intents and slots to a suggestion, and may present the mapped suggestion to Alice (e.g., with a pop-up, or inline with the message by displaying a “Save” button below the message). Although this disclosure describes identifying information related to an entity from a messaging thread and storing the information in a particular manner, this disclosure contemplates identifying information related to an entity from a messaging thread and storing the information in any suitable manner. 
       FIG. 4  illustrates an example scenario for presenting a suggestion to store values for entity information associated with an entity to a user. In the example illustrated in  FIG. 4 , Charles, a first user, is exchanging messages with his former colleague Alice, a second user.  FIG. 4  illustrates a client system  130  of Alice. Alice has just created a messaging thread  410  on a messaging application on her client system  130  for the messaging session with Charles. The messaging application may be integrated with the assistant system  140 . Alice sent a message  420  to Charles reading “Hey, I heard that you got a new job. Congrats!!”, and Charles responded with a message  430  to Alice reading “Thanks, my new email address is charles@newcompany.com”. The message  430  comprises a new email address of Charles. The assistant system  140  may identify the new email address of Charles in the message  430  and may send instructions for presenting Alice a suggestion “Do you want to save charles@newcompany.com as Charles&#39;s new email address?” with a pop-up window  440  on the messaging context. The pop-up window  440  may comprise a ‘YES’ button  450  for confirming the suggestion and a ‘NO’ button  460  for rejecting the suggestion. If Alice clicks the ‘YES’ button  450 , the assistant system  140  may store the new email address of Charles in a data store. When Alice searches for the email address of Charles, the assistant system  140  may present Alice the stored email address of Charles by retrieving the stored email address of Charles from the data store. 
     In particular embodiments, the assistant system  140  may receive a message  430  sent from a first user to a second user from a first client system  130  associated with a first user. The message  430  may be sent in a messaging thread  410  of a messaging application that is integrated with the assistant system  140 . In order to provide messaging service with minimal delay, the assistant system  140  may send instructions for presenting the message from the first user to the second client system  130  associated with the second user. As an example and not by way of limitation, as illustrated in  FIG. 4 , the assistant system  140  may receive a message  430  from the first client system  130  associated with Charles, the first user. The assistant system  140  may send instructions for presenting the message  430  to Alice, the second user, to the second client system  130  associated with Alice. Although this disclosure describes receiving a messaging and delivering the received message to the client systems associated with the one or more recipient users in a particular manner, this disclosure contemplates receiving a messaging and delivering the received message to the client systems associated with the one or more recipient users in any suitable manner. 
     In particular embodiments, the assistant system  140  may analyze, the message  430  from the first user to identify one or more intents and one or more slots of the received message. After delivering the received message to the second user, a recipient of the message  430 , the messaging platform  205  may send the message  430  to a natural-language understanding (NLU) module  220 . Th messaging platform  250  may send the message  430  to NLU module  220  through an assistant xbot  215 . The NLU module  220  may identify one or more intents, and one or more slots from the message  430  in a personalized and context-aware manner. At least one of the identified intents may be an intent to offer entity information, and one or more of the slots comprise values for entity information of a particular information type associated with an entity. The entity may belong to a particular entity domain. The particular information type may comprise name, address, location, phone number, event, date, time, email address or any suitable information type. The particular entity domain may comprise person, group, object, place, or any suitable entity domain. In particular embodiments, the NLU module  220  of the assistant system  140  may perform a semantic parsing on the received message to extract the one or more intents and the one or more slots. In particular embodiments, the NLU module  220  of the assistant system  140  may process the received message with a natural-language processing (NLP) algorithm to identify the one or more intents and the one or more slots. The NLU module  220  may interact with the SIA module  230 , the UCE  225  and the dialog engine  235  to analyze the received message  430  as disclosed above. The dialog engine  235  may communicate with an entity resolution module  240  to resolve entities associated with the one or more slots. As an example and not by way of limitation, continuing with a prior example, the NLU module  220  of the assistant system  140  may identify an intent [IN:offer_information] and two slots [SL:person(Charles Smith)] and [SL:email_address (charles@newcompany.com)] by performing a semantic parsing on the received message  430 . In particular embodiments, the NLU module  220  of the assistant system  140  may identify an intent [IN:offer_information] and two slots [SL:person(Charles Smith)] and [SL:email_address (charles@newcompany.com)] by processing the received message with a natural-language processing (NLP) algorithm. Although this disclosure describes analyzing the received message to identify one or more intents and one or more slots of the received message in a particular manner, this disclosure contemplates analyzing the received message to identify one or more intents and one or more slots of the received message in any suitable manner. 
     In particular embodiments, the assistant system  140  may compute a confidence score for the intent to offer entity information based on user behavior history records associated with the second user. On determining that at least one of the identified intents may be an intent to offer entity information, the dialog engine  235  may forward the identified one or more intents and the identified one or more slots to a first-party agent  250  responsible for storing entity information. The first-party agent  250  may compute a confidence score for the intent to offer entity information based on user behavior history records associated with the second user. In particular embodiments, the user behavior history records may comprise information on past responses of the second user to suggestions to store values for entity information of the particular information type in association with profile records for entities of the particular entity domain. As an example and not by way of limitation, continuing with a prior example, the dialog engine  235  forwards the identified intent and slots to the first-party agent  250  that is in charge of storing entity information. The first-party agent  250  computes a confidence score for the intent to offer entity information based on information on past responses of Alice to suggestions to store an email address of a friend or a colleague. Because Alice has usually confirmed such suggestions, the computed confidence score may be higher than a confidence score computed for an intent to offer entity information that Alice usually rejects to store. In particular embodiments, the user behavior history records may comprise information on an interaction rate of the second user with entity information of the particular information type associated with entities of the particular entity domain. As another example and not by way of limitation, the first-party agent  250  may compute a confidence score for an intent to offer an address of a restaurant. If the user has not searched any restaurant address over a pre-determined period of time, the computed confidence score for the intent to offer the address of the restaurant may be lower than a confidence score computed for an intent to offer entity information that the user has interacted with often over the same period of time. Although this disclosure describes computing a confidence score for an intent to offer entity information based on user behavior history records associated with the user in a particular manner, this disclosure contemplates computing a confidence score for an intent to offer entity information based on user behavior history records associated with the user in any suitable manner. 
     In particular embodiments, the first-party agent  250  of the assistant system  140  may, if the confidence score exceeds a threshold score, generate a suggestion to store the values for entity information in association with a profile record for the entity based on the at least one of the identified intents to offer entity information and the values for entity information associated with the entity. In particular embodiments, the assistant system  140  may determine whether presenting the suggestion to store the values for entity information in association with the profile record for the entity to the second user is allowed based on privacy settings associated with the entity. As an example and not by way of limitation, continuing with a prior example, the privacy settings associated with Charles may prevent the social-networking system  160  from sharing any of his personal information with anyone. The first-party agent  250  may not present the suggestion to Alice in such a case. As another example and not by way of limitation, continuing with a prior example, the privacy settings associated with Charles may allow the social-networking system  160  to share his personal information with his friends and colleagues. The first-party agent  250  of the assistant system  140  may cause the messaging platform  205  to send instructions for presenting the suggestion to the second user to the second client system  130  associated with the second user in such a case. In particular embodiments, the first-party agent  250  may communicate with an associated first-party provider  260  to cause the messaging platform  205  to send the instructions to the second client system  130 . Although this disclosure describes presenting a suggestion to store the values for entity information to the user in a particular manner, this disclosure contemplates presenting a suggestion to store the values for entity information to the user in any suitable manner. 
     In particular embodiments, the assistant system  140  may receive from the second client system  130  associated with the second user an indication from the second user confirming the values for entity information should be stored with the profile record for the entity. The assistant system  140  may store the values for entity information in association with the profile record for the entity to a data store. In particular embodiments, the data store may be the user context engine  225 . In particular embodiments, the assistant system  140  may store the values for entity information in association with an identifier for a messaging thread in that the values for entity information are offered. As an example and not by way of limitation, continuing with a prior example, Alice may click the ‘YES’ button to confirm that the new email address of Charles, charles@newcompany.com, should be stored. The second client system  130  associated with Alice may send an indication that Alice has confirmed storing the new email address of Charles to the assistant system  140 . On receiving the indication from the second client system  130 , the first-party agent  250  of the assistant system  140  may store the new email address of Charles to the user context engine  225  or a data store  310  of the assistant system  140 . Although this disclosure describes storing values for entity information on receiving a confirmation from the user in a particular manner, this disclosure contemplates storing values for entity information on receiving a confirmation from the user in any suitable manner. 
