Patent Publication Number: US-11658835-B2

Title: Using a single request for multi-person calling in assistant systems

Description:
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, motion, 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, motion) in stateful and multi-turn conversations to get assistance. As an example and not by way of limitation, the assistant system may support both audio (verbal) input and nonverbal input, such as vision, location, gesture, motion, or hybrid/multi-modal input. 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 of the user 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 advance 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, without a user input, tasks that are relevant to user interests and preferences based on the user profile, at a time relevant for the user. 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 assist the user via a hybrid architecture built upon both client-side processes and server-side processes. The client-side processes and the server-side processes may be two parallel workflows for processing a user input and providing assistance to the user. In particular embodiments, the client-side processes may be performed locally on a client system associated with a user. By contrast, the server-side processes may be performed remotely on one or more computing systems. In particular embodiments, an arbitrator on the client system may coordinate receiving user input (e.g., an audio signal), determine whether to use a client-side process, a server-side process, or both, to respond to the user input, and analyze the processing results from each process. The arbitrator may instruct agents on the client-side or server-side to execute tasks associated with the user input based on the aforementioned analyses. The execution results may be further rendered as output to the client system. By leveraging both client-side and server-side processes, the assistant system can effectively assist a user with optimal usage of computing resources while at the same time protecting user privacy and enhancing security. 
     In particular embodiments, the assistant system may enable a user to call multiple people simultaneously via the assistant system with a single request. Existing conventional systems require users to add people to a group call one-at-a-time or to set up a conference call for people to dial into. In contrast, the assistant system may enable the user to initiate the call by specifying, in a single request, two or more entity names, nicknames, or a combination of entity names and nicknames of these people. As an example and not by way of limitation, the request may be “call John and Lee”, “call my brother and Lee”, or “call my brother and my brother in law”. The assistant system may then initiate the group call without further user input needed. In addition, the nickname may refer a single person or a group of people. As an example and not by way of limitation, the request may be “call my besties”. The user may even request the call by specifying individuals together with a group. As an example and not by way of limitation, the request may be “call Danna, Mr. Serious, and my besties.” Upon receiving a request to call multiple people, the assistant system may first disambiguate the request to identify the targeted people that the user wants to call. The assistant system may further confirm with the user about the disambiguation and start a group call to these targeted people after it receives the user&#39;s confirmation. The user may call multiple people simultaneously with a single request using different applications supported by the assistant system, e.g., a messaging application, a video conferencing application, etc., and the call may be either a voice call or a video call. Although this disclosure describes enabling particular multi-person calling via particular systems in particular manners, this disclosure contemplates providing any suitable multi-person calling via any suitable system in any suitable manner. 
     In particular embodiments, the assistant system may receive, from a client system associated with a first user via an assistant xbot, a user request to call a plurality of second users simultaneously. The user request may comprise one or more references referring the plurality of second users. In particular embodiments, the assistant system may identify the plurality of second users by resolving the one or more references to a plurality of entity identifiers associated with the plurality of second users, respectively. The assistant system may further initiate, by the assistant xbot, a group call to the plurality of second users simultaneously based on the plurality of entity identifiers. 
     Certain technical challenges exist for enabling a multi-person call. One technical challenge may include disambiguating an ambiguous mention of the user request. The solution presented by the embodiments disclosed herein to address this challenge may be determining candidate users based on a personalized machine-learning model with each candidate user associated with a confidence score and confirming with the user when the confidence scores are lower than a threshold score, as the personalized machine-learning model may use user profile information, historical interactions with the assistant system, and contextual information to determine candidate users and the confirmation from the user may provide further clarification. 
     Certain embodiments disclosed herein may provide one or more technical advantages. A technical advantage of the embodiments may include improving user experience with the assistant system by enabling a user to call multiple people simultaneously without any additional input as the user can simply submit a single request in any modality and the assistant system may effectively identify the people the user intends to call and initiate such a call accordingly. Certain embodiments disclosed herein may provide none, some, or all of the above technical advantages. One or more other technical advantages may be readily apparent to one skilled in the art in view of the figures, descriptions, and claims of the present disclosure. 
     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 server-side processes of the assistant system. 
         FIG.  4    illustrates an example diagram flow of processing a user input by the assistant system. 
         FIG.  5 A  illustrate an example interaction for calling two people. 
         FIG.  5 B  illustrate another example interaction for calling two people. 
         FIG.  6 A  illustrate an example interaction for calling two people with a nickname. 
         FIG.  6 B  illustrate an example interaction for calling two people with disambiguation of a nickname. 
         FIG.  7 A  illustrate an example interaction for calling a group of people with a nickname. 
         FIG.  7 B  illustrate another example interaction for calling a group of people with a nickname. 
         FIG.  7 C  illustrate an example interaction for calling a group of people by creating the group. 
         FIG.  8 A  illustrate an example interaction for calling multiple people with disambiguation of first and last names. 
         FIG.  8 B  illustrate another example interaction for calling multiple people with disambiguation of first and last names. 
         FIG.  8 C  illustrate another example interaction for calling multiple people with disambiguation of first and last names. 
         FIG.  9    illustrates an example interaction for calling multiple people with a mixture of a nickname for one person and a nickname for a group. 
         FIG.  10    illustrates an example method for enabling a multi-person call. 
         FIG.  11    illustrates an example social graph. 
         FIG.  12    illustrates an example view of an embedding space. 
         FIG.  13    illustrates an example artificial neural network. 
         FIG.  14    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, virtual reality (VR) headset, augment reality (AR) smart glasses, 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 Ser. No. 16/153,574, filed 5 Oct. 2018, U.S. Design patent application Ser. No. 29/631,910, filed 3 Jan. 2018, U.S. Design patent application Ser. No. 29/631,747, filed 2 Jan. 2018, U.S. Design patent application Ser. No. 29/631,913, filed 3 Jan. 2018, and U.S. Design patent application Ser. No. 29/631,914, filed 3 Jan. 2018, each of which is 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, combinations of markup language and scripts, 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, motion, orientation, 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) by speaking into a microphone of the client system  130 . The assistant application  136  may then communicate the request to the assistant system  140 . The assistant system  140  may accordingly generate a response and send it back to the assistant application  136 . The assistant application  136  may further present the response to the user in text and/or images on a display of the client system  130 . 
     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 , an assistant system  140 , 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, a third-party content provider may use one or more third-party agents to provide content objects and/or services. A third-party agent may be an implementation that is hosted and executing on the third-party system  170 . 
     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, for example, an assistant system  140  or 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 of an 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, motion) in stateful and multi-turn conversations to get assistance. As an example and not by way of limitation, the assistant system  140  may support both audio input (verbal) and nonverbal input, such as vision, location, gesture, motion, or hybrid/multi-modal input. 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 of the user 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, without a user input, pre-authorized tasks that are relevant to user interests and preferences based on the user profile, at a time relevant for the user. 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 Ser. No. 16/182,542, filed 6 Nov. 2018, which is incorporated by reference. 
     In particular embodiments, the assistant system  140  may assist the user via a hybrid architecture built upon both client-side processes and server-side processes. The client-side processes and the server-side processes may be two parallel workflows for processing a user input and providing assistances to the user. In particular embodiments, the client-side processes may be performed locally on a client system  130  associated with a user. By contrast, the server-side processes may be performed remotely on one or more computing systems. In particular embodiments, an assistant orchestrator on the client system  130  may coordinate receiving user input (e.g., audio signal) and determine whether to use client-side processes, server-side processes, or both, to respond to the user input. A dialog arbitrator may analyze the processing results from each process. The dialog arbitrator may instruct agents on the client-side or server-side to execute tasks associated with the user input based on the aforementioned analyses. The execution results may be further rendered as output to the client system  130 . By leveraging both client-side and server-side processes, the assistant system  140  can effectively assist a user with optimal usage of computing resources while at the same time protecting user privacy and enhancing security. 
     In particular embodiments, the assistant system  140  may receive a user input from a 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. The user input may be verbal, nonverbal, or a combination thereof. As an example and not by way of limitation, the nonverbal user input may be based on the user&#39;s voice, vision, location, activity, gesture, motion, or a combination thereof. If the user input is based on the user&#39;s voice (e.g., the user may speak to the client system  130 ), such user input may be first processed by a system audio API  202  (application programming interface). The system audio API  202  may conduct echo cancellation, noise removal, beam forming, and self-user voice activation, speaker identification, voice activity detection (VAD), and any other acoustic techniques to generate audio data that is readily processable by the assistant system  140 . In particular embodiments, the system audio API  202  may perform wake-word detection  204  from the user input. As an example and not by way of limitation, a wake-word may be “hey assistant”. If such wake-word is detected, the assistant system  140  may be activated accordingly. In alternative embodiments, the user may activate the assistant system  140  via a visual signal without a wake-word. The visual signal may be received at a low-power sensor (e.g., a camera) that can detect various visual signals. As an example and not by way of limitation, the visual signal may be a barcode, a QR code or a universal product code (UPC) detected by the client system  130 . As another example and not by way of limitation, the visual signal may be the user&#39;s gaze at an object. As yet another example and not by way of limitation, the visual signal may be a user gesture, e.g., the user pointing at an object. 
     In particular embodiments, the audio data from the system audio API  202  may be sent to an assistant orchestrator  206 . The assistant orchestrator  206  may be executing on the client system  130 . In particular embodiments, the assistant orchestrator  206  may determine whether to respond to the user input by using client-side processes, server-side processes, or both. As indicated in  FIG.  2   , the client-side processes are illustrated below the dashed line  207  whereas the server-side processes are illustrated above the dashed line  207 . The assistant orchestrator  206  may also determine to respond to the user input by using both the client-side processes and the server-side processes simultaneously. Although  FIG.  2    illustrates the assistant orchestrator  206  as being a client-side process, the assistant orchestrator  206  may be a server-side process or may be a hybrid process split between client- and server-side processes. 
     In particular embodiments, the server-side processes may be as follows after audio data is generated from the system audio API  202 . The assistant orchestrator  206  may send the audio data to a remote computing system that hosts different modules of the assistant system  140  to respond to the user input. In particular embodiments, the audio data may be received at a remote automatic speech recognition (ASR) module  208 . The ASR module  208  may allow a user to dictate and have speech transcribed as written text, have a document synthesized as an audio stream, or issue commands that are recognized as such by the system. The ASR module  208  may use statistical models to determine the most likely sequences of words that correspond to a given portion of speech received by the assistant system  140  as audio input. The models may include one or more of hidden Markov models, neural networks, deep learning models, or any combination thereof. The received audio input may be encoded into digital data at a particular sampling rate (e.g., 16, 44.1, or 96 kHz) and with a particular number of bits representing each sample (e.g., 8, 16, of 24 bits). 
     In particular embodiments, the ASR module  208  may comprise different components. The ASR module  208  may comprise one or more of a grapheme-to-phoneme (G2P) model, a pronunciation learning model, a personalized acoustic model, a personalized language model (PLM), or an end-pointing model. In particular embodiments, the G2P model may be used to determine a user&#39;s grapheme-to-phoneme style, e.g., what it may sound like when a particular user speaks a particular word. The personalized acoustic model may be a model of the relationship between audio signals and the sounds of phonetic units in the language. Therefore, such personalized acoustic model may identify how a user&#39;s voice sounds. The personalized acoustical model may be generated using training data such as training speech received as audio input and the corresponding phonetic units that correspond to the speech. The personalized acoustical model may be trained or refined using the voice of a particular user to recognize that user&#39;s speech. In particular embodiments, the personalized language model may then determine the most likely phrase that corresponds to the identified phonetic units for a particular audio input. The personalized language model may be a model of the probabilities that various word sequences may occur in the language. The sounds of the phonetic units in the audio input may be matched with word sequences using the personalized language model, and greater weights may be assigned to the word sequences that are more likely to be phrases in the language. The word sequence having the highest weight may be then selected as the text that corresponds to the audio input. In particular embodiments, the personalized language model may be also used to predict what words a user is most likely to say given a context. In particular embodiments, the end-pointing model may detect when the end of an utterance is reached. 
