Patent Grant
12131523
This disclosure generally relates to databases and file management within network environments, and in particular relates to hardware and software for smart assistant systems.
An assistant system can provide information or services on behalf of a user based on a combination of user input, location awareness, and the ability to access information from a variety of online sources (such as weather conditions, traffic congestion, news, stock prices, user schedules, retail prices, etc.). The user input may include text (e.g., online chat), especially in an instant messaging application or other applications, voice, images, or a combination of them. The assistant system may perform concierge-type services (e.g., making dinner reservations, purchasing event tickets, making travel arrangements) or provide information based on the user input. The assistant system may also perform management or data-handling tasks based on online information and events without user initiation or interaction. Examples of those tasks that may be performed by an assistant system may include schedule management (e.g., sending an alert to a dinner date that a user is running late due to traffic conditions, update schedules for both parties, and change the restaurant reservation time). The assistant system may be enabled by the combination of computing devices, application programming interfaces (APIs), and the proliferation of applications on user devices.
A social-networking system, which may include a social-networking website, may enable its users (such as persons or organizations) to interact with it and with each other through it. The social-networking system may, with input from a user, create and store in the social-networking system a user profile associated with the user. The user profile may include demographic information, communication-channel information, and information on personal interests of the user. The social-networking system may also, with input from a user, create and store a record of relationships of the user with other users of the social-networking system, as well as provide services (e.g. profile/news feed posts, photo-sharing, event organization, messaging, games, or advertisements) to facilitate social interaction between or among users.
The social-networking system may send over one or more networks content or messages related to its services to a mobile or other computing device of a user. A user may also install software applications on a mobile or other computing device of the user for accessing a user profile of the user and other data within the social-networking system. The social-networking system may generate a personalized set of content objects to display to a user, such as a newsfeed of aggregated stories of other users connected to the user.
In particular embodiments, the assistant system may assist a user to obtain information or services. The assistant system may enable the user to interact with it with multi-modal user input (such as voice, text, image, video) in stateful and multi-turn conversations to get assistance. The assistant system may create and store a user profile comprising both personal and contextual information associated with the user. In particular embodiments, the assistant system may analyze the user input using natural-language understanding. The analysis may be based on the user profile for more personalized and context-aware understanding. The assistant system may resolve entities associated with the user input based on the analysis. In particular embodiments, the assistant system may interact with different agents to obtain information or services that are associated with the resolved entities. The assistant system may generate a response for the user regarding the information or services by using natural-language generation. Through the interaction with the user, the assistant system may use dialog management techniques to manage and forward the conversation flow with the user. In particular embodiments, the assistant system may further assist the user to effectively and efficiently digest the obtained information by summarizing the information. The assistant system may also assist the user to be more engaging with an online social network by providing tools that help the user interact with the online social network (e.g., creating posts, comments, messages). The assistant system may additionally assist the user to manage different tasks such as keeping track of events. In particular embodiments, the assistant system may proactively execute tasks that are relevant to user interests and preferences based on the user profile without a user input. In particular embodiments, the assistant system may check privacy settings to ensure that accessing a user's profile or other user information and executing different tasks are permitted subject to the user's privacy settings.
In particular embodiment, the client system may access a particular assistant system out of a plurality of multiple assistant systems installed on the client system by using a corresponding “wake word” for the particular assistant system. Each assistant system may have its own function set (i.e., set of tasks it can execute). There may be different assistant systems available for a user to interact with for various experiences (e.g., third-party smart assistants), each assistant system with access to different function sets (e.g., querying for general information, social-networking, purchasing items, etc.). To better accommodate user needs, a client system of the user may include multiple assistant systems (e.g. pre-installed or added by the user). The inclusion of multiple assistant systems may provide the users with more functions associated with each assistant system. As an example and not by way of limitation, a client system may include a social-networking assistant (e.g., M-Assistant/Portal), which may include a function set that comprises knowledge queries, social-networking, and chatting (e.g., via a messenger or video-chat interface). Additionally, the client system may also include a shopping assistant, which may include a function set that comprises knowledge queries and shopping. In order to differentiate between accessing one assistant system over the other assistant system, a user may use different wake words to access the desired assistant xbot. For instance, a user may say “hey Portal” to access the social-networking assistant and the user may say “hey Assistant” to access the shopping assistant. Different users may have access to different assistant xbots. As an example and not by way of limitation, all users may be able to access the shopping assistant, but only an authenticated/primary user may be able to access the social-networking assistant. Users may become familiar with the function set of each assistant xbot available on the client system and be able to invoke the appropriate assistant xbot by saying the wake word corresponding to the respective assistant system. Although this disclosure describes accessing an assistant system out of a plurality of assistant systems in a particular manner, this disclosure contemplates accessing an assistant system out of a plurality of assistant systems in any suitable manner.
In particular embodiments, the client system may receive a first user input from a first user. The first user may be a user of an online-social network. In particular embodiments, the client system may parse the first user input to identify an n-gram associated with a first wake word from a plurality of wake words corresponding to a plurality of assistant systems associated with the client system. Each assistant system may provide a particular set of functions. In particular embodiments, the client system may determine that the first wake word corresponds to a first assistant system of the plurality of assistant systems. The first assistant system may provide a first set of functions. In particular embodiments, the client system may send, to the first assistant system, a request to set an assistant xbot of the first assistant system into a listening mode. In particular embodiments, the client system may receive, from the first assistant system, an indication that the assistant xbot is in listening mode responsive to a determination that the first user has permission to access the first assistant system.
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.
System Overview
This disclosure contemplates any suitable network 110. As an example and not by way of limitation, one or more portions of a network 110 may include an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a cellular telephone network, or a combination of two or more of these. A network 110 may include one or more networks 110.
Links 150 may connect a client system 130, an assistant system 140, a social-networking system 160, and a third-party system 170 to a communication network 110 or to each other. This disclosure contemplates any suitable links 150. In particular embodiments, one or more links 150 include one or more wireline (such as for example Digital Subscriber Line (DSL) or Data Over Cable Service Interface Specification (DOCSIS)), wireless (such as for example Wi-Fi or Worldwide Interoperability for Microwave Access (WiMAX)), or optical (such as for example Synchronous Optical Network (SONET) or Synchronous Digital Hierarchy (SDH)) links. In particular embodiments, one or more links 150 each include an ad hoc network, an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, a portion of the Internet, a portion of the PSTN, a cellular technology-based network, a satellite communications technology-based network, another link 150, or a combination of two or more such links 150. Links 150 need not necessarily be the same throughout a network environment 100. One or more first links 150 may differ in one or more respects from one or more second links 150.
In particular embodiments, a client system 130 may be an electronic device including hardware, software, or embedded logic components or a combination of two or more such components and capable of carrying out the appropriate functionalities implemented or supported by a client system 130. As an example and not by way of limitation, a client system 130 may include a computer system such as a desktop computer, notebook or laptop computer, netbook, a tablet computer, e-book reader, GPS device, camera, personal digital assistant (PDA), handheld electronic device, cellular telephone, smartphone, smart speaker, other suitable electronic device, or any suitable combination thereof. In particular embodiments, the client system 130 may be a smart assistant device. More information on smart assistant devices may be found in U.S. patent application Ser. No. 15/949,011, filed 9 Apr. 2018, U.S. Patent Application No. 62/655,751, filed 10 Apr. 2018, U.S. 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's social connections (e.g., friends, followers, followed accounts, contacts, etc.). The user at the client system 130 may also use the social-networking application 134 to interact with a plurality of content objects (e.g., posts, news articles, ephemeral content, etc.) on the online social network. As an example and not by way of limitation, the user may browse trending topics and breaking news using the social-networking application 134.
In particular embodiments, a client system 130 may include an assistant application 136. A user at a client system 130 may use the assistant application 136 to interact with the assistant system 140. In particular embodiments, the assistant application 136 may comprise a stand-alone application. In particular embodiments, the assistant application 136 may be integrated into the social-networking application 134 or another suitable application (e.g., a messaging application). In particular embodiments, the assistant application 136 may be also integrated into the client system 130, an assistant hardware device, or any other suitable hardware devices. In particular embodiments, the assistant application 136 may be accessed via the web browser 132. In particular embodiments, the user may provide input via different modalities. As an example and not by way of limitation, the modalities may include audio, text, image, video, etc. The assistant application 136 may communicate the user input to the assistant system 140. Based on the user input, the assistant system 140 may generate responses. The assistant system 140 may send the generated responses to the assistant application 136. The assistant application 136 may then present the responses to the user at the client system 130. The presented responses may be based on different modalities such as audio, text, image, and video. As an example and not by way of limitation, the user may verbally ask the assistant application 136 about the traffic information (i.e., via an audio modality). The assistant application 136 may then communicate the request to the assistant system 140. The assistant system 140 may accordingly generate the result and send it back to the assistant application 136. The assistant application 136 may further present the result to the user in text.
In particular embodiments, an assistant system 140 may assist users to retrieve information from different sources. The assistant system 140 may also assist user to request services from different service providers. In particular embodiments, the assist system 140 may receive a user request for information or services via the assistant application 136 in the client system 130. The assist system 140 may use natural-language understanding to analyze the user request based on user'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
In particular embodiments, the social-networking system 160 may be a network-addressable computing system that can host an online social network. The social-networking system 160 may generate, store, receive, and send social-networking data, such as, for example, user-profile data, concept-profile data, social-graph information, or other suitable data related to the online social network. The social-networking system 160 may be accessed by the other components of network environment 100 either directly or via a network 110. As an example and not by way of limitation, a client system 130 may access the social-networking system 160 using a web browser 132, or a native application associated with the social-networking system 160 (e.g., a mobile social-networking application, a messaging application, another suitable application, or any combination thereof) either directly or via a network 110. In particular embodiments, the social-networking system 160 may include one or more servers 162. Each server 162 may be a unitary server or a distributed server spanning multiple computers or multiple datacenters. Servers 162 may be of various types, such as, for example and without limitation, web server, news server, mail server, message server, advertising server, file server, application server, exchange server, database server, proxy server, another server suitable for performing functions or processes described herein, or any combination thereof. In particular embodiments, each server 162 may include hardware, software, or embedded logic components or a combination of two or more such components for carrying out the appropriate functionalities implemented or supported by server 162. In particular embodiments, the social-networking system 160 may include one or more data stores 164. Data stores 164 may be used to store various types of information. In particular embodiments, the information stored in data stores 164 may be organized according to specific data structures. In particular embodiments, each data store 164 may be a relational, columnar, correlation, or other suitable database. Although this disclosure describes or illustrates particular types of databases, this disclosure contemplates any suitable types of databases. Particular embodiments may provide interfaces that enable a client system 130, a social-networking system 160, or a third-party system 170 to manage, retrieve, modify, add, or delete, the information stored in data store 164.
In particular embodiments, the social-networking system 160 may store one or more social graphs in one or more data stores 164. In particular embodiments, a social graph may include multiple nodes-which may include multiple user nodes (each corresponding to a particular user) or multiple concept nodes (each corresponding to a particular concept)—and multiple edges connecting the nodes. The social-networking system 160 may provide users of the online social network the ability to communicate and interact with other users. In particular embodiments, users may join the online social network via the social-networking system 160 and then add connections (e.g., relationships) to a number of other users of the social-networking system 160 whom they want to be connected to. Herein, the term “friend” may refer to any other user of the social-networking system 160 with whom a user has formed a connection, association, or relationship via the social-networking system 160.
In particular embodiments, the social-networking system 160 may provide users with the ability to take actions on various types of items or objects, supported by the social-networking system 160. As an example and not by way of limitation, the items and objects may include groups or social networks to which users of the social-networking system 160 may belong, events or calendar entries in which a user might be interested, computer-based applications that a user may use, transactions that allow users to buy or sell items via the service, interactions with advertisements that a user may perform, or other suitable items or objects. A user may interact with anything that is capable of being represented in the social-networking system 160 or by an external system of a third-party system 170, which is separate from the social-networking system 160 and coupled to the social-networking system 160 via a network 110.
In particular embodiments, the social-networking system 160 may be capable of linking a variety of entities. As an example and not by way of limitation, the social-networking system 160 may enable users to interact with each other as well as receive content from third-party systems 170 or other entities, or to allow users to interact with these entities through an application programming interfaces (API) or other communication channels.
In particular embodiments, a third-party system 170 may include one or more types of servers, one or more data stores, one or more interfaces, including but not limited to APIs, one or more web services, one or more content sources, one or more networks, or any other suitable components, e.g., that servers may communicate with. A third-party system 170 may be operated by a different entity from an entity operating the social-networking system 160. In particular embodiments, however, the social-networking system 160 and third-party systems 170 may operate in conjunction with each other to provide social-networking services to users of the social-networking system 160 or third-party systems 170. In this sense, the social-networking system 160 may provide a platform, or backbone, which other systems, such as third-party systems 170, may use to provide social-networking services and functionality to users across the Internet.
In particular embodiments, a third-party system 170 may include a third-party content object provider. A third-party content object provider may include one or more sources of content objects, which may be communicated to a client system 130. As an example and not by way of limitation, content objects may include information regarding things or activities of interest to the user, such as, for example, movie show times, movie reviews, restaurant reviews, restaurant menus, product information and reviews, or other suitable information. As another example and not by way of limitation, content objects may include incentive content objects, such as coupons, discount tickets, gift certificates, or other suitable incentive objects.
In particular embodiments, the social-networking system 160 also includes user-generated content objects, which may enhance a user's interactions with the social-networking system 160. User-generated content may include anything a user can add, upload, send, or “post” to the social-networking system 160. As an example and not by way of limitation, a user communicates posts to the social-networking system 160 from a client system 130. Posts may include data such as status updates or other textual data, location information, photos, videos, links, music or other similar data or media. Content may also be added to the social-networking system 160 by a third-party through a “communication channel,” such as a newsfeed or stream.
In particular embodiments, the social-networking system 160 may include a variety of servers, sub-systems, programs, modules, logs, and data stores. In particular embodiments, the social-networking system 160 may include one or more of the following: a web server, action logger, API-request server, relevance-and-ranking engine, content-object classifier, notification controller, action log, third-party-content-object-exposure log, inference module, authorization/privacy server, search module, advertisement-targeting module, user-interface module, user-profile store, connection store, third-party content store, or location store. The social-networking system 160 may also include suitable components such as network interfaces, security mechanisms, load balancers, failover servers, management-and-network-operations consoles, other suitable components, or any suitable combination thereof. In particular embodiments, the social-networking system 160 may include one or more user-profile stores for storing user profiles. A user profile may include, for example, biographic information, demographic information, behavioral information, social information, or other types of descriptive information, such as work experience, educational history, hobbies or preferences, interests, affinities, or location. Interest information may include interests related to one or more categories. Categories may be general or specific. As an example and not by way of limitation, if a user “likes” an article about a brand of shoes the category may be the brand, or the general category of “shoes” or “clothing.” A connection store may be used for storing connection information about users. The connection information may indicate users who have similar or common work experience, group memberships, hobbies, educational history, or are in any way related or share common attributes. The connection information may also include user-defined connections between different users and content (both internal and external). A web server may be used for linking the social-networking system 160 to one or more client systems 130 or one or more third-party systems 170 via a network 110. The web server may include a mail server or other messaging functionality for receiving and routing messages between the social-networking system 160 and one or more client systems 130. An API-request server may allow a third-party system 170 to access information from the social-networking system 160 by calling one or more APIs. An action logger may be used to receive communications from a web server about a user'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
In particular embodiments, the assistant system 140 may receive a user input from the assistant application 136 in the client system 130 associated with the user. In particular embodiments, the user input may be a user generated input that is sent to the assistant system 140 in a single turn. If the user input is based on a text modality, the assistant system 140 may receive it at a messaging platform 205. If the user input is based on an audio modality (e.g., the user may speak to the assistant application 136 or send a video including speech to the assistant application 136), the assistant system 140 may process it using an audio speech recognition (ASR) module 210 to convert the user input into text. If the user input is based on an image or video modality, the assistant system 140 may process it using optical character recognition techniques within the messaging platform 205 to convert the user input into text. The output of the messaging platform 205 or the ASR module 210 may be received at an assistant xbot 215. More information on handling user input based on different modalities may be found in U.S. patent application Ser. No. 16/053,600, filed 2 Aug. 2018, which is incorporated by reference.
In particular embodiments, the assistant xbot 215 may be a type of chat bot. The assistant xbot 215 may comprise a programmable service channel, which may be a software code, logic, or routine that functions as a personal assistant to the user. The assistant xbot 215 may work as the user's portal to the assistant system 140. The assistant xbot 215 may therefore be considered as a type of conversational agent. In particular embodiments, the assistant xbot 215 may send the textual user input to a natural-language understanding (NLU) module 220 to interpret the user input. In particular embodiments, the NLU module 220 may get information from a user context engine 225 and a semantic information aggregator (SIA) 230 to accurately understand the user input. The user context engine 225 may store the user profile of the user. The user profile of the user may comprise user-profile data including demographic information, social information, and contextual information associated with the user. The user-profile data may also include user interests and preferences on a plurality of topics, aggregated through conversations on news feed, search logs, messaging platform 205, etc. The usage of a user profile may be protected behind a privacy check module 245 to ensure that a user's information can be used only for his/her benefit, and not shared with anyone else. More information on user profiles may be found in U.S. patent application Ser. No. 15/967,239, filed 30 Apr. 2018, which is incorporated by reference. The semantic information aggregator 230 may provide ontology data associated with a plurality of predefined domains, intents, and slots to the NLU module 220. In particular embodiments, a domain may denote a social context of interaction, e.g., education. An intent may be an element in a pre-defined taxonomy of semantic intentions, which may indicate a purpose of a user interacting with the assistant system 140. In particular embodiments, an intent may be an output of the NLU module 220 if the user input comprises a text/speech input. The NLU module 220 may classify the text/speech input into a member of the pre-defined taxonomy, e.g., for the input “Play Beethoven's 5th,” the NLU module 220 may classify the input as having the intent [IN:play_music]. In particular embodiments, a domain may be conceptually a namespace for a set of intents, e.g., music. A slot may be a named sub-string with the user input, representing a basic semantic entity. For example, a slot for “pizza” may be [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 [IN:play_music], a slot may be [SL:song_name]. The semantic information aggregator 230 may additionally extract information from a social graph, a knowledge graph, and a concept graph, and retrieve a user's profile from the user context engine 225. The semantic information aggregator 230 may further process information from these different sources by determining what information to aggregate, annotating n-grams of the user input, ranking the n-grams with confidence scores based on the aggregated information, formulating the ranked n-grams into features that can be used by the NLU module 220 for understanding the user input. More information on aggregating semantic information may be found in U.S. patent application Ser. No. 15/967,342, filed 30 Apr. 2018, which is incorporated by reference. Based on the output of the user context engine 225 and the semantic information aggregator 230, the NLU module 220 may identify a domain, an intent, and one or more slots from the user input in a personalized and context-aware manner. As an example and not by way of limitation, a user input may comprise “show me how to get to the movies”. The NLU module 220 may identify the particular movie theatre that the user wants to go based on the user's personal information and the associated contextual information. In particular embodiments, the NLU module 220 may comprise a lexicon of language and a parser and grammar rules to partition sentences into an internal representation. The NLU module 220 may also comprise one or more programs that perform naive semantics or stochastic semantic analysis to the use of pragmatics to understand a user input. In particular embodiments, the parser may be based on a deep learning architecture comprising multiple long-short term memory (LSTM) networks. As an example and not by way of limitation, the parser may be based on a recurrent neural network grammar (RNNG) model, which is a type of recurrent and recursive LSTM algorithm. More information on natural-language understanding may be found in U.S. patent application Ser. No. 16/011,062, filed 18 Jun. 2018, U.S. patent application Ser. No. 16/025,317, filed 2 Jul. 2018, and U.S. patent application Ser. No. 16/038,120, filed 17 Jul. 2018, each of which is incorporated by reference.