     In particular embodiments, the assistant system  140  may update the user behavior history records associated with the second user based on the received indication from the second user. The updated user behavior history records may comprise information on the particular information type, the particular entity domain, a suggestion type, and the received indication. The assistant system  140  may store the updated user behavior history records in association with a user profile of the second user. The stored updated user behavior history records may be used for computing confidence scores for intents to offer entity information in the future. As an example and not by way of limitation, continuing with a prior example, after storing the new email address of Charles to the user context engine  225 , the assistant system  140  may update the user behavior history records associated with Alice. The updated user behavior history records may comprise an indication that Alice confirmed a suggestion to store an email address of a friend. As another example and not by way of limitation, if a user rejects a suggestion to store an address of a restaurant, the assistant system  140  may update the user behavior history records associated with the user. The updated user behavior history records may comprise an indication that the user rejected a suggestion to store an address of a restaurant. Although this disclosure describes updating the user behavior history records associated with the user based on the received indication from the user in a particular manner, this disclosure contemplates updating the user behavior history records associated with the user based on the received indication from the user in any suitable manner. 
     In particular embodiments, privacy settings associated with the second user may indicate that values of entity information can automatically be stored with the profile records for the respective entities. The messaging application on the client system  130  associated with the user may have an interface to allow the user to opt in the automatic entity information saving. When the user opts in, the client system  130  associated with the user may send an indication that the user opted in to the assistant system  140 . The assistant system  140  may update privacy settings associated with the user based on the received indication from the client system  130  associated with the user. If privacy settings associated with the second user indicate that values of entity information can automatically be stored with the profile records for the respective entities, the assistant system  140  may skip sending instructions for presenting the suggestion to the second user to store the values for entity information in association with a profile record for the entity to the second client system  130  associated with the second user. The assistant system  140  may treating as if an indication from the second user confirming the values for entity information should be stored with the profile record for the entity is received if the confidence score exceeds the threshold score. Thus, the assistant system  140  may store the values for entity information in association with the profile record for the entity to the data store. In particular embodiments, the assistant system  140  may update the user behavior history records associated with the second user. As an example and not by way of limitation, David, a social network user using a messaging application integrated with the assistant system  140  on his client system  130 , may select an ‘automatic saving information on messages’ option on his messaging application. When David is exchanging messages with Eve, a friend of David, on the messaging application, Eve sent a message “My home phone number changed. It is 123-456-7890.” After delivering the message to David, the assistant system  140  may compute a confidence score for an intent to offer the new home phone number of Eve. Because David searches phone numbers of his friends often, the computed confidence score may be higher than the threshold score. The assistant system  140  may store the new home phone number of Eve to the data store without explicitly getting a confirmation from David because privacy settings associated with David indicates that the values of entity information can be automatically stored. In particular embodiments, the assistant system  140  may also update the user behavior history records associated with David. The updated user behavior history records may comprise that a phone number of a friend is stored because David opted in. Although this disclosure describes storing the values of entity information automatically based on privacy settings in a particular manner, this disclosure contemplates storing the values of entity information automatically based on privacy settings in any suitable manner. 
     In particular embodiments, the assistant system  140  may receive a search query for the profile record for the entity from the second client system  130  associated with the second user. The second user may need the values for entity information stored in association with the profile record for the entity in the data store. The second user may search the values for entity information associated with the entity using a search query on her client system  130 . The second client system  130  associated with the second user may send the search query to the assistant system  140 . On receiving the search query, the assistant system  140  may retrieve the profile record for the entity comprising the values for entity information from the data store. The assistant system  140  may send instructions for presenting the profile record for the entity with the values for entity information to the second client system  130  associated with the second user. As an example and not by way of limitation, continuing with a prior example, Alice may want to send an email to Charles at a later time. She remembers that Charles has changed his job and got a new email address, but does not remember the exact email address. Alice may search “email address of Charles Smith” with her smartphone, the client system  130 . The client system  130  associated with Alice may send the search query to the assistant system  140 . The assistant system  140  may retrieve the stored email address of Charles. The assistant system  140  may send the client system  130  associated with Alice instructions for presenting the retrieved email address to Alice. In particular embodiments, Alice may search the email address of Charles by speaking to the assistant application  136 . The assistant system  140  may process the input from Alice using the ASR module  210 . Although this disclosure describes presenting the stored values of entity information in response to a search query from the user in a particular manner, this disclosure contemplates presenting the stored values of entity information in response to a search query from the user in any suitable manner. 
     In particular embodiments, the assistant system  140  may update the user behavior history records associated with the second user based on the received search query. The updated user behavior history records may comprise information on the particular information type as an information type and the particular entity domain as an entity domain associated with the search query. The assistant system  140  may store the updated user behavior history records in association with a user profile of the second user. The stored updated user behavior history records may be used for computing confidence scores for intents to offer entity information in the future. As an example and not by way of limitation, continuing with a prior example, the assistant system  140  update the user behavior history records associated with Alice based on the received search query from Alice. The updated user behavior history records may comprise an indication that Alice searches an email address of a friend. When the assistant system  140  computes a confidence score for an intent to offer an email address of a person in the future, the assistant system  140  may utilize the user behavior history records. Although this disclosure describes updating the user behavior history records associated with the user based on a received search query from the user in a particular manner, this disclosure contemplates updating the user behavior history records associated with the user based on a received search query from the user in any suitable manner. 
     In particular embodiments, the assistant system  140  may determine that the values for entity information is associated with a future event. The assistant system  140  may send instructions for presenting a suggestion to the second user to set a reminder for the future event to the second client system  130  associated with the second user. The suggestion may comprise the values for entity information. The assistant system  140  may receive an indication from the second user confirming the reminder for the future event should be set from the second client system  130  associated with the second user. The assistant system  140  may set a timer for the reminder at a determined reminder time. The assistant system  140  may, at the expiration of the timer, send instructions for presenting a reminder comprising the values for entity information to the second user to the second client system  130  associated with the second user. In particular embodiments, the assistant system  140  may generate the suggestion to set a reminder for the future event in addition to a suggestion to store the values for entity information associated with the entity. In particular embodiments, the assistant system  140  may generate the suggestion to set a reminder for the future event. The assistant system  140  may store the values of entity information in association with the profile record for the entity to the data store on receiving an indication from the second user confirming the reminder for the future event should be set. In particular embodiments, privacy settings associated with the second user may indicate that values of entity information can automatically be stored with the profile records for the respective entities. The assistant system  140  may set a reminder automatically without sending the suggestion to set the reminder to the second client system  130  associated with the second user.  FIG. 5  illustrates an example scenario for presenting a suggestion to set a reminder for a future event. As an example and not by way of limitation, as illustrated in  FIG. 5 , Bob, a first user, is exchanging messages with his colleague Alice, a second user, regarding their next project.  FIG. 5  illustrates a client system  130  of Alice. Alice has joined a messaging thread  510  of a messaging application on her client system  130  for the messaging session with Bob. Alice and Bob agreed to have a meeting at 2:00 in the afternoon tomorrow. The meeting place is going to be the meeting room A. The assistant system  140  may identify an intent [IN:exchange_information] and slots [SL:event(“A meeting with Bob”)], [SL:time(14:00 30 Jul. 2018)], and [SL:place(“The meeting room A”)] by analyzing the series of exchanged messages. The assistant system  140  may send the client system  130  associated with Alice instructions for presenting Alice a suggestion “Do you want to get a reminder 30 minutes before the meeting with Bob at 2:00 PM Jul. 30, 2018 at the meeting room A?” with a pop-up window  520  on the messaging context. The pop-up window  520  may comprise a ‘YES’ button  530  for confirming the suggestion and a ‘NO’ button  540  for rejecting the suggestion. The suggestion message may comprise a list box  550  to allow Alice to adjust the reminder time. If Alice confirms the suggestion by clicking the ‘YES’ button  530 , the client system  130  associated with Alice may send an indication that Alice confirms to set a reminder for the meeting to the assistant system  140 . The assistant system  140  may store the details for the meeting including the meeting time and location to a data store and set a timer to the determined reminder time. On expiration of the timer, the assistant system  140  may send instructions for presenting a reminder to Alice to the client system  130  associated with Alice. Although this disclosure describes presenting a suggestion to set a reminder for a future event after identifying values for entity information associated with the future event in a particular manner, this disclosure contemplates presenting a suggestion to set a reminder for a future event after identifying values for entity information associated with the future event in any suitable manner. 