     In particular embodiments, the output of the ASR module  208  may be sent to a remote natural-language understanding (NLU) module  210 . The NLU module  210  may perform named entity resolution (NER). The NLU module  210  may additionally consider contextual information when analyzing the user input. In particular embodiments, an intent and/or a slot may be an output of the NLU module  210 . 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 . The NLU module  210  may classify a user input into a member of the pre-defined taxonomy, e.g., for the input “Play Beethoven&#39;s 5th,” the NLU module  210  may classify the input as having the intent [IN:play music]. In particular embodiments, a domain may denote a social context of interaction, e.g., education, or a namespace for a set of intents, e.g., music. A slot may be a named sub-string corresponding to a character string within the user input, representing a basic semantic entity. For example, a slot for “pizza” may be [SL: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 the intent [IN:play music], a valid slot may be [SL:song_name]. In particular embodiments, the NLU module  210  may additionally extract information from one or more of a social graph, a knowledge graph, or a concept graph, and retrieve a user&#39;s profile from one or more remote data stores  212 . The NLU module  210  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, and formulating the ranked n-grams into features that can be used by the NLU module  210  for understanding the user input. 
     In particular embodiments, the NLU module  210  may identify one or more of a domain, an intent, or a slot from the user input in a personalized and context-aware manner. As an example and not by way of limitation, a user input may comprise “show me how to get to the coffee shop”. The NLU module  210  may identify the particular coffee shop that the user wants to go based on the user&#39;s personal information and the associated contextual information. In particular embodiments, the NLU module  210  may comprise a lexicon of a particular language and a parser and grammar rules to partition sentences into an internal representation. The NLU module  210  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 output of the NLU module  210  may be sent to a remote reasoning module  214 . The reasoning module  214  may comprise a dialog manager and an entity resolution component. In particular embodiments, the dialog manager may have complex dialog logic and product-related business logic. The dialog manager may manage the dialog state and flow of the conversation between the user and the assistant system  140 . The dialog manager may additionally store previous conversations between the user and the assistant system  140 . In particular embodiments, the dialog manager may communicate with the entity resolution component to resolve entities associated with the one or more slots, which supports the dialog manager to advance the flow of the conversation between the user and the assistant system  140 . In particular embodiments, the entity resolution component may access one or more of the social graph, the knowledge graph, or 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 book “Alice&#39;s Adventures”, which includes information that has been extracted from multiple content sources (e.g., an online social network, online encyclopedias, book review sources, media databases, and entertainment 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 “fantasy” attribute value which indicates the genre of the book “Alice&#39;s Adventures”. 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. 
     In particular embodiments, the entity resolution component may check the privacy constraints to guarantee that the resolving of the entities does not violate 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 component may not return that user&#39;s identifier in response to a request. Based on the information obtained from the social graph, the knowledge graph, the concept graph, and the user profile, and subject to applicable privacy policies, the entity resolution component may therefore resolve the entities associated with the user input in a personalized, context-aware, and privacy-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) corresponding to a particular user (e.g., a unique username or user ID number). 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 manager may conduct dialog optimization and assistant state tracking. Dialog optimization is the problem of using data to understand what the most likely branching in a dialog should be. As an example and not by way of limitation, with dialog optimization the assistant system  140  may not need to confirm who a user wants to call because the assistant system  140  has high confidence that a person inferred based on dialog optimization would be very likely whom the user wants to call. In particular embodiments, the dialog manager may use reinforcement learning for dialog optimization. Assistant state tracking aims to keep track of a state that changes over time as a user interacts with the world and the assistant system  140  interacts with the user. As an example and not by way of limitation, assistant state tracking may track what a user is talking about, whom the user is with, where the user is, what tasks are currently in progress, and where the user&#39;s gaze is at, etc., subject to applicable privacy policies. In particular embodiments, the dialog manager may use a set of operators to track the dialog state. The operators may comprise the necessary data and logic to update the dialog state. Each operator may act as delta of the dialog state after processing an incoming request. In particular embodiments, the dialog manager may further comprise a dialog state tracker and an action selector. In alternative embodiments, the dialog state tracker may replace the entity resolution component and resolve the references/mentions and keep track of the state. 
     In particular embodiments, the reasoning module  214  may further conduct false trigger mitigation. The goal of false trigger mitigation is to detect false triggers (e.g., wake-word) of assistance requests and to avoid generating false records when a user actually does not intend to invoke the assistant system  140 . As an example and not by way of limitation, the reasoning module  214  may achieve false trigger mitigation based on a nonsense detector. If the nonsense detector determines that a wake-word makes no sense at this point in the interaction with the user, the reasoning module  214  may determine that inferring the user intended to invoke the assistant system  140  may be incorrect. In particular embodiments, the output of the reasoning module  214  may be sent a remote dialog arbitrator  216 . 
     In particular embodiments, each of the ASR module  208 , NLU module  210 , and reasoning module  214  may access the remote data store  212 , which comprises user episodic memories to determine how to assist a user more effectively. More information on episodic memories may be found in U.S. patent application Ser. No. 16/552,559, filed 27 Aug. 2019, which is incorporated by reference. The data store  212  may additionally 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 platforms, etc. The usage of a user profile may be subject to privacy constraints 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. 
     In particular embodiments, parallel to the aforementioned server-side process involving the ASR module  208 , NLU module  210 , and reasoning module  214 , the client-side process may be as follows. In particular embodiments, the output of the assistant orchestrator  206  may be sent to a local ASR module  216  on the client system  130 . The ASR module  216  may comprise a personalized language model (PLM), a G2P model, and an end-pointing model. Because of the limited computing power of the client system  130 , the assistant system  140  may optimize the personalized language model at run time during the client-side process. As an example and not by way of limitation, the assistant system  140  may pre-compute a plurality of personalized language models for a plurality of possible subjects a user may talk about. When a user requests assistance, the assistant system  140  may then swap these pre-computed language models quickly so that the personalized language model may be optimized locally by the assistant system  140  at run time based on user activities. As a result, the assistant system  140  may have a technical advantage of saving computational resources while efficiently determining what the user may be talking about. In particular embodiments, the assistant system  140  may also re-learn user pronunciations quickly at run time. 
     In particular embodiments, the output of the ASR module  216  may be sent to a local NLU module  218 . In particular embodiments, the NLU module  218  herein may be more compact compared to the remote NLU module  210  supported on the server-side. When the ASR module  216  and NLU module  218  process the user input, they may access a local assistant memory  220 . The local assistant memory  220  may be different from the user memories stored on the data store  212  for the purpose of protecting user privacy. In particular embodiments, the local assistant memory  220  may be syncing with the user memories stored on the data store  212  via the network  110 . As an example and not by way of limitation, the local assistant memory  220  may sync a calendar on a user&#39;s client system  130  with a server-side calendar associate with the user. In particular embodiments, any secured data in the local assistant memory  220  may be only accessible to the modules of the assistant system  140  that are locally executing on the client system  130 . 
     In particular embodiments, the output of the NLU module  218  may be sent to a local reasoning module  222 . The reasoning module  222  may comprise a dialog manager and an entity resolution component. Due to the limited computing power, the reasoning module  222  may conduct on-device learning that is based on learning algorithms particularly tailored for client systems  130 . As an example and not by way of limitation, federated learning may be used by the reasoning module  222 . Federated learning is a specific category of distributed machine learning approaches which trains machine learning models using decentralized data residing on end devices such as mobile phones. In particular embodiments, the reasoning module  222  may use a particular federated learning model, namely federated user representation learning, to extend existing neural-network personalization techniques to federated learning. Federated user representation learning can personalize models in federated learning by learning task-specific user representations (i.e., embeddings) or by personalizing model weights. Federated user representation learning is a simple, scalable, privacy-preserving, and resource-efficient. Federated user representation learning may divide model parameters into federated and private parameters. Private parameters, such as private user embeddings, may be trained locally on a client system  130  instead of being transferred to or averaged on a remote server. Federated parameters, by contrast, may be trained remotely on the server. In particular embodiments, the reasoning module  222  may use another particular federated learning model, namely active federated learning to transmit a global model trained on the remote server to client systems  130  and calculate gradients locally on these client systems  130 . Active federated learning may enable the reasoning module to minimize the transmission costs associated with downloading models and uploading gradients. For active federated learning, in each round client systems are selected not uniformly at random, but with a probability conditioned on the current model and the data on the client systems to maximize efficiency. In particular embodiments, the reasoning module  222  may use another particular federated learning model, namely federated Adam. Conventional federated learning model may use stochastic gradient descent (SGD) optimizers. By contrast, the federated Adam model may use moment-based optimizers. Instead of using the averaged model directly as what conventional work does, federated Adam model may use the averaged model to compute approximate gradients. These gradients may be then fed into the federated Adam model, which may de-noise stochastic gradients and use a per-parameter adaptive learning rate. Gradients produced by federated learning may be even noisier than stochastic gradient descent (because data may be not independent and identically distributed), so federated Adam model may help even more deal with the noise. The federated Adam model may use the gradients to take smarter steps towards minimizing the objective function. The experiments show that conventional federated learning on a benchmark has 1.6% drop in ROC (Receiver Operating Characteristics) curve whereas federated Adam model has only 0.4% drop. In addition, federated Adam model has no increase in communication or on-device computation. In particular embodiments, the reasoning module  222  may also perform false trigger mitigation. This false trigger mitigation may help detect false activation requests, e.g., wake-word, on the client system  130  when the user&#39;s speech input comprises data that is subject to privacy constraints. As an example and not by way of limitation, when a user is in a voice call, the user&#39;s conversation is private and the false trigger detection based on such conversation can only occur locally on the user&#39;s client system  130 . 
     In particular embodiments, the assistant system  140  may comprise a local context engine  224 . The context engine  224  may process all the other available signals to provide more informative cues to the reasoning module  222 . As an example and not by way of limitation, the context engine  224  may have information related to people, sensory data from client system  130  sensors (e.g., microphone, camera) that are further analyzed by computer vision technologies, geometry constructions, activity data, inertial data (e.g., collected by a VR headset), location, etc. In particular embodiments, the computer vision technologies may comprise human skeleton reconstruction, face detection, facial recognition, hand tracking, eye tracking, etc. In particular embodiments, geometry constructions may comprise constructing objects surrounding a user using data collected by a client system  130 . As an example and not by way of limitation, the user may be wearing AR glasses and geometry construction may aim to determine where the floor is, where the wall is, where the user&#39;s hands are, etc. In particular embodiments, inertial data may be data associated with linear and angular motions. As an example and not by way of limitation, inertial data may be captured by AR glasses which measures how a user&#39;s body parts move. 
