Facebook, Inc.

Microsoft Technology Licensing, LLC

Meta Platforms, Inc.

Yahoo! Inc.

Benoit F Dumoulin

Rajen Subba

Xiaohu Liu

Fuchun Peng

Baiyang Liu

Shuangyu Chang

Michael Levit

Michael Robert Hanson

Wenhai Yang

Sarangarajan Parthasarathy

Emmanouil Koukoumidis

Jinsong Yu

Anuj Kumar

Carlos Garcia Jurado Suarez

Kun Han

Rushin N Shah

Heidi Young

Jeremy Gillmor Kahn

Chandrasekhar Iyer

Xing Wei

Dilek Zeynep Hakkani-Tur

Gokhan Tur

Larry Paul Heck

Antoine Roland Raux

Omer Emre Velipasaoglu

Mohsen M Agsen

Savas Parastatidis

Bo Zeng

Murat Akbacak

Stefan Nelson-Lindall

Hernan Guelman

Abhik Lahiri

Barlas Oguz

Jiedan Zhu

Ying Zhang

Elizabeth F Churchill

William Ramsey

Balmanohar Paluri

Nicholas Eric Craswell

Su-Lin Wu

Rosie Jones

Milad Shokouhi

Lei Duan

Richard Kasperski

Merlyn Deng

Yumao Lu

Nick Craswell

Karthik Raghunathan

Rosemary Ellen Jones

Umut Ozertem

Aram Grigoryan

Fan Li

Irina-Elena Veliche

Jiefeng Shen

Dario Garcia Garcia

George Harlow Mills

Ali Ahmadi

Reena Philip

Fei Sha

Henry Feild

Jeffrey Bardzell

Chi-Hoon Lee

Eric Crestan

In one embodiment, a method includes receiving a user query inputted on a head-mounted device from the head-mounted device, wherein the user query corresponds to multiple dialog-intents, executing multiple tasks corresponding to the multiple dialog-intents, generating a multi-perspective response by a stitching model based on two or more of execution results of the multiple tasks, wherein the stitching model combines the two or more of the execution results based on natural language processing, and wherein the multi-perspective response comprises a natural-language response combining the two or more execution results, and sending instructions to the head-mounted device for presenting the multi-perspective response on the head-mounted device.

Generating multi-perspective responses by assistant systems

In one embodiment, a method includes receiving a first user input from a first user, wherein the first user input comprises a partial request, presenting one or more suggested intent auto-completions corresponding to the partial request, receiving a selection by the first user of a first suggested intent auto-completion of the suggested intent auto-completions and a second user input, presenting one or more suggested slot auto-completions corresponding to one or more candidate slot-hypotheses corresponding to the second user input, respectively, wherein each of the candidate slot-hypotheses comprise a slot-suggestion, and wherein each suggested slot auto-completion comprises the second user input and the corresponding candidate slot-hypothesis, receiving a selection by the first user of a first suggested slot auto-completion of the suggested slot auto-completions, and presenting execution results of one or more tasks corresponding to the first suggested intent auto-completion and the first suggested slot auto-completion.

Contextual auto-completion for assistant systems

In one embodiment, a method includes receiving a user request from a first user at a client system, wherein the user request is associated with a semantic-intent, identifying dialog-intents associated with the user request by the client system based on the semantic-intent and context information associated with the user request, wherein each dialog-intent is a sub-intent of the semantic-intent; determining agents for executing tasks associated with the dialog-intents by the client system, and presenting information returned from the agents responsive to executing the tasks at the client system.

Intent identification for agent matching by assistant systems

In one embodiment, a method includes, by a client system, receiving, by an assistant xbot of the client system, a request from a first user for a summary of user content from a first content source, retrieving, from the first content source, a plurality of content items corresponding to the request, generating a personalized summary of the retrieved content items, wherein the personalization of the summary is based on a user profile of the first user, and presenting, by the assistant xbot, the personalized summary responsive to the request within a separate communication interface between the assistant xbot and the first user, wherein the personalized summary is interactable by the first user to react to one or more of the plurality of content items.

Content summarization for assistant systems

In one embodiment, a method includes receiving a user query associated with dialog-intents at a client system, executing tasks corresponding to the dialog-intents, generating a multi-perspective response by a stitching model based on two or more of execution results of the tasks, wherein the multi-perspective response comprises a natural-language response combining the two or more execution results, and presenting the multi-perspective response at the client system.

In one embodiment, a method includes receiving a user request from a client system associated with a first user, wherein the user request is associated with a semantic-intent, identifying one or more dialog-intents associated with the user request based on the semantic-intent and context information associated with the user request, wherein each dialog-intent is a sub-intent of the semantic-intent, determining one or more agents for executing one or more tasks associated with the one or more dialog-intents, and sending instructions for presenting information returned from the one or more agents responsive to executing the one or more tasks to the client system.