     In particular embodiments, the assistant system  140  may receive an indication that the second user returns to the messaging thread after being away for more than a pre-determined amount of time from the client system  130  associated with the second user. The assistant system  140  may retrieve all the values for entity information stored in association with the identifier for the messaging thread. The assistant system  140  may send instructions to display the retrieved values for entity information on a screen of the client system  130  to the client system  130  associated with the second user. The displayed values for entity information may refresh memory of the second user. As an example and not by way of limitation, continuing with a prior example, Alice has switched the context of the client system  130  from the messaging thread with Bob to something else. Alice switches the context back to the messaging thread with Bob in the evening of that day. The client system  130  associated with Alice may send an indication that Alice returns to the messaging thread to the assistant system  140 . The indication may comprise an identifier uniquely identifying the messaging thread between Alice and Bob. The assistant system  140  may retrieve all the stored values for entity information associated with the messaging thread identifier. The assistant system  140  may send the client system  130  associated with Alice instructions for presenting the retrieved values for entity information to Alice. Alice may recall what has been discussed with Bob on the message thread based on the presented values for entity information. Although this disclosure describes presenting stored values for entity information to the user when the user returns to the messaging thread in a particular manner, this disclosure contemplates presenting stored values for entity information to the user when the user returns to the messaging thread in any suitable manner. 
       FIG. 6  illustrates an example method  600  for presenting a suggestion to store values of entity information identified in exchanged messages. The method may begin at step  610 , where the assistant system  140  may receive, from a first client system associated with a first user, a message sent from the first user to a second user. At step  620 , the assistant system  140  may analyze the message from the first user to identify one or more intents and one or more slots of the received message, wherein at least one of the identified intents is an intent to offer entity information, and wherein one or more of the slots comprise values for entity information of a particular information type associated with an entity, and wherein the entity belongs to a particular entity domain. At step  630 , the assistant system  140  may compute a confidence score for the intent to offer entity information based on user behavior history records associated with the second user. At step  640 , the assistant system  140  may send, to a second client system associated with the second user, if the confidence score exceeds a threshold score, instructions for presenting a suggestion to the second user to store the values for entity information in association with a profile record for the entity. At step  650 , the assistant system  140  may receive, from the second client system associated with the second user, an indication from the second user confirming the values for entity information should be stored with the profile record for the entity. Particular embodiments may repeat one or more steps of the method of  FIG. 6 , where appropriate. Although this disclosure describes and illustrates particular steps of the method of  FIG. 6  as occurring in a particular order, this disclosure contemplates any suitable steps of the method of  FIG. 6  occurring in any suitable order. Moreover, although this disclosure describes and illustrates an example method for presenting a suggestion to store values of entity information identified in exchanged messages including the particular steps of the method of  FIG. 6 , this disclosure contemplates any suitable method for presenting a suggestion to store values of entity information identified in exchanged messages including any suitable steps, which may include all, some, or none of the steps of the method of  FIG. 6 , where appropriate. Furthermore, although this disclosure describes and illustrates particular components, devices, or systems carrying out particular steps of the method of  FIG. 6 , this disclosure contemplates any suitable combination of any suitable components, devices, or systems carrying out any suitable steps of the method of  FIG. 6 . 
     More information on messenger systems may be found in U.S. patent application Ser. No. 15/900,703, filed 20 Feb. 2018, U.S. Patent Application No. 61/980,355, filed 16 Apr. 2014, U.S. Patent Application No. 62/020,354, filed 2 Jul. 2014, U.S. Patent Application No. 62/050,073, filed 12 Sep. 2014, U.S. patent application Ser. No. 14/686,771, filed 14 Apr. 2015, U.S. patent application Ser. No. 14/686,770, filed 14 Apr. 2015, U.S. patent application Ser. No. 15/654,007, filed 19 Jul. 2017, U.S. patent application Ser. No. 15/391,250, filed 27 Dec. 2016, U.S. patent application Ser. No. 15/077,814, filed 22 Mar. 2016, U.S. Patent Application No. 62/320,869, filed 11 Apr. 2016, U.S. patent application Ser. No. 15/485,812, filed 12 Apr. 2017, U.S. patent application Ser. No. 15/485,665, filed 12 Apr. 2017, U.S. patent application Ser. No. 15/485,738, filed 12 Apr. 2017, U.S. patent application Ser. No. 15/879,368, filed 24 Jan. 2018, U.S. patent application Ser. No. 15/485,853, filed 12 Apr. 2017, U.S. patent application Ser. No. 15/920,970, filed 14 Mar. 2018, U.S. patent application Ser. No. 15/485,880, filed 12 Apr. 2017, U.S. Patent Application No. 62/485,450, filed 14 Apr. 2017, U.S. patent application Ser. No. 15/489,354, filed 17 Apr. 2017, U.S. patent application Ser. No. 15/380,112, filed 15 Dec. 2016, U.S. patent application Ser. No. 15/648,488, filed 13 Jul. 2017, U.S. Patent Application No. 62/486,262, filed 17 Apr. 2017, U.S. Patent Application No. 62/486,202, filed 17 Apr. 2017, U.S. patent application Ser. No. 15/813,552, filed 15 Nov. 2017, and U.S. Design patent application Ser. No. 29/550,163, filed 30 Dec. 2015, which are incorporated by reference. 
     Social Graphs 
       FIG. 7  illustrates an example social graph  700 . In particular embodiments, the social-networking system  160  may store one or more social graphs  700  in one or more data stores. In particular embodiments, the social graph  700  may include multiple nodes—which may include multiple user nodes  702  or multiple concept nodes  704 —and multiple edges  706  connecting the nodes. Each node may be associated with a unique entity (i.e., user or concept), each of which may have a unique identifier (ID), such as a unique number or username. The example social graph  700  illustrated in  FIG. 7  is shown, for didactic purposes, in a two-dimensional visual map representation. In particular embodiments, a social-networking system  160 , a client system  130 , an assistant system  170 , or a third-party system  170  may access the social graph  700  and related social-graph information for suitable applications. The nodes and edges of the social graph  700  may be stored as data objects, for example, in a data store (such as a social-graph database). Such a data store may include one or more searchable or queryable indexes of nodes or edges of the social graph  700 . 
     In particular embodiments, a user node  702  may correspond to a user of the social-networking system  160  or the assistant system  170 . As an example and not by way of limitation, a user may be an individual (human user), an entity (e.g., an enterprise, business, or third-party application), or a group (e.g., of individuals or entities) that interacts or communicates with or over the social-networking system  160  or the assistant system  170 . In particular embodiments, when a user registers for an account with the social-networking system  160 , the social-networking system  160  may create a user node  702  corresponding to the user, and store the user node  702  in one or more data stores. Users and user nodes  702  described herein may, where appropriate, refer to registered users and user nodes  702  associated with registered users. In addition or as an alternative, users and user nodes  702  described herein may, where appropriate, refer to users that have not registered with the social-networking system  160 . In particular embodiments, a user node  702  may be associated with information provided by a user or information gathered by various systems, including the social-networking system  160 . As an example and not by way of limitation, a user may provide his or her name, profile picture, contact information, birth date, sex, marital status, family status, employment, education background, preferences, interests, or other demographic information. In particular embodiments, a user node  702  may be associated with one or more data objects corresponding to information associated with a user. In particular embodiments, a user node  702  may correspond to one or more web interfaces. 