In particular embodiments, the identified domain, intent, and one or more slots from the NLU module 220 may be sent to a dialog engine 235. In particular embodiments, the dialog engine 235 may manage the dialog state and flow of the conversation between the user and the assistant xbot 215. The dialog engine 235 may additionally store previous conversations between the user and the assistant xbot 215. In particular embodiments, the dialog engine 235 may communicate with an entity resolution module 240 to resolve entities associated with the one or more slots, which supports the dialog engine 235 to forward the flow of the conversation between the user and the assistant xbot 215. In particular embodiments, the entity resolution module 240 may access the social graph, the knowledge graph, and the concept graph when resolving the entities. Entities may include, for example, unique users or concepts, each of which may have a unique identifier (ID). As an example and not by way of limitation, the knowledge graph may comprise a plurality of entities. Each entity may comprise a single record associated with one or more attribute values. The particular record may be associated with a unique entity identifier. Each record may have diverse values for an attribute of the entity. Each attribute value may be associated with a confidence probability. A confidence probability for an attribute value represents a probability that the value is accurate for the given attribute. Each attribute value may be also associated with a semantic weight. A semantic weight for an attribute value may represent how the value semantically appropriate for the given attribute considering all the available information. For example, the knowledge graph may comprise an entity of a movie “The Martian” (2015), which includes information that has been extracted from multiple content sources (e.g., online social network, online encyclopedia, movie 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 space attribute value which indicates the genre of the movie “The Martian” (2015). More information on the knowledge graph may be found in U.S. patent application Ser. No. 16/048,049, filed 27 Jul. 2018, and U.S. patent application Ser. No. 16/048,101, filed 27 Jul. 2018, each of which is incorporated by reference. The entity resolution module 240 may additionally request a user profile of the user associated with the user input from the user context engine 225. In particular embodiments, the entity resolution module 240 may communicate with a privacy check module 245 to guarantee that the resolving of the entities does not violate privacy policies. In particular embodiments, the privacy check module 245 may use an authorization/privacy server to enforce privacy policies. As an example and not by way of limitation, an entity to be resolved may be another user who specifies in his/her privacy settings that his/her identity should not be searchable on the online social network, and thus the entity resolution module 240 may not return that user's identifier in response to a request. Based on the information obtained from the social graph, knowledge graph, concept graph, and user profile, and subject to applicable privacy policies, the entity resolution module 240 may therefore accurately resolve the entities associated with the user input in a personalized and context-aware manner. In particular embodiments, each of the resolved entities may be associated with one or more identifiers hosted by the social-networking system 160. As an example and not by way of limitation, an identifier may comprise a unique user identifier (ID). In particular embodiments, each of the resolved entities may be also associated with a confidence score. More information on resolving entities may be found in U.S. patent application Ser. No. 16/048,049, filed 27 Jul. 2018, and U.S. patent application Ser. No. 16/048,072, filed 27 Jul. 2018, each of which is incorporated by reference.
In particular embodiments, the dialog engine 235 may communicate with different agents based on the identified intent and domain, and the resolved entities. In particular embodiments, an agent may be an implementation that serves as a broker across a plurality of content providers for one domain. A content provider may be an entity responsible for carrying out an action associated with an intent or completing a task associated with the intent. As an example and not by way of limitation, multiple device-specific implementations (e.g., real-time calls for a client system 130 or a messaging application on the client system 130) may be handled internally by a single agent. Alternatively, these device-specific implementations may be handled by multiple agents associated with multiple domains. In particular embodiments, the agents may comprise first-party agents 250 and third-party agents 255. In particular embodiments, first-party agents 250 may comprise internal agents that are accessible and controllable by the assistant system 140 (e.g. agents associated with services provided by the online social network). In particular embodiments, third-party agents 255 may comprise external agents that the assistant system 140 has no control over (e.g., music streams agents, ticket sales agents). The first-party agents 250 may be associated with first-party providers 260 that provide content objects and/or services hosted by the social-networking system 160. The third-party agents 255 may be associated with third-party providers 265 that provide content objects and/or services hosted by the third-party system 170.
In particular embodiments, the communication from the dialog engine 235 to the first-party agents 250 may comprise requesting particular content objects and/or services provided by the first-party providers 260. As a result, the first-party agents 250 may retrieve the requested content objects from the first-party providers 260 and/or execute tasks that command the first-party providers 260 to perform the requested services. In particular embodiments, the communication from the dialog engine 235 to the third-party agents 255 may comprise requesting particular content objects and/or services provided by the third-party providers 265. As a result, the third-party agents 255 may retrieve the requested content objects from the third-party providers 265 and/or execute tasks that command the third-party providers 265 to perform the requested services. The third-party agents 255 may access the privacy check module 245 to guarantee no privacy violations before interacting with the third-party providers 265. As an example and not by way of limitation, the user associated with the user input may specify in his/her privacy settings that his/her profile information is invisible to any third-party content providers. Therefore, when retrieving content objects associated with the user input from the third-party providers 265, the third-party agents 255 may complete the retrieval without revealing to the third-party providers 265 which user is requesting the content objects.
In particular embodiments, each of the first-party agents 250 or third-party agents 255 may be designated for a particular domain. As an example and not by way of limitation, the domain may comprise weather, transportation, music, etc. In particular embodiments, the assistant system 140 may use a plurality of agents collaboratively to respond to a user input. As an example and not by way of limitation, the user input may comprise “direct me to my next meeting.” The assistant system 140 may use a calendar agent to retrieve the location of the next meeting. The assistant system 140 may then use a navigation agent to direct the user to the next meeting.
In particular embodiments, each of the first-party agents 250 or third-party agents 255 may retrieve a user profile from the user context engine 225 to execute tasks in a personalized and context-aware manner. As an example and not by way of limitation, a user input may comprise “book me a ride to the airport.” A transportation agent may execute the task of booking the ride. The transportation agent may retrieve the user profile of the user from the user context engine 225 before booking the ride. For example, the user profile may indicate that the user prefers taxis, so the transportation agent may book a taxi for the user. As another example, the contextual information associated with the user profile may indicate that the user is in a hurry so the transportation agent may book a ride from a ride-sharing service for the user since it may be faster to get a car from a ride-sharing service than a taxi company. In particular embodiment, each of the first-party agents 250 or third-party agents 255 may take into account other factors when executing tasks. As an example and not by way of limitation, other factors may comprise price, rating, efficiency, partnerships with the online social network, etc.
In particular embodiments, the dialog engine 235 may communicate with a conversational understanding composer (CU composer) 270. The dialog engine 235 may send the requested content objects and/or the statuses of the requested services to the CU composer 270. In particular embodiments, the dialog engine 235 may send the requested content objects and/or the statuses of the requested services as a <k, c, u, d> tuple, in which k indicates a knowledge source, c indicates a communicative goal, u indicates a user model, and d indicates a discourse model. In particular embodiments, the CU composer 270 may comprise a natural-language generator (NLG) 271 and a user interface (UI) payload generator 272. The natural-language generator 271 may generate a communication content based on the output of the dialog engine 235. In particular embodiments, the NLG 271 may comprise a content determination component, a sentence planner, and a surface realization component. The content determination component may determine the communication content based on the knowledge source, communicative goal, and the user's expectations. As an example and not by way of limitation, the determining may be based on a description logic. The description logic may comprise, for example, three fundamental notions which are individuals (representing objects in the domain), concepts (describing sets of individuals), and roles (representing binary relations between individuals or concepts). The description logic may be characterized by a set of constructors that allow the natural-language generator 271 to build complex concepts/roles from atomic ones. In particular embodiments, the content determination component may perform the following tasks to determine the communication content. The first task may comprise a translation task, in which the input to the natural-language generator 271 may be translated to concepts. The second task may comprise a selection task, in which relevant concepts may be selected among those resulted from the translation task based on the user model. The third task may comprise a verification task, in which the coherence of the selected concepts may be verified. The fourth task may comprise an instantiation task, in which the verified concepts may be instantiated as an executable file that can be processed by the natural-language generator 271. The sentence planner may determine the organization of the communication content to make it human understandable. The surface realization component may determine specific words to use, the sequence of the sentences, and the style of the communication content. The UI payload generator 272 may determine a preferred modality of the communication content to be presented to the user. In particular embodiments, the CU composer 270 may communicate with the privacy check module 245 to make sure the generation of the communication content follows the privacy policies. In particular embodiments, the CU composer 270 may retrieve a user profile from the user context engine 225 when generating the communication content and determining the modality of the communication content. As a result, the communication content may be more natural, personalized, and context-aware for the user. As an example and not by way of limitation, the user profile may indicate that the user likes short sentences in conversations so the generated communication content may be based on short sentences. As another example and not by way of limitation, the contextual information associated with the user profile may indicated that the user is using a device that only outputs audio signals so the UI payload generator 272 may determine the modality of the communication content as audio. More information on natural-language generation may be found in U.S. patent application Ser. No. 15/967,279, filed 30 Apr. 2018, and U.S. patent application Ser. No. 15/966,455, filed 30 Apr. 2018, each of which is incorporated by reference.
In particular embodiments, the CU composer 270 may send the generated communication content to the assistant xbot 215. In particular embodiments, the assistant xbot 215 may send the communication content to the messaging platform 205. The messaging platform 205 may further send the communication content to the client system 130 via the assistant application 136. In alternative embodiments, the assistant xbot 215 may send the communication content to a text-to-speech (TTS) module 275. The TTS module 275 may convert the communication content to an audio clip. The TTS module 275 may further send the audio clip to the client system 130 via the assistant application 136.
In particular embodiments, the assistant xbot 215 may interact with a proactive inference layer 280 without receiving a user input. The proactive inference layer 280 may infer user interests and preferences based on the user profile that is retrieved from the user context engine 225. In particular embodiments, the proactive inference layer 280 may further communicate with proactive agents 285 regarding the inference. The proactive agents 285 may execute proactive tasks based on the inference. As an example and not by way of limitation, the proactive tasks may comprise sending content objects or providing services to the user. In particular embodiments, each proactive task may be associated with an agenda item. The agenda item may comprise a recurring item such as a daily digest. The agenda item may also comprise a one-time item. In particular embodiments, a proactive agent 285 may retrieve the user profile from the user context engine 225 when executing the proactive task. Therefore, the proactive agent 285 may execute the proactive task in a personalized and context-aware manner. As an example and not by way of limitation, the proactive inference layer may infer that the user likes the band Maroon 5 and the proactive agent 285 may generate a recommendation of Maroon 5's new song/album to the user.
In particular embodiments, the proactive agent 285 may generate candidate entities associated with the proactive task based on a user profile. The generation may be based on a straightforward backend query using deterministic filters to retrieve the candidate entities from a structured data store. The generation may be alternatively based on a machine-learning model that is trained based on the user profile, entity attributes, and relevance between users and entities. As an example and not by way of limitation, the machine-learning model may be based on support vector machines (SVM). As another example and not by way of limitation, the machine-learning model may be based on a regression model. As another example and not by way of limitation, the machine-learning model may be based on a deep convolutional neural network (DCNN). In particular embodiments, the proactive agent 285 may also rank the generated candidate entities based on the user profile and the content associated with the candidate entities. The ranking may be based on the similarities between a user's interests and the candidate entities. As an example and not by way of limitation, the assistant system 140 may generate a feature vector representing a user's interest and feature vectors representing the candidate entities. The assistant system 140 may then calculate similarity scores (e.g., based on cosine similarity) between the feature vector representing the user's interest and the feature vectors representing the candidate entities. The ranking may be alternatively based on a ranking model that is trained based on user feedback data.
In particular embodiments, the proactive task may comprise recommending the candidate entities to a user. The proactive agent 285 may schedule the recommendation, thereby associating a recommendation time with the recommended candidate entities. The recommended candidate entities may be also associated with a priority and an expiration time. In particular embodiments, the recommended candidate entities may be sent to a proactive scheduler. The proactive scheduler may determine an actual time to send the recommended candidate entities to the user based on the priority associated with the task and other relevant factors (e.g., clicks and impressions of the recommended candidate entities). In particular embodiments, the proactive scheduler may then send the recommended candidate entities with the determined actual time to an asynchronous tier. The asynchronous tier may temporarily store the recommended candidate entities as a job. In particular embodiments, the asynchronous tier may send the job to the dialog engine 235 at the determined actual time for execution. In alternative embodiments, the asynchronous tier may execute the job by sending it to other surfaces (e.g., other notification services associated with the social-networking system 160). In particular embodiments, the dialog engine 235 may identify the dialog intent, state, and history associated with the user. Based on the dialog intent, the dialog engine 235 may select some candidate entities among the recommended candidate entities to send to the client system 130. In particular embodiments, the dialog state and history may indicate if the user is engaged in an ongoing conversation with the assistant xbot 215. If the user is engaged in an ongoing conversation and the priority of the task of recommendation is low, the dialog engine 235 may communicate with the proactive scheduler to reschedule a time to send the selected candidate entities to the client system 130. If the user is engaged in an ongoing conversation and the priority of the task of recommendation is high, the dialog engine 235 may initiate a new dialog session with the user in which the selected candidate entities may be presented. As a result, the interruption of the ongoing conversation may be prevented. When it is determined that sending the selected candidate entities is not interruptive to the user, the dialog engine 235 may send the selected candidate entities to the CU composer 270 to generate a personalized and context-aware communication content comprising the selected candidate entities, subject to the user's privacy settings. In particular embodiments, the CU composer 270 may send the communication content to the assistant xbot 215 which may then send it to the client system 130 via the messaging platform 205 or the TTS module 275. More information on proactively assisting users may be found in U.S. patent application Ser. No. 15/967,193, filed 30 Apr. 2018, and U.S. patent application Ser. No. 16/036,827, filed 16 Jul. 2018, each of which is incorporated by reference.
In particular embodiments, the assistant xbot 215 may communicate with a proactive agent 285 in response to a user input. As an example and not by way of limitation, the user may ask the assistant xbot 215 to set up a reminder. The assistant xbot 215 may request a proactive agent 285 to set up such reminder and the proactive agent 285 may proactively execute the task of reminding the user at a later time.
In particular embodiments, the assistant system 140 may comprise a summarizer 290. The summarizer 290 may provide customized news feed summaries to a user. In particular embodiments, the summarizer 290 may comprise a plurality of meta agents. The plurality of meta agents may use the first-party agents 250, third-party agents 255, or proactive agents 285 to generated news feed summaries. In particular embodiments, the summarizer 290 may retrieve user interests and preferences from the proactive inference layer 280. The summarizer 290 may then retrieve entities associated with the user interests and preferences from the entity resolution module 240. The summarizer 290 may further retrieve a user profile from the user context engine 225. Based on the information from the proactive inference layer 280, the entity resolution module 240, and the user context engine 225, the summarizer 290 may generate personalized and context-aware summaries for the user. In particular embodiments, the summarizer 290 may send the summaries to the CU composer 270. The CU composer 270 may process the summaries and send the processing results to the assistant xbot 215. The assistant xbot 215 may then send the processed summaries to the client system 130 via the messaging platform 205 or the TTS module 275. More information on summarization may be found in U.S. patent application Ser. No. 15/967,290, filed 30 Apr. 2018, which is incorporated by reference.
In particular embodiments, the request manger 305 may send the generated content objects to the NLU module 220. The NLU module 220 may perform a plurality of steps to process the content objects. At step 221, the NLU module 220 may generate a whitelist for the content objects. In particular embodiments, the whitelist may comprise interpretation data matching the user request. At step 222, the NLU module 220 may perform a featurization based on the whitelist. At step 223, the NLU module 220 may perform domain classification/selection on user request based on the features resulted from the featurization to classify the user request into predefined domains. The domain classification/selection results may be further processed based on two related procedures. At step 224a, the NLU module 220 may process the domain classification/selection result using an intent classifier. The intent classifier may determine the user'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 224b, the NLU module may process the domain classification/selection result using a meta-intent classifier. The meta-intent classifier may determine categories that describe the user'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 225a, the NLU module 220 may use a slot tagger to annotate one or more slots associated with the user request. In particular embodiments, the slot tagger may annotate the one or more slots for the n-grams of the user request. At step 225b, the NLU module 220 may use a meta slot tagger to annotate one or more slots for the classification result from the meta-intent classifier. In particular embodiments, the meta slot tagger may tag generic slots such as references to items (e.g., the first), the type of slot, the value of the slot, etc. As an example and not by way of limitation, a user request may comprise “change 500 dollars in my account to Japanese yen.” The intent classifier may take the user request as input and formulate it into a vector. The intent classifier may then calculate probabilities of the user request being associated with different predefined intents based on a vector comparison between the vector representing the user request and the vectors representing different predefined intents. In a similar manner, the slot tagger may take the user request as input and formulate each word into a vector. The intent classifier may then calculate probabilities of each word being associated with different predefined slots based on a vector comparison between the vector representing the word and the vectors representing different predefined slots. The intent of the user may be classified as “changing money”. The slots of the user request may comprise “500”, “dollars”, “account”, and “Japanese yen”. The meta-intent of the user may be classified as “financial service”. The meta slot may comprise “finance”.