     In particular embodiments, the output of the local reasoning module  222  may be sent to the dialog arbitrator  216 . The dialog arbitrator  216  may function differently in three scenarios. In the first scenario, the assistant orchestrator  206  determines to use server-side process, for which the dialog arbitrator  216  may transmit the output of the reasoning module  214  to a remote action execution module  226 . In the second scenario, the assistant orchestrator  206  determines to use both server-side processes and client-side processes, for which the dialog arbitrator  216  may aggregate output from both reasoning modules (i.e., remote reasoning module  214  and local reasoning module  222 ) of both processes and analyze them. As an example and not by way of limitation, the dialog arbitrator  216  may perform ranking and select the best reasoning result for responding to the user input. In particular embodiments, the dialog arbitrator  216  may further determine whether to use agents on the server-side or on the client-side to execute relevant tasks based on the analysis. In the third scenario, the assistant orchestrator  206  determines to use client-side processes and the dialog arbitrator  216  needs to evaluate the output of the local reasoning module  222  to determine if the client-side processes can complete the task of handling the user input. In alternative embodiments, the output of the reasoning module  222  may be not sent to the dialog arbitrator  216  if the assistant orchestrator  206  determines to use client-side processes and that client-side processes are fully capable of processing the user input. 
     In particular embodiments, for the first and second scenarios mentioned above, the dialog arbitrator  216  may determine that the agents on the server-side are necessary to execute tasks responsive to the user input. Accordingly, the dialog arbitrator  216  may send necessary information regarding the user input to the action execution module  226 . The action execution module  226  may call one or more agents to execute the tasks. In alternative embodiments, the action selector of the dialog manager may determine actions to execute and instruct the action execution module  226  accordingly. 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. In particular embodiments, the agents may comprise first-party agents and third-party agents. In particular embodiments, first-party agents 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, such as messaging services or photo-share services). In particular embodiments, third-party agents may comprise external agents that the assistant system  140  has no control over (e.g., third-party online music application agents, ticket sales agents). The first-party agents may be associated with first-party providers that provide content objects and/or services hosted by the social-networking system  160 . The third-party agents may be associated with third-party providers that provide content objects and/or services hosted by the third-party system  170 . In particular embodiments, each of the first-party agents or third-party agents 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, for the second and third scenarios mentioned above, the dialog arbitrator  216  may determine that the agents on the client-side are capable of executing tasks responsive to the user input but additional information is needed (e.g., response templates) or that the tasks can be only handled by the agents on the server-side. If the dialog arbitrator  216  determines that the tasks can be only handled by the agents on the server-side, the dialog arbitrator  216  may send necessary information regarding the user input to the action execution module  226 . If the dialog arbitrator  216  determines that the agents on the client-side are capable of executing tasks but response templates are needed, the dialog arbitrator  216  may send necessary information regarding the user input to a remote response template generation module  228 . The output of the response template generation module  228  may be further sent to a local action execution module  230  executing on the client system  130 . In particular embodiments, if the assistant orchestrator  206  determines to use client-side processes and that client-side processes are fully capable of processing the user input, the output of the reasoning module  222  may be directly sent to the action execution module  230 . 
     In particular embodiments, the action execution module  230  may call local agents to execute tasks. A local agent on the client system  130  may be able to execute simpler tasks compared to an agent on the server-side. 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 action execution module  230  may additionally perform a set of general executable dialog actions. The set of executable dialog actions may interact with agents, users and the assistant system  140  itself. These dialog actions may comprise dialog actions for slot request, confirmation, disambiguation, agent execution, etc. The dialog actions may be independent of the underlying implementation of the action selector or dialog policy. Both tree-based policy and model-based policy may generate the same basic dialog actions, with a callback function hiding any action selector specific implementation details. 
     In particular embodiments, the output from the remote action execution module  226  on the server-side may be sent to a remote response execution module  232 . In particular embodiments, the action execution module  226  may communicate back to the dialog arbitrator  216  for more information. The response execution module  232  may be based on a remote conversational understanding (CU) composer. In particular embodiments, the output from the action execution module  226  may be formulated 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 may comprise a natural-language generation (NLG) module and a user interface (UI) payload generator. The natural-language generator may generate a communication content based on the output of the action execution module  226  using different language models and/or language templates. In particular embodiments, the generation of the communication content may be application specific and also personalized for each user. The CU composer may also determine a modality of the generated communication content using the UI payload generator. In particular embodiments, the NLG module 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 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 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. 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 may determine a preferred modality of the communication content to be presented to the user. In particular embodiments, the CU composer may check privacy constraints associated with the user to make sure the generation of the communication content follows the privacy policies. 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 output from the local action execution module  230  on the client system  130  may be sent to a local response execution module  234 . The response execution module  234  may be based on a local conversational understanding (CU) composer. The CU composer may comprise a natural-language generation (NLG) module. As the computing power of a client system  130  may be limited, the NLG module may be simple for the consideration of computational efficiency. Because the NLG module may be simple, the output of the response execution module  234  may be sent to a local response expansion module  236 . The response expansion module  236  may further expand the result of the response execution module  234  to make a response more natural and contain richer semantic information. 
     In particular embodiments, if the user input is based on audio signals, the output of the response execution module  232  on the server-side may be sent to a remote text-to-speech (TTS) module  238 . Similarly, the output of the response expansion module  236  on the client-side may be sent to a local TTS module  240 . Both TTS modules may convert a response to audio signals. In particular embodiments, the output from the response execution module  232 , the response expansion module  236 , or the TTS modules on both sides, may be finally sent to a local render output module  242 . The render output module  242  may generate a response that is suitable for the client system  130 . As an example and not by way of limitation, the output of the response execution module  232  or the response expansion module  236  may comprise one or more of natural-language strings, speech, actions with parameters, or rendered images or videos that can be displayed in a VR headset or AR smart glasses. As a result, the render output module  242  may determine what tasks to perform based on the output of CU composer to render the response appropriately for displaying on the VR headset or AR smart glasses. For example, the response may be visual-based modality (e.g., an image or a video clip) that can be displayed via the VR headset or AR smart glasses. As another example, the response may be audio signals that can be played by the user via VR headset or AR smart glasses. As yet another example, the response may be augmented-reality data that can be rendered VR headset or AR smart glasses for enhancing user experience. 
     In particular embodiments, the assistant system  140  may have a variety of capabilities including audio cognition, visual cognition, signals intelligence, reasoning, and memories. In particular embodiments, the capability of audio recognition may enable the assistant system  140  to understand a user&#39;s input associated with various domains in different languages, understand a conversation and be able to summarize it, perform on-device audio cognition for complex commands, identify a user by voice, extract topics from a conversation and auto-tag sections of the conversation, enable audio interaction without a wake-word, filter and amplify user voice from ambient noise and conversations, understand which client system  130  (if multiple client systems  130  are in vicinity) a user is talking to. 
     In particular embodiments, the capability of visual cognition may enable the assistant system  140  to perform face detection and tracking, recognize a user, recognize most people of interest in major metropolitan areas at varying angles, recognize majority of interesting objects in the world through a combination of existing machine-learning models and one-shot learning, recognize an interesting moment and auto-capture it, achieve semantic understanding over multiple visual frames across different episodes of time, provide platform support for additional capabilities in people, places, objects recognition, recognize full set of settings and micro-locations including personalized locations, recognize complex activities, recognize complex gestures to control a client system  130 , handle images/videos from egocentric cameras (e.g., with motion, capture angles, resolution, etc.), accomplish similar level of accuracy and speed regarding images with lower resolution, conduct one-shot registration and recognition of people, places, and objects, and perform visual recognition on a client system  130 . 
     In particular embodiments, the assistant system  140  may leverage computer vision techniques to achieve visual cognition. Besides computer vision techniques, the assistant system  140  may explore options that can supplement these techniques to scale up the recognition of objects. In particular embodiments, the assistant system  140  may use supplemental signals such as optical character recognition (OCR) of an object&#39;s labels, GPS signals for places recognition, signals from a user&#39;s client system  130  to identify the user. In particular embodiments, the assistant system  140  may perform general scene recognition (home, work, public space, etc.) to set context for the user and reduce the computer-vision search space to identify top likely objects or people. In particular embodiments, the assistant system  140  may guide users to train the assistant system  140 . For example, crowdsourcing may be used to get users to tag and help the assistant system  140  recognize more objects over time. As another example, users can register their personal objects as part of initial setup when using the assistant system  140 . The assistant system  140  may further allow users to provide positive/negative signals for objects they interact with to train and improve personalized models for them. 
     In particular embodiments, the capability of signals intelligence may enable the assistant system  140  to determine user location, understand date/time, determine family locations, understand users&#39; calendars and future desired locations, integrate richer sound understanding to identify setting/context through sound alone, build signals intelligence models at run time which may be personalized to a user&#39;s individual routines. 
     In particular embodiments, the capability of reasoning may enable the assistant system  140  to have the ability to pick up any previous conversation threads at any point in the future, synthesize all signals to understand micro and personalized context, learn interaction patterns and preferences from users&#39; historical behavior and accurately suggest interactions that they may value, generate highly predictive proactive suggestions based on micro-context understanding, understand what content a user may want to see at what time of a day, understand the changes in a scene and how that may impact the user&#39;s desired content. 
     In particular embodiments, the capabilities of memories may enable the assistant system  140  to remember which social connections a user previously called or interacted with, write into memory and query memory at will (i.e., open dictation and auto tags), extract richer preferences based on prior interactions and long-term learning, remember a user&#39;s life history, extract rich information from egocentric streams of data and auto catalog, and write to memory in structured form to form rich short, episodic and long-term memories. 
       FIG.  3    illustrates an example diagram flow of server-side processes of the assistant system  140 . In particular embodiments, a server-assistant service module  301  may access a request manager  302  upon receiving a user request. In alternative embodiments, the user request may be first processed by the remote ASR module  208  if the user request is based on audio signals. In particular embodiments, the request manager  302  may comprise a context extractor  303  and a conversational understanding object generator (CU object generator)  304 . The context extractor  303  may extract contextual information associated with the user request. The context extractor  303  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 whether an alarm is set on the client system  130 . As another example and not by way of limitation, the update of contextual information may comprise whether a song is playing on the client system  130 . The CU object generator  304  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  302  may store the contextual information and the generated content objects in data store  212  which is a particular data store implemented in the assistant system  140 . 
     In particular embodiments, the request manger  302  may send the generated content objects to the remote NLU module  210 . The NLU module  210  may perform a plurality of steps to process the content objects. At step  305 , the NLU module  210  may generate a whitelist for the content objects. In particular embodiments, the whitelist may comprise interpretation data matching the user request. At step  306 , the NLU module  210  may perform a featurization based on the whitelist. At step  307 , the NLU module  210  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  308   a , the NLU module  210  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  308   b , the NLU module  210  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  309   a , the NLU module  210  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  309   b , the NLU module  210  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  210  may comprise a semantic information aggregator  310 . The semantic information aggregator  310  may help the NLU module  210  improve the domain classification/selection of the content objects by providing semantic information. In particular embodiments, the semantic information aggregator  310  may aggregate semantic information in the following way. The semantic information aggregator  310  may first retrieve information from a user context engine  315 . In particular embodiments, the user context engine  315  may comprise offline aggregators and an online inference service. The offline aggregators 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 during a predetermined timeframe (e.g., from a prior 90-day window). The processing result may be stored in the user context engine  315  as part of the user profile. The online inference service 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  315  also as part of the user profile. In particular embodiments, both the offline aggregators and online inference service 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  310  may then process the retrieved information, i.e., a user profile, from the user context engine  315  in the following steps. At step  311 , the semantic information aggregator  310  may process the retrieved information from the user context engine  315  based on natural-language processing (NLP). In particular embodiments, the semantic information aggregator  310  may tokenize text by text normalization, extract syntax features from text, and extract semantic features from text based on NLP. The semantic information aggregator  310  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  310  may further conduct global word embedding, domain-specific embedding, and/or dynamic embedding based on the contextual information. At step  312 , the processing result may be annotated with entities by an entity tagger. Based on the annotations, the semantic information aggregator  310  may generate dictionaries for the retrieved information at step  313 . In particular embodiments, the dictionaries may comprise global dictionary features which can be updated dynamically offline. At step  314 , the semantic information aggregator  310  may rank the entities tagged by the entity tagger. In particular embodiments, the semantic information aggregator  310  may communicate with different graphs  320  including one or more of the social graph, the knowledge graph, or the concept graph to extract ontology data that is relevant to the retrieved information from the user context engine  315 . In particular embodiments, the semantic information aggregator  310  may aggregate the user profile, the ranked entities, and the information from the graphs  320 . The semantic information aggregator  310  may then provide the aggregated information to the NLU module  210  to facilitate the domain classification/selection. 