     In particular embodiments, a concept node  704  may correspond to a concept. As an example and not by way of limitation, a concept may correspond to a place (such as, for example, a movie theater, restaurant, landmark, or city); a website (such as, for example, a website associated with the social-networking system  160  or a third-party website associated with a web-application server); an entity (such as, for example, a person, business, group, sports team, or celebrity); a resource (such as, for example, an audio file, video file, digital photo, text file, structured document, or application) which may be located within the social-networking system  160  or on an external server, such as a web-application server; real or intellectual property (such as, for example, a sculpture, painting, movie, game, song, idea, photograph, or written work); a game; an activity; an idea or theory; another suitable concept; or two or more such concepts. A concept node  704  may be associated with information of a concept provided by a user or information gathered by various systems, including the social-networking system  160  and the assistant system  170 . As an example and not by way of limitation, information of a concept may include a name or a title; one or more images (e.g., an image of the cover page of a book); a location (e.g., an address or a geographical location); a website (which may be associated with a URL); contact information (e.g., a phone number or an email address); other suitable concept information; or any suitable combination of such information. In particular embodiments, a concept node  704  may be associated with one or more data objects corresponding to information associated with concept node  704 . In particular embodiments, a concept node  704  may correspond to one or more web interfaces. 
     In particular embodiments, a node in the social graph  700  may represent or be represented by a web interface (which may be referred to as a “profile interface”). Profile interfaces may be hosted by or accessible to the social-networking system  160  or the assistant system  170 . Profile interfaces may also be hosted on third-party websites associated with a third-party system  170 . As an example and not by way of limitation, a profile interface corresponding to a particular external web interface may be the particular external web interface and the profile interface may correspond to a particular concept node  704 . Profile interfaces may be viewable by all or a selected subset of other users. As an example and not by way of limitation, a user node  702  may have a corresponding user-profile interface in which the corresponding user may add content, make declarations, or otherwise express himself or herself. As another example and not by way of limitation, a concept node  704  may have a corresponding concept-profile interface in which one or more users may add content, make declarations, or express themselves, particularly in relation to the concept corresponding to concept node  704 . 
     In particular embodiments, a concept node  704  may represent a third-party web interface or resource hosted by a third-party system  170 . The third-party web interface or resource may include, among other elements, content, a selectable or other icon, or other inter-actable object (which may be implemented, for example, in JavaScript, AJAX, or PHP codes) representing an action or activity. As an example and not by way of limitation, a third-party web interface may include a selectable icon such as “like,” “check-in,” “eat,” “recommend,” or another suitable action or activity. A user viewing the third-party web interface may perform an action by selecting one of the icons (e.g., “check-in”), causing a client system  130  to send to the social-networking system  160  a message indicating the user&#39;s action. In response to the message, the social-networking system  160  may create an edge (e.g., a check-in-type edge) between a user node  702  corresponding to the user and a concept node  704  corresponding to the third-party web interface or resource and store edge  706  in one or more data stores. 
     In particular embodiments, a pair of nodes in the social graph  700  may be connected to each other by one or more edges  706 . An edge  706  connecting a pair of nodes may represent a relationship between the pair of nodes. In particular embodiments, an edge  706  may include or represent one or more data objects or attributes corresponding to the relationship between a pair of nodes. As an example and not by way of limitation, a first user may indicate that a second user is a “friend” of the first user. In response to this indication, the social-networking system  160  may send a “friend request” to the second user. If the second user confirms the “friend request,” the social-networking system  160  may create an edge  706  connecting the first user&#39;s user node  702  to the second user&#39;s user node  702  in the social graph  700  and store edge  706  as social-graph information in one or more of data stores  167 . In the example of  FIG. 7 , the social graph  700  includes an edge  706  indicating a friend relation between user nodes  702  of user “A” and user “B” and an edge indicating a friend relation between user nodes  702  of user “C” and user “B.” Although this disclosure describes or illustrates particular edges  706  with particular attributes connecting particular user nodes  702 , this disclosure contemplates any suitable edges  706  with any suitable attributes connecting user nodes  702 . As an example and not by way of limitation, an edge  706  may represent a friendship, family relationship, business or employment relationship, fan relationship (including, e.g., liking, etc.), follower relationship, visitor relationship (including, e.g., accessing, viewing, checking-in, sharing, etc.), subscriber relationship, superior/subordinate relationship, reciprocal relationship, non-reciprocal relationship, another suitable type of relationship, or two or more such relationships. Moreover, although this disclosure generally describes nodes as being connected, this disclosure also describes users or concepts as being connected. Herein, references to users or concepts being connected may, where appropriate, refer to the nodes corresponding to those users or concepts being connected in the social graph  700  by one or more edges  706 . 
     In particular embodiments, an edge  706  between a user node  702  and a concept node  704  may represent a particular action or activity performed by a user associated with user node  702  toward a concept associated with a concept node  704 . As an example and not by way of limitation, as illustrated in  FIG. 7 , a user may “like,” “attended,” “played,” “listened,” “cooked,” “worked at,” or “watched” a concept, each of which may correspond to an edge type or subtype. A concept-profile interface corresponding to a concept node  704  may include, for example, a selectable “check in” icon (such as, for example, a clickable “check in” icon) or a selectable “add to favorites” icon. Similarly, after a user clicks these icons, the social-networking system  160  may create a “favorite” edge or a “check in” edge in response to a user&#39;s action corresponding to a respective action. As another example and not by way of limitation, a user (user “C”) may listen to a particular song (“Imagine”) using a particular application (an online music application). In this case, the social-networking system  160  may create a “listened” edge  706  and a “used” edge (as illustrated in  FIG. 7 ) between user nodes  702  corresponding to the user and concept nodes  704  corresponding to the song and application to indicate that the user listened to the song and used the application. Moreover, the social-networking system  160  may create a “played” edge  706  (as illustrated in  FIG. 7 ) between concept nodes  704  corresponding to the song and the application to indicate that the particular song was played by the particular application. In this case, “played” edge  706  corresponds to an action performed by an external application on an external audio file (the song “Imagine”). Although this disclosure describes particular edges  706  with particular attributes connecting user nodes  702  and concept nodes  704 , this disclosure contemplates any suitable edges  706  with any suitable attributes connecting user nodes  702  and concept nodes  704 . Moreover, although this disclosure describes edges between a user node  702  and a concept node  704  representing a single relationship, this disclosure contemplates edges between a user node  702  and a concept node  704  representing one or more relationships. As an example and not by way of limitation, an edge  706  may represent both that a user likes and has used at a particular concept. Alternatively, another edge  706  may represent each type of relationship (or multiples of a single relationship) between a user node  702  and a concept node  704  (as illustrated in  FIG. 7  between user node  702  for user “E” and concept node  704 ). 
     In particular embodiments, the social-networking system  160  may create an edge  706  between a user node  702  and a concept node  704  in the social graph  700 . As an example and not by way of limitation, a user viewing a concept-profile interface (such as, for example, by using a web browser or a special-purpose application hosted by the user&#39;s client system  130 ) may indicate that he or she likes the concept represented by the concept node  704  by clicking or selecting a “Like” icon, which may cause the user&#39;s client system  130  to send to the social-networking system  160  a message indicating the user&#39;s liking of the concept associated with the concept-profile interface. In response to the message, the social-networking system  160  may create an edge  706  between user node  702  associated with the user and concept node  704 , as illustrated by “like” edge  706  between the user and concept node  704 . In particular embodiments, the social-networking system  160  may store an edge  706  in one or more data stores. In particular embodiments, an edge  706  may be automatically formed by the social-networking system  160  in response to a particular user action. As an example and not by way of limitation, if a first user uploads a picture, watches a movie, or listens to a song, an edge  706  may be formed between user node  702  corresponding to the first user and concept nodes  704  corresponding to those concepts. Although this disclosure describes forming particular edges  706  in particular manners, this disclosure contemplates forming any suitable edges  706  in any suitable manner. 