In particular embodiments, the NLU module 220 may improve the domain classification/selection of the content objects by extracting semantic information from the semantic information aggregator 230. In particular embodiments, the semantic information aggregator 230 may aggregate semantic information in the following way. The semantic information aggregator 230 may first retrieve information from the user context engine 225. In particular embodiments, the user context engine 225 may comprise offline aggregators 226 and an online inference service 227. The offline aggregators 226 may process a plurality of data associated with the user that are collected from a prior time window. As an example and not by way of limitation, the data may include news feed posts/comments, interactions with news feed posts/comments, search history, etc. that are collected from a prior 90-day window. The processing result may be stored in the user context engine 225 as part of the user profile. The online inference service 227 may analyze the conversational data associated with the user that are received by the assistant system 140 at a current time. The analysis result may be stored in the user context engine 225 also as part of the user profile. In particular embodiments, both the offline aggregators 226 and online inference service 227 may extract personalization features from the plurality of data. The extracted personalization features may be used by other modules of the assistant system 140 to better understand user input. In particular embodiments, the semantic information aggregator 230 may then process the retrieved information, i.e., a user profile, from the user context engine 225 in the following steps. At step 231, the semantic information aggregator 230 may process the retrieved information from the user context engine 225 based on natural-language processing (NLP). In particular embodiments, the semantic information aggregator 230 may tokenize text by text normalization, extract syntax features from text, and extract semantic features from text based on NLP. The semantic information aggregator 230 may additionally extract features from contextual information, which is accessed from dialog history between a user and the assistant system 140. The semantic information aggregator 230 may further conduct global word embedding, domain-specific embedding, and/or dynamic embedding based on the contextual information. At step 232, the processing result may be annotated with entities by an entity tagger. Based on the annotations, the semantic information aggregator 230 may generate dictionaries for the retrieved information at step 233. In particular embodiments, the dictionaries may comprise global dictionary features which can be updated dynamically offline. At step 234, the semantic information aggregator 230 may rank the entities tagged by the entity tagger. In particular embodiments, the semantic information aggregator 230 may communicate with different graphs 330 including social graph, knowledge graph, and concept graph to extract ontology data that is relevant to the retrieved information from the user context engine 225. In particular embodiments, the semantic information aggregator 230 may aggregate the user profile, the ranked entities, and the information from the graphs 330. The semantic information aggregator 230 may then send the aggregated information to the NLU module 220 to facilitate the domain classification/selection.
In particular embodiments, the output of the NLU module 220 may be sent to a co-reference module 315 to interpret references of the content objects associated with the user request. In particular embodiments, the co-reference module 315 may be used to identify an item to which the user request refers. The co-reference module 315 may comprise reference creation 316 and reference resolution 317. In particular embodiments, the reference creation 316 may create references for entities determined by the NLU module 220. The reference resolution 317 may resolve these references accurately. As an example and not by way of limitation, a user request may comprise “find me the nearest gas station and direct me there”. The co-reference module 315 may interpret “there” as “the nearest gas station”. In particular embodiments, the co-reference module 315 may access the user context engine 225 and the dialog engine 235 when necessary to interpret references with improved accuracy.
In particular embodiments, the identified domains, intents, meta-intents, slots, and meta slots, along with the resolved references may be sent to the entity resolution module 240 to resolve relevant entities. The entity resolution module 240 may execute generic and domain-specific entity resolution. In particular embodiments, the entity resolution module 240 may comprise domain entity resolution 241 and generic entity resolution 242. The domain entity resolution 241 may resolve the entities by categorizing the slots and meta slots into different domains. In particular embodiments, entities may be resolved based on the ontology data extracted from the graphs 330. The ontology data may comprise the structural relationship between different slots/meta-slots and domains. The ontology may also comprise information of how the slots/meta-slots may be grouped, related within a hierarchy where the higher level comprises the domain, and subdivided according to similarities and differences. The generic entity resolution 242 may resolve the entities by categorizing the slots and meta slots into different generic topics. In particular embodiments, the resolving may be also based on the ontology data extracted from the graphs 330. The ontology data may comprise the structural relationship between different slots/meta-slots and generic topics. The ontology may also comprise information of how the slots/meta-slots may be grouped, related within a hierarchy where the higher level comprises the topic, and subdivided according to similarities and differences. As an example and not by way of limitation, in response to the input of an inquiry of the advantages of a brand of electric car, the generic entity resolution 242 may resolve the brand of electric car as vehicle and the domain entity resolution 241 may resolve the brand of electric car as electric car.
In particular embodiments, the output of the entity resolution module 240 may be sent to the dialog engine 235 to forward the flow of the conversation with the user. The dialog engine 235 may comprise dialog intent resolution 236 and dialog state update/ranker 237. In particular embodiments, the dialog intent resolution 236 may resolve the user intent associated with the current dialog session based on dialog history between the user and the assistant system 140. The dialog intent resolution 236 may map intents determined by the NLU module 220 to different dialog intents. The dialog intent resolution 236 may further rank dialog intents based on signals from the NLU module 220, the entity resolution module 240, and dialog history between the user and the assistant system 140. In particular embodiments, the dialog state update/ranker 237 may update/rank the dialog state of the current dialog session. As an example and not by way of limitation, the dialog state update/ranker 237 may update the dialog state as “completed” if the dialog session is over. As another example and not by way of limitation, the dialog state update/ranker 237 may rank the dialog state based on a priority associated with it.
In particular embodiments, the dialog engine 235 may communicate with a task completion module 335 about the dialog intent and associated content objects. In particular embodiments, the task completion module 335 may rank different dialog hypotheses for different dialog intents. The task completion module 335 may comprise an action selection component 336. In particular embodiments, the dialog engine 235 may additionally check against dialog policies 320 regarding the dialog state. In particular embodiments, a dialog policy 320 may comprise a data structure that describes an execution plan of an action by an agent 340. An agent 340 may select among registered content providers to complete the action. The data structure may be constructed by the dialog engine 235 based on an intent and one or more slots associated with the intent. A dialog policy 320 may further comprise multiple goals related to each other through logical operators. In particular embodiments, a goal may be an outcome of a portion of the dialog policy and it may be constructed by the dialog engine 235. A goal may be represented by an identifier (e.g., string) with one or more named arguments, which parameterize the goal. As an example and not by way of limitation, a goal with its associated goal argument may be represented as {confirm_artist, args:{artist: “Madonna” }}. In particular embodiments, a dialog policy may be based on a tree-structured representation, in which goals are mapped to leaves of the tree. In particular embodiments, the dialog engine 235 may execute a dialog policy 320 to determine the next action to carry out. The dialog policies 320 may comprise generic policy 321 and domain specific policies 322, both of which may guide how to select the next system action based on the dialog state. In particular embodiments, the task completion module 335 may communicate with dialog policies 320 to obtain the guidance of the next system action. In particular embodiments, the action selection component 336 may therefore select an action based on the dialog intent, the associated content objects, and the guidance from dialog policies 320.
In particular embodiments, the output of the task completion module 335 may be sent to the CU composer 270. In alternative embodiments, the selected action may require one or more agents 340 to be involved. As a result, the task completion module 335 may inform the agents 340 about the selected action. Meanwhile, the dialog engine 235 may receive an instruction to update the dialog state. As an example and not by way of limitation, the update may comprise awaiting agents' response. In particular embodiments, the CU composer 270 may generate a communication content for the user using the NLG 271 based on the output of the task completion module 335. In particular embodiments, the NLG 271 may use different language models and/or language templates to generate natural language outputs. The generation of natural language outputs may be application specific. The generation of natural language outputs may be also personalized for each user. The CU composer 270 may also determine a modality of the generated communication content using the UI payload generator 272. Since the generated communication content may be considered as a response to the user request, the CU composer 270 may additionally rank the generated communication content using a response ranker 273. As an example and not by way of limitation, the ranking may indicate the priority of the response.
In particular embodiments, the output of the CU composer 270 may be sent to a response manager 325. The response manager 325 may perform different tasks including storing/updating the dialog state 326 retrieved from data store 310 and generating responses 327. In particular embodiments, the output of CU composer 270 may comprise one or more of natural-language strings, speech, or actions with parameters. As a result, the response manager 325 may determine what tasks to perform based on the output of CU composer 270. In particular embodiments, the generated response and the communication content may be sent to the assistant xbot 215. In alternative embodiments, the output of the CU composer 270 may be additionally sent to the TTS module 275 if the determined modality of the communication content is audio. The speech generated by the TTS module 275 and the response generated by the response manager 325 may be then sent to the assistant xbot 215.
Multiple Wake Words for Assistant Systems
In particular embodiments, the client system 130 may access a particular assistant system 140 out of a plurality of multiple assistant systems 140 installed on the client system 130 by using a corresponding “wake word” for the particular assistant system 140. Each assistant system 140 may have its own function set (i.e., set of tasks it can execute). There may be different assistant systems 140 available for a user to interact with for various experiences (e.g., third-party smart assistants), each assistant system 140 with access to different function sets (e.g., querying for general information, social-networking, purchasing items, etc.). To better accommodate user needs, a client system 130 of the user may include multiple assistant systems 140 (e.g. pre-installed or added by the user). The inclusion of multiple assistant systems 140 may provide the users with more functions associated with each assistant system 140. As an example and not by way of limitation, a client system 130 may include a social-networking assistant (e.g., M-Assistant/Portal), which may include a function set that comprises knowledge queries, social-networking, and chatting (e.g., via a messenger or video-chat interface). Additionally, the client system 130 may also include a shopping assistant, which may include a function set that comprises knowledge queries and shopping. In order to differentiate between accessing one assistant system 140 over the other assistant system 140, a user may use different wake words to access the desired assistant xbot 215. For instance, a user may say “hey Portal” to access the social-networking assistant and the user may say “hey Assistant” to access the shopping assistant. Different users may have access to different assistant xbots 215. As an example and not by way of limitation, all users may be able to access the shopping assistant, but only an authenticated/primary user may be able to access the social-networking assistant. Users may become familiar with the function set of each assistant xbot 215 available on the client system 130 and be able to invoke the appropriate assistant xbot 215 by saying the wake word corresponding to the respective assistant system 140. Although this disclosure describes accessing an assistant system 140 out of a plurality of assistant systems 140 in a particular manner, this disclosure contemplates accessing an assistant system 140 out of a plurality of assistant systems 140 in any suitable manner.
In particular embodiments, the client system 130 may receive a user input from a user. The user may interact with the client system 130 to use the assistant system 140. In particular embodiments, the user input may be at least one of a text input, an audio input, or a visual input. As an example and not by way of limitation, the user may type text into an interface to use an assistant system 140. As another example and not by way of limitation, the user may speak to the client system 130 to interact with the assistant system 140. In particular embodiments, the client system 130 may receive the user input at a user input analysis module. In particular embodiments, the assistant systems 140 may be installed on the client system 130. As an example and not by way of limitation, the software to run the assistant system 140 may all be installed onto the client system 130. In particular embodiments, the assistant systems 140 may be accessible to the client system 130. As an example and not by way of limitation, the assistant system 140 may be run by a server and the client system 130 may have an assistant application 136 installed to interact with the assistant system 140. Although this disclosure describes receiving a user input in a particular manner, this disclosure contemplates receiving a user input in any suitable manner.
In particular embodiments, the client system 130 may parse the user input to identify an n-gram associated with a wake word. In particular embodiments, there may be a plurality of wake words that correspond to a plurality of assistant systems 140 that are associated with the client system 130. As an example and not by way of limitation, a wake word may be used to “wake” or prepare an assistant system 140 to receive further user input, such as a request for information. Each wake word may be associated with one assistant system 140. In particular embodiments, each assistant system 140 may provide a particular set of functions. As an example and not by way of limitation, one assistant system 140 may comprise shopping functions and another assistant system 140 may comprise social-networking functions. To access the respective functions, the user may wake the respective assistant system 140. In particular embodiments, the client system 130 may compare the parsed user input to identify any n-grams that match a wake word from a plurality of wake words. As an example and not by way of limitation, the client system 130 may use a list of wake words to compare any identified n-gram to the wake words. In particular embodiments, the user may specify customized wake words for a plurality of assistant systems 140. As an example and not by way of limitation, the user may select “Portal” to be a wake word for a social-networking assistant system 140. To specify wake words, the user may go through a set up process to set particular wake words to access particular assistant systems 140. In particular embodiments, during a set up process or specifying wake words, a voice profile may be generated for the user. In particular embodiments, there may be general wake words that apply to particular assistant systems 140 and they may come with default functions that anyone who accesses the assistant system 140 may use. As an example and not by way of limitation, if a general wake word for a shopping assistant is “Assistant” and a default function is a fact search, then any user may say “Hey Assistant” to wake the shopping assistant and use the fact search function, such as “What is the biggest city in California?” In particular embodiments, a primary user may set the amount of functions available for other users. As an example and not by way of limitation, the primary user may have access to all functions in relation to assistant systems 140 installed on the client system 130 and set certain functions available to other users who use the client system 130. In particular embodiments, the amount of functions available to a user may correspond to a user profile of the user associated with a particular assistant system 140. As an example and not by way of limitation, for a user who has linked to a particular shopping assistant system 140 previously, the user may use his particular wake word on any client system 130 with the particular shopping assistant system 140 installed to access a function set that is available to the user. In particular embodiments, the function set may be based on the user and client system 130 combination that is being used to access the particular assistant system 140. In particular embodiments, the use of a particular wake word may provide access to a subset of functions out of a set of functions available to an assistant system 140. In particular embodiments, the combination of the user and wake word determines the function set available to the user. As an example and not by way of limitation, if a user “Jim” is given access to all available functions for a shopping assistant with the wake word “Alex,” then another user “Tom” may be given only a subset of all available functions with the same wake word “Alex”. In particular embodiments, one set of wake words may correspond to one assistant system 140 and another set of wake words may correspond to a different assistant system 140. Although this disclosure describes parsing the user input to identify an n-gram in a particular manner, this disclosure contemplates parsing the user input to identify an n-gram in any suitable manner.
In particular embodiments, the client system 130 may determine that a wake word corresponds to a particular assistant system 140. After identifying a wake word from the received user input, the client system 130 may determine which assistant system 140 the wake word corresponds to. As an example and not by way of limitation, the client system 130 may match the identified wake word to a list of wake words that correspond to any assistant system 140 that is accessible by the client system 130. For instance, if “Alex”, “Portal”, or “Assistant” all correspond to an assistant system 140 (one or more assistant systems) accessible by the client system 130, then a user may use any of the wake words “Alex”, “Portal”, or “Assistant” to access their respective assistant system 140. However, continuing the example, if a user says “Hey Helper” then the assistant system 140 may not respond to the user input since no wake word is received. In another example and not by way of limitation, if “Portal” corresponds to a shopping assistant system 140 and “Assistant” correspond to a social-networking assistant system 140, then the user may use “Portal” to access the shopping assistant system 140 or “Assistant” to access the social-networking assistant system 140. In particular embodiments, each of the various assistant systems 140 that are associated with the client system 130 may have their own set of functions that are available to them. Although this disclosure describes determining that a wake word corresponds to a particular assistant system in a particular manner, this disclosure contemplates determining that a wake word corresponds to a particular assistant system in any suitable manner.
In particular embodiments, the client system 140 may send a request to set an assistant system 140 into a listening mode. In particular embodiments, the listening mode is indicative of the client system 130 to receive further input to send to the assistant system 140 through an assistant xbot 215 of the assistant system 140. In particular embodiments, the listening mode sets the assistant system 140 to wait for another input from the client system 130. In particular embodiments, the request may comprise the user input for the assistant system 140 to analyze. In particular embodiments, the client system 130 sends the request to the assistant xbot 215 of the assistant system 140 that is determined to correspond to the wake word identified from the user input. In particular embodiments, the request may be to set the assistant xbot 215 of the assistant system 140 into a listening mode. Although this disclosure describes sending a request in a particular manner, this disclosure contemplates sending a request in any suitable manner.
In particular embodiments, the client system 130 may receive an indication that the assistant system 140 is in a listening mode. In particular embodiments, the client system 130 may receive an indication that the assistant xbot 215 is set into a listening mode to receive further inputs from the user. In particular embodiments, the further inputs may comprise a request, a query, and the like. In particular embodiments, once the assistant system 140 receives the request to set the assistant xbot 215 into a listening mode, the assistant system 140 may determine whether the user has permission to access the assistant system 140. In particular embodiments, the assistant system 140 may determine a voiceprint of the user based on the received user input from the client system 130 and compare the voiceprint to the voiceprints stored on the assistant system 140. In particular embodiments, the voiceprints are stored from when the user sets up the wake word to correspond to the respective assistant system 140. More information on user authentication with voiceprints may be found in U.S. patent application Ser. No. 15/386,282, filed 21 Dec. 2018. In particular embodiments, the determination that the user has permission to access the assistant system 140 may comprise one or more of accessing a plurality of voiceprints that correspond to a plurality of users of the assistant system 140, comparing the received user input to the plurality of voiceprints to identify the user identifier (ID) of the user, and determining the user has permission based on a comparison the user ID to a list of authorized user IDs associated with the assistant system 140. In particular embodiments, the user may be provided access to a subset of the functions available on the assistant system 140 based on the user ID. In particular embodiments, if a user is not authorized to access the assistant system 140, the assistant system 140 may send instructions to the client system 130 to notify the user is not authorized to use the assistant system 140. As an example and not by way of limitation, the client system 130 may state “You're not an authorized user.” As another example and not by way of limitation, the client system 130 may present to the user that an error occurred. In particular embodiments, the assistant system 140 may send instructions to the client system 130 to present an indication that the assistant system 140 is in a listening mode. As an example and not by way of limitation, the client system 130 may present a circle to indicate one or more of the client system 130, assistant xbot 215, or the assistant system 140 is in a listening mode. Although this disclosure describes receiving an indication that the assistant system 140 is in a listening mode in a particular manner, this disclosure contemplates receiving an indication that the assistant system 140 is in a listening mode in any suitable manner.
In particular embodiments, the client system 130 may receive a user request from the user while the assistant xbot 215 of the assistant system 140 is in listening mode. In particular embodiments, the user request may comprise a query, a command, or the like. In particular embodiments, the client system 130 may send the user request to the assistant system 140 to process. In particular embodiments, the user request may correspond to a function of the set of functions available to the user. In particular embodiments, if the user request is a function not available to the user and/or not available on the assistant system 140, the assistant system 140 may send instructions to the client system 130 to notify the user of an unavailable request for the user. In particular embodiments, an unavailable request may turn off the listening mode. In particular embodiments, the assistant system 140 may respond to an unavailable request and proceed to wait for further user input from the user. In particular embodiments, the assistant system 140 may wait a predetermined time period prior to turning off the listening mode. In particular embodiments, the assistant system 140 may process the user request as described above and send the results of the user request to the client system 130 to present to the user. Although this disclosure describes receiving a user request in a particular manner, this disclosure contemplates receiving a user request in any suitable manner.
In particular embodiments, the client system 130 may include a user input analysis module 402. The client system 130 may receive a user input from the user. In particular embodiments, the user input may be one or more of a text input, an audio input, and a visual input. The user input analysis module 402 may receive the user input and parse the user input to identify any n-grams in the user input. After identifying one or more n-grams, the client system 130 may determine whether any wake words have been received from the user. In particular embodiments, the user input analysis module 402 may compare identified n-grams to a list of wake words that correspond to assistant systems 140a-140b that are associated with the client system 130. In particular embodiments, each of the wake words of the list of wake words may correspond to one of the assistant systems 140a-140b. As an example and not by way of limitation, a wake word “Portal” may correspond to assistant system 140a and a wake word “Assistant” may correspond to assistant system 140b. After the client system 130 identifies a wake word from the list of wake words, the client system 130 may determine which assistant system 140a-140b that the wake word corresponds to. In particular embodiments, the client system 130 may subsequently send a request to set the determined assistant system 140a or 140b into a listening mode. The listening mode may be a state in which the assistant system 140a-140b is prepared to receive further user input.