     In particular embodiments, the output of the NLU module  210  may be sent to the remote reasoning module  214 . The reasoning module  214  may comprise a co-reference component  325 , an entity resolution component  330 , and a dialog manager  335 . The output of the NLU module  210  may be first received at the co-reference component  325  to interpret references of the content objects associated with the user request. In particular embodiments, the co-reference component  325  may be used to identify an item to which the user request refers. The co-reference component  325  may comprise reference creation  326  and reference resolution  327 . In particular embodiments, the reference creation  326  may create references for entities determined by the NLU module  210 . The reference resolution  327  may resolve these references accurately. As an example and not by way of limitation, a user request may comprise “find me the nearest grocery store and direct me there”. The co-reference component  325  may interpret “there” as “the nearest grocery store”. In particular embodiments, the co-reference component  325  may access the user context engine  315  and the dialog manager  335  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 component  330  to resolve relevant entities. The entity resolution component  330  may execute generic and domain-specific entity resolution. In particular embodiments, the entity resolution component  330  may comprise domain entity resolution  331  and generic entity resolution  332 . The domain entity resolution  331  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  320 . 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  332  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  320 . 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 particular brand of electric car, the generic entity resolution  332  may resolve the referenced brand of electric car as vehicle and the domain entity resolution  331  may resolve the referenced brand of electric car as electric car. 
     In particular embodiments, the output of the entity resolution component  330  may be sent to the dialog manager  335  to advance the flow of the conversation with the user. The dialog manager  335  may be an asynchronous state machine that repeatedly updates the state and selects actions based on the new state. The dialog manager  335  may comprise dialog intent resolution  336  and dialog state tracker  337 . In particular embodiments, the dialog manager  335  may execute the selected actions and then call the dialog state tracker  337  again until the action selected requires a user response, or there are no more actions to execute. Each action selected may depend on the execution result from previous actions. In particular embodiments, the dialog intent resolution  336  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  336  may map intents determined by the NLU module  210  to different dialog intents. The dialog intent resolution  336  may further rank dialog intents based on signals from the NLU module  210 , the entity resolution component  330 , and dialog history between the user and the assistant system  140 . In particular embodiments, instead of directly altering the dialog state, the dialog state tracker  337  may be a side-effect free component and generate n-best candidates of dialog state update operators that propose updates to the dialog state. The dialog state tracker  337  may comprise intent resolvers containing logic to handle different types of NLU intent based on the dialog state and generate the operators. In particular embodiments, the logic may be organized by intent handler, such as a disambiguation intent handler to handle the intents when the assistant system  140  asks for disambiguation, a confirmation intent handler that comprises the logic to handle confirmations, etc. Intent resolvers may combine the turn intent together with the dialog state to generate the contextual updates for a conversation with the user. A slot resolution component may then recursively resolve the slots in the update operators with resolution providers including the knowledge graph and domain agents. In particular embodiments, the dialog state tracker  337  may update/rank the dialog state of the current dialog session. As an example and not by way of limitation, the dialog state tracker  337  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 tracker  337  may rank the dialog state based on a priority associated with it. 
     In particular embodiments, the reasoning module  214  may communicate with the remote action execution module  226  and the dialog arbitrator  216 , respectively. In particular embodiments, the dialog manager  335  of the reasoning module  214  may communicate with a task completion component  340  of the action execution module  226  about the dialog intent and associated content objects. In particular embodiments, the task completion module  340  may rank different dialog hypotheses for different dialog intents. The task completion module  340  may comprise an action selector  341 . In alternative embodiments, the action selector  341  may be comprised in the dialog manager  335 . In particular embodiments, the dialog manager  335  may additionally check against dialog policies  345  comprised in the dialog arbitrator  216  regarding the dialog state. In particular embodiments, a dialog policy  345  may comprise a data structure that describes an execution plan of an action by an agent  350 . The dialog policy  345  may comprise a general policy  346  and task policies  347 . In particular embodiments, the general policy  346  may be used for actions that are not specific to individual tasks. The general policy  346  may comprise handling low confidence intents, internal errors, unacceptable user response with retries, skipping or inserting confirmation based on ASR or NLU confidence scores, etc. The general policy  346  may also comprise the logic of ranking dialog state update candidates from the dialog state tracker  337  output and pick the one to update (such as picking the top ranked task intent). In particular embodiments, the assistant system  140  may have a particular interface for the general policy  346 , which allows for consolidating scattered cross-domain policy/business-rules, especial those found in the dialog state tracker  337 , into a function of the action selector  341 . The interface for the general policy  346  may also allow for authoring of self-contained sub-policy units that may be tied to specific situations or clients, e.g., policy functions that may be easily switched on or off based on clients, situation, etc. The interface for the general policy  346  may also allow for providing a layering of policies with back-off, i.e. multiple policy units, with highly specialized policy units that deal with specific situations being backed up by more general policies  346  that apply in wider circumstances. In this context the general policy  346  may alternatively comprise intent or task specific policy. In particular embodiments, a task policy  347  may comprise the logic for action selector  341  based on the task and current state. In particular embodiments, there may be the following four types of task policies  347 : 1) manually crafted tree-based dialog plans; 2) coded policy that directly implements the interface for generating actions; 3) configurator-specified slot-filling tasks; and 4) machine-learning model based policy learned from data. In particular embodiments, the assistant system  140  may bootstrap new domains with rule-based logic and later refine the task policies  347  with machine-learning models. In particular embodiments, a dialog policy  345  may a tree-based policy, which is a pre-constructed dialog plan. Based on the current dialog state, a dialog policy  345  may choose a node to execute and generate the corresponding actions. As an example and not by way of limitation, the tree-based policy may comprise topic grouping nodes and dialog action (leaf) nodes. 
     In particular embodiments, the action selector  341  may take candidate operators of dialog state and consult the dialog policy  345  to decide what action should be executed. The assistant system  140  may use a hierarchical dialog policy with general policy  346  handling the cross-domain business logic and task policies  347  handles the task/domain specific logic. In particular embodiments, the general policy  346  may pick one operator from the candidate operators to update the dialog state, followed by the selection of a user facing action by a task policy  347 . Once a task is active in the dialog state, the corresponding task policy  347  may be consulted to select right actions. In particular embodiments, both the dialog state tracker  337  and the action selector  341  may not change the dialog state until the selected action is executed. This may allow the assistant system  140  to execute the dialog state tracker  337  and the action selector  341  for processing speculative ASR results and to do n-best ranking with dry runs. In particular embodiments, the action selector  341  may take the dialog state update operators as part of the input to select the dialog action. The execution of the dialog action may generate a set of expectation to instruct the dialog state tracker  337  to handler future turns. In particular embodiments, an expectation may be used to provide context to the dialog state tracker  337  when handling the user input from next turn. As an example and not by way of limitation, slot request dialog action may have the expectation of proving a value for the requested slot. 
     In particular embodiments, the dialog manager  335  may support multi-turn compositional resolution of slot mentions. For a compositional parse from the NLU  210 , the resolver may recursively resolve the nested slots. The dialog manager  335  may additionally support disambiguation for the nested slots. As an example and not by way of limitation, the user request may be “remind me to call Alex”. The resolver may need to know which Alex to call before creating an actionable reminder to-do entity. The resolver may halt the resolution and set the resolution state when further user clarification is necessary for a particular slot. The general policy  346  may examine the resolution state and create corresponding dialog action for user clarification. In dialog state tracker  337 , based on the user request and the last dialog action, the dialog manager may update the nested slot. This capability may allow the assistant system  140  to interact with the user not only to collect missing slot values but also to reduce ambiguity of more complex/ambiguous utterances to complete the task. In particular embodiments, the dialog manager may further support requesting missing slots in a nested intent and multi-intent user requests (e.g., “take this photo and send it to Dad”). In particular embodiments, the dialog manager  335  may support machine-learning models for more robust dialog experience. As an example and not by way of limitation, the dialog state tracker  337  may use neural network based models (or any other suitable machine-learning models) to model belief over task hypotheses. As another example and not by way of limitation, for action selector  341 , highest priority policy units may comprise white-list/black-list overrides, which may have to occur by design; middle priority units may comprise machine-learning models designed for action selection; and lower priority units may comprise rule-based fallbacks when the machine-learning models elect not to handle a situation. In particular embodiments, machine-learning model based general policy unit may help the assistant system  140  reduce redundant disambiguation or confirmation steps, thereby reducing the number of turns to execute the user request. 
     In particular embodiments, the action execution module  226  may call different agents  350  for task execution. An agent  350  may select among registered content providers to complete the action. The data structure may be constructed by the dialog manager  335  based on an intent and one or more slots associated with the intent. A dialog policy  345  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 manager  335 . 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 manager  335  may execute a dialog policy  345  to determine the next action to carry out. The dialog policies  345  may comprise generic policy  346  and domain specific policies  347 , both of which may guide how to select the next system action based on the dialog state. In particular embodiments, the task completion component  340  of the action execution module  226  may communicate with dialog policies  345  comprised in the dialog arbitrator  216  to obtain the guidance of the next system action. In particular embodiments, the action selection component  341  may therefore select an action based on the dialog intent, the associated content objects, and the guidance from dialog policies  345 . 
     In particular embodiments, the output of the action execution module  226  may be sent to the remote response execution module  232 . Specifically, the output of the task completion component  340  of the action execution module  226  may be sent to the CU composer  355  of the response execution module  226 . In alternative embodiments, the selected action may require one or more agents  350  to be involved. As a result, the task completion module  340  may inform the agents  350  about the selected action. Meanwhile, the dialog manager  335  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;  350  response. In particular embodiments, the CU composer  355  may generate a communication content for the user using a natural-language generation (NLG) module  356  based on the output of the task completion module  340 . In particular embodiments, the NLG module  356  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  355  may also determine a modality of the generated communication content using the UI payload generator  357 . Since the generated communication content may be considered as a response to the user request, the CU composer  355  may additionally rank the generated communication content using a response ranker  358 . As an example and not by way of limitation, the ranking may indicate the priority of the response. 
     In particular embodiments, the response execution module  232  may perform different tasks based on the output of the CU composer  355 . These tasks may include writing (i.e., storing/updating) the dialog state  361  retrieved from data store  212  and generating responses  362 . In particular embodiments, the output of CU composer  355  may comprise one or more of natural-language strings, speech, actions with parameters, or rendered images or videos that can be displayed in a VR headset or AR smart glass. As a result, the response execution module  232  may determine what tasks to perform based on the output of CU composer  355 . In particular embodiments, the generated response and the communication content may be sent to the local render output module  242  by the response execution module  232 . In alternative embodiments, the output of the CU composer  355  may be additionally sent to the remote TTS module  238  if the determined modality of the communication content is audio. The speech generated by the TTS module  238  and the response generated by the response execution module  232  may be then sent to the render output module  242 . 