     Vector Spaces and Embeddings 
       FIG. 8  illustrates an example view of a vector space  800 . In particular embodiments, an object or an n-gram may be represented in a d-dimensional vector space, where d denotes any suitable number of dimensions. Although the vector space  800  is illustrated as a three-dimensional space, this is for illustrative purposes only, as the vector space  800  may be of any suitable dimension. In particular embodiments, an n-gram may be represented in the vector space  800  as a vector referred to as a term embedding. Each vector may comprise coordinates corresponding to a particular point in the vector space  800  (i.e., the terminal point of the vector). As an example and not by way of limitation, vectors  810 ,  820 , and  880  may be represented as points in the vector space  800 , as illustrated in  FIG. 8 . An n-gram may be mapped to a respective vector representation. As an example and not by way of limitation, n-grams t 1  and t 2  may be mapped to vectors   and   in the vector space  800 , respectively, by applying a function   defined by a dictionary, such that  = (t 1 ) and  = (t 2 ). As another example and not by way of limitation, a dictionary trained to map text to a vector representation may be utilized, or such a dictionary may be itself generated via training. As another example and not by way of limitation, a model, such as Word2vec, may be used to map an n-gram to a vector representation in the vector space  800 . In particular embodiments, an n-gram may be mapped to a vector representation in the vector space  800  by using a machine leaning model (e.g., a neural network). The machine learning model may have been trained using a sequence of training data (e.g., a corpus of objects each comprising n-grams). 
     In particular embodiments, an object may be represented in the vector space  800  as a vector referred to as a feature vector or an object embedding. As an example and not by way of limitation, objects e 1  and e 2  may be mapped to vectors   and   in the vector space  800 , respectively, by applying a function  , such that  = (e 1 ) and  = (e 2 ). In particular embodiments, an object may be mapped to a vector based on one or more properties, attributes, or features of the object, relationships of the object with other objects, or any other suitable information associated with the object. As an example and not by way of limitation, a function   may map objects to vectors by feature extraction, which may start from an initial set of measured data and build derived values (e.g., features). As an example and not by way of limitation, an object comprising a video or an image may be mapped to a vector by using an algorithm to detect or isolate various desired portions or shapes of the object. Features used to calculate the vector may be based on information obtained from edge detection, corner detection, blob detection, ridge detection, scale-invariant feature transformation, edge direction, changing intensity, autocorrelation, motion detection, optical flow, thresholding, blob extraction, template matching, Hough transformation (e.g., lines, circles, ellipses, arbitrary shapes), or any other suitable information. As another example and not by way of limitation, an object comprising audio data may be mapped to a vector based on features such as a spectral slope, a tonality coefficient, an audio spectrum centroid, an audio spectrum envelope, a Mel-frequency cepstrum, or any other suitable information. In particular embodiments, when an object has data that is either too large to be efficiently processed or comprises redundant data, a function   may map the object to a vector using a transformed reduced set of features (e.g., feature selection). In particular embodiments, a function   may map an object e to a vector  (e) based on one or more n-grams associated with object e. Although this disclosure describes representing an n-gram or an object in a vector space in a particular manner, this disclosure contemplates representing an n-gram or an object in a vector space in any suitable manner. 
     In particular embodiments, the social-networking system  160  may calculate a similarity metric of vectors in vector space  800 . A similarity metric may be a cosine similarity, a Minkowski distance, a Mahalanobis distance, a Jaccard similarity coefficient, or any suitable similarity metric. As an example and not by way of limitation, a similarity metric of   and   may be a cosine similarity 
     
       
         
           
             
               
                 
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     As another example and not by way of limitation, a similarity metric of   and   may be a Euclidean distance ∥ − ∥. A similarity metric of two vectors may represent how similar the two objects or n-grams corresponding to the two vectors, respectively, are to one another, as measured by the distance between the two vectors in the vector space  800 . As an example and not by way of limitation, vector  810  and vector  820  may correspond to objects that are more similar to one another than the objects corresponding to vector  810  and vector  880 , based on the distance between the respective vectors. Although this disclosure describes calculating a similarity metric between vectors in a particular manner, this disclosure contemplates calculating a similarity metric between vectors in any suitable manner. 
     More information on vector spaces, embeddings, feature vectors, and similarity metrics may be found in U.S. patent application Ser. No. 14/949,436, filed 23 Nov. 2015, U.S. patent application Ser. No. 15/286,315, filed 5 Oct. 2016, and U.S. patent application Ser. No. 15/365,789, filed 30 Nov. 2016, each of which is incorporated by reference. 
     Artificial Neural Networks 
       FIG. 9  illustrates an example artificial neural network (“ANN”)  900 . In particular embodiments, an ANN may refer to a computational model comprising one or more nodes. Example ANN  900  may comprise an input layer  910 , hidden layers  920 ,  930 ,  960 , and an output layer  950 . Each layer of the ANN  900  may comprise one or more nodes, such as a node  905  or a node  915 . In particular embodiments, each node of an ANN may be connected to another node of the ANN. As an example and not by way of limitation, each node of the input layer  910  may be connected to one of more nodes of the hidden layer  920 . In particular embodiments, one or more nodes may be a bias node (e.g., a node in a layer that is not connected to and does not receive input from any node in a previous layer). In particular embodiments, each node in each layer may be connected to one or more nodes of a previous or subsequent layer. Although  FIG. 9  depicts a particular ANN with a particular number of layers, a particular number of nodes, and particular connections between nodes, this disclosure contemplates any suitable ANN with any suitable number of layers, any suitable number of nodes, and any suitable connections between nodes. As an example and not by way of limitation, although  FIG. 9  depicts a connection between each node of the input layer  910  and each node of the hidden layer  920 , one or more nodes of the input layer  910  may not be connected to one or more nodes of the hidden layer  920 . 
     In particular embodiments, an ANN may be a feedforward ANN (e.g., an ANN with no cycles or loops where communication between nodes flows in one direction beginning with the input layer and proceeding to successive layers). As an example and not by way of limitation, the input to each node of the hidden layer  920  may comprise the output of one or more nodes of the input layer  910 . As another example and not by way of limitation, the input to each node of the output layer  950  may comprise the output of one or more nodes of the hidden layer  960 . In particular embodiments, an ANN may be a deep neural network (e.g., a neural network comprising at least two hidden layers). In particular embodiments, an ANN may be a deep residual network. A deep residual network may be a feedforward ANN comprising hidden layers organized into residual blocks. The input into each residual block after the first residual block may be a function of the output of the previous residual block and the input of the previous residual block. As an example and not by way of limitation, the input into residual block N may be F(x)+x, where F(x) may be the output of residual block N−1, x may be the input into residual block N−1. Although this disclosure describes a particular ANN, this disclosure contemplates any suitable ANN. 
     In particular embodiments, an activation function may correspond to each node of an ANN. An activation function of a node may define the output of a node for a given input. In particular embodiments, an input to a node may comprise a set of inputs. As an example and not by way of limitation, an activation function may be an identity function, a binary step function, a logistic function, or any other suitable function. As another example and not by way of limitation, an activation function for a node k may be the sigmoid function 
     
       
         
           
             
               
                 
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     the rectifier F k (s k )=max (0, s k ), or any other suitable function F k (s k ), where s k  may be the effective input to node k. In particular embodiments, the input of an activation function corresponding to a node may be weighted. Each node may generate output using a corresponding activation function based on weighted inputs. In particular embodiments, each connection between nodes may be associated with a weight. As an example and not by way of limitation, a connection  925  between the node  905  and the node  915  may have a weighting coefficient of 0.4, which may indicate that 0.4 multiplied by the output of the node  905  is used as an input to the node  915 . As another example and not by way of limitation, the output y k  of node k may be y k =F k (s k ), where F k  may be the activation function corresponding to node k, s k =Σ j  (w jk x j ) may be the effective input to node k, x j  may be the output of a node j connected to node k, and w jk  may be the weighting coefficient between node j and node k. In particular embodiments, the input to nodes of the input layer may be based on a vector representing an object. Although this disclosure describes particular inputs to and outputs of nodes, this disclosure contemplates any suitable inputs to and outputs of nodes. Moreover, although this disclosure may describe particular connections and weights between nodes, this disclosure contemplates any suitable connections and weights between nodes. 
     In particular embodiments, an ANN may be trained using training data. As an example and not by way of limitation, training data may comprise inputs to the ANN  900  and an expected output. As another example and not by way of limitation, training data may comprise vectors each representing a training object and an expected label for each training object. In particular embodiments, training an ANN may comprise modifying the weights associated with the connections between nodes of the ANN by optimizing an objective function. As an example and not by way of limitation, a training method may be used (e.g., the conjugate gradient method, the gradient descent method, the stochastic gradient descent) to backpropagate the sum-of-squares error measured as a distances between each vector representing a training object (e.g., using a cost function that minimizes the sum-of-squares error). In particular embodiments, an ANN may be trained using a dropout technique. As an example and not by way of limitation, one or more nodes may be temporarily omitted (e.g., receive no input and generate no output) while training. For each training object, one or more nodes of the ANN may have some probability of being omitted. The nodes that are omitted for a particular training object may be different than the nodes omitted for other training objects (e.g., the nodes may be temporarily omitted on an object-by-object basis). Although this disclosure describes training an ANN in a particular manner, this disclosure contemplates training an ANN in any suitable manner. 