In particular embodiments, a client system 130 may include two assistant applications 136a-136b. In particular embodiments, the assistant applications 136a-136b may correspond to the respective assistant systems 140a-140b. A user at a client system 130 may use either assistant applications 136a-136b to interact with the corresponding assistant system 140a or 140b. In particular embodiments, either assistant application 136a-136b may comprise a stand-alone application. In particular embodiments, the assistant applications 136a-136b may be integrated into a suitable application (e.g., a messaging application). In particular embodiments, the assistant applications 136a-136b may be also integrated into the client system 130, an assistant hardware device, or any other suitable hardware devices. In particular embodiments, the assistant applications 136a-136b may be accessed via a web browser (not shown). In particular embodiments, the user may provide input via different modalities. As an example and not by way of limitation, the modalities may include audio, text, image, video, etc. The assistant applications 136a-136b may communicate the user input to their respective assistant systems 140a-140b. Based on the user input, the assistant systems 140a-140b may generate responses. The assistant systems 140a-140b may send the generated responses to the respective assistant applications 136a-136b. The assistant applications 136a-136b 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 136a about the traffic information (i.e., via an audio modality). The assistant application 136a may then communicate the request to the assistant system 140a. The assistant system 140a may accordingly generate the result and send it back to the assistant application 136a. The assistant application 136a may further present the result to the user in text.
In particular embodiments, the voiceprint identifier module 515 may authenticate a user and identify who the user associated with the user input is based on a comparison of a voiceprint associated with the use input and a database of user voiceprints. More information related to voice authentication 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 speech recognition module 520 may compare the user input 505 to a database of voiceprints to identify a user. In particular embodiments, the speech recognition module 520 may generate and send a user confidence 530 to the assistant xbot 215. In particular embodiments, the user confidence 530 may be indicative of the probability that the user matches a voiceprint of a user. In particular embodiments, the ASR module 210 may generate a text user input 535 from the received user input 505 and send the user input 535 to the assistant xbot 215.
In particular embodiments, the assistant xbot 215 may determine who is identified as a user based on the received user confidence 525, 530. In particular embodiments, the assistant xbot 215 may generate a request 540 to determine whether an identified user is authorized to use the assistant system 140. In particular embodiments, the assistant xbot 215 may generate a request 540 that includes the user confidences 525, 530 and user input 535 and send the request to the user verification module 545. In particular embodiments, the modules 510, 515 may be communicatively coupled to the user verification module 545. In particular embodiments, the user verification module 545 may confirm an identified user based on the received user confidences 525, 530. In particular embodiments, the user verification module 545 may determine whether an identified user is authorized to use the assistant system 140. As an example and not by way of limitation, the user verification module 545 may use a list of authorized users and compare a user identifier associated with the identified user to compare to user identifiers on the list of authorized users. In particular embodiments, the user verification module 545 may determine the user is authorized to use the assistant system 140 and send a response 550 to the assistant xbot 215 to pass to the client system 130 through TTS module 275. The response 550 may include an indication that the user is authorized to use the assistant system 140 and the assistant xbot 215 may set the assistant system 140 into a listening mode based on the response 550. In particular embodiments, the assistant xbot 215 may send a user output 555 indicative of the result of the determination to the TTS module 275. In particular embodiments, the TTS module 275 may generate a user output 560 based on the received user output 555 and send the user output 560 to the client system 130. As an example and not by way of limitation, the user output 555 may be an indication that the assistant system 140 is set in a listening mode and the user output 560 may be instructions to present the user output 555, such as “Portal is ready to receive further input” or a visual indication that the assistant system 140 is set into a listening mode. In particular embodiments, the client system 130 may present the user output 560 to the user. In particular embodiments, the user verification module 545 may determine the user is not authorized to use the assistant system 140 and send a response 550 indicative of a failed authorization. In particular embodiments, the assistant xbot 215 may generate a user output 555 to notify the user that he or she is not authorized to use the assistant system 140, which is passed to TTS module 275 and subsequently passed to the client system 130 as user output 560. In particular embodiments, the assistant xbot 215 may have no response when there is no determination that the user is not an authorized user of the assistant system 140. In particular embodiments, the client system 130 may provide an indication of a failed authorization. As an example and not by way of limitation, the client system 130 may state there is an error that occurred. As another example and not by way of limitation, the client system 130 may initially present a visual change in response to the user input 505 to indicate reception of the user input 505 and return to a default screen after the user authorization has failed to indicate nothing has happened.
Social Graphs
In particular embodiments, a user node 802 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 802 corresponding to the user, and store the user node 802 in one or more data stores. Users and user nodes 802 described herein may, where appropriate, refer to registered users and user nodes 802 associated with registered users. In addition or as an alternative, users and user nodes 802 described herein may, where appropriate, refer to users that have not registered with the social-networking system 160. In particular embodiments, a user node 802 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 802 may be associated with one or more data objects corresponding to information associated with a user. In particular embodiments, a user node 802 may correspond to one or more web interfaces.
In particular embodiments, a concept node 804 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 804 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 804 may be associated with one or more data objects corresponding to information associated with concept node 804. In particular embodiments, a concept node 804 may correspond to one or more web interfaces.
In particular embodiments, a node in the social graph 800 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 804. 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 802 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 804 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 804.
In particular embodiments, a concept node 804 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'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 802 corresponding to the user and a concept node 804 corresponding to the third-party web interface or resource and store edge 806 in one or more data stores.
In particular embodiments, a pair of nodes in the social graph 800 may be connected to each other by one or more edges 806. An edge 806 connecting a pair of nodes may represent a relationship between the pair of nodes. In particular embodiments, an edge 806 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 806 connecting the first user's user node 802 to the second user's user node 802 in the social graph 800 and store edge 806 as social-graph information in one or more of data stores 164. In the example of
In particular embodiments, an edge 806 between a user node 802 and a concept node 804 may represent a particular action or activity performed by a user associated with user node 802 toward a concept associated with a concept node 804. As an example and not by way of limitation, as illustrated in
In particular embodiments, the social-networking system 160 may create an edge 806 between a user node 802 and a concept node 804 in the social graph 800. 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's client system 130) may indicate that he or she likes the concept represented by the concept node 804 by clicking or selecting a “Like” icon, which may cause the user's client system 130 to send to the social-networking system 160 a message indicating the user'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 806 between user node 802 associated with the user and concept node 804, as illustrated by “like” edge 806 between the user and concept node 804. In particular embodiments, the social-networking system 160 may store an edge 806 in one or more data stores. In particular embodiments, an edge 806 may be automatically formed by the social-networking system 160 in response to a particular user action. As an example and not by way of limitation, if a first user uploads a picture, watches a movie, or listens to a song, an edge 806 may be formed between user node 802 corresponding to the first user and concept nodes 804 corresponding to those concepts. Although this disclosure describes forming particular edges 806 in particular manners, this disclosure contemplates forming any suitable edges 806 in any suitable manner.
Vector Spaces and Embeddings
and
in the vector space 900, respectively, by applying a function
defined by a dictionary, such that
=
(t1) and
=
(t2). As another example and not by way of limitation, a dictionary trained to map text to a vector representation may be utilized, or such a dictionary may be itself generated via training. As another example and not by way of limitation, a model, such as Word2vec, may be used to map an n-gram to a vector representation in the vector space 900. In particular embodiments, an n-gram may be mapped to a vector representation in the vector space 900 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 900 as a vector referred to as a feature vector or an object embedding. As an example and not by way of limitation, objects e1 and e2 may be mapped to vectors and
in the vector space 900, respectively, by applying a function
, such that
=
(e1) and
=
(e2). 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 e 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 900. 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
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 900. As an example and not by way of limitation, vector 910 and vector 920 may correspond to objects that are more similar to one another than the objects corresponding to vector 910 and vector 930, 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
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 1020 may comprise the output of one or more nodes of the input layer 1010. As another example and not by way of limitation, the input to each node of the output layer 1050 may comprise the output of one or more nodes of the hidden layer 1060. 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
the hyperbolic tangent function
the rectifier Fk(sk)=max (0, sk), or any other suitable function Fk(sk), where sk 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 1025 between the node 1005 and the node 1015 may have a weighting coefficient of 0.4, which may indicate that 0.4 multiplied by the output of the node 1005 is used as an input to the node 1015. As another example and not by way of limitation, the output yk of node k may be yk=Fk(sk), where Fk may be the activation function corresponding to node k, sk=Σj (wjkxj) may be the effective input to node k, xj may be the output of a node j connected to node k, and wjk 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 1000 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 804 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 800. A privacy setting may be specified for one or more edges 806 or edge-types of the social graph 800, or with respect to one or more nodes 802, 804 or node-types of the social graph 800. The privacy settings applied to a particular edge 806 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 804 connected to a user node 802 of the first user by an edge 806. The first user may specify privacy settings that apply to a particular edge 806 connecting to the concept node 804 of the object, or may specify privacy settings that apply to all edges 806 connecting to the concept node 804. 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'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'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'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's status updates are public, but any images shared by the first user are visible only to the first user'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'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'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'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's default privacy settings may specify that the social-networking system 160 or assistant system 140 may use location information provided from a client device 130 of the first user to provide the location-based services, but that the social-networking system 160 or assistant system 140 may not store the location information of the first user or provide it to any third-party system 170. The first user may then update the privacy settings to allow location information to be used by a third-party image-sharing application in order to geo-tag photos.
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'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'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's privacy setting may specify that such voice recording may be used only for a limited purpose (e.g., authentication, tagging the user in photos), and further specify that such voice recording may not be shared with any third-party system 170 or used by other processes or applications associated with the social-networking system 160.
Systems and Methods
This disclosure contemplates any suitable number of computer systems 1100. This disclosure contemplates computer system 1100 taking any suitable physical form. As example and not by way of limitation, computer system 1100 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 1100 may include one or more computer systems 1100; 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 1100 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 1100 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 1100 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 1100 includes a processor 1102, memory 1104, storage 1106, an input/output (I/O) interface 1108, a communication interface 1110, and a bus 1112. 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 1102 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 1102 may retrieve (or fetch) the instructions from an internal register, an internal cache, memory 1104, or storage 1106; decode and execute them; and then write one or more results to an internal register, an internal cache, memory 1104, or storage 1106. In particular embodiments, processor 1102 may include one or more internal caches for data, instructions, or addresses. This disclosure contemplates processor 1102 including any suitable number of any suitable internal caches, where appropriate. As an example and not by way of limitation, processor 1102 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 1104 or storage 1106, and the instruction caches may speed up retrieval of those instructions by processor 1102. Data in the data caches may be copies of data in memory 1104 or storage 1106 for instructions executing at processor 1102 to operate on; the results of previous instructions executed at processor 1102 for access by subsequent instructions executing at processor 1102 or for writing to memory 1104 or storage 1106; or other suitable data. The data caches may speed up read or write operations by processor 1102. The TLBs may speed up virtual-address translation for processor 1102. In particular embodiments, processor 1102 may include one or more internal registers for data, instructions, or addresses. This disclosure contemplates processor 1102 including any suitable number of any suitable internal registers, where appropriate. Where appropriate, processor 1102 may include one or more arithmetic logic units (ALUs); be a multi-core processor; or include one or more processors 1102. Although this disclosure describes and illustrates a particular processor, this disclosure contemplates any suitable processor.
In particular embodiments, memory 1104 includes main memory for storing instructions for processor 1102 to execute or data for processor 1102 to operate on. As an example and not by way of limitation, computer system 1100 may load instructions from storage 1106 or another source (such as, for example, another computer system 1100) to memory 1104. Processor 1102 may then load the instructions from memory 1104 to an internal register or internal cache. To execute the instructions, processor 1102 may retrieve the instructions from the internal register or internal cache and decode them. During or after execution of the instructions, processor 1102 may write one or more results (which may be intermediate or final results) to the internal register or internal cache. Processor 1102 may then write one or more of those results to memory 1104. In particular embodiments, processor 1102 executes only instructions in one or more internal registers or internal caches or in memory 1104 (as opposed to storage 1106 or elsewhere) and operates only on data in one or more internal registers or internal caches or in memory 1104 (as opposed to storage 1106 or elsewhere). One or more memory buses (which may each include an address bus and a data bus) may couple processor 1102 to memory 1104. Bus 1112 may include one or more memory buses, as described below. In particular embodiments, one or more memory management units (MMUs) reside between processor 1102 and memory 1104 and facilitate accesses to memory 1104 requested by processor 1102. In particular embodiments, memory 1104 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 1104 may include one or more memories 1104, where appropriate. Although this disclosure describes and illustrates particular memory, this disclosure contemplates any suitable memory.
In particular embodiments, storage 1106 includes mass storage for data or instructions. As an example and not by way of limitation, storage 1106 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 1106 may include removable or non-removable (or fixed) media, where appropriate. Storage 1106 may be internal or external to computer system 1100, where appropriate. In particular embodiments, storage 1106 is non-volatile, solid-state memory. In particular embodiments, storage 1106 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 1106 taking any suitable physical form. Storage 1106 may include one or more storage control units facilitating communication between processor 1102 and storage 1106, where appropriate. Where appropriate, storage 1106 may include one or more storages 1106. Although this disclosure describes and illustrates particular storage, this disclosure contemplates any suitable storage.
In particular embodiments, I/O interface 1108 includes hardware, software, or both, providing one or more interfaces for communication between computer system 1100 and one or more I/O devices. Computer system 1100 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 1100. 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 1108 for them. Where appropriate, I/O interface 1108 may include one or more device or software drivers enabling processor 1102 to drive one or more of these I/O devices. I/O interface 1108 may include one or more I/O interfaces 1108, 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 1110 includes hardware, software, or both providing one or more interfaces for communication (such as, for example, packet-based communication) between computer system 1100 and one or more other computer systems 1100 or one or more networks. As an example and not by way of limitation, communication interface 1110 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 1110 for it. As an example and not by way of limitation, computer system 1100 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 1100 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 1100 may include any suitable communication interface 1110 for any of these networks, where appropriate. Communication interface 1110 may include one or more communication interfaces 1110, where appropriate. Although this disclosure describes and illustrates a particular communication interface, this disclosure contemplates any suitable communication interface.
In particular embodiments, bus 1112 includes hardware, software, or both coupling components of computer system 1100 to each other. As an example and not by way of limitation, bus 1112 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 1112 may include one or more buses 1112, 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.
This application is a continuation under 35 U.S.C. § 120 of U.S. patent application Ser. No. 16/183,650, filed 7 Nov. 2018, which claims the benefit, under 35 U.S.C. § 119(e), of U.S. Provisional Patent Application No. 62/660,876, filed 20 Apr. 2018, which is incorporated herein by reference.