A computing device receives a message including a request for a recommendation. A representation of a hypothetical ideal recommendation to provide in response to the message is determined based on the message content. Data regarding entities that are potential recommendations are retrieved from a data store, the data regarding each entity including a representation of the entity (e.g., a vector) derived from factual information about the entity and opinions of other users of the entity. Ranking scores are determined for at least a subset of the entities based on the difference between the entity representations and the representation of the hypothetical ideal recommendation. An entity to recommend is selected based on the ranking scores and a reply to the message is sent that identifies the selected entity.

Combining machine-learning and social data to generate personalized recommendations

In one embodiment, a method includes receiving a user query from a client system associated with a first user, determining a plurality of dialog-intents, each being associated with a particular agent of a plurality of agents, executing a plurality of tasks corresponding to the user query via the plurality of agents corresponding to the plurality of dialog-intents, receiving a plurality of execution results corresponding to the plurality of tasks from the plurality of agents, respectively, selecting two or more of the plurality of execution results for combination, generating a multi-perspective response based on the selected execution results by a stitching model, wherein the multi-perspective response comprises a natural-language response combining the selected execution results, and sending instructions for presenting the multi-perspective response to the first user to the client system in response to the user query.

In one embodiment, a method includes, by one or more computing systems, receiving, from a client system associated with a user, a request for a summary of user communications from a content source, accessing a plurality of user communications from the content source, identifying a plurality of segments associated with the plurality of user communications, wherein the plurality of segments is associated with a plurality of topics, respectively, calculating, for each segment of the plurality of segments, a user interest score for the segment, selecting one or more of the segments for summarization based on their user interest scores, generating one or more personalized summaries of the one or more selected segments, wherein the personalization of the summary is based on the user profile of the user and sending, to the client system, instructions to present the personalized summaries to the user responsive to the request.

In one embodiment, a method includes receiving a user request for a summarization of a particular type of content objects from a client system associated with a first user, determining one or more modalities associated with the user request, selecting a plurality of content objects of the particular type based on a user profile of the first user, wherein the user profile comprises one or more confidence scores associated with one or more subjects associated with the first user, respectively, and wherein the plurality of content objects are selected based on the one or more confidence scores, generating a summary of each content object based on the user profile and the determined modalities, and sending, to the client system in response to the user request, instructions for presenting the summaries of the plurality of content objects, wherein the summaries are presented via one or more of the determined modalities.

Generating personalized content summaries for users

A user interacts with a virtual digital assistant with the intent that it provides assistance with a task. The user sends messages to the virtual digital assistant that include content obtained via user input at a client device. An intent determination model is applied to the content to identify the user's intent. The virtual digital assistant identifies agents that are capable of servicing the intent are identified and retrieves contextual data relating to the message from a data store. An intent arbitration model is used to select one of the agents which is activated to provide assistance with the task. The contextual information may include global metrics of agent performance and/or information regarding the user's preferences.

Intent arbitration for a virtual assistant

In one embodiment, a method includes receiving a user input including a partial request from a client system of a first user, analyzing the user input to generate one or more candidate hypotheses based on a personalized language model where each of the candidate hypotheses includes one or more of an intent-suggestion or a slot-suggestion, sending instructions for presenting one or more suggested auto-completions corresponding to one or more of the candidate hypotheses, respectively, to the client system, where each suggested auto-completion comprises the partial request and the corresponding candidate hypothesis, receiving an indication of a selection by the first user of a first suggested auto-completion of the suggested auto-completions from the client system, and executing one or more tasks based on the first suggested auto-completion selected by the first user via one or more agents.

In one embodiment, a method includes receiving from a client system a user request from a first user, determining a necessity for resolving the first user to a known entity to execute one or more tasks associated with the user request based on privacy restrictions associated with the user request, determining a set of candidate entities for the first user based on one or more machine-learning models, each candidate entity being associated with a respective confidence score greater than a threshold score, sending instructions for prompting the first user to select a candidate entity from the set of candidate entities, resolving the first user to a selected candidate entity responsive to receiving a selection from the first user, and executing the one or more tasks associated with the user request based on a user profile associated with the selected candidate entity.

Identifying users through conversations for assistant systems

An online system receives a request to generate presentation content for presentation to a user. The online system receives a set of content items and identifies a surface for presenting the presentation information to the user. For example, the surface may be a voice only surface, a voice and graphical display, a graphical display only. Based on the identified surface, the online system ranks the set of content items. The online system then determines presentation information for a subset of the content items and transmits instructions to present the presentation information at the surface.

Applying templates to customize presentation of content based on surface type

Exemplary embodiments relate to methods, mediums, and systems for managing a conversation. In an embodiment, a computer-implemented input interface is provided to receive an input comprising information in natural language. A dialog manager is configured to determine an intent of the input, determine information to fulfill the intent, and identify one or both of information available to the dialog manager or information that is unavailable to the dialog manager. A conversational understanding document documents the intent and the identified information. An output interface forwards the conversational understanding document towards a task completion handler separate and distinct from the dialog manager. Other embodiments are described and claimed.

Conversational understanding

Growing community of inventors

Palo Alto, CA, United States of America

Benoit F Dumoulin