     Privacy 
     In particular embodiments, one or more objects (e.g., content or other types of objects) of a computing system may be associated with one or more privacy settings. The one or more objects may be stored on or otherwise associated with any suitable computing system or application, such as, for example, a social-networking system  160 , a client system  130 , an assistant system  140 , a third-party system  170 , a social-networking application, an assistant application, a messaging application, a photo-sharing application, or any other suitable computing system or application. Although the examples discussed herein are in the context of an online social network, these privacy settings may be applied to any other suitable computing system. Privacy settings (or “access settings”) for an object may be stored in any suitable manner, such as, for example, in association with the object, in an index on an authorization server, in another suitable manner, or any suitable combination thereof. A privacy setting for an object may specify how the object (or particular information associated with the object) can be accessed, stored, or otherwise used (e.g., viewed, shared, modified, copied, executed, surfaced, or identified) within the online social network. When privacy settings for an object allow a particular user or other entity to access that object, the object may be described as being “visible” with respect to that user or other entity. As an example and not by way of limitation, a user of the online social network may specify privacy settings for a user-profile page that identify a set of users that may access work-experience information on the user-profile page, thus excluding other users from accessing that information. 
     In particular embodiments, privacy settings for an object may specify a “blocked list” of users or other entities that should not be allowed to access certain information associated with the object. In particular embodiments, the blocked list may include third-party entities. The blocked list may specify one or more users or entities for which an object is not visible. As an example and not by way of limitation, a user may specify a set of users who may not access photo albums associated with the user, thus excluding those users from accessing the photo albums (while also possibly allowing certain users not within the specified set of users to access the photo albums). In particular embodiments, privacy settings may be associated with particular social-graph elements. Privacy settings of a social-graph element, such as a node or an edge, may specify how the social-graph element, information associated with the social-graph element, or objects associated with the social-graph element can be accessed using the online social network. As an example and not by way of limitation, a particular concept node  704  corresponding to a particular photo may have a privacy setting specifying that the photo may be accessed only by users tagged in the photo and friends of the users tagged in the photo. In particular embodiments, privacy settings may allow users to opt in to or opt out of having their content, information, or actions stored/logged by the social-networking system  160  or assistant system  140  or shared with other systems (e.g., a third-party system  170 ). Although this disclosure describes using particular privacy settings in a particular manner, this disclosure contemplates using any suitable privacy settings in any suitable manner. 
     In particular embodiments, privacy settings may be based on one or more nodes or edges of a social graph  700 . A privacy setting may be specified for one or more edges  706  or edge-types of the social graph  700 , or with respect to one or more nodes  702 ,  704  or node-types of the social graph  700 . The privacy settings applied to a particular edge  706  connecting two nodes may control whether the relationship between the two entities corresponding to the nodes is visible to other users of the online social network. Similarly, the privacy settings applied to a particular node may control whether the user or concept corresponding to the node is visible to other users of the online social network. As an example and not by way of limitation, a first user may share an object to the social-networking system  160 . The object may be associated with a concept node  704  connected to a user node  702  of the first user by an edge  706 . The first user may specify privacy settings that apply to a particular edge  706  connecting to the concept node  704  of the object, or may specify privacy settings that apply to all edges  706  connecting to the concept node  704 . As another example and not by way of limitation, the first user may share a set of objects of a particular object-type (e.g., a set of images). The first user may specify privacy settings with respect to all objects associated with the first user of that particular object-type as having a particular privacy setting (e.g., specifying that all images posted by the first user are visible only to friends of the first user and/or users tagged in the images). 
     In particular embodiments, the social-networking system  160  may present a “privacy wizard” (e.g., within a webpage, a module, one or more dialog boxes, or any other suitable interface) to the first user to assist the first user in specifying one or more privacy settings. The privacy wizard may display instructions, suitable privacy-related information, current privacy settings, one or more input fields for accepting one or more inputs from the first user specifying a change or confirmation of privacy settings, or any suitable combination thereof. In particular embodiments, the social-networking system  160  may offer a “dashboard” functionality to the first user that may display, to the first user, current privacy settings of the first user. The dashboard functionality may be displayed to the first user at any appropriate time (e.g., following an input from the first user summoning the dashboard functionality, following the occurrence of a particular event or trigger action). The dashboard functionality may allow the first user to modify one or more of the first user&#39;s current privacy settings at any time, in any suitable manner (e.g., redirecting the first user to the privacy wizard). 
     Privacy settings associated with an object may specify any suitable granularity of permitted access or denial of access. As an example and not by way of limitation, access or denial of access may be specified for particular users (e.g., only me, my roommates, my boss), users within a particular degree-of-separation (e.g., friends, friends-of-friends), user groups (e.g., the gaming club, my family), user networks (e.g., employees of particular employers, students or alumni of particular university), all users (“public”), no users (“private”), users of third-party systems  170 , particular applications (e.g., third-party applications, external websites), other suitable entities, or any suitable combination thereof. Although this disclosure describes particular granularities of permitted access or denial of access, this disclosure contemplates any suitable granularities of permitted access or denial of access. 
     In particular embodiments, one or more servers  162  may be authorization/privacy servers for enforcing privacy settings. In response to a request from a user (or other entity) for a particular object stored in a data store  164 , the social-networking system  160  may send a request to the data store  164  for the object. The request may identify the user associated with the request and the object may be sent only to the user (or a client system  130  of the user) if the authorization server determines that the user is authorized to access the object based on the privacy settings associated with the object. If the requesting user is not authorized to access the object, the authorization server may prevent the requested object from being retrieved from the data store  164  or may prevent the requested object from being sent to the user. In the search-query context, an object may be provided as a search result only if the querying user is authorized to access the object, e.g., if the privacy settings for the object allow it to be surfaced to, discovered by, or otherwise visible to the querying user. In particular embodiments, an object may represent content that is visible to a user through a newsfeed of the user. As an example and not by way of limitation, one or more objects may be visible to a user&#39;s “Trending” page. In particular embodiments, an object may correspond to a particular user. The object may be content associated with the particular user, or may be the particular user&#39;s account or information stored on the social-networking system  160 , or other computing system. As an example and not by way of limitation, a first user may view one or more second users of an online social network through a “People You May Know” function of the online social network, or by viewing a list of friends of the first user. As an example and not by way of limitation, a first user may specify that they do not wish to see objects associated with a particular second user in their newsfeed or friends list. If the privacy settings for the object do not allow it to be surfaced to, discovered by, or visible to the user, the object may be excluded from the search results. Although this disclosure describes enforcing privacy settings in a particular manner, this disclosure contemplates enforcing privacy settings in any suitable manner. 
     In particular embodiments, different objects of the same type associated with a user may have different privacy settings. Different types of objects associated with a user may have different types of privacy settings. As an example and not by way of limitation, a first user may specify that the first user&#39;s status updates are public, but any images shared by the first user are visible only to the first user&#39;s friends on the online social network. As another example and not by way of limitation, a user may specify different privacy settings for different types of entities, such as individual users, friends-of-friends, followers, user groups, or corporate entities. As another example and not by way of limitation, a first user may specify a group of users that may view videos posted by the first user, while keeping the videos from being visible to the first user&#39;s employer. In particular embodiments, different privacy settings may be provided for different user groups or user demographics. As an example and not by way of limitation, a first user may specify that other users who attend the same university as the first user may view the first user&#39;s pictures, but that other users who are family members of the first user may not view those same pictures. 
     In particular embodiments, the social-networking system  160  may provide one or more default privacy settings for each object of a particular object-type. A privacy setting for an object that is set to a default may be changed by a user associated with that object. As an example and not by way of limitation, all images posted by a first user may have a default privacy setting of being visible only to friends of the first user and, for a particular image, the first user may change the privacy setting for the image to be visible to friends and friends-of-friends. 