| Number | Name | Date | Kind |
|---|---|---|---|
| 1544305 | Fisher | Jun 1925 | A |
| 5721827 | Logan et al. | Feb 1998 | A |
| 5872850 | Klein et al. | Feb 1999 | A |
| 6026424 | Circenis | Feb 2000 | A |
| 6115458 | Taskett | Sep 2000 | A |
| 6233575 | Agrawal et al. | May 2001 | B1 |
| 6243761 | Mogul et al. | Jun 2001 | B1 |
| 6484136 | Kanevsky et al. | Nov 2002 | B1 |
| 6510451 | Wu et al. | Jan 2003 | B2 |
| 6578025 | Pollack et al. | Jun 2003 | B1 |
| 6665640 | Bennett et al. | Dec 2003 | B1 |
| 6901364 | Nguyen et al. | May 2005 | B2 |
| 6968333 | Abbott et al. | Nov 2005 | B2 |
| 6990513 | Belfiore et al. | Jan 2006 | B2 |
| 7069215 | Bangalore et al. | Jun 2006 | B1 |
| 7080004 | Wang et al. | Jul 2006 | B2 |
| 7124123 | Roskind et al. | Oct 2006 | B1 |
| 7158678 | Nagel et al. | Jan 2007 | B2 |
| 7397912 | Aasman et al. | Jul 2008 | B2 |
| 7406408 | Lackey et al. | Jul 2008 | B1 |
| 7426537 | Lee et al. | Sep 2008 | B2 |
| 7467087 | Gillick et al. | Dec 2008 | B1 |
| 7624007 | Bennett | Nov 2009 | B2 |
| 7702508 | Bennett | Apr 2010 | B2 |
| 8019748 | Wu et al. | Sep 2011 | B1 |
| 8027451 | Arendsen et al. | Sep 2011 | B2 |
| 8112275 | Kennewick et al. | Feb 2012 | B2 |
| 8190627 | Platt et al. | May 2012 | B2 |
| 8195468 | Weider et al. | Jun 2012 | B2 |
| 8370162 | Faisman et al. | Feb 2013 | B2 |
| 8396282 | Huber et al. | Mar 2013 | B1 |
| 8478581 | Chen | Jul 2013 | B2 |
| 8504349 | Manu et al. | Aug 2013 | B2 |
| 8560564 | Hoelzle et al. | Oct 2013 | B1 |
| 8619767 | Ohashi | Dec 2013 | B2 |
| 8677377 | Cheyer et al. | Mar 2014 | B2 |
| 8817951 | Goffin | Aug 2014 | B2 |
| 8868592 | Weininger et al. | Oct 2014 | B1 |
| 8935192 | Ventilla et al. | Jan 2015 | B1 |
| 8938394 | Faaborg et al. | Jan 2015 | B1 |
| 8949250 | Garg et al. | Feb 2015 | B1 |
| 8983383 | Haskin | Mar 2015 | B1 |
| 8995981 | Aginsky et al. | Mar 2015 | B1 |
| 9026145 | Duleba et al. | May 2015 | B1 |
| 9098575 | Ilyas et al. | Aug 2015 | B2 |
| 9154739 | Nicolaou et al. | Oct 2015 | B1 |
| 9171341 | Trandal et al. | Oct 2015 | B1 |
| 9177291 | Martinazzi et al. | Nov 2015 | B2 |
| 9195436 | Mytkowicz et al. | Nov 2015 | B2 |
| 9251471 | Pinckney et al. | Feb 2016 | B2 |
| 9299059 | Marra et al. | Mar 2016 | B1 |
| 9304736 | Whiteley et al. | Apr 2016 | B1 |
| 9338242 | Suchland et al. | May 2016 | B1 |
| 9338493 | Van Os et al. | May 2016 | B2 |
| 9344338 | Quillen et al. | May 2016 | B2 |
| 9354709 | Heller et al. | May 2016 | B1 |
| 9367806 | Cosic | Jun 2016 | B1 |
| 9390724 | List | Jul 2016 | B2 |
| 9406103 | Gray et al. | Aug 2016 | B1 |
| 9418658 | David et al. | Aug 2016 | B1 |
| 9443527 | Watanabe | Sep 2016 | B1 |
| 9472206 | Ady | Oct 2016 | B2 |
| 9479931 | Ortiz, Jr. et al. | Oct 2016 | B2 |
| 9508341 | Parlikar et al. | Nov 2016 | B1 |
| 9536522 | Hall et al. | Jan 2017 | B1 |
| 9576574 | Van Os | Feb 2017 | B2 |
| 9639608 | Freeman | May 2017 | B2 |
| 9659577 | Langhammer | May 2017 | B1 |
| 9660950 | Archibong et al. | May 2017 | B2 |
| 9686577 | Tseng et al. | Jun 2017 | B2 |
| 9720955 | Cao et al. | Aug 2017 | B1 |
| 9747895 | Jansche et al. | Aug 2017 | B1 |
| 9767309 | Patel et al. | Sep 2017 | B1 |
| 9785717 | DeLuca | Oct 2017 | B1 |
| 9792281 | Sarikaya | Oct 2017 | B2 |
| 9824321 | Raghunathan et al. | Nov 2017 | B2 |
| 9858925 | Gruber et al. | Jan 2018 | B2 |
| 9865260 | Vuskovic et al. | Jan 2018 | B1 |
| 9875233 | Tomkins et al. | Jan 2018 | B1 |
| 9875741 | Gelfenbeyn et al. | Jan 2018 | B2 |
| 9881077 | Alfonseca et al. | Jan 2018 | B1 |
| 9886953 | Lemay et al. | Feb 2018 | B2 |
| 9916753 | Aginsky et al. | Mar 2018 | B2 |
| 9959328 | Jain et al. | May 2018 | B2 |
| 9986394 | Taylor et al. | May 2018 | B1 |
| 9990591 | Gelfenbeyn et al. | Jun 2018 | B2 |
| 10042032 | Scott et al. | Aug 2018 | B2 |
| 10088908 | Poupyrev et al. | Oct 2018 | B1 |
| 10108707 | Chu et al. | Oct 2018 | B1 |
| 10109273 | Rajasekaram et al. | Oct 2018 | B1 |
| 10127220 | Bellegarda et al. | Nov 2018 | B2 |
| 10127227 | Badr et al. | Nov 2018 | B1 |
| 10133613 | Surti et al. | Nov 2018 | B2 |
| 10134388 | Lilly | Nov 2018 | B1 |
| 10134395 | Typrin | Nov 2018 | B2 |
| 10162886 | Wang et al. | Dec 2018 | B2 |
| 10199051 | Binder et al. | Feb 2019 | B2 |
| 10220303 | Schmidt et al. | Mar 2019 | B1 |
| 10229680 | Gillespie et al. | Mar 2019 | B1 |
| 10241752 | Lemay et al. | Mar 2019 | B2 |
| 10255365 | Campbell et al. | Apr 2019 | B2 |
| 10262062 | Chang et al. | Apr 2019 | B2 |
| 10276170 | Gruber et al. | Apr 2019 | B2 |
| 10336856 | Stache et al. | Jul 2019 | B2 |
| 10348658 | Rodriguez et al. | Jul 2019 | B2 |
| 10354307 | Ye et al. | Jul 2019 | B2 |
| 10387464 | Weston et al. | Aug 2019 | B2 |
| 10409818 | Hayes et al. | Sep 2019 | B1 |
| 10412026 | Sherrets et al. | Sep 2019 | B2 |
| 10418032 | Mohajer et al. | Sep 2019 | B1 |
| 10462422 | Harrison et al. | Oct 2019 | B1 |
| 10467282 | Shorman et al. | Nov 2019 | B2 |
| 10482182 | Jankowski, Jr. | Nov 2019 | B1 |
| 10504513 | Gray et al. | Dec 2019 | B1 |
| 10511808 | Harrison et al. | Dec 2019 | B2 |
| 10515625 | Metallinou et al. | Dec 2019 | B1 |
| 10574613 | Leiba et al. | Feb 2020 | B2 |
| 10579688 | Green | Mar 2020 | B2 |
| 10600406 | Shapiro et al. | Mar 2020 | B1 |
| 10649985 | Cornell, Jr. et al. | May 2020 | B1 |
| 10679008 | Dubey et al. | Jun 2020 | B2 |
| 10719786 | Treseler et al. | Jul 2020 | B1 |
| 10761866 | Liu et al. | Sep 2020 | B2 |
| 10762903 | Kahan et al. | Sep 2020 | B1 |
| 10782986 | Martin | Sep 2020 | B2 |
| 10810256 | Goldberg et al. | Oct 2020 | B1 |
| 10839098 | Borup et al. | Nov 2020 | B2 |
| 10841249 | Lim et al. | Nov 2020 | B2 |
| 10854206 | Liu et al. | Dec 2020 | B1 |
| 10855485 | Zhou et al. | Dec 2020 | B1 |
| 10867256 | Bugay et al. | Dec 2020 | B2 |
| 10878337 | Katsuki et al. | Dec 2020 | B2 |
| 10896295 | Shenoy | Jan 2021 | B1 |
| 10949616 | Shenoy et al. | Mar 2021 | B1 |
| 10957329 | Liu | Mar 2021 | B1 |
| 10958599 | Penov et al. | Mar 2021 | B1 |
| 10963273 | Peng et al. | Mar 2021 | B2 |
| 10977258 | Liu et al. | Apr 2021 | B1 |
| 10977711 | Verma et al. | Apr 2021 | B1 |
| 10978056 | Challa et al. | Apr 2021 | B1 |
| 11009961 | Moscarillo | May 2021 | B2 |
| 11038974 | Koukoumidis et al. | Jun 2021 | B1 |
| 11042554 | Balakrishnan et al. | Jun 2021 | B1 |
| 11068659 | Cutting et al. | Jul 2021 | B2 |
| 11086858 | Koukoumidis et al. | Aug 2021 | B1 |
| 11087756 | Presant et al. | Aug 2021 | B1 |
| 11120158 | Hockey et al. | Sep 2021 | B2 |
| 11159767 | Kamisetty et al. | Oct 2021 | B1 |
| 11245646 | Koukoumidis | Feb 2022 | B1 |
| 11308169 | Koukoumidis et al. | Apr 2022 | B1 |
| 11334165 | Clements | May 2022 | B1 |
| 11436300 | Felt et al. | Sep 2022 | B1 |
| 11443358 | Chow | Sep 2022 | B2 |
| 11727677 | Liu et al. | Aug 2023 | B2 |
| 20010036297 | Ikegami et al. | Nov 2001 | A1 |
| 20020015480 | Daswani et al. | Feb 2002 | A1 |
| 20020111917 | Hoffman et al. | Aug 2002 | A1 |
| 20020141621 | Lane | Oct 2002 | A1 |
| 20020152067 | Viikki et al. | Oct 2002 | A1 |
| 20020165912 | Wenocur et al. | Nov 2002 | A1 |
| 20030005174 | Coffman et al. | Jan 2003 | A1 |
| 20030046083 | Devinney, Jr. et al. | Mar 2003 | A1 |
| 20030126330 | Balasuriya | Jul 2003 | A1 |
| 20030182125 | Phillips et al. | Sep 2003 | A1 |
| 20030220095 | Engelhart | Nov 2003 | A1 |
| 20040019487 | Kleindienst et al. | Jan 2004 | A1 |
| 20040019489 | Funk | Jan 2004 | A1 |
| 20040044516 | Kennewick | Mar 2004 | A1 |
| 20040075690 | Cirne | Apr 2004 | A1 |
| 20040085162 | Agarwal et al. | May 2004 | A1 |
| 20040098253 | Balentine et al. | May 2004 | A1 |
| 20040186819 | Baker | Sep 2004 | A1 |
| 20040230572 | Omoigui | Nov 2004 | A1 |
| 20040236580 | Bennett | Nov 2004 | A1 |
| 20050004907 | Bruno et al. | Jan 2005 | A1 |
| 20050010393 | Danieli et al. | Jan 2005 | A1 |
| 20050135595 | Bushey et al. | Jun 2005 | A1 |
| 20050144284 | Ludwig et al. | Jun 2005 | A1 |
| 20050149327 | Roth et al. | Jul 2005 | A1 |
| 20060047617 | Bacioiu et al. | Mar 2006 | A1 |
| 20060294546 | Ro et al. | Dec 2006 | A1 |
| 20070028264 | Lowe | Feb 2007 | A1 |
| 20070073678 | Scott et al. | Mar 2007 | A1 |
| 20070073745 | Scott et al. | Mar 2007 | A1 |
| 20070124147 | Gopinath et al. | May 2007 | A1 |
| 20070124263 | Katariya et al. | May 2007 | A1 |
| 20070136058 | Jeong et al. | Jun 2007 | A1 |
| 20070185712 | Jeong et al. | Aug 2007 | A1 |
| 20070239432 | Soong et al. | Oct 2007 | A1 |
| 20070239454 | Paek et al. | Oct 2007 | A1 |
| 20070270126 | Forbes et al. | Nov 2007 | A1 |
| 20070300224 | Aggarwal et al. | Dec 2007 | A1 |
| 20080015864 | Ross et al. | Jan 2008 | A1 |
| 20080028036 | Slawson et al. | Jan 2008 | A1 |
| 20080095187 | Jung et al. | Apr 2008 | A1 |
| 20080107255 | Geva et al. | May 2008 | A1 |
| 20080178126 | Beeck et al. | Jul 2008 | A1 |
| 20080189269 | Olsen | Aug 2008 | A1 |
| 20080209577 | Vrielink et al. | Aug 2008 | A1 |
| 20080222142 | O'Donnell | Sep 2008 | A1 |
| 20080240379 | Maislos et al. | Oct 2008 | A1 |
| 20080243885 | Harger et al. | Oct 2008 | A1 |
| 20080300884 | Smith | Dec 2008 | A1 |
| 20080313162 | Bahrami et al. | Dec 2008 | A1 |
| 20090007127 | Roberts et al. | Jan 2009 | A1 |
| 20090070113 | Gupta et al. | Mar 2009 | A1 |
| 20090119581 | Velusamy | May 2009 | A1 |
| 20090150153 | Li et al. | Jun 2009 | A1 |
| 20090150322 | Bower et al. | Jun 2009 | A1 |
| 20090178011 | Ording et al. | Jul 2009 | A1 |
| 20090191521 | Paul et al. | Jul 2009 | A1 |
| 20090228439 | Manolescu et al. | Sep 2009 | A1 |
| 20090265299 | Hadad et al. | Oct 2009 | A1 |
| 20090282033 | Alshawi | Nov 2009 | A1 |
| 20090300525 | Jolliff | Dec 2009 | A1 |
| 20090326945 | Tian | Dec 2009 | A1 |
| 20100031143 | Rao et al. | Feb 2010 | A1 |
| 20100064345 | Bentley et al. | Mar 2010 | A1 |
| 20100125456 | Weng et al. | May 2010 | A1 |
| 20100138215 | Williams | Jun 2010 | A1 |
| 20100138224 | Bedingfield, Sr. | Jun 2010 | A1 |
| 20100199320 | Ramanathan et al. | Aug 2010 | A1 |
| 20100217794 | Strandell et al. | Aug 2010 | A1 |
| 20100217837 | Ansari et al. | Aug 2010 | A1 |
| 20100241431 | Weng et al. | Sep 2010 | A1 |
| 20100241639 | Kifer et al. | Sep 2010 | A1 |
| 20100299329 | Emanuel et al. | Nov 2010 | A1 |
| 20100306191 | LeBeau et al. | Dec 2010 | A1 |
| 20100306249 | Hill et al. | Dec 2010 | A1 |
| 20110010675 | Hamilton | Jan 2011 | A1 |
| 20110040751 | Chandrasekar et al. | Feb 2011 | A1 |
| 20110078615 | Bier | Mar 2011 | A1 |
| 20110093264 | Gopalakrishnan | Apr 2011 | A1 |
| 20110093820 | Zhang et al. | Apr 2011 | A1 |
| 20110119216 | Wigdor | May 2011 | A1 |
| 20110137918 | Yasrebi et al. | Jun 2011 | A1 |
| 20110153324 | Ballinger et al. | Jun 2011 | A1 |
| 20110153423 | Elvekrog et al. | Jun 2011 | A1 |
| 20110153863 | Khan et al. | Jun 2011 | A1 |
| 20110161348 | Oron | Jun 2011 | A1 |
| 20110169726 | Holmdahl et al. | Jul 2011 | A1 |
| 20110184718 | Chen | Jul 2011 | A1 |
| 20110184768 | Norton et al. | Jul 2011 | A1 |
| 20110238754 | Dasilva et al. | Sep 2011 | A1 |
| 20110246383 | Gibson et al. | Oct 2011 | A1 |
| 20110264522 | Chan et al. | Oct 2011 | A1 |
| 20120016678 | Gruber et al. | Jan 2012 | A1 |
| 20120019446 | Wu et al. | Jan 2012 | A1 |
| 20120022872 | Gruber et al. | Jan 2012 | A1 |
| 20120023120 | Kanefsky | Jan 2012 | A1 |
| 20120030301 | Herold et al. | Feb 2012 | A1 |
| 20120041907 | Wang et al. | Feb 2012 | A1 |
| 20120053945 | Gupta et al. | Mar 2012 | A1 |
| 20120069131 | Abelow | Mar 2012 | A1 |
| 20120078889 | Chu-Carroll et al. | Mar 2012 | A1 |
| 20120078891 | Brown et al. | Mar 2012 | A1 |
| 20120081303 | Cassar et al. | Apr 2012 | A1 |
| 20120101806 | Davis et al. | Apr 2012 | A1 |
| 20120101865 | Zhakov | Apr 2012 | A1 |
| 20120105257 | Murillo et al. | May 2012 | A1 |
| 20120109858 | Makadia et al. | May 2012 | A1 |
| 20120117051 | Liu et al. | May 2012 | A1 |
| 20120159507 | Kwon et al. | Jun 2012 | A1 |
| 20120159635 | He et al. | Jun 2012 | A1 |
| 20120163224 | Long | Jun 2012 | A1 |
| 20120179453 | Ghani et al. | Jul 2012 | A1 |
| 20120179481 | Patel et al. | Jul 2012 | A1 |
| 20120179633 | Ghani et al. | Jul 2012 | A1 |
| 20120179694 | Sciacca et al. | Jul 2012 | A1 |
| 20120190386 | Anderson | Jul 2012 | A1 |
| 20120205436 | Thomas et al. | Aug 2012 | A1 |
| 20120232885 | Barbosa et al. | Sep 2012 | A1 |
| 20120233188 | Majumdar | Sep 2012 | A1 |
| 20120245944 | Gruber et al. | Sep 2012 | A1 |
| 20120246191 | Xiong | Sep 2012 | A1 |
| 20120254188 | Koperski et al. | Oct 2012 | A1 |
| 20120265528 | Gruber et al. | Oct 2012 | A1 |
| 20120265787 | Hsu et al. | Oct 2012 | A1 |
| 20120278164 | Spivack et al. | Nov 2012 | A1 |
| 20120290526 | Gupta et al. | Nov 2012 | A1 |
| 20120290950 | Rapaport et al. | Nov 2012 | A1 |
| 20120294477 | Yang et al. | Nov 2012 | A1 |
| 20120297294 | Scott et al. | Nov 2012 | A1 |
| 20120303356 | Boyle et al. | Nov 2012 | A1 |
| 20120311035 | Guha et al. | Dec 2012 | A1 |
| 20120311126 | Jadallah et al. | Dec 2012 | A1 |
| 20130035930 | Ferrucci et al. | Feb 2013 | A1 |
| 20130046544 | Kay et al. | Feb 2013 | A1 |
| 20130054631 | Govani et al. | Feb 2013 | A1 |
| 20130073387 | Heath | Mar 2013 | A1 |
| 20130073389 | Heath | Mar 2013 | A1 |
| 20130073400 | Heath | Mar 2013 | A1 |
| 20130073473 | Heath | Mar 2013 | A1 |
| 20130096910 | Stan et al. | Apr 2013 | A1 |
| 20130117832 | Gandhi et al. | May 2013 | A1 |
| 20130124538 | Lee et al. | May 2013 | A1 |
| 20130191250 | Bradley et al. | Jul 2013 | A1 |
| 20130191416 | Lee et al. | Jul 2013 | A1 |
| 20130191790 | Kawalkar | Jul 2013 | A1 |
| 20130194193 | Kawalkar | Aug 2013 | A1 |
| 20130204813 | Master et al. | Aug 2013 | A1 |
| 20130204894 | Faith et al. | Aug 2013 | A1 |
| 20130226892 | Ehsani et al. | Aug 2013 | A1 |
| 20130234945 | Goktekin | Sep 2013 | A1 |
| 20130238332 | Chen | Sep 2013 | A1 |
| 20130246521 | Schacht et al. | Sep 2013 | A1 |
| 20130254139 | Lei | Sep 2013 | A1 |
| 20130262752 | Talagala et al. | Oct 2013 | A1 |
| 20130265218 | Moscarillo | Oct 2013 | A1 |
| 20130268624 | Yagiura | Oct 2013 | A1 |
| 20130268839 | Lefebvre et al. | Oct 2013 | A1 |
| 20130275138 | Gruber et al. | Oct 2013 | A1 |
| 20130275164 | Gruber et al. | Oct 2013 | A1 |
| 20130275441 | Agrawal et al. | Oct 2013 | A1 |
| 20130277564 | Teshima et al. | Oct 2013 | A1 |
| 20130282811 | Lessin et al. | Oct 2013 | A1 |
| 20130289999 | Hymel | Oct 2013 | A1 |
| 20130290205 | Bonmassar et al. | Oct 2013 | A1 |
| 20130296057 | Gagner et al. | Nov 2013 | A1 |
| 20130317858 | Hasan et al. | Nov 2013 | A1 |
| 20130346077 | Mengibar et al. | Dec 2013 | A1 |
| 20130346877 | Borovoy et al. | Dec 2013 | A1 |
| 20140012926 | Narayanan et al. | Jan 2014 | A1 |
| 20140019459 | Gradin et al. | Jan 2014 | A1 |
| 20140025702 | Curtiss et al. | Jan 2014 | A1 |