       FIG.  4    illustrates an example diagram flow of processing a user input by the assistant system  140 . As an example and not by way of limitation, the user input may be based on audio signals. In particular embodiments, a mic array  402  of the client system  130  may receive the audio signals (e.g., speech). The audio signals may be transmitted to a process loop  404  in a format of audio frames. In particular embodiments, the process loop  404  may send the audio frames for voice activity detection (VAD)  406  and wake-on-voice (WoV) detection  408 . The detection results may be returned to the process loop  404 . If the WoV detection  408  indicates the user wants to invoke the assistant system  140 , the audio frames together with the VAD  406  result may be sent to an encode unit  410  to generate encoded audio data. After encoding, the encoded audio data may be sent to an encrypt unit  412  for privacy and security purpose, followed by a link unit  414  and decrypt unit  416 . After decryption, the audio data may be sent to a mic driver  418 , which may further transmit the audio data to an audio service module  420 . In alternative embodiments, the user input may be received at a wireless device (e.g., Bluetooth device) paired with the client system  130 . Correspondingly, the audio data may be sent from a wireless-device driver  422  (e.g., Bluetooth driver) to the audio service module  420 . In particular embodiments, the audio service module  420  may determine that the user input can be fulfilled by an application executing on the client system  130 . Accordingly, the audio service module  420  may send the user input to a real-time communication (RTC) module  424 . The RTC module  424  may deliver audio packets to a video or audio communication system (e.g., VOIP or video call). The RTC module  424  may call a relevant application (App)  426  to execute tasks related to the user input. 
     In particular embodiments, the audio service module  420  may determine that the user is requesting assistance that needs the assistant system  140  to respond. Accordingly, the audio service module  420  may inform the client-assistant service module  426 . In particular embodiments, the client-assistant service module  426  may communicate with the assistant orchestrator  206 . The assistant orchestrator  206  may determine whether to use client-side processes or server-side processes to respond to the user input. In particular embodiments, the assistant orchestrator  206  may determine to use client-side processes and inform the client-assistant service module  426  about such decision. As a result, the client-assistant service module  426  may call relevant modules to respond to the user input. 
     In particular embodiments, the client-assistant service module  426  may use the local ASR module  216  to analyze the user input. The ASR module  216  may comprise a grapheme-to-phoneme (G2P) model, a pronunciation learning model, a personalized language model (PLM), an end-pointing model, and a personalized acoustic model. In particular embodiments, the client-assistant service module  426  may further use the local NLU module  218  to understand the user input. The NLU module  218  may comprise a named entity resolution (NER) component and a contextual session-based NLU component. In particular embodiments, the client-assistant service module  426  may use an intent broker  428  to analyze the user&#39;s intent. To be accurate about the user&#39;s intent, the intent broker  428  may access an entity store  430  comprising entities associated with the user and the world. In alternative embodiments, the user input may be submitted via an application  432  executing on the client system  130 . In this case, an input manager  434  may receive the user input and analyze it by an application environment (App Env) module  436 . The analysis result may be sent to the application  432  which may further send the analysis result to the ASR module  216  and NLU module  218 . In alternative embodiments, the user input may be directly submitted to the client-assistant service module  426  via an assistant application  438  executing on the client system  130 . Then the client-assistant service module  426  may perform similar procedures based on modules as aforementioned, i.e., the ASR module  216 , the NLU module  218 , and the intent broker  428 . 
     In particular embodiments, the assistant orchestrator  206  may determine to user server-side process. Accordingly, the assistant orchestrator  206  may send the user input to one or more computing systems that host different modules of the assistant system  140 . In particular embodiments, a server-assistant service module  301  may receive the user input from the assistant orchestrator  206 . The server-assistant service module  301  may instruct the remote ASR module  208  to analyze the audio data of the user input. The ASR module  208  may comprise a grapheme-to-phoneme (G2P) model, a pronunciation learning model, a personalized language model (PLM), an end-pointing model, and a personalized acoustic model. In particular embodiments, the server-assistant service module  301  may further instruct the remote NLU module  210  to understand the user input. In particular embodiments, the server-assistant service module  301  may call the remote reasoning model  214  to process the output from the ASR module  208  and the NLU module  210 . In particular embodiments, the reasoning model  214  may perform entity resolution and dialog optimization. In particular embodiments, the output of the reasoning model  314  may be sent to the agent  350  for executing one or more relevant tasks. 
     In particular embodiments, the agent  350  may access an ontology module  440  to accurately understand the result from entity resolution and dialog optimization so that it can execute relevant tasks accurately. The ontology module  440  may provide ontology data associated with a plurality of predefined domains, intents, and slots. The ontology data may also comprise the structural relationship between different slots and domains. The ontology data may further comprise information of how the slots may be grouped, related within a hierarchy where the higher level comprises the domain, and subdivided according to similarities and differences. The ontology data may also comprise information of how the slots may be grouped, related within a hierarchy where the higher level comprises the topic, and subdivided according to similarities and differences. Once the tasks are executed, the agent  350  may return the execution results together with a task completion indication to the reasoning module  214 . 
     The embodiments disclosed herein may include or be implemented in conjunction with an artificial reality system. Artificial reality is a form of reality that has been adjusted in some manner before presentation to a user, which may include, e.g., a virtual reality (VR), an augmented reality (AR), a mixed reality (MR), a hybrid reality, or some combination and/or derivatives thereof. Artificial reality content may include completely generated content or generated content combined with captured content (e.g., real-world photographs). The artificial reality content may include video, audio, haptic feedback, or some combination thereof, and any of which may be presented in a single channel or in multiple channels (such as stereo video that produces a three-dimensional effect to the viewer). Additionally, in some embodiments, artificial reality may be associated with applications, products, accessories, services, or some combination thereof, that are, e.g., used to create content in an artificial reality and/or used in (e.g., perform activities in) an artificial reality. The artificial reality system that provides the artificial reality content may be implemented on various platforms, including a head-mounted display (HMD) connected to a host computer system, a standalone HMD, a mobile device or computing system, or any other hardware platform capable of providing artificial reality content to one or more viewers. 
     Using a Single Request for Multi-Person Calling in Assistant Systems 
     In particular embodiments, the assistant system  140  may enable a user to call multiple people simultaneously via the assistant system  140  with a single request. Existing conventional systems require users to add people to a group call one-at-a-time or to set up a conference call for people to dial into. In contrast, the assistant system  140  may enable the user to initiate the call by specifying, in a single request, two or more entity names, nicknames, or a combination of entity names and nicknames of these people. As an example and not by way of limitation, the request may be “call John and Lee”, “call my brother and Lee”, or “call my brother and my brother in law”. The assistant system  140  may then initiate the group call without further user input needed. In addition, the nickname may refer a single person or a group of people. As an example and not by way of limitation, the request may be “call my besties”. The user may even request the call by specifying individuals together with a group. As an example and not by way of limitation, the request may be “call Danna, Mr. Serious, and my besties.” Upon receiving a request to call multiple people, the assistant system  140  may first disambiguate the request to identify the targeted people that the user wants to call. The assistant system  140  may further confirm with the user about the disambiguation and start a group call to these targeted people after it receives the user&#39;s confirmation. The user may call multiple people simultaneously with a single request using different applications supported by the assistant system  140 , e.g., a messaging application, a video conferencing application, etc., and the call may be either a voice call or a video call. Although this disclosure describes enabling particular multi-person calling via particular systems in particular manners, this disclosure contemplates providing any suitable multi-person calling via any suitable system in any suitable manner. 
     In particular embodiments, the assistant system  140  may receive, from a client system  130  associated with a first user via an assistant xbot, a user request to call a plurality of second users simultaneously. The user request may comprise one or more references referring the plurality of second users. In particular embodiments, the assistant system  140  may identify the plurality of second users by resolving the one or more references to a plurality of entity identifiers associated with the plurality of second users, respectively. The assistant system  140  may further initiate, by the assistant xbot, a group call to the plurality of second users simultaneously based on the plurality of entity identifiers. 
     In particular embodiments, the assistant xbot may be a type of chat bot. The assistant xbot 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 may work as the user&#39;s portal to the assistant system  140 . The assistant xbot may therefore be considered as a type of conversational agent. Although this disclosure describes particular xbot in a particular manner, this disclosure contemplates any suitable xbot in any suitable manner. Although this disclosure describes particular xbot in particular systems in particular manners, this disclosure contemplates any suitable xbot in any suitable system in any suitable manner. 
     In particular embodiments, the user request may comprise only a single request. Initiating the group call to the plurality of second users simultaneously may be responsive to the single request. In particular embodiments, initiating the group call to the plurality of second users simultaneously may comprise determining that any additional user input is not necessary for initiating the group call. In particular embodiments, initiating the group call to the plurality of second users simultaneously may comprise the following steps. The assistant system  140  may simultaneously generate a plurality of communication requests from the first user to the plurality of second users, respectively. The assistant system  140  may further send, to a plurality of client systems  130  associated with the plurality of second users via a network, the plurality of communication requests simultaneously. The assistant system  140  may have a technical advantage of improving user experience with the assistant system  140  by enabling a user to call multiple people simultaneously without any additional input as the user can simply submit a single request in any modality and the assistant system  140  may effectively identify the people the user intends to call and initiate such a call accordingly. Although this disclosure describes initiating particular calls via particular systems in a particular manner, this disclosure contemplates initiating any suitable calls via any suitable system in any suitable manner. 
     In particular embodiments, the multi-person calling function may be implemented for both server-side and client-side processes of the assistant system  140 . In particular embodiments, identifying the plurality of second users by resolving the one or more references to the plurality of entity identifiers associated with the plurality of second users, respectively, may comprise the following steps. The assistant system  140  may first access one or more contact lists associated with the first user. Each contact list may comprise a plurality of names associated with a plurality of entity identifiers. In particular embodiments, the assistant system  140  may compare the one or more references with the plurality of names to determine the plurality of entity identifiers for the plurality of second users. As an example and not by way of limitation, the entity identifiers may comprise one or more of a phone number, a social-networking identifier, an identifier for a messaging application, or any suitable identifier. In particular embodiments, the one or more contact lists may be stored on one or more of the client system  130 , one or more of the computing systems, or one or more third-party computing systems  170 . Specifically, some applications may keep a user&#39;s contact list only on the client-side, some may keep the contact list only on the server-side, and others may keep the contact list both on the client side and the server side. Therefore, if the user request specifies which application to use for the multi-person call, the assistant orchestrator  206  may route the request to either the client side or the server side according to where the application keeps the contact list. If a specific application is not indicated in the user request, the assistant system  140  may first try to execute the multi-person call on the server side and then fall back to the client side if contact information doesn&#39;t exist on the server side. Alternatively, the assistant orchestrator  206  may send the user request to both the client side and the server side and have them both try to execute the multi-person call. After that, the outputs from both the client-side and the server-side modules may be sent to the dialog arbitrator  206 , which may decide how to complete the execution of the multi-person call. Although this disclosure describes implementing particular calls in a particular manner, this disclosure contemplates implementing any suitable call in any suitable manner. 