     In particular embodiments, privacy settings may allow a first user to specify (e.g., by opting out, by not opting in) whether the social-networking system  160  or assistant system  140  may receive, collect, log, or store particular objects or information associated with the user for any purpose. In particular embodiments, privacy settings may allow the first user to specify whether particular applications or processes may access, store, or use particular objects or information associated with the user. The privacy settings may allow the first user to opt in or opt out of having objects or information accessed, stored, or used by specific applications or processes. The social-networking system  160  or assistant system  140  may access such information in order to provide a particular function or service to the first user, without the social-networking system  160  or assistant system  140  having access to that information for any other purposes. Before accessing, storing, or using such objects or information, the social-networking system  160  or assistant system  140  may prompt the user to provide privacy settings specifying which applications or processes, if any, may access, store, or use the object or information prior to allowing any such action. As an example and not by way of limitation, a first user may transmit a message to a second user via an application related to the online social network (e.g., a messaging app), and may specify privacy settings that such messages should not be stored by the social-networking system  160  or assistant system  140 . 
     In particular embodiments, a user may specify whether particular types of objects or information associated with the first user may be accessed, stored, or used by the social-networking system  160  or assistant system  140 . As an example and not by way of limitation, the first user may specify that images sent by the first user through the social-networking system  160  or assistant system  140  may not be stored by the social-networking system  160  or assistant system  140 . As another example and not by way of limitation, a first user may specify that messages sent from the first user to a particular second user may not be stored by the social-networking system  160  or assistant system  140 . As yet another example and not by way of limitation, a first user may specify that all objects sent via a particular application may be saved by the social-networking system  160  or assistant system  140 . 
     In particular embodiments, privacy settings may allow a first user to specify whether particular objects or information associated with the first user may be accessed from particular client systems  130  or third-party systems  170 . The privacy settings may allow the first user to opt in or opt out of having objects or information accessed from a particular device (e.g., the phone book on a user&#39;s smart phone), from a particular application (e.g., a messaging app), or from a particular system (e.g., an email server). The social-networking system  160  or assistant system  140  may provide default privacy settings with respect to each device, system, or application, and/or the first user may be prompted to specify a particular privacy setting for each context. As an example and not by way of limitation, the first user may utilize a location-services feature of the social-networking system  160  or assistant system  140  to provide recommendations for restaurants or other places in proximity to the user. The first user&#39;s default privacy settings may specify that the social-networking system  160  or assistant system  140  may use location information provided from a client device  130  of the first user to provide the location-based services, but that the social-networking system  160  or assistant system  140  may not store the location information of the first user or provide it to any third-party system  170 . The first user may then update the privacy settings to allow location information to be used by a third-party image-sharing application in order to geo-tag photos. 
     Privacy Settings Based on Location 
     In particular embodiments, privacy settings may allow a user to specify one or more geographic locations from which objects can be accessed. Access or denial of access to the objects may depend on the geographic location of a user who is attempting to access the objects. As an example and not by way of limitation, a user may share an object and specify that only users in the same city may access or view the object. As another example and not by way of limitation, a first user may share an object and specify that the object is visible to second users only while the first user is in a particular location. If the first user leaves the particular location, the object may no longer be visible to the second users. As another example and not by way of limitation, a first user may specify that an object is visible only to second users within a threshold distance from the first user. If the first user subsequently changes location, the original second users with access to the object may lose access, while a new group of second users may gain access as they come within the threshold distance of the first user. 
     Privacy Settings for User-Authentication and Experience-Personalization Information 
     In particular embodiments, the social-networking system  160  or assistant system  140  may have functionalities that may use, as inputs, personal or biometric information of a user for user-authentication or experience-personalization purposes. A user may opt to make use of these functionalities to enhance their experience on the online social network. As an example and not by way of limitation, a user may provide personal or biometric information to the social-networking system  160  or assistant system  140 . The user&#39;s privacy settings may specify that such information may be used only for particular processes, such as authentication, and further specify that such information may not be shared with any third-party system  170  or used for other processes or applications associated with the social-networking system  160  or assistant system  140 . As another example and not by way of limitation, the social-networking system  160  may provide a functionality for a user to provide voice-print recordings to the online social network. As an example and not by way of limitation, if a user wishes to utilize this function of the online social network, the user may provide a voice recording of his or her own voice to provide a status update on the online social network. The recording of the voice-input may be compared to a voice print of the user to determine what words were spoken by the user. The user&#39;s privacy setting may specify that such voice recording may be used only for voice-input purposes (e.g., to authenticate the user, to send voice messages, to improve voice recognition in order to use voice-operated features of the online social network), and further specify that such voice recording may not be shared with any third-party system  170  or used by other processes or applications associated with the social-networking system  160 . As another example and not by way of limitation, the social-networking system  160  may provide a functionality for a user to provide a reference image (e.g., a facial profile, a retinal scan) to the online social network. The online social network may compare the reference image against a later-received image input (e.g., to authenticate the user, to tag the user in photos). The user&#39;s privacy setting may specify that such voice recording may be used only for a limited purpose (e.g., authentication, tagging the user in photos), and further specify that such voice recording may not be shared with any third-party system  170  or used by other processes or applications associated with the social-networking system  160 . 
     Systems and Methods 
       FIG. 10  illustrates an example computer system  1000 . In particular embodiments, one or more computer systems  1000  perform one or more steps of one or more methods described or illustrated herein. In particular embodiments, one or more computer systems  1000  provide functionality described or illustrated herein. In particular embodiments, software running on one or more computer systems  1000  performs one or more steps of one or more methods described or illustrated herein or provides functionality described or illustrated herein. Particular embodiments include one or more portions of one or more computer systems  1000 . Herein, reference to a computer system may encompass a computing device, and vice versa, where appropriate. Moreover, reference to a computer system may encompass one or more computer systems, where appropriate. 
     This disclosure contemplates any suitable number of computer systems  1000 . This disclosure contemplates computer system  1000  taking any suitable physical form. As example and not by way of limitation, computer system  1000  may be an embedded computer system, a system-on-chip (SOC), a single-board computer system (SBC) (such as, for example, a computer-on-module (COM) or system-on-module (SOM)), a desktop computer system, a laptop or notebook computer system, an interactive kiosk, a mainframe, a mesh of computer systems, a mobile telephone, a personal digital assistant (PDA), a server, a tablet computer system, or a combination of two or more of these. Where appropriate, computer system  1000  may include one or more computer systems  1000 ; be unitary or distributed; span multiple locations; span multiple machines; span multiple data centers; or reside in a cloud, which may include one or more cloud components in one or more networks. Where appropriate, one or more computer systems  1000  may perform without substantial spatial or temporal limitation one or more steps of one or more methods described or illustrated herein. As an example and not by way of limitation, one or more computer systems  1000  may perform in real time or in batch mode one or more steps of one or more methods described or illustrated herein. One or more computer systems  1000  may perform at different times or at different locations one or more steps of one or more methods described or illustrated herein, where appropriate. 
     In particular embodiments, computer system  1000  includes a processor  1002 , memory  1004 , storage  1006 , an input/output (I/O) interface  1008 , a communication interface  1010 , and a bus  1012 . Although this disclosure describes and illustrates a particular computer system having a particular number of particular components in a particular arrangement, this disclosure contemplates any suitable computer system having any suitable number of any suitable components in any suitable arrangement. 
     In particular embodiments, processor  1002  includes hardware for executing instructions, such as those making up a computer program. As an example and not by way of limitation, to execute instructions, processor  1002  may retrieve (or fetch) the instructions from an internal register, an internal cache, memory  1004 , or storage  1006 ; decode and execute them; and then write one or more results to an internal register, an internal cache, memory  1004 , or storage  1006 . In particular embodiments, processor  1002  may include one or more internal caches for data, instructions, or addresses. This disclosure contemplates processor  1002  including any suitable number of any suitable internal caches, where appropriate. As an example and not by way of limitation, processor  1002  may include one or more instruction caches, one or more data caches, and one or more translation lookaside buffers (TLBs). Instructions in the instruction caches may be copies of instructions in memory  1004  or storage  1006 , and the instruction caches may speed up retrieval of those instructions by processor  1002 . Data in the data caches may be copies of data in memory  1004  or storage  1006  for instructions executing at processor  1002  to operate on; the results of previous instructions executed at processor  1002  for access by subsequent instructions executing at processor  1002  or for writing to memory  1004  or storage  1006 ; or other suitable data. The data caches may speed up read or write operations by processor  1002 . The TLBs may speed up virtual-address translation for processor  1002 . In particular embodiments, processor  1002  may include one or more internal registers for data, instructions, or addresses. This disclosure contemplates processor  1002  including any suitable number of any suitable internal registers, where appropriate. Where appropriate, processor  1002  may include one or more arithmetic logic units (ALUs); be a multi-core processor; or include one or more processors  1002 . Although this disclosure describes and illustrates a particular processor, this disclosure contemplates any suitable processor. 