| 20140032659 | Marini et al. | Jan 2014 | A1 |
| 20140039895 | Aravamudan et al. | Feb 2014 | A1 |
| 20140040748 | Lemay et al. | Feb 2014 | A1 |
| 20140074483 | Van Os | Mar 2014 | A1 |
| 20140074934 | Van Hoff et al. | Mar 2014 | A1 |
| 20140094307 | Doolittle et al. | Apr 2014 | A1 |
| 20140104177 | Ouyang et al. | Apr 2014 | A1 |
| 20140108307 | Raghunathan et al. | Apr 2014 | A1 |
| 20140108453 | Venkataraman et al. | Apr 2014 | A1 |
| 20140108562 | Panzer | Apr 2014 | A1 |
| 20140108989 | Bi et al. | Apr 2014 | A1 |
| 20140115410 | Kealy et al. | Apr 2014 | A1 |
| 20140122622 | Castera et al. | May 2014 | A1 |
| 20140129266 | Perl et al. | May 2014 | A1 |
| 20140143879 | Milman et al. | May 2014 | A1 |
| 20140164506 | Tesch et al. | Jun 2014 | A1 |
| 20140195371 | Kageyama et al. | Jul 2014 | A1 |
| 20140207860 | Wang et al. | Jul 2014 | A1 |
| 20140214410 | Jang et al. | Jul 2014 | A1 |
| 20140222415 | Legat | Aug 2014 | A1 |
| 20140222422 | Sarikaya et al. | Aug 2014 | A1 |
| 20140225931 | Plagemann et al. | Aug 2014 | A1 |
| 20140236591 | Yue et al. | Aug 2014 | A1 |
| 20140244712 | Walters et al. | Aug 2014 | A1 |
| 20140255887 | Xu et al. | Sep 2014 | A1 |
| 20140258191 | Gubin et al. | Sep 2014 | A1 |
| 20140278786 | Liu-Qiu-Yan | Sep 2014 | A1 |
| 20140280001 | Stein | Sep 2014 | A1 |
| 20140280017 | Indarapu et al. | Sep 2014 | A1 |
| 20140282153 | Christiansen et al. | Sep 2014 | A1 |
| 20140282276 | Drucker et al. | Sep 2014 | A1 |
| 20140282956 | Kennedy et al. | Sep 2014 | A1 |
| 20140297284 | Gruber | Oct 2014 | A1 |
| 20140310470 | Rash et al. | Oct 2014 | A1 |
| 20140310614 | Jones | Oct 2014 | A1 |
| 20140310788 | Ricci | Oct 2014 | A1 |
| 20140314283 | Harding | Oct 2014 | A1 |
| 20140330832 | Viau | Nov 2014 | A1 |
| 20140354539 | Skogöet al. | Dec 2014 | A1 |
| 20140358539 | Rao et al. | Dec 2014 | A1 |
| 20140358843 | Subramaniam et al. | Dec 2014 | A1 |
| 20140358890 | Chen et al. | Dec 2014 | A1 |
| 20140365216 | Gruber et al. | Dec 2014 | A1 |
| 20140365919 | Shaw et al. | Dec 2014 | A1 |
| 20140379340 | Timem et al. | Dec 2014 | A1 |
| 20150006286 | Liu et al. | Jan 2015 | A1 |
| 20150012345 | Bhagat et al. | Jan 2015 | A1 |
| 20150012524 | Heymans et al. | Jan 2015 | A1 |
| 20150019227 | Anandarajah | Jan 2015 | A1 |
| 20150032504 | Elango et al. | Jan 2015 | A1 |
| 20150046841 | Sharon et al. | Feb 2015 | A1 |
| 20150058720 | Smadja et al. | Feb 2015 | A1 |
| 20150067502 | Yang et al. | Mar 2015 | A1 |
| 20150073907 | Purves et al. | Mar 2015 | A1 |
| 20150078613 | Forutanpour et al. | Mar 2015 | A1 |
| 20150079554 | Lee et al. | Mar 2015 | A1 |
| 20150081321 | Jain | Mar 2015 | A1 |
| 20150081674 | Ali et al. | Mar 2015 | A1 |
| 20150082229 | Ouyang et al. | Mar 2015 | A1 |
| 20150088593 | Raghunathan et al. | Mar 2015 | A1 |
| 20150088665 | Karlsson | Mar 2015 | A1 |
| 20150095033 | Boies et al. | Apr 2015 | A1 |
| 20150100524 | Pantel et al. | Apr 2015 | A1 |
| 20150142420 | Sarikaya et al. | May 2015 | A1 |
| 20150142447 | Kennewick et al. | May 2015 | A1 |
| 20150142704 | London | May 2015 | A1 |
| 20150149182 | Kalns et al. | May 2015 | A1 |
| 20150154001 | Knox et al. | Jun 2015 | A1 |
| 20150169284 | Quast | Jun 2015 | A1 |
| 20150169744 | Walkingshaw et al. | Jun 2015 | A1 |
| 20150170050 | Price | Jun 2015 | A1 |
| 20150179168 | Hakkani-Tur et al. | Jun 2015 | A1 |
| 20150185827 | Sayed | Jul 2015 | A1 |
| 20150186156 | Brown et al. | Jul 2015 | A1 |
| 20150199333 | Nekhay | Jul 2015 | A1 |
| 20150199715 | Caron et al. | Jul 2015 | A1 |
| 20150207765 | Brantingham et al. | Jul 2015 | A1 |
| 20150220777 | Kauffmann et al. | Aug 2015 | A1 |
| 20150220888 | Iyer | Aug 2015 | A1 |
| 20150227519 | Clark et al. | Aug 2015 | A1 |
| 20150228275 | Watanabe et al. | Aug 2015 | A1 |
| 20150242525 | Perlegos | Aug 2015 | A1 |
| 20150242787 | Bernstein et al. | Aug 2015 | A1 |
| 20150248476 | Weissinger et al. | Sep 2015 | A1 |
| 20150256636 | Spivack et al. | Sep 2015 | A1 |
| 20150278691 | Xia et al. | Oct 2015 | A1 |
| 20150278922 | Isaacson et al. | Oct 2015 | A1 |
| 20150279389 | Lebeau et al. | Oct 2015 | A1 |
| 20150286747 | Anastasakos et al. | Oct 2015 | A1 |
| 20150293597 | Mishra et al. | Oct 2015 | A1 |
| 20150293995 | Chen et al. | Oct 2015 | A1 |
| 20150317302 | Liu et al. | Nov 2015 | A1 |
| 20150331853 | Palmonari et al. | Nov 2015 | A1 |
| 20150332670 | Akbacak et al. | Nov 2015 | A1 |
| 20150332672 | Akbacak et al. | Nov 2015 | A1 |
| 20150339303 | Perlegos | Nov 2015 | A1 |
| 20150347375 | Tremblay et al. | Dec 2015 | A1 |
| 20150347572 | Yang et al. | Dec 2015 | A1 |
| 20150348543 | Zhao et al. | Dec 2015 | A1 |
| 20150348551 | Gruber et al. | Dec 2015 | A1 |
| 20150363393 | Williams et al. | Dec 2015 | A1 |
| 20150370798 | Ju et al. | Dec 2015 | A1 |
| 20150373565 | Safavi | Dec 2015 | A1 |
| 20150379426 | Steele et al. | Dec 2015 | A1 |
| 20150379568 | Balasubramanian | Dec 2015 | A1 |
| 20150379981 | Balasubramanian | Dec 2015 | A1 |
| 20160004862 | Almehmadi et al. | Jan 2016 | A1 |
| 20160019290 | Ratnaparkhi et al. | Jan 2016 | A1 |
| 20160021179 | James et al. | Jan 2016 | A1 |
| 20160026720 | Lehrer et al. | Jan 2016 | A1 |
| 20160037311 | Cho | Feb 2016 | A1 |
| 20160042069 | Lee-Goldman et al. | Feb 2016 | A1 |
| 20160042249 | Babenko et al. | Feb 2016 | A1 |
| 20160048527 | Li | Feb 2016 | A1 |
| 20160063118 | Campbell et al. | Mar 2016 | A1 |
| 20160063553 | Pesochinsky | Mar 2016 | A1 |
| 20160070449 | Christiansen et al. | Mar 2016 | A1 |
| 20160080485 | Hamedi | Mar 2016 | A1 |
| 20160092160 | Graff et al. | Mar 2016 | A1 |
| 20160092598 | Mishra | Mar 2016 | A1 |
| 20160093298 | Naik et al. | Mar 2016 | A1 |
| 20160093304 | Kim | Mar 2016 | A1 |
| 20160098482 | Moon et al. | Apr 2016 | A1 |
| 20160110381 | Chen et al. | Apr 2016 | A1 |
| 20160117360 | Kunc et al. | Apr 2016 | A1 |
| 20160156574 | Hum et al. | Jun 2016 | A1 |
| 20160156584 | Hum et al. | Jun 2016 | A1 |
| 20160163311 | Crook et al. | Jun 2016 | A1 |
| 20160170989 | Bishop et al. | Jun 2016 | A1 |
| 20160173568 | Penilla et al. | Jun 2016 | A1 |
| 20160173578 | Sharma | Jun 2016 | A1 |
| 20160188565 | Robichaud et al. | Jun 2016 | A1 |
| 20160188671 | Gupta et al. | Jun 2016 | A1 |
| 20160188727 | Waibel et al. | Jun 2016 | A1 |
| 20160188730 | Delli Santi | Jun 2016 | A1 |
| 20160196491 | Chandrasekaran et al. | Jul 2016 | A1 |
| 20160203002 | Kannan et al. | Jul 2016 | A1 |
| 20160203238 | Cherniavskii et al. | Jul 2016 | A1 |
| 20160210363 | Rambhia et al. | Jul 2016 | A1 |
| 20160210963 | Kim et al. | Jul 2016 | A1 |
| 20160217124 | Sarikaya et al. | Jul 2016 | A1 |
| 20160219078 | Porras et al. | Jul 2016 | A1 |
| 20160225370 | Kannan et al. | Aug 2016 | A1 |
| 20160239751 | Mosterman et al. | Aug 2016 | A1 |
| 20160253630 | Oliveri et al. | Sep 2016 | A1 |
| 20160255082 | Rathod | Sep 2016 | A1 |
| 20160259717 | Nychis et al. | Sep 2016 | A1 |
| 20160261545 | Bastide et al. | Sep 2016 | A1 |
| 20160269472 | Byron et al. | Sep 2016 | A1 |
| 20160283016 | Zaitsev et al. | Sep 2016 | A1 |
| 20160292582 | Kozloski et al. | Oct 2016 | A1 |
| 20160306505 | Mgneras et al. | Oct 2016 | A1 |
| 20160307571 | Mizumoto et al. | Oct 2016 | A1 |
| 20160308799 | Schubert et al. | Oct 2016 | A1 |
| 20160320853 | Lien et al. | Nov 2016 | A1 |
| 20160328096 | Tran et al. | Nov 2016 | A1 |
| 20160336006 | Levit et al. | Nov 2016 | A1 |
| 20160336008 | Menezes et al. | Nov 2016 | A1 |
| 20160364382 | Sarikaya | Dec 2016 | A1 |
| 20160371054 | Beaumont et al. | Dec 2016 | A1 |
| 20160371378 | Fan et al. | Dec 2016 | A1 |
| 20160372116 | Summerfield | Dec 2016 | A1 |
| 20160378849 | Myslinski | Dec 2016 | A1 |
| 20160378861 | Eledath et al. | Dec 2016 | A1 |
| 20160379213 | Isaacson et al. | Dec 2016 | A1 |
| 20160381220 | Kurganov | Dec 2016 | A1 |
| 20170005803 | Brownewell et al. | Jan 2017 | A1 |
| 20170019362 | Kim et al. | Jan 2017 | A1 |
| 20170025117 | Hong | Jan 2017 | A1 |
| 20170026318 | Daniel et al. | Jan 2017 | A1 |
| 20170034112 | Perlegos | Feb 2017 | A1 |
| 20170061294 | Weston et al. | Mar 2017 | A1 |
| 20170061956 | Sarikaya et al. | Mar 2017 | A1 |
| 20170063887 | Muddu et al. | Mar 2017 | A1 |
| 20170091168 | Bellegarda et al. | Mar 2017 | A1 |
| 20170091171 | Perez | Mar 2017 | A1 |
| 20170092008 | Djorgovski et al. | Mar 2017 | A1 |
| 20170092264 | Hakkani-Tur et al. | Mar 2017 | A1 |
| 20170098236 | Lee et al. | Apr 2017 | A1 |
| 20170125012 | Kanthak et al. | May 2017 | A1 |
| 20170132019 | Karashchuk et al. | May 2017 | A1 |
| 20170132688 | Freund et al. | May 2017 | A1 |
| 20170139938 | Balasubramanian et al. | May 2017 | A1 |
| 20170147676 | Jaidka et al. | May 2017 | A1 |
| 20170147696 | Evnine | May 2017 | A1 |
| 20170148073 | Nomula et al. | May 2017 | A1 |
| 20170161018 | Lemay et al. | Jun 2017 | A1 |
| 20170169013 | Sarikaya et al. | Jun 2017 | A1 |
| 20170169354 | Diamanti et al. | Jun 2017 | A1 |
| 20170177715 | Chang et al. | Jun 2017 | A1 |
| 20170178626 | Gruber et al. | Jun 2017 | A1 |
| 20170180276 | Gershony et al. | Jun 2017 | A1 |
| 20170185375 | Martel et al. | Jun 2017 | A1 |
| 20170188101 | Srinivasaraghavan | Jun 2017 | A1 |
| 20170193390 | Weston et al. | Jul 2017 | A1 |
| 20170206271 | Jain | Jul 2017 | A1 |
| 20170206405 | Molchanov et al. | Jul 2017 | A1 |
| 20170220426 | Sankar et al. | Aug 2017 | A1 |
| 20170221475 | Bruguier et al. | Aug 2017 | A1 |
| 20170228240 | Khan et al. | Aug 2017 | A1 |
| 20170228366 | Bui et al. | Aug 2017 | A1 |
| 20170235360 | George-Svahn | Aug 2017 | A1 |
| 20170235726 | Wang et al. | Aug 2017 | A1 |
| 20170235740 | Seth et al. | Aug 2017 | A1 |
| 20170235848 | Van Dusen et al. | Aug 2017 | A1 |
| 20170242847 | Li et al. | Aug 2017 | A1 |
| 20170243107 | Jolley et al. | Aug 2017 | A1 |
| 20170249059 | Houseworth | Aug 2017 | A1 |
| 20170255272 | Flagg et al. | Sep 2017 | A1 |
| 20170255536 | Weissinger et al. | Sep 2017 | A1 |
| 20170255580 | Parekh et al. | Sep 2017 | A1 |
| 20170263242 | Nagao | Sep 2017 | A1 |
| 20170270180 | State | Sep 2017 | A1 |
| 20170270919 | Parthasarathi et al. | Sep 2017 | A1 |
| 20170270929 | Aleksic et al. | Sep 2017 | A1 |
| 20170278514 | Mathias et al. | Sep 2017 | A1 |
| 20170286401 | He et al. | Oct 2017 | A1 |
| 20170287474 | Maergner et al. | Oct 2017 | A1 |
| 20170290077 | Nilsson et al. | Oct 2017 | A1 |
| 20170293834 | Raison et al. | Oct 2017 | A1 |
| 20170295114 | Goldberg | Oct 2017 | A1 |
| 20170300831 | Gelfenbeyn et al. | Oct 2017 | A1 |
| 20170308589 | Liu et al. | Oct 2017 | A1 |
| 20170316159 | Hooker | Nov 2017 | A1 |
| 20170344645 | Appel et al. | Nov 2017 | A1 |
| 20170351786 | Quattoni et al. | Dec 2017 | A1 |
| 20170351969 | Parmar et al. | Dec 2017 | A1 |
| 20170353469 | Selekman et al. | Dec 2017 | A1 |
| 20170357637 | Nell et al. | Dec 2017 | A1 |
| 20170357661 | Hornkvist et al. | Dec 2017 | A1 |
| 20170358293 | Chua et al. | Dec 2017 | A1 |
| 20170358304 | Castillo Sanchez et al. | Dec 2017 | A1 |
| 20170359707 | Diaconu et al. | Dec 2017 | A1 |
| 20170364563 | Gao et al. | Dec 2017 | A1 |
| 20170366479 | Ladha et al. | Dec 2017 | A1 |
| 20170373999 | Abou Mahmoud et al. | Dec 2017 | A1 |
| 20180013699 | Sapoznik et al. | Jan 2018 | A1 |
| 20180018562 | Jung | Jan 2018 | A1 |
| 20180018987 | Zass | Jan 2018 | A1 |
| 20180020285 | Zass | Jan 2018 | A1 |
| 20180040020 | Kurian et al. | Feb 2018 | A1 |
| 20180047091 | Ogden et al. | Feb 2018 | A1 |
| 20180052824 | Ferrydiansyah et al. | Feb 2018 | A1 |
| 20180052842 | Hewavitharana et al. | Feb 2018 | A1 |
| 20180054523 | Zhang et al. | Feb 2018 | A1 |
| 20180060029 | Kogan et al. | Mar 2018 | A1 |
| 20180060439 | Kula et al. | Mar 2018 | A1 |
| 20180061401 | Sarikaya et al. | Mar 2018 | A1 |
| 20180061419 | Melendo Casado | Mar 2018 | A1 |
| 20180062862 | Lu et al. | Mar 2018 | A1 |
| 20180067638 | Klein et al. | Mar 2018 | A1 |
| 20180068019 | Novikoff et al. | Mar 2018 | A1 |
| 20180068661 | Printz | Mar 2018 | A1 |
| 20180075844 | Kim et al. | Mar 2018 | A1 |
| 20180075847 | Lee et al. | Mar 2018 | A1 |
| 20180082191 | Pearmain et al. | Mar 2018 | A1 |
| 20180083894 | Fung et al. | Mar 2018 | A1 |
| 20180088663 | Zhang et al. | Mar 2018 | A1 |
| 20180088677 | Zhang et al. | Mar 2018 | A1 |
| 20180089164 | Iida et al. | Mar 2018 | A1 |
| 20180096071 | Green | Apr 2018 | A1 |
| 20180096072 | He et al. | Apr 2018 | A1 |
| 20180101893 | Dagan et al. | Apr 2018 | A1 |
| 20180107917 | Hewavitharana et al. | Apr 2018 | A1 |
| 20180108358 | Humphreys et al. | Apr 2018 | A1 |
| 20180115598 | Shariat et al. | Apr 2018 | A1 |
| 20180121508 | Halstvedt | May 2018 | A1 |
| 20180130194 | Kochura et al. | May 2018 | A1 |
| 20180146019 | Chen et al. | May 2018 | A1 |
| 20180150233 | Hanzawa et al. | May 2018 | A1 |
| 20180150280 | Rhee et al. | May 2018 | A1 |
| 20180150551 | Wang et al. | May 2018 | A1 |
| 20180150739 | Wu | May 2018 | A1 |
| 20180157759 | Zheng et al. | Jun 2018 | A1 |
| 20180157981 | Albertson et al. | Jun 2018 | A1 |
| 20180165723 | Wright et al. | Jun 2018 | A1 |
| 20180176614 | Lin et al. | Jun 2018 | A1 |
| 20180189628 | Kaufmann et al. | Jul 2018 | A1 |
| 20180189629 | Yatziv et al. | Jul 2018 | A1 |
| 20180196854 | Burks | Jul 2018 | A1 |
| 20180210874 | Fuxman et al. | Jul 2018 | A1 |
| 20180218428 | Xie et al. | Aug 2018 | A1 |
| 20180232435 | Papangelis et al. | Aug 2018 | A1 |
| 20180232662 | Solomon et al. | Aug 2018 | A1 |
| 20180233139 | Finkelstein et al. | Aug 2018 | A1 |
| 20180233141 | Solomon et al. | Aug 2018 | A1 |
| 20180239837 | Wang | Aug 2018 | A1 |
| 20180240014 | Strope et al. | Aug 2018 | A1 |
| 20180246953 | Oh et al. | Aug 2018 | A1 |
| 20180246983 | Rathod | Aug 2018 | A1 |
| 20180260086 | Leme et al. | Sep 2018 | A1 |
| 20180260481 | Rathod | Sep 2018 | A1 |
| 20180260680 | Finkelstein et al. | Sep 2018 | A1 |
| 20180260856 | Balasubramanian et al. | Sep 2018 | A1 |
| 20180268298 | Johansen et al. | Sep 2018 | A1 |
| 20180287968 | Koukoumidis et al. | Oct 2018 | A1 |
| 20180293221 | Finkelstein et al. | Oct 2018 | A1 |
| 20180293484 | Wang et al. | Oct 2018 | A1 |
| 20180300040 | Mate et al. | Oct 2018 | A1 |
| 20180301151 | Mont-Reynaud et al. | Oct 2018 | A1 |
| 20180309779 | Benyo et al. | Oct 2018 | A1 |
| 20180329512 | Liao et al. | Nov 2018 | A1 |
| 20180329982 | Patel et al. | Nov 2018 | A1 |
| 20180329998 | Thomson et al. | Nov 2018 | A1 |
| 20180330280 | Erenrich et al. | Nov 2018 | A1 |
| 20180330714 | Paulik et al. | Nov 2018 | A1 |
| 20180330721 | Thomson et al. | Nov 2018 | A1 |
| 20180330737 | Paulik et al. | Nov 2018 | A1 |
| 20180332118 | Phipps et al. | Nov 2018 | A1 |
| 20180341871 | Maitra et al. | Nov 2018 | A1 |