     In particular embodiments, the workflow for multi-person calling may be as follows. In particular embodiments, the user request may be based on a voice input. Upon receiving such user request, the server-side ASR module  208  or the client-side ASR module  216  may take the voice input and generate text transcripts of the user request. The text transcripts may be then sent to the server-side NLU module  210  or the client-side NLU module  218 . In particular embodiments, the NLU module  210  or NLU module  218  may process the transcripts in different ways. In particular embodiments, the assistant system  140  may identify, based on the NLU module  210  or NLU module  218 , one or more separators associated with the user request. As an example and not by way of limitation, a separator may be “and” which is between different entities. Accordingly, identifying the plurality of second users may be based on the one or more separators. In particular embodiments, the NLU module  210  or NLU module  218  may enable the intent to call or start a call to accept multiple slots of individuals or groups of people. That is to say, instead of an intent of [IN:call/start_call(user)], the intent may be [IN:call/start_call(user_1, user_2, . . . , user_n)], [IN:call/start_call(user_group_1)], [IN:call/start_call(user_group_1, user_group_2, . . . , user_group_n)], or even [IN:call/start_call(user_1, . . . , user_n, user_group_1, . . . , user_group_n)]. In particular embodiments, the output of the NLU module  210  or NLU module  218  may be then received at the server-side reasoning module  214  or the client-side reasoning module  222 . Although this disclosure describes determining particular intents and slots in particular systems in a particular manner, this disclosure contemplates determining any suitable intent and slot in any suitable system in any suitable manner. 
     In particular embodiments, the reasoning module  214  or reasoning module  222  may determine how to process the output of the NLU module  210  or NLU module  218  to identify multiple entity names. In particular embodiments, the one or more references of the user request may comprise at least an ambiguous mention. The ambiguous mention may correspond to a particular second user from the plurality of second users. As an example and not by way of limitation, the ambiguous mention may be “my bestie”. In particular embodiments, the reasoning module  214  or reasoning module  222  may need to disambiguate such ambiguous mention to identify the particular second user. The disambiguation may comprise the following steps. In particular embodiments, the reasoning module  214  or reasoning module  222  may determine a plurality of candidate users based on a personalized machine-learning model. The personalized machine-learning model may be based on user profile information, historical interactions with the assistant system  140 , and contextual information. Each candidate user is associated with a confidence score. During the disambiguation, there may be a particular problem of differentiating between first and last names. As an example and not by way of limitation, if a user says “call Ben Thomas and Dan,” the user could mean to call two users (i.e., user1=Ben Thomas; user2=Dan) or call three users (i.e., user1=Ben; user2=Thomas; user3=Dan). In particular embodiments, the reasoning module  214  or reasoning module  222  may parse the user request to identify all the possible combinations of first and last names to generate candidate users. In particular embodiments, the reasoning module  214  or reasoning module  222  may then rank the plurality of candidate users based on their respective confidence scores. The reasoning module  214  or reasoning module  222  may further identify the particular second user from the plurality of candidate users based on their respective rankings. Continuing with the previous example, the reasoning module  214  or reasoning module  222  may determine a few candidate users such as “John”, “Lisa”, and “Alex” for the ambiguous mention “my bestie” with each being associated with a respective confidence score. The reasoning module  214  or reasoning module  222  may then rank them as “Alex”, “Lisa”, and “John”. If these candidate users have low confidence scores, the assistant system  140  may further ask the user for disambiguation. In particular embodiments, the assistant system  140  may send, to the client system  130  via the assistant xbot, instructions for prompting the first user for a confirmation of the particular second user. In alternative embodiments, the assistant system  140  may directly present the prompt for the confirmation at the client system  130  via the assistant xbot using the client-side process. In particular embodiments, the reasoning module  214  or reasoning module  222  may find candidate users that the user wants to call through multiple iterations of disambiguation and then identify the particular user from these candidate entities. Determining candidate users based on a personalized machine-learning model with each candidate user associated with a confidence score and confirming with the user when the confidence scores are lower than a threshold score may be effective solutions for addressing the technical challenge of disambiguating an ambiguous mention of the user request as the personalized machine-learning model may use user profile information, historical interactions with the assistant system  140 , and contextual information to determine candidate users and the confirmation from the user may provide further clarification. Although this disclosure describes disambiguating particular user input via particular systems in particular manners, this disclosure contemplates disambiguating any suitable user input via any suitable system in any suitable manner. 
     In particular embodiments, the one or more references may comprise at least an ambiguous mention. The ambiguous mention may correspond to a group of second users from among the plurality of second users. In particular embodiments, the reasoning module  214  or reasoning module  222  may disambiguate the ambiguous mention to identify the group of second users. The disambiguation may comprise comparing the ambiguous mention to a list of group identifiers corresponding to a plurality of predetermined groups, respectively. In particular embodiments, each predetermined group may comprise a plurality of users. As an example and not by way of limitation, the user request may be “call my team” which has an ambiguous mention of “my team”. The reasoning module  214  or reasoning module  222  may accordingly disambiguate “my team” by comparing it with a list of group identifiers to identify the team members. As another example and not by way of limitation, the user may say “start a group call”. The assistant xbot may ask “who is the first person?” The user may provide a name, for which the assistant system  140  may disambiguate it if necessary. The assistant xbot may continue asking “who is the second person?” Such process may continue until the user indicates that all the people that user intended to call are added to the group. In particular embodiments, the reasoning module  214  or reasoning module  222  may determine a group identifier corresponding to the ambiguous mention does not exist, i.e., the user didn&#39;t pre-define the group. The reasoning module  214  or reasoning module  222  may then send, to the client system  130  via the assistant xbot, instructions for prompting the first user to create the group. In alternative embodiments, the assistant system  140  may directly present the prompt for creating the group at the client system  130  via the assistant xbot using the client-side process. In particular embodiments, the reasoning module  214  or reasoning module  222  may receive, from the client system  130  via the assistant xbot, one or more user inputs comprising information associated with each of the second users in the group. The reasoning module  214  or reasoning module  222  may then create the group of the second users based on the one or more user inputs. In particular embodiments, the reasoning module  214  or reasoning module  222  may further store the created group in a data store  212 . The group may be associated with a group identifier. Continuing with the previous example, the reasoning module  214  or reasoning module  222  may determine that the group of “my team” doesn&#39;t exist. Therefore, the assistant system  140  may ask the user to create this group by providing names and contact information of the group members. Although this disclosure describes disambiguating particular user input via particular systems in particular manners, this disclosure contemplates disambiguating any suitable user input via any suitable system in any suitable manner. 
     In particular embodiments, the assistant system  140  may use a confirmation dialog to let the first user have the final confirmation of calling the second users including the disambiguated ones. The confirmation from the dialog may be explicit or implicit. For implicit confirmation, the assistant system  140  may use one-shot calling. As an example and not by way of limitation, the assistant system  140  may say “calling Ben Thomas and Dan now.” The one-shot calling may give the user some time (e.g., 5 seconds) to cancel the call. In particular embodiments, the dialog arbitrator  216  may take the confirmation and instruct the server-side action execution module  226  or the client-side action execution module  230  to complete the multi-person call. Although this disclosure describes particular confirmations via particular systems in particular manners, this disclosure contemplates any suitable confirmation via any suitable system in any suitable manner. 
     In particular embodiments, the multi-person calling function may be enabled on different applications (e.g., a messaging application, a video conferencing application, etc.) corresponding to different agents. The action execution module  226  or action execution module  230  may need to further determine which application to use. As an example and not by way of limitation, the group call may be a voice call using a voice-based application or a video call using a video-based application. In particular embodiments, the user request may specify which application the first user intended to use for the multi-person call. Accordingly, the action execution module  226  or action execution module  230  may select an agent from a plurality of agents to execute a task corresponding to the group call based on the user request. As an example and not by way of limitation, the user request may be “call my besties on the messaging app.” Accordingly, an agent corresponding to the “messaging app” may be selected. If the first user doesn&#39;t specify which application to use, the action execution module  226  or action execution module  230  may select an agent from a plurality of agents to execute a task corresponding to the group call based on one or more of a default setting associated with the first user, a user preference associated with the first user, a historical interaction between the first user and the computing systems, or a predetermined rule. As an example and not by way of limitation, the default setting may indicate the call should be completed via a particular voice-calling application, for which an agent corresponding to such voice application may be selected. As another example and not by way of limitation, the historical interactions may indicate the user always used a particular conferencing application to call others. Accordingly, an agent corresponding to the conferencing application may be selected. Although this disclosure describes using particular applications by particular systems in particular manners, this disclosure contemplates using any suitable application by any suitable system in any suitable manner. 
       FIGS.  5 A- 5 B  illustrate example interactions for calling two people.  FIG.  5 A  illustrate an example interaction for calling two people. A user  502  may speak a single request  504  to the assistant system  140  via the client system  130 . The client system  130  may be a smart phone. The single request  504  may be “hey assistant, call Matt and Kevin.” The assistant system  140  may identify the targets referred by “Matt” and “Kevin” and execute the task of calling them simultaneously. The assistant system  140  may further generate a response  506  which is “calling Matt and Kevin now.”  FIG.  5 B  illustrate another example interaction for calling two people. The user  502  may speak the single request  504  to the assistant system  140  via the client system  130 . The single request  504  may be “hey assistant, call Matt and Kevin.” The assistant system  140  may identify the targets referred by “Matt” and “Kevin” and execute the task of calling them simultaneously. The assistant system  140  may further generate a response  506  which is “calling Matt and Kevin now.” The response  506  may implicitly give the user  502  a chance to have the final confirmation. As an example and not by way of limitation, the user  504  may choose to cancel the call by saying “wait! They probably are in a meeting now.  508 ” Although this disclosure describes particular interactions via particular systems in particular manners, this disclosure contemplates any suitable interaction via any suitable system in any suitable manner. 
       FIGS.  6 A- 6 B  illustrate example interactions for calling two people with a nickname.  FIG.  6 A  illustrate an example interaction for calling two people with a nickname. A user  602  may speak a single request  604  to the assistant system  140  via the client system  130 . The client system  130  may be a smart dock. The single request  604  may be “hey assistant, call Nancy and Dollface,” in which “Dollface” is a nickname. The assistant system  140  may disambiguate such nickname and resolve it to “Jessica”. The assistant system  140  may have a high confidence that “Dollface” refers to “Jessica”. As such, the assistant system  140  may execute the task of calling Nancy and Jessica simultaneously. The assistant system  140  may further generate a response  606 , which may be “calling Nancy and Jessica now.”  FIG.  6 B  illustrate an example interaction for calling two people with disambiguation of a nickname. The user  602  may speak a single request  604  to the assistant system  140  via the client system  130 . The single request  604  may be “hey assistant, call Nancy and Dollface,” in which “Dollface” is a nickname. The assistant system  140  may disambiguate such nickname with a low confidence. As such, the assistant system  140  may generate a response  608  to confirm with the user whether the user means “Jessica” by “Dollface”. The response  608  may be “do you mean Jessica when you said ‘Dollface’?” The user  602  may confirm with “yes.  610 ” Once receiving the user&#39;s  602  confirmation, the assistant system  140  may execute the task of calling Nancy and Jessica simultaneously. The assistant system  140  may further generate the response  612 , which may be “calling Nancy and Jessica now.” Although this disclosure describes particular interactions via particular systems in particular manners, this disclosure contemplates any suitable interaction via any suitable system in any suitable manner. 