     In particular embodiments, memory  1004  includes main memory for storing instructions for processor  1002  to execute or data for processor  1002  to operate on. As an example and not by way of limitation, computer system  1000  may load instructions from storage  1006  or another source (such as, for example, another computer system  1000 ) to memory  1004 . Processor  1002  may then load the instructions from memory  1004  to an internal register or internal cache. To execute the instructions, processor  1002  may retrieve the instructions from the internal register or internal cache and decode them. During or after execution of the instructions, processor  1002  may write one or more results (which may be intermediate or final results) to the internal register or internal cache. Processor  1002  may then write one or more of those results to memory  1004 . In particular embodiments, processor  1002  executes only instructions in one or more internal registers or internal caches or in memory  1004  (as opposed to storage  1006  or elsewhere) and operates only on data in one or more internal registers or internal caches or in memory  1004  (as opposed to storage  1006  or elsewhere). One or more memory buses (which may each include an address bus and a data bus) may couple processor  1002  to memory  1004 . Bus  1012  may include one or more memory buses, as described below. In particular embodiments, one or more memory management units (MMUs) reside between processor  1002  and memory  1004  and facilitate accesses to memory  1004  requested by processor  1002 . In particular embodiments, memory  1004  includes random access memory (RAM). This RAM may be volatile memory, where appropriate. Where appropriate, this RAM may be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, where appropriate, this RAM may be single-ported or multi-ported RAM. This disclosure contemplates any suitable RAM. Memory  1004  may include one or more memories  1004 , where appropriate. Although this disclosure describes and illustrates particular memory, this disclosure contemplates any suitable memory. 
     In particular embodiments, storage  1006  includes mass storage for data or instructions. As an example and not by way of limitation, storage  1006  may include a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disc, a magneto-optical disc, magnetic tape, or a Universal Serial Bus (USB) drive or a combination of two or more of these. Storage  1006  may include removable or non-removable (or fixed) media, where appropriate. Storage  1006  may be internal or external to computer system  1000 , where appropriate. In particular embodiments, storage  1006  is non-volatile, solid-state memory. In particular embodiments, storage  1006  includes read-only memory (ROM). Where appropriate, this ROM may be mask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), electrically alterable ROM (EAROM), or flash memory or a combination of two or more of these. This disclosure contemplates mass storage  1006  taking any suitable physical form. Storage  1006  may include one or more storage control units facilitating communication between processor  1002  and storage  1006 , where appropriate. Where appropriate, storage  1006  may include one or more storages  1006 . Although this disclosure describes and illustrates particular storage, this disclosure contemplates any suitable storage. 
     In particular embodiments, I/O interface  1008  includes hardware, software, or both, providing one or more interfaces for communication between computer system  1000  and one or more I/O devices. Computer system  1000  may include one or more of these I/O devices, where appropriate. One or more of these I/O devices may enable communication between a person and computer system  1000 . As an example and not by way of limitation, an I/O device may include a keyboard, keypad, microphone, monitor, mouse, printer, scanner, speaker, still camera, stylus, tablet, touch screen, trackball, video camera, another suitable I/O device or a combination of two or more of these. An I/O device may include one or more sensors. This disclosure contemplates any suitable I/O devices and any suitable I/O interfaces  1008  for them. Where appropriate, I/O interface  1008  may include one or more device or software drivers enabling processor  1002  to drive one or more of these I/O devices. I/O interface  1008  may include one or more I/O interfaces  1008 , where appropriate. Although this disclosure describes and illustrates a particular I/O interface, this disclosure contemplates any suitable I/O interface. 
     In particular embodiments, communication interface  1010  includes hardware, software, or both providing one or more interfaces for communication (such as, for example, packet-based communication) between computer system  1000  and one or more other computer systems  1000  or one or more networks. As an example and not by way of limitation, communication interface  1010  may include a network interface controller (NIC) or network adapter for communicating with an Ethernet or other wire-based network or a wireless NIC (WNIC) or wireless adapter for communicating with a wireless network, such as a WI-FI network. This disclosure contemplates any suitable network and any suitable communication interface  1010  for it. As an example and not by way of limitation, computer system  1000  may communicate with an ad hoc network, a personal area network (PAN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), or one or more portions of the Internet or a combination of two or more of these. One or more portions of one or more of these networks may be wired or wireless. As an example, computer system  1000  may communicate with a wireless PAN (WPAN) (such as, for example, a BLUETOOTH WPAN), a WI-FI network, a WI-MAX network, a cellular telephone network (such as, for example, a Global System for Mobile Communications (GSM) network), or other suitable wireless network or a combination of two or more of these. Computer system  1000  may include any suitable communication interface  1010  for any of these networks, where appropriate. Communication interface  1010  may include one or more communication interfaces  1010 , where appropriate. Although this disclosure describes and illustrates a particular communication interface, this disclosure contemplates any suitable communication interface. 
     In particular embodiments, bus  1012  includes hardware, software, or both coupling components of computer system  1000  to each other. As an example and not by way of limitation, bus  1012  may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT) interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBAND interconnect, a low-pin-count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCIe) bus, a serial advanced technology attachment (SATA) bus, a Video Electronics Standards Association local (VLB) bus, or another suitable bus or a combination of two or more of these. Bus  1012  may include one or more buses  1012 , where appropriate. Although this disclosure describes and illustrates a particular bus, this disclosure contemplates any suitable bus or interconnect. 
     Herein, a computer-readable non-transitory storage medium or media may include one or more semiconductor-based or other integrated circuits (ICs) (such, as for example, field-programmable gate arrays (FPGAs) or application-specific ICs (ASICs)), hard disk drives (HDDs), hybrid hard drives (HHDs), optical discs, optical disc drives (ODDs), magneto-optical discs, magneto-optical drives, floppy diskettes, floppy disk drives (FDDs), magnetic tapes, solid-state drives (SSDs), RAM-drives, memory storage cards or drives, any other suitable computer-readable non-transitory storage media, or any suitable combination of two or more of these, where appropriate. A computer-readable non-transitory storage medium may be volatile, non-volatile, or a combination of volatile and non-volatile, where appropriate. 
     Miscellaneous 
     Herein, “or” is inclusive and not exclusive, unless expressly indicated otherwise or indicated otherwise by context. Therefore, herein, “A or B” means “A, B, or both,” unless expressly indicated otherwise or indicated otherwise by context. Moreover, “and” is both joint and several, unless expressly indicated otherwise or indicated otherwise by context. Therefore, herein, “A and B” means “A and B, jointly or severally,” unless expressly indicated otherwise or indicated otherwise by context. 
     The scope of this disclosure encompasses all changes, substitutions, variations, alterations, and modifications to the example embodiments described or illustrated herein that a person having ordinary skill in the art would comprehend. The scope of this disclosure is not limited to the example embodiments described or illustrated herein. Moreover, although this disclosure describes and illustrates respective embodiments herein as including particular components, elements, feature, functions, operations, or steps, any of these embodiments may include any combination or permutation of any of the components, elements, features, functions, operations, or steps described or illustrated anywhere herein that a person having ordinary skill in the art would comprehend. Furthermore, reference in the appended claims to an apparatus or system or a component of an apparatus or system being adapted to, arranged to, capable of, configured to, enabled to, operable to, or operative to perform a particular function encompasses that apparatus, system, component, whether or not it or that particular function is activated, turned on, or unlocked, as long as that apparatus, system, or component is so adapted, arranged, capable, configured, enabled, operable, or operative. Additionally, although this disclosure describes or illustrates particular embodiments as providing particular advantages, particular embodiments may provide none, some, or all of these advantages.