| 20180349962 | Adderly et al. | Dec 2018 | A1 |
| 20180350144 | Rathod | Dec 2018 | A1 |
| 20180350377 | Karazoun | Dec 2018 | A1 |
| 20180367483 | Rodriguez et al. | Dec 2018 | A1 |
| 20190005024 | Somech et al. | Jan 2019 | A1 |
| 20190005549 | Goldshtein et al. | Jan 2019 | A1 |
| 20190007208 | Surla et al. | Jan 2019 | A1 |
| 20190034406 | Singh et al. | Jan 2019 | A1 |
| 20190034849 | Romaine et al. | Jan 2019 | A1 |
| 20190035390 | Howard et al. | Jan 2019 | A1 |
| 20190051289 | Yoneda et al. | Feb 2019 | A1 |
| 20190065588 | Lin et al. | Feb 2019 | A1 |
| 20190073363 | Perez et al. | Mar 2019 | A1 |
| 20190080698 | Miller | Mar 2019 | A1 |
| 20190082221 | Jain et al. | Mar 2019 | A1 |
| 20190087491 | Bax | Mar 2019 | A1 |
| 20190095500 | Pandey et al. | Mar 2019 | A1 |
| 20190095785 | Sarkar et al. | Mar 2019 | A1 |
| 20190104093 | Lim et al. | Apr 2019 | A1 |
| 20190115008 | Jiang et al. | Apr 2019 | A1 |
| 20190121907 | Brunn et al. | Apr 2019 | A1 |
| 20190132265 | Nowak-Przygodzki et al. | May 2019 | A1 |
| 20190139150 | Brownhill et al. | May 2019 | A1 |
| 20190146647 | Ramchandran et al. | May 2019 | A1 |
| 20190147849 | Talwar et al. | May 2019 | A1 |
| 20190149489 | Akbulut et al. | May 2019 | A1 |
| 20190156204 | Bresch et al. | May 2019 | A1 |
| 20190156206 | Graham et al. | May 2019 | A1 |
| 20190163691 | Brunet et al. | May 2019 | A1 |
| 20190171655 | Psota et al. | Jun 2019 | A1 |
| 20190182195 | Avital et al. | Jun 2019 | A1 |
| 20190205368 | Wang et al. | Jul 2019 | A1 |
| 20190205464 | Zhao et al. | Jul 2019 | A1 |
| 20190206401 | Liu et al. | Jul 2019 | A1 |
| 20190206412 | Li et al. | Jul 2019 | A1 |
| 20190213490 | White et al. | Jul 2019 | A1 |
| 20190236167 | Hu et al. | Aug 2019 | A1 |
| 20190237068 | Canim et al. | Aug 2019 | A1 |
| 20190242608 | Laftchiev et al. | Aug 2019 | A1 |
| 20190258710 | Biyani et al. | Aug 2019 | A1 |
| 20190266185 | Rao et al. | Aug 2019 | A1 |
| 20190281001 | Miller et al. | Sep 2019 | A1 |
| 20190287526 | Ren et al. | Sep 2019 | A1 |
| 20190311036 | Shanmugam et al. | Oct 2019 | A1 |
| 20190311301 | Pyati | Oct 2019 | A1 |
| 20190311710 | Eraslan et al. | Oct 2019 | A1 |
| 20190313054 | Harrison et al. | Oct 2019 | A1 |
| 20190318729 | Chao et al. | Oct 2019 | A1 |
| 20190318735 | Chao et al. | Oct 2019 | A1 |
| 20190324527 | Presant et al. | Oct 2019 | A1 |
| 20190324553 | Liu et al. | Oct 2019 | A1 |
| 20190324780 | Zhu et al. | Oct 2019 | A1 |
| 20190325042 | Yu et al. | Oct 2019 | A1 |
| 20190325081 | Liu et al. | Oct 2019 | A1 |
| 20190325084 | Peng et al. | Oct 2019 | A1 |
| 20190325864 | Anders et al. | Oct 2019 | A1 |
| 20190327330 | Natarajan et al. | Oct 2019 | A1 |
| 20190327331 | Natarajan et al. | Oct 2019 | A1 |
| 20190332946 | Han et al. | Oct 2019 | A1 |
| 20190341021 | Shang et al. | Nov 2019 | A1 |
| 20190348033 | Chen et al. | Nov 2019 | A1 |
| 20190361408 | Tokuchi | Nov 2019 | A1 |
| 20190385051 | Wabgaonkar et al. | Dec 2019 | A1 |
| 20200012681 | McInerney et al. | Jan 2020 | A1 |
| 20200027443 | Raux | Jan 2020 | A1 |
| 20200035220 | Liu | Jan 2020 | A1 |
| 20200045119 | Weldemariam et al. | Feb 2020 | A1 |
| 20200081736 | Gopalan et al. | Mar 2020 | A1 |
| 20200104427 | Long et al. | Apr 2020 | A1 |
| 20200175990 | Fanty | Jun 2020 | A1 |
| 20200184959 | Yasa et al. | Jun 2020 | A1 |
| 20200202171 | Hughes et al. | Jun 2020 | A1 |
| 20200382449 | Taylor et al. | Dec 2020 | A1 |
| 20200388282 | Secker-Walker et al. | Dec 2020 | A1 |
| 20200410012 | Moon et al. | Dec 2020 | A1 |
| 20210011967 | Rathod | Jan 2021 | A1 |
| 20210110115 | Hermann et al. | Apr 2021 | A1 |
| 20210117214 | Presant et al. | Apr 2021 | A1 |
| 20210117479 | Liu et al. | Apr 2021 | A1 |
| 20210117623 | Aly et al. | Apr 2021 | A1 |
| 20210117624 | Aghajanyan et al. | Apr 2021 | A1 |
| 20210117681 | Poddar et al. | Apr 2021 | A1 |
| 20210117712 | Huang et al. | Apr 2021 | A1 |
| 20210117780 | Malik et al. | Apr 2021 | A1 |
| 20210118440 | Peng et al. | Apr 2021 | A1 |
| 20210118442 | Poddar et al. | Apr 2021 | A1 |
| 20210119955 | Penov et al. | Apr 2021 | A1 |
| 20210120206 | Liu et al. | Apr 2021 | A1 |
| 20210281632 | Brewer et al. | Sep 2021 | A1 |
| 20210303512 | Barday et al. | Sep 2021 | A1 |
| 20220006661 | Rathod | Jan 2022 | A1 |
| 20220038615 | Chaudhri et al. | Feb 2022 | A1 |
| 20220092131 | Koukoumidis et al. | Mar 2022 | A1 |
| 20220188361 | Botros et al. | Jun 2022 | A1 |
| 20220254338 | Gruber et al. | Aug 2022 | A1 |
| 20220382376 | Wang et al. | Dec 2022 | A1 |
| Number | Date | Country |
|---|---|---|
| 2017203668 | Jan 2018 | AU |
| 101292282 | Oct 2008 | CN |
| 101375273 | Feb 2009 | CN |
| 101465749 | Jun 2009 | CN |
| 101772792 | Jul 2010 | CN |
| 103262156 | Aug 2013 | CN |
| 103294195 | Sep 2013 | CN |
| 103608837 | Feb 2014 | CN |
| 103703466 | Apr 2014 | CN |
| 104718765 | Jun 2015 | CN |
| 104981681 | Oct 2015 | CN |
| 105830150 | Aug 2016 | CN |
| 105843801 | Aug 2016 | CN |
| 105917330 | Aug 2016 | CN |
| 106055114 | Oct 2016 | CN |
| 106462909 | Feb 2017 | CN |
| 106527709 | Mar 2017 | CN |
| 106997236 | Aug 2017 | CN |
| 107003723 | Aug 2017 | CN |
| 107148554 | Sep 2017 | CN |
| 107210033 | Sep 2017 | CN |
| 107408324 | Nov 2017 | CN |
| 107490971 | Dec 2017 | CN |
| 107633042 | Jan 2018 | CN |
| 107704559 | Feb 2018 | CN |
| 107750360 | Mar 2018 | CN |
| 107909061 | Apr 2018 | CN |
| 107924552 | Apr 2018 | CN |
| 2530870 | Dec 2012 | EP |
| 3122001 | Jan 2017 | EP |
| H1173297 | Mar 1999 | JP |
| 2015095232 | May 2015 | JP |
| 2015191561 | Nov 2015 | JP |
| 2016151736 | Aug 2016 | JP |
| 20070043673 | Apr 2007 | KR |
| 101350712 | Jan 2014 | KR |
| 20170140079 | Dec 2017 | KR |
| 2012116241 | Aug 2012 | WO |
| 2012116241 | Nov 2012 | WO |
| 2014190297 | Nov 2014 | WO |
| 2016065020 | Apr 2016 | WO |
| 2016195739 | Dec 2016 | WO |
| 2017053208 | Mar 2017 | WO |
| 2017112003 | Jun 2017 | WO |
| 2017116488 | Jul 2017 | WO |
| 2017129149 | Aug 2017 | WO |
| 2018053502 | Mar 2018 | WO |
| 2018067402 | Apr 2018 | WO |
| 2018235191 | Dec 2018 | WO |
| Entry |
|---|
| International Search Report and Written Opinion for International Application No. PCT/US2018/039268, mailed Jan. 18, 2019, 14 Pages. |
| International Search Report and Written Opinion for International Application No. PCT/US2018/042906, mailed Feb. 27, 2019, 10 Pages. |
| International Search Report and Written Opinion for International Application No. PCT/US2018/045177, mailed Jan. 16, 2019, 15 Pages. |
| International Search Report and Written Opinion for International Application No. PCT/US2018/049568, mailed Feb. 11, 2019, 25 Pages. |
| International Search Report and Written Opinion for International Application No. PCT/US2018/054322, mailed Feb. 8, 2019, 15 Pages. |
| International Search Report and Written Opinion for International Application No. PCT/US2019/028187, mailed Aug. 12, 2019, 11 Pages. |
| International Search Report and Written Opinion for International Application No. PCT/US2019/028387, mailed Aug. 21, 2019, 12 Pages. |
| Komer B., et al., “Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-Learn,” Proceedings of the 13th Python in Science Conference, 2014, vol. 13, pp. 34-40. |
| Leung K.W-T., et al., “Deriving Concept-Based User Profiles from Search Engine Logs,” IEEE Transactions on Knowledge and Data Engineering, Jul. 2010, vol. 22 (7), pp. 969-982. |
| Li T., et al., “Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads,” arXiv: 1708.07308v1, Aug. 24, 2017, pp. 1-17. |
| Liebman E., et al., “DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation,” ArXiv, 2015, pp. 1-9. |
| Light M., et al., “Personalized Multimedia Information Access,” Communications of the Association for Computing Machinery, May 2002, vol. 45 (5), pp. 54-59. |
| Mahajan D., et al., “LogUCB: An Explore-Exploit Algorithm for Comments Recommendation,” Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM '12), 2012, pp. 6-15. |
| McInerney J., et al., “Explore, Exploit, and Explain: Personalizing Explainable Recommendations with Bandits,” RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems, Sep. 2018, vol. 12, pp. 31-39. |
| Mesnil G., et al., “Using Recurrent Neural Networks for Slot Filling in Spoken Language Understanding, ” IEEE/ACM Transactions on Audio, Speech, and Language Processing, Mar. 2015, vol. 23 (3), pp. 530-539. |
| Moon T., et al., “Online Learning for Recency Search Ranking Using Real-Time User Feedback,” Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM '10), 2010, pp. 1501-1504. |
| Mun H., et al., “Accelerating Smart Speaker Service with Content Prefetching and Local Control,” In IEEE 17th Annual Consumer Communications & Networking Conference (CCNC), 2020, 6 pages. |
| Nanas N., et al., “Multi-topic Information Filtering with a Single User Profile,” Springer-Verlag Berlin Germany, SETN, LNAI 3025, 2004, pp. 400-409. |
| Ren H., et al., “Dialog State Tracking using Conditional Random Fields,” Proceedings of the SIGDIAL Conference, Association for Computational Linguistics, 2013, pp. 457-461. |
| So C.F., et al., “Ontological User Profiling and Language Modeling for Personalized Information Services,” 2009 IEEE International Conference on e-Business Engineering, Macau, China, 2009, pp. 559-564. |
| Sood A., et al., “Topic-Focused Summarization of Chat Conversations,” ECIR, LNCS 7814, Springer-Verlag, Berlin, Germany, 2013, pp. 800-803. |
| Tepper N., et al., “Collabot: Personalized Group Chat Summarization,” In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, Feb. 5, 2018, pp. 771-774. |
| U.S. Appl. No. 62/660,879, inventor Anuj; Kumar, filed on Apr. 20, 2018. |
| U.S. Appl. No. 62/923,342, inventors Hanson; Michael Robert et al., filed on Oct. 18, 2019. |
| Vanchinathan H.P., et al., “Explore-Exploit in Top-N Recommender Systems via Gaussian Processes,” Proceedings of the 8th ACM Conference on Recommender systems (RecSys '14), 2014, pp. 225-232. |
| Wang W., et al., “Rafiki: Machine Learning as an Analytics Service System,” arXiv: 1804.06087v1, Apr. 17, 2018, pp. 1-13. |
| Wang X., et al., “Exploration in Interactive Personalized Music Recommendation: A Reinforcement Learning Approach,” ACM Transactions on Multimedia Computing, Communications, and Applications, arXiv: 1311.6355v1, Oct. 2013, vol. 2 (3), pp. 1-24. |
| Bang J., et al., “Example-Based Chat-Oriented Dialogue System With Personalized Long-Term Memory,” International Conference on Big Data and Smart Computing (BIGCOMP), Jeju, South Korea, 2015, pp. 238-243. |
| Candito M.H., et al., “Can the TAG Derivation Tree Represent a Semantic Graph? An Answer in the Light of Meaning-Text Theory,” Proceedings of the Fourth International Workshop on Tree Adjoining Grammars and Related Frameworks (TAG+ 4), 1998, 04 pages. |
| Chen Y.N., et al., “Knowledge as a Teacher: Knowledge-Guided Structural Attention Networks,” arXiv preprint arXiv: 1609.03286, 2016, 12 pages. |
| Chen Y.N., et al., “Matrix Factorization with Domain Knowledge and Behavioral Patterns for Intent Modeling,” NIPS Workshop on Machine Learning for SLU and Interaction, 2015, pp. 1-7. |
| Co-pending U.S. Appl. No. 15/953,957, inventors Kemal; El Moujahid et al., filed on Apr. 16, 2018. |
| Co-pending U.S. Appl. No. 16/173,155, inventors Emmanouil; Koukoumidis et al., filed on Oct. 29, 2018. |
| Co-pending U.S. Appl. No. 16/557,055, inventors Moon; Seungwhan et al., filed on Aug. 30, 2019. |
| Co-pending U.S. Appl. No. 16/659,070, inventors Huang; Lisa Xiaoyi et al., filed on Oct. 21, 2019. |
| Co-pending U.S. Appl. No. 16/659,203, inventors Huang; Lisa Xiaoyi et al., filed on Oct. 21, 2019. |
| Co-pending U.S. Appl. No. 16/659,419, inventor Huang; Lisa Xiaoyi, filed on Oct. 21, 2019. |
| Co-pending U.S. Appl. No. 16/741,630, inventors Crook; Paul Anthony et al., filed on Jan. 13, 2020. |
| Co-pending U.S. Appl. No. 16/742,769, inventors Liu; Xiaohu et al., filed on Jan. 14, 2020. |
| Co-pending U.S. Appl. No. 16/790,497, inventors Gao; Yang et al., filed on Feb. 13, 2020. |
| Co-pending U.S. Appl. No. 16/815,960, inventors Malik; Kshitiz et al., filed on Mar. 11, 2020. |
| Co-pending U.S. Appl. No. 16/842,366, inventors Sravani; Kamisetty et al., filed on Apr. 7, 2020. |
| Co-pending U.S. Appl. No. 16/914,966, inventor Behar; Noam Yakob, filed on Jun. 29, 2020. |
| Co-pending U.S. Appl. No. 16/917,664, inventors Liu; Xiaohu et al., filed on Jun. 30, 2020. |
| Co-pending U.S. Appl. No. 16/921,665, inventors Liu; Honglei et al., filed on Jul. 6, 2020. |
| Co-pending U.S. Appl. No. 16/998,423, inventors Armen; Aghajanyan et al., filed on Aug. 20, 2020. |
| Co-pending U.S. Appl. No. 17/006,260, inventors William; Presant et al., filed on Aug. 28, 2020. |
| Co-pending U.S. Appl. No. 17/006,339, inventors Shivani; Poddar et al., filed on Aug. 28, 2020. |
| Co-pending U.S. Appl. No. 17/006,377, inventors Shivani; Poddar et al., filed on Aug. 28, 2020. |
| Co-pending U.S. Appl. No. 17/009,542, inventor Kottur; Satwik, filed on Sep. 1, 2020. |
| Co-pending U.S. Appl. No. 17/035,253, inventors Khemka; Piyush et al., filed on Sep. 28, 2020. |
| Co-pending U.S. Appl. No. 17/120,013, inventors Botros; Fadi et al., filed on Dec. 11, 2020. |
| Co-pending U.S. Appl. No. 17/136,636, inventors Greenberg; Michael et al., filed on Dec. 29, 2020. |
| Co-pending U.S. Appl. No. 17/139,363, inventors Cheng; Daniel Manhon et al., filed on Dec. 31, 2020. |
| Co-pending U.S. Appl. No. 17/186,459, inventors Liu; Bing et al., filed on Feb. 26, 2021. |
| Co-pending U.S. Appl. No. 17/336,716, inventors Chaland; Christophe et al., filed on Jun. 2, 2021. |
| Co-pending U.S. Appl. No. 17/351,501, inventors Sethi; Pooja et al., filed on Jun. 18, 2021. |
| Co-pending U.S. Appl. No. 17/391,765, inventors Pu; Yiming et al., filed on Aug. 2, 2021. |
| Co-pending U.S. Appl. No. 17/394,096, inventors Wang; Emily et al., filed on Aug. 4, 2021. |
| Co-pending U.S. Appl. No. 17/394,159, inventors Santoro; Elizabeth Kelsey et al., filed on Aug. 4, 2021. |
| Co-pending U.S. Appl. No. 17/407,922, inventors Pu; Yiming et al., filed on Aug. 20, 2021. |
| Co-pending U.S. Appl. No. 17/504,276, inventors Kottur; Satwik et al., filed on Oct. 18, 2021. |
| Co-pending U.S. Appl. No. 17/512,478, inventors Chen; Zhiyu et al., filed on Oct. 27, 2021. |
| Co-pending U.S. Appl. No. 17/512,508, inventors Vincent; Joshuah et al., filed on Oct. 27, 2021. |
| Co-Pending U.S. Appl. No. 16/150,184, inventors Francislav; P. Penov et al., filed on Oct. 2, 2018, 78 pages. |
| Co-Pending U.S. Appl. No. 16/173,155, filed Oct. 29, 2018, 79 pages. |
| Co-Pending U.S. Appl. No. 62/660,879, inventor Anuj; Kumar, filed on Apr. 20, 2018. |