       FIGS.  7 A- 7 B  illustrate example interactions for calling a group of people with a nickname.  FIG.  7 A  illustrate an example interaction for calling a group of people with a nickname. A user  702  may speak a single request  704  to the assistant system  140  via the client system  130 . The client system  130  may be a tablet. The single request  704  may be “hey assistant, call my besties,” in which “besties” is a nickname for a group of people. The assistant system  140  may disambiguate such nickname and resolve it to a group of people. The assistant system  140  may have a high confidence which group “besties” refers to. As such, the assistant system  140  may execute the task of calling the user&#39;s  702  besties simultaneously. The assistant system  140  may further generate a response  706 , which may be “calling your besties now.”  FIG.  7 B  illustrate another example interaction for calling a group of people with a nickname. The user  702  may speak the single request  704  to the assistant system  140  via the client system  130 . The single request  704  may be “hey assistant, call my besties,” in which “besties” is a nickname for a group of people. The assistant system  140  may disambiguate such nickname and resolve it to a group of people. The assistant system  140  may have a high confidence which group “besties” refers to. As such, the assistant system  140  may execute the task of calling the user&#39;s  702  besties simultaneously. The assistant system  140  may further generate a response  708  with clear identification of who “besties” refers to. The response  708  may be “calling Danny, Anna and Andrew now,” in which Danny, Anna, and Andrew are the user&#39;s  702  besties.  FIG.  7 C  illustrate an example interaction for calling a group of people by creating the group. The user  702  may speak the single request  704  to the assistant system  140  via the client system  130 . The single request  704  may be “hey assistant, call my besties,” in which “besties” is a nickname for a group of people. The assistant system  140  may determine that such group does not exist. Therefore, the assistant system  140  may generate a response  710  to ask the user  702  to create a group for the user&#39;s  702  besties. The response  710  may be “I&#39;m not sure who your besties are. Can you tell me their names?” The user  702  may then reply “Danny, Anna and Andrew.  712 ” The assistant system  140  may create a group of besties including Danny, Anna and Andrew. The assistant system  140  may then execute the tasking of calling these three people simultaneously. In addition, the assistant system  140  may generate a response  714  notifying the user  702  that their information is saved and the assistant system  140  is going to call them. The response  714  may be “thanks. I&#39;ve saved their information as your besties. Calling them now.” Although this disclosure describes particular interactions via particular systems in particular manners, this disclosure contemplates any suitable interaction via any suitable system in any suitable manner. 
       FIGS.  8 A- 8 B  illustrate example interactions for calling multiple people with the disambiguation of first and last names.  FIG.  8 A  illustrate an example interaction for calling multiple people with disambiguation of first and last names. A user  802  may speak a single request  804  to the assistant system  140  via the client system  130 . The client system  130  may be a pair of AR/VR glasses. The single request  804  may be “hey assistant, call Ben Thomas and Jack.” The assistant system  140  may determine that “Ben Thomas” is one person with “Ben” as first name and “Thomas” as last name. The assistant system  140  may have a high confidence with such determination. Consequently, the assistant system  140  may execute the task of calling Ben and Jack simultaneously. The assistant system  140  may further generate a response  806 , which may be “calling Ben and Jack now,” in which the assistant system  140  only mentions the first names.  FIG.  8 B  illustrates another example interaction for calling multiple people with disambiguation of first and last names. The user  802  may speak the single request  804  to the assistant system  140  via the client system  130 . The single request  804  may be “hey assistant, call Ben Thomas and Jack.” The assistant system  140  may determine that “Ben Thomas” is one person with “Ben” as first name and “Thomas” as last name. The assistant system  140  may have a high confidence with such determination. Consequently, the assistant system  140  may execute the task of calling Ben and Jack simultaneously. The assistant system  140  may further generate a response  808 , which may be “calling Ben Thomas and Jack Hernandez now,” in which the assistant system  140  mentions the full names for both the two people. As result, it may be clearer to the user  802  whom the assistant system  140  is calling.  FIG.  8 C  illustrates another example interaction for calling multiple people with disambiguation of first and last names. The user  802  may speak the single request  804  to the assistant system  140  via the client system  130 . The single request  804  may be “hey assistant, call Ben Thomas and Jack.” The assistant system  140  may have low confidence determining if “Ben Thomas” refers to one person or two people. Therefore, the assistant system  140  may ask the user  802  for confirmation. The confirmation question  810  may be “do you mean Ben Lu and Thomas Smith or just Ben Thomas?” The user  802  may confirm with “just Ben Thomas.  812 ” Once receiving the user&#39;s  802  confirmation, the assistant system  140  may execute the task of calling Ben Thomas and Jack simultaneously. The assistant system  140  may further generate a response  814  which may be “ok. Calling Ben Thomas and Jack Hernandez now.” Although this disclosure describes particular interactions via particular systems in particular manners, this disclosure contemplates any suitable interaction via any suitable system in any suitable manner. 
       FIG.  9    illustrates an example interaction for calling multiple people with a mixture of a nickname for one person and a nickname for a group. A user  902  may speak a single request  904  to the assistant system  140  via the client system  130 . The client system  130  may be a smart speaker. The single request  904  may be “hey assistant, call Alice, Dollface, and my besties.” The assistant system  140  may disambiguate the nickname “Dollface” and resolve it to “Jessica” while disambiguating the nickname “besties” and resolving it to a group of people. The assistant system  140  may have a high confidence about the disambiguation. As such, the assistant system  140  may execute the task of calling Alice, Jessica, and the user&#39;s  902  besties simultaneously. The assistant system  140  may further generate a response  906 , which may be “calling Alice, Jessica, and your besties now.” Although this disclosure describes particular interactions via particular systems in particular manners, this disclosure contemplates any suitable interaction via any suitable system in any suitable manner. 
       FIG.  10    illustrates an example method  1000  for enabling a multi-person call. The method may begin at step  1010 , where the assistant system  140  may receive, from a client system  130  associated with a first user via an assistant xbot, a user request based on a voice input to call a plurality of second users simultaneously, wherein the user request comprises one or more references referring to the plurality of second users, wherein the one or more references comprise at least an ambiguous mention, and wherein the user request comprises only a single request. At step  1020 , the assistant system  140  may disambiguate an ambiguous mention corresponding to a particular second user to identify the particular second user, wherein the disambiguation comprises determining a plurality of candidate users based on a personalized machine-learning model, wherein each candidate user is associated with a confidence score, ranking the plurality of candidate users based on their respective confidence scores, and identifying the particular second user from the plurality of candidate users based on their respective rankings. At step  1030 , the assistant system  140  may send, to the client system  130  via the assistant xbot, instructions for prompting the first user for a confirmation of the particular second user. Parallel to steps  1020  and  1030 , at step  1040 , the assistant system  140  may disambiguate an ambiguous mention corresponding to a group of second users to identify the group of second users, wherein the disambiguation comprises comparing the ambiguous mention to a list of group identifiers corresponding to a plurality of predetermined groups, respectively, wherein each predetermined group comprises a plurality of users. At step  1050 , the assistant system  140  may determine a group identifier corresponding to the ambiguous mention does not exist, send, to the client system  130  via the assistant xbot, instructions for prompting the first user to create the group, receive, from the client system  130  via the assistant xbot, one or more user inputs comprising information associated with each of the second users in the group, create the group of the second users based on the one or more user inputs, and store the created group in a data store  212 , wherein the group is associated with a group identifier. At step  1060 , the assistant system  140  may identify, based on a natural-language understanding module, one or more separators associated with the user request. At step  1070 , the assistant system  140  may identify the plurality of second users by resolving the one or more references to a plurality of entity identifiers associated with the plurality of second users, respectively, based on the one or more separators, wherein the identification comprises accessing one or more contact lists associated with the first user, wherein each contact list comprises a plurality of names associated with a plurality of entity identifiers, and comparing the one or more references with the plurality of names. At step  1080 , the assistant system  140  may initiate, by the assistant xbot, a group call to the plurality of second users simultaneously based on the plurality of entity identifiers responsive to the single request, wherein the initiation comprises determining that any additional user input is not necessary for initiating the group call, simultaneously generating a plurality of communication requests from the first user to the plurality of second users, respectively, and sending, to a plurality of client systems  130  associated with the plurality of second users via a network, the plurality of communication requests simultaneously, where the group call is a voice call or a video call. Particular embodiments may repeat one or more steps of the method of  FIG.  10   , where appropriate. Although this disclosure describes and illustrates particular steps of the method of  FIG.  10    as occurring in a particular order, this disclosure contemplates any suitable steps of the method of  FIG.  10    occurring in any suitable order. Moreover, although this disclosure describes and illustrates an example method for enabling a multi-person call including the particular steps of the method of  FIG.  10   , this disclosure contemplates any suitable method for enabling a multi-person call including any suitable steps, which may include all, some, or none of the steps of the method of  FIG.  10   , where appropriate. Furthermore, although this disclosure describes and illustrates particular components, devices, or systems carrying out particular steps of the method of  FIG.  10   , this disclosure contemplates any suitable combination of any suitable components, devices, or systems carrying out any suitable steps of the method of  FIG.  10   . 
     Social Graphs 
       FIG.  11    illustrates an example social graph  1100 . In particular embodiments, the social-networking system  160  may store one or more social graphs  1100  in one or more data stores. In particular embodiments, the social graph  1100  may include multiple nodes—which may include multiple user nodes  1102  or multiple concept nodes  1104 —and multiple edges  1106  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  1100  illustrated in  FIG.  11    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  140 , or a third-party system  170  may access the social graph  1100  and related social-graph information for suitable applications. The nodes and edges of the social graph  1100  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  1100 . 
     In particular embodiments, a user node  1102  may correspond to a user of the social-networking system  160  or the assistant system  140 . 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  140 . 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  1102  corresponding to the user, and store the user node  1102  in one or more data stores. Users and user nodes  1102  described herein may, where appropriate, refer to registered users and user nodes  1102  associated with registered users. In addition or as an alternative, users and user nodes  1102  described herein may, where appropriate, refer to users that have not registered with the social-networking system  160 . In particular embodiments, a user node  1102  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  1102  may be associated with one or more data objects corresponding to information associated with a user. In particular embodiments, a user node  1102  may correspond to one or more web interfaces. 
     In particular embodiments, a concept node  1104  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  1104  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  140 . 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  1104  may be associated with one or more data objects corresponding to information associated with concept node  1104 . In particular embodiments, a concept node  1104  may correspond to one or more web interfaces. 
     In particular embodiments, a node in the social graph  1100  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  140 . 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  1104 . 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  1102  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  1104  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  1104 . 
     In particular embodiments, a concept node  1104  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 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  1102  corresponding to the user and a concept node  1104  corresponding to the third-party web interface or resource and store edge  1106  in one or more data stores. 
     In particular embodiments, a pair of nodes in the social graph  1100  may be connected to each other by one or more edges  1106 . An edge  1106  connecting a pair of nodes may represent a relationship between the pair of nodes. In particular embodiments, an edge  1106  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  1106  connecting the first user&#39;s user node  1102  to the second user&#39;s user node  1102  in the social graph  1100  and store edge  1106  as social-graph information in one or more of data stores  164 . In the example of  FIG.  11   , the social graph  1100  includes an edge  1106  indicating a friend relation between user nodes  1102  of user “A” and user “B” and an edge indicating a friend relation between user nodes  1102  of user “C” and user “B.” Although this disclosure describes or illustrates particular edges  1106  with particular attributes connecting particular user nodes  1102 , this disclosure contemplates any suitable edges  1106  with any suitable attributes connecting user nodes  1102 . As an example and not by way of limitation, an edge  1106  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  1100  by one or more edges  1106 . The degree of separation between two objects represented by two nodes, respectively, is a count of edges in a shortest path connecting the two nodes in the social graph  1100 . As an example and not by way of limitation, in the social graph  1100 , the user node  1102  of user “C” is connected to the user node  1102  of user “A” via multiple paths including, for example, a first path directly passing through the user node  1102  of user “B,” a second path passing through the concept node  1104  of company “A1me” and the user node  1102  of user “D,” and a third path passing through the user nodes  1102  and concept nodes  1104  representing school “Stateford,” user “G,” company “A1me,” and user “D.” User “C” and user “A” have a degree of separation of two because the shortest path connecting their corresponding nodes (i.e., the first path) includes two edges  1106 . 