| Csaky R.K., “Deep Learning Based Chatbot Models,” Budapest University of Technology and Economics, Nov. 2017, 69 pages, Retrieved from the Internet: URL: https://www.researchgate.net/publication/323587007_Deep_Learning_Based_Chatbot_Models. |
| Golovin D., et al., “Google Vizier: A Service for Black-Box Optimization,” Proceedings of the 23rd ACM International Conference on Knowledge Discovery and Data Mining, Aug. 13-17, 2017, vol. 23, pp. 1487-1495. |
| Grow your Business with Social Bots, Digital Marketing Institute, Nov. 20, 2017, 14 pages, Retrieved from the Internet: URL: https://digitalmarketinginstitute.com/blog/grow-your-business-with-social-bots. |
| Guo D.Z., et al., “Joint Semantic Utterance Classification and Slot Filling With Recursive Neural Networks,” IEEE Spoken Language Technology Workshop (SLT), 2014, pp. 554-559. |
| Hazen J.T., et al., “Pronunciation Modeling using a Finite-State Transducer Representation,” Speech Communication, vol. 46, 2005, pp. 189-203. |
| Huang Z., et al., “Bidirectional LSTM-CRF Models for Sequence Tagging,” arXiv preprint, arXiv:1508.01991, 2015, 10 pages. |
| International Search Report and Written Opinion for International Application No. PCT/US2018/033116, mailed Jan. 17, 2019, 10 Pages. |
| International Search Report and Written Opinion for International Application No. PCT/US2018/034604, mailed Jan. 18, 2019, 13 Pages. |
| International Search Report and Written Opinion for International Application No. PCT/US2018/038396, mailed Jan. 21, 2019, 13 Pages. |
| Anonymous, “Semantic Parsing,” Wikipedia, Mar. 22, 2018, 5 pages, Retrieved from the Internet: URL: https://en.wikipedia.org/w/index.php?title=Semantic_parsing&oldid=831890029, [Retrieved on May 24, 2022]. |
| Anonymous, “Semantic Role Labeling,” Wikipedia, Jan. 27, 2018, 2 pages, Retrieved from the Internet: URL: https://en.wikipedia.org/w/index.php?title=Semantic_role_labeling&oldid=822626564, [Retrieved on May 24, 2022]. |
| Armentano M.G., et al., “A Framework for Attaching Personal Assistants to Existing Applications,” Computer Languages, Systems & Structures, 2009, vol. 35(4), pp. 448-463. |
| Patel A., et al., “Cross-Lingual Phoneme Mapping for Language Robust Contextual Speech Recognition,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018, pp. 5924-5928. |
| Trottier D., “A Research Agenda for Social Media Surveillance,” Fast Capitalism, 2011, vol. 8, Issue 1, pp. 59-68. |
| Wong Y.W., et al., “Scalable Attribute-Value Extraction from Semi-Structured Text,” IEEE International Conference on Data Mining Workshops, 2009, pp. 302-307. |
| Batrinca B., et al., “Social media analytics: a survey of techniques, tools and platforms,”, AI Society [Online], Feb. 2015, vol. 30 (1), pp. 89-116, [Retrieved on Mar. 28, 2019], Retrieved from the Internet: URL: https://link.springer.com/article/10.1007/s00146-014-0549-4. |
| Kar R., et al., “Applying Chatbots to the Internet of Things: Opportunities and Architectural Elements,” arXiv: 1611.03799v1, submitted on Nov. 11, 2016, (2017), 9 pages. |
| Klopfenstein L.C., et al., “The Rise of Bots: A survey of Conversational Interfaces, Patterns, and Paradigms,” DIS 2017 Proceedings of the 2017 Conference on Designing Interactive Systems, Jun. 10, 2017, pp. 555-565. |
| U.S. Pat. No. 11,915,021, Feb. 27, 2024, Xiaohu; Liu et al., (Withdrawn). |
| Bailey K., “Conversational AI and the Road Ahead,” TechCrunch [Online], Feb. 15, 2018 [Retrieved on Jun. 14, 2019], 13 Pages, Retrieved from Internet: URL: https://techcrunch.com/2017/02/25/conversational-ai-and-the-road-ahead/. |
| Challa A., et al., “Generate, Filter, and Rank: Grammaticality Classification for Production-Ready NLG Systems,” Facebook Conversational AI, Apr. 9, 2019, 10 Pages. |
| “Chat Extensions,” [online], Apr. 18, 2017, 8 Pages, Retrieved from the Internet: URL: https://developers.facebook.com/docs/messenger-platform/guides/chat-extensions/. |
| Co-pending U.S. Appl. No. 14/593,723, inventors Colin; Patrick Treseler et al., filed Jan. 9, 2015. |
| Co-pending U.S. Appl. No. 15/808,638, inventors Ryan; Brownhill et al., filed Nov. 9, 2017. |
| Co-pending U.S. Appl. No. 15/966,455, inventor Scott; Martin, filed Apr. 30, 2018. |
| Co-pending U.S. Appl. No. 15/967,193, inventors Testuggine; Davide et al., filed Apr. 30, 2018. |
| Co-pending U.S. Appl. No. 15/967,239, inventors Vivek; Natarajan et al., filed Apr. 30, 2018. |
| Co-pending U.S. Appl. No. 15/967,279, inventors Fuchun; Peng et al., filed Apr. 30, 2018. |
| Co-pending U.S. Appl. No. 15/967,290, inventors Fuchun; Peng et al., filed Apr. 30, 2018. |
| Co-pending U.S. Appl. No. 15/967,342, inventors Vivek; Natarajan et al., filed Apr. 30, 2018. |
| Co-pending U.S. Appl. No. 16/011,062, inventors Jinsong; Yu et al., filed Jun. 18, 2018. |
| Co-pending U.S. Appl. No. 16/025,317, inventors Gupta; Sonal et al., filed Jul. 2, 2018. |
| Co-pending U.S. Appl. No. 16/036,827, inventors Emmanouil; Koukoumidis et al., filed Jul. 16, 2018. |
| Co-pending U.S. Appl. No. 16/038,120, inventors Schissel; Jason et al., filed Jul. 17, 2018. |
| Co-pending U.S. Appl. No. 16/048,049, inventor Markku; Salkola, filed Jul. 27, 2018. |
| Co-pending U.S. Appl. No. 16/048,072, inventor Salkola; Markku , filed Jul. 27, 2018. |
| Co-pending U.S. Appl. No. 16/048,101, inventor Salkola; Markku , filed Jul. 27, 2018. |
| Co-pending U.S. Appl. No. 16/053,600, inventors Vivek; Natarajan et al., filed Aug. 2, 2018. |
| Co-pending U.S. Appl. No. 16/057,414, inventors Jeremy; Gillmor Kahn et al., filed Aug. 7, 2018. |
| Co-pending U.S. Appl. No. 16/103,775, inventors Zheng; Zhou et al., filed Aug. 14, 2018. |
| Co-pending U.S. Appl. No. 16/107,601, inventor Rajesh; Krishna Shenoy, filed Aug. 21, 2018. |
| Co-pending U.S. Appl. No. 16/107,847, inventors Rajesh; Krishna Shenoy et al., filed Aug. 21, 2018. |
| Co-pending U.S. Appl. No. 16/118, 169, inventors Baiyang; Liu et al., filed Aug. 30, 2018. |
| Co-pending U.S. Appl. No. 16/121,393, inventors Zhou; Zheng et al., filed Sep. 4, 2018. |
| Co-pending U.S. Appl. No. 16/127,173, inventors Zheng; Zhou et al., filed Sep. 10, 2018. |
| Co-pending U.S. Appl. No. 16/129,638, inventors Vivek; Natarajan et al., filed Sep. 12, 2018. |
| Co-pending U.S. Appl. No. 16/150,069, inventors Jiedan; Zhu et al., filed Oct. 2, 2018. |
| Co-pending U.S. Appl. No. 16/151,040, inventors Brian; Nelson et al., filed Oct. 3, 2018. |
| Co-pending U.S. Appl. No. 16/168,536, inventors Dumoulin; Benoit F. et al., filed Oct. 23, 2018. |
| Co-pending U.S. Appl. No. 16/176,081, inventors Anusha; Balakrishnan et al., filed Oct. 31, 2018. |
| Co-pending U.S. Appl. No. 16/176,312, inventors Koukoumidis; Emmanouil et al., filed Oct. 31, 2018. |
| Co-pending U.S. Appl. No. 16/182,542, inventors Michael; Hanson et al., filed Nov. 6, 2018. |
| Co-pending U.S. Appl. No. 16/183,650, inventors Xiaohu; Liu et al., filed Nov. 7, 2018. |
| Co-pending U.S. Appl. No. 16/192,538, inventor Koukoumidis; Emmanouil, filed Nov. 15, 2018. |
| Co-pending U.S. Appl. No. 16/222,923, inventors Jason; Schissel et al., filed Dec. 17, 2018. |
| Co-pending U.S. Appl. No. 16/222,957, inventors Emmanouil; Koukoumidis et al., filed Dec. 17, 2018. |
| Co-pending U.S. Appl. No. 16/229,828, inventors Xiaohu; Liu et al., filed Dec. 21, 2018. |
| Co-pending U.S. Appl. No. 16/247,439, inventors Xiaohu; Liu et al., filed Jan. 14, 2019. |
| Co-pending U.S. Appl. No. 16/264,173, inventors Ashwini; Challa et al., filed Jan. 31, 2019. |
| Co-pending U.S. Appl. No. 16/376,832, inventors Liu; Honglei et al., filed Apr. 5, 2019. |
| Co-pending U.S. Appl. No. 16/388,130, inventors Xiaohu; Liu et al., filed Apr. 18, 2019. |
| Co-pending U.S. Appl. No. 16/389,634, inventors Crook; Paul Anthony et al., filed Apr. 19, 2019. |
| Co-pending U.S. Appl. No. 16/389,708, inventors William; Crosby Presant et al., filed Apr. 19, 2019. |
| Co-pending U.S. Appl. No. 16/389,728, inventors William; Presant et al., filed Apr. 19, 2019. |
| Co-pending U.S. Appl. No. 16/389,769, inventors Hongeli; Liu et al., filed Apr. 19, 2019. |
| Co-pending U.S. Appl. No. 16/434,010, inventors Dogaru; Sergiu et al., filed Jun. 6, 2019. |
| Co-Pending U.S. Appl. No. 15/953,957, filed Apr. 16, 2018, 117 pages. |
| Dubin R., et al., “Adaptation Logic for HTTP Dynamic Adaptive Streaming using Geo-Predictive Crowdsourcing,” Springer, Feb. 5, 2016, 11 Pages. |
| Dyer C., et al., “Recurrent Neural Network Grammars,” Proceedings of NAACL-HLT, San Diego, California, Jun. 12-17, 2016, pp. 199-209. |
| European Search Report for European Application No. 18203627.7, mailed Feb. 5, 2019, 8 pages. |
| Extended European Search Report for European Application No. 18201683.2, mailed Feb. 4, 2019, 9 pages. |
| Extended European Search Report for European Application No. 18201685.7, mailed Feb. 4, 2019, 8 pages. |
| Extended European Search Report for European Application No. 18201805.1, mailed Feb. 4, 2019, 8 Pages. |
| Extended European Search Report for European Application No. 18201808.5, mailed Feb. 4, 2019, 9 Pages. |
| Extended European Search Report for European Application No. 18201820, mailed Feb. 5, 2019, 8 pages. |
| Extended European Search Report for European Application No. 18201826.7, mailed Feb. 4, 2019, 08 pages. |
| Extended European Search Report for European Application No. 18203675.6, mailed Feb. 5, 2019, 8 pages. |
| Extended European Search Report for European Application No. 19155094.6, mailed Mar. 19, 2019, 6 Pages. |
| Germanakos P., et al., “Realizing Comprehensive User Profile as the Core Element of Adaptive and Personalized Communication Environments and Systems,” The Computer Journal, Oct. 31, 2009, vol. 52, No. 7, pp. 749-770. |
| Glass J., “A Brief Introduction to Automatic Speech Recognition,” Nov. 13, 2007, 22 Pages, Retrieved from Internet: http://www.cs.columbia.edu/˜mcollins/6864/slides/asr.pdf. |
| Google Allo Makes Conversations Eeasier, Productive, and more Expressive, May 19, 2016 [Retrieved on Jul. 11, 2019], 13 Pages, Retrieved from Internet: URL: https://www.trickyways.com/2016/05/google-allo-makes-conversations-eeasier-productive-expressive/. |
| Honglei L., et al., “Explore-Exploit: A Framework for Interactive and Online Learning,” Dec. 1, 2018, 7 pages. |
| Huang S., “Word2Vec and FastText Word Embedding with Gensim,” Towards Data Science [Online], Feb. 4, 2018 [Retrieved on Jun. 14, 2019], 11 Pages, Retrieved from Internet: URL: https://towardsdatascience.com/word-embedding-with-word2vec-and-fasttext-a209c1d3e12c. |
| Li; et al., “A Persona-based Neural Conversation Model,” arXiv preprint arXiv: 1603.06155, Jun. 8, 2016, 10 Pages. |
| McCross T., “Dialog Management,” Feb. 15, 2018 [Retrieved on Jun. 14, 2019], 12 Pages, Retrieved from Internet: https://tutorials.botsfloor.com/dialog-management-799c20a39aad. |
| Office Action mailed Mar. 4, 2024 for Chinese Application No. 201880094832.0, filed Oct. 4, 2018, 17 pages. |
| Office Action mailed Jan. 15, 2024 for Chinese Application No. 201880094857.0, filed Jun. 20, 2018, 18 pages. |
| Office Action mailed Jan. 17, 2024 for Chinese Application No. 201880094827.X, filed Jul. 19, 2018, 6 pages. |
| Office Action mailed Dec. 21, 2023 for Chinese Application No. 201880094771.8, filed Jun. 25, 2018, 23 pages. |
| Office Action mailed Dec. 26, 2023 for Chinese Application No. 201880094305.X, filed May 25, 2018, 7 pages. |
| Office Action mailed Dec. 29, 2023 for Chinese Application No. 201880094828.4, filed Aug. 3, 2018, 28 pages. |
| Ostendorf M., et al., “Human Language Technology: Opportunities and Challenges,” IEEE International Conference on Acoustics Speech and Signal Processing, Mar. 23, 2005, 5 Pages. |
| “Overview of Language Technology,” Feb. 15, 2018, 1 Page, Retrieved from Internet: URL: https://www.dfki.de/It/It-generaL.php,. |
| Poliak A., et al., “Efficient, Compositional, Order-Sensitive n-gram Embeddings,” Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Apr. 3-7, 2017, vol. 2, pp. 503-508. |
| Takahashi Y., et al., “User and Device Adaptation for Sports Video Content,” 2007 IEEE International Conference on Multimedia and Expo, Jul. 5, 2007, pp. 1051-1054. |
| Turniski F., et al., “Analysis of 3G and 4G Download Throughput in Pedestrian Zones,” Proceedings Elmar-2013, Sep. 12, 2016, pp. 9-12, XP032993723. |
| U.S. Appl. No. 62/660,876, inventors Kumar; Anuj et al., filed Apr. 20, 2018. |
| U.S. Appl. No. 62/675,090, inventors Hanson; Michael Robert et al., filed May 22, 2018. |
| U.S. Appl. No. 62/747,628, inventors Liu; Honglei et al., filed Oct. 18, 2018. |
| U.S. Appl. No. 62/749,608, inventors Challa; Ashwini et al., filed Oct. 23, 2018. |
| U.S. Appl. No. 62/750,746, inventors Liu; Honglei et al., filed Oct. 25, 2018. |
| Wen; et al., “Personalized Language Modeling by Crowd Sourcing with Social Network Data for Voice Access of Cloud Applications,” 2012 IEEE Spoken Language Technology Workshop (SLT), Dec. 2, 2012, pp. 188-193. |
| “What is Conversational AI?,” Glia Blog [Online], Sep. 13, 2017 [Retrieved on Jun. 14, 2019], 3 Pages, Retrieved from Internet: URL: https://blog.salemove.com/what-is-conversational-ai/. |
| Wikipedia, “Dialog Manager,” Dec. 16, 2006-Mar. 13, 2018 [Retrieved on Jun. 14, 2019], 8 Pages, Retrieved from Internet: https://en.wikipedia.org/wiki/Dialog_manager. |
| Wikipedia, “Natural-Language Understanding,” Nov. 11, 2018-Apr. 3, 2019 [Retrieved on Jun. 14, 2019], 5 Pages, Retrieved from Internet: URL: https://en.wikipedia.org/wiki/Natural-language_understanding. |
| Wikipedia, “Speech Synthesis,” Jan. 24, 2004-Jun. 5, 2019 [Retrieved on Jun. 14, 2019], 19 Pages, Retrieved from Internet: URL: https://en.wikipedia.org/wiki/Speech_synthesis. |
| Wikipedia, “Question Answering,” Feb. 15, 2018 [Retrieved on Jun. 14, 2019], 6 Pages, Retrieved from Internet: URL: https://en.wikipedia.org/wiki/Question_answering. |
| CNIPA—Office Action mailed May 15, 2024 for Chinese Application No. 201880094771.8, filed Jun. 25, 2018, 7 pages. |
| Sarikaya R., “The Technology Behind Personal Digital Assistants: An Overview of the System Architecture and Key Components,” IEEE Signal Processing Magazine, Jan. 1, 2017, vol. 34 (1), pp. 67-81, XP011639190. |
| Liu S., “User Intention oriented Social Image Retrieval,” Apr. 2016, 102 pages. [Cover page and Abstract only in English translation]. |
| CNIPA—Office Action mailed Jul. 25, 2024 for Chinese Application No. 201980040312.6, filed Apr. 19, 2019, 9 pages. |
| Number | Date | Country | |
|---|---|---|---|
| 20210183397 A1 | Jun 2021 | US |
| Number | Date | Country | |
|---|---|---|---|
| 62660876 | Apr 2018 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 16183650 | Nov 2018 | US |
| Child | 17182951 | US |