     In particular embodiments, an edge  1106  between a user node  1102  and a concept node  1104  may represent a particular action or activity performed by a user associated with user node  1102  toward a concept associated with a concept node  1104 . As an example and not by way of limitation, as illustrated in  FIG.  11   , a user may “like,” “attended,” “played,” “listened,” “cooked,” “worked at,” or “read” a concept, each of which may correspond to an edge type or subtype. A concept-profile interface corresponding to a concept node  1104  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 (a third-party online music application). In this case, the social-networking system  160  may create a “listened” edge  1106  and a “used” edge (as illustrated in  FIG.  11   ) between user nodes  1102  corresponding to the user and concept nodes  1104  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  1106  (as illustrated in  FIG.  11   ) between concept nodes  1104  corresponding to the song and the application to indicate that the particular song was played by the particular application. In this case, “played” edge  1106  corresponds to an action performed by an external application (the third-party online music application) on an external audio file (the song “Imagine”). Although this disclosure describes particular edges  1106  with particular attributes connecting user nodes  1102  and concept nodes  1104 , this disclosure contemplates any suitable edges  1106  with any suitable attributes connecting user nodes  1102  and concept nodes  1104 . Moreover, although this disclosure describes edges between a user node  1102  and a concept node  1104  representing a single relationship, this disclosure contemplates edges between a user node  1102  and a concept node  1104  representing one or more relationships. As an example and not by way of limitation, an edge  1106  may represent both that a user likes and has used at a particular concept. Alternatively, another edge  1106  may represent each type of relationship (or multiples of a single relationship) between a user node  1102  and a concept node  1104  (as illustrated in  FIG.  11    between user node  1102  for user “E” and concept node  1104  for “online music application”). 
     In particular embodiments, the social-networking system  160  may create an edge  1106  between a user node  1102  and a concept node  1104  in the social graph  1100 . 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  1104  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  1106  between user node  1102  associated with the user and concept node  1104 , as illustrated by “like” edge  1106  between the user and concept node  1104 . In particular embodiments, the social-networking system  160  may store an edge  1106  in one or more data stores. In particular embodiments, an edge  1106  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, reads a book, watches a movie, or listens to a song, an edge  1106  may be formed between user node  1102  corresponding to the first user and concept nodes  1104  corresponding to those concepts. Although this disclosure describes forming particular edges  1106  in particular manners, this disclosure contemplates forming any suitable edges  1106  in any suitable manner. 
     Vector Spaces and Embeddings 
       FIG.  12    illustrates an example view of a vector space  1200 . 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  1200  is illustrated as a three-dimensional space, this is for illustrative purposes only, as the vector space  1200  may be of any suitable dimension. In particular embodiments, an n-gram may be represented in the vector space  1200  as a vector referred to as a term embedding. Each vector may comprise coordinates corresponding to a particular point in the vector space  1200  (i.e., the terminal point of the vector). As an example and not by way of limitation, vectors  1210 ,  1220 , and  1230  may be represented as points in the vector space  1200 , as illustrated in  FIG.  12   . 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  1200 , 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 word-embeddings model may be used to map an n-gram to a vector representation in the vector space  1200 . In particular embodiments, an n-gram may be mapped to a vector representation in the vector space  1200  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  1200  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  1200 , 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  1200 . 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 
                     v   1     ⇀     ·       v   2     ⇀         ||       v   1     ⇀     ||   ||       v   2     ⇀     ||       .         
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  1200 . As an example and not by way of limitation, vector  1210  and vector  1220  may correspond to objects that are more similar to one another than the objects corresponding to vector  1210  and vector  1230 , 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.  13    illustrates an example artificial neural network (“ANN”)  1300 . In particular embodiments, an ANN may refer to a computational model comprising one or more nodes. Example ANN  1300  may comprise an input layer  1310 , hidden layers  1320 ,  1330 ,  1340 , and an output layer  1350 . Each layer of the ANN  1300  may comprise one or more nodes, such as a node  1305  or a node  1315 . 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  1310  may be connected to one of more nodes of the hidden layer  1320 . 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.  13    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.  13    depicts a connection between each node of the input layer  1310  and each node of the hidden layer  1320 , one or more nodes of the input layer  1310  may not be connected to one or more nodes of the hidden layer  1320 . 
     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  1320  may comprise the output of one or more nodes of the input layer  1310 . As another example and not by way of limitation, the input to each node of the output layer  1350  may comprise the output of one or more nodes of the hidden layer  1340 . 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 
                   F   k     ⁡     (     s   k     )       =     1     1   +     e     -     s   k               ,         
the hyperbolic tangent function
 
                   F   k     ⁡     (     s   k     )       =         e     s   k       -     e     -     s   k               e     s   k       +     e     -     s   k               ,         
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  1325  between the node  1305  and the node  1315  may have a weighting coefficient of 0.4, which may indicate that 0.4 multiplied by the output of the node  1305  is used as an input to the node  1315 . 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  1300  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  1104  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  1100 . A privacy setting may be specified for one or more edges  1106  or edge-types of the social graph  1100 , or with respect to one or more nodes  1102 ,  1104  or node-types of the social graph  1100 . The privacy settings applied to a particular edge  1106  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  1104  connected to a user node  1102  of the first user by an edge  1106 . The first user may specify privacy settings that apply to a particular edge  1106  connecting to the concept node  1104  of the object, or may specify privacy settings that apply to all edges  1106  connecting to the concept node  1104 . 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 system  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. 
     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. 
     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 image may be used only for a limited purpose (e.g., authentication, tagging the user in photos), and further specify that such image 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.  14    illustrates an example computer system  1400 . In particular embodiments, one or more computer systems  1400  perform one or more steps of one or more methods described or illustrated herein. In particular embodiments, one or more computer systems  1400  provide functionality described or illustrated herein. In particular embodiments, software running on one or more computer systems  1400  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  1400 . 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  1400 . This disclosure contemplates computer system  1400  taking any suitable physical form. As example and not by way of limitation, computer system  1400  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  1400  may include one or more computer systems  1400 ; 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  1400  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  1400  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  1400  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  1400  includes a processor  1402 , memory  1404 , storage  1406 , an input/output (I/O) interface  1408 , a communication interface  1410 , and a bus  1412 . 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  1402  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  1402  may retrieve (or fetch) the instructions from an internal register, an internal cache, memory  1404 , or storage  1406 ; decode and execute them; and then write one or more results to an internal register, an internal cache, memory  1404 , or storage  1406 . In particular embodiments, processor  1402  may include one or more internal caches for data, instructions, or addresses. This disclosure contemplates processor  1402  including any suitable number of any suitable internal caches, where appropriate. As an example and not by way of limitation, processor  1402  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  1404  or storage  1406 , and the instruction caches may speed up retrieval of those instructions by processor  1402 . Data in the data caches may be copies of data in memory  1404  or storage  1406  for instructions executing at processor  1402  to operate on; the results of previous instructions executed at processor  1402  for access by subsequent instructions executing at processor  1402  or for writing to memory  1404  or storage  1406 ; or other suitable data. The data caches may speed up read or write operations by processor  1402 . The TLBs may speed up virtual-address translation for processor  1402 . In particular embodiments, processor  1402  may include one or more internal registers for data, instructions, or addresses. This disclosure contemplates processor  1402  including any suitable number of any suitable internal registers, where appropriate. Where appropriate, processor  1402  may include one or more arithmetic logic units (ALUs); be a multi-core processor; or include one or more processors  1402 . Although this disclosure describes and illustrates a particular processor, this disclosure contemplates any suitable processor. 
     In particular embodiments, memory  1404  includes main memory for storing instructions for processor  1402  to execute or data for processor  1402  to operate on. As an example and not by way of limitation, computer system  1400  may load instructions from storage  1406  or another source (such as, for example, another computer system  1400 ) to memory  1404 . Processor  1402  may then load the instructions from memory  1404  to an internal register or internal cache. To execute the instructions, processor  1402  may retrieve the instructions from the internal register or internal cache and decode them. During or after execution of the instructions, processor  1402  may write one or more results (which may be intermediate or final results) to the internal register or internal cache. Processor  1402  may then write one or more of those results to memory  1404 . In particular embodiments, processor  1402  executes only instructions in one or more internal registers or internal caches or in memory  1404  (as opposed to storage  1406  or elsewhere) and operates only on data in one or more internal registers or internal caches or in memory  1404  (as opposed to storage  1406  or elsewhere). One or more memory buses (which may each include an address bus and a data bus) may couple processor  1402  to memory  1404 . Bus  1412  may include one or more memory buses, as described below. In particular embodiments, one or more memory management units (MMUs) reside between processor  1402  and memory  1404  and facilitate accesses to memory  1404  requested by processor  1402 . In particular embodiments, memory  1404  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  1404  may include one or more memories  1404 , where appropriate. Although this disclosure describes and illustrates particular memory, this disclosure contemplates any suitable memory. 
     In particular embodiments, storage  1406  includes mass storage for data or instructions. As an example and not by way of limitation, storage  1406  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  1406  may include removable or non-removable (or fixed) media, where appropriate. Storage  1406  may be internal or external to computer system  1400 , where appropriate. In particular embodiments, storage  1406  is non-volatile, solid-state memory. In particular embodiments, storage  1406  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  1406  taking any suitable physical form. Storage  1406  may include one or more storage control units facilitating communication between processor  1402  and storage  1406 , where appropriate. Where appropriate, storage  1406  may include one or more storages  1406 . Although this disclosure describes and illustrates particular storage, this disclosure contemplates any suitable storage. 
     In particular embodiments, I/O interface  1408  includes hardware, software, or both, providing one or more interfaces for communication between computer system  1400  and one or more I/O devices. Computer system  1400  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  1400 . 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  1408  for them. Where appropriate, I/O interface  1408  may include one or more device or software drivers enabling processor  1402  to drive one or more of these I/O devices. I/O interface  1408  may include one or more I/O interfaces  1408 , 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  1410  includes hardware, software, or both providing one or more interfaces for communication (such as, for example, packet-based communication) between computer system  1400  and one or more other computer systems  1400  or one or more networks. As an example and not by way of limitation, communication interface  1410  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  1410  for it. As an example and not by way of limitation, computer system  1400  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  1400  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  1400  may include any suitable communication interface  1410  for any of these networks, where appropriate. Communication interface  1410  may include one or more communication interfaces  1410 , where appropriate. Although this disclosure describes and illustrates a particular communication interface, this disclosure contemplates any suitable communication interface. 
     In particular embodiments, bus  1412  includes hardware, software, or both coupling components of computer system  1400  to each other. As an example and not by way of limitation, bus  1412  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  1412  may include one or more buses  1412 , 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, SECURE DIGITAL 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.