Microsoft Technology Licensing, LLC

Uber Technologies, Inc.

Jaime Brooks Teevan

Susan Theresa Dumais

Daniel John Liebling

John Charles Krumm

Alice Jane Bernheim Brush

Amy Kathleen Karlson

Shahriyar J Amini

Ryen William White

Meredith June Morris

Eric J Horvitz

Nissanka Arachchige Bodhi Priyantha

Brent Jaron Hecht

Krysta Marie Svore

Ido Priness

Shiraz Jitendra Cupala

Adi L Miller

Eytan Adar

Saleema Amin Amershi

Richard Lloyd Hughes

Amir Kantor

Lan Ye

Kira Radinsky

Longqi Yang

Kevin Michael Carter

Shai Ifrach

Mengting Wan

Shlomi Maliah

Nissanka Arachige Bodhi Priyantha

Yasemin Cenberoglu

Kumud Ranjan

Shyam Gupta

Gautam Jain

Xiao Yan Lu

Paul Nathan Bennett

Emre Mehmet Kiciman

Victor Poznanski

Bernhard S J Kohlmeier

Patrick Pantel

James Wiliamson Mickens

Michael S Bernstein

Kristina Nikolova Toutanova

Mark James Encarnacion

Semiha Ece Kamar Eden

Robert Warren Gruen

Pallavi Choudhury

Jin-Woo Jeong

Shamsi Tamara Iqbal

Ann M Paradiso

Anthony Lee Carbary

Gayathri Ravichandran Geetha

Matthew Jonathan Gardner

Woon Kiat Wong

Sarah K Tyler

Luis Carlos Vargas Herring

Jing Ma

Darren Francis Gehring

Kiran Tomlinson

Cao Lu

Shiri Azenkot

Eleonora Shtotland

Gilad Gildin

Ofek David

James W Mickens

A system for providing a personalized assistant within a network-based communication service includes one or more processors and a memory storage device storing instructions thereon. During a network-based communication session, the system receives a query from a computing device of a first communication session participant and processes the query by determining that a second communication session participant has shared content via a content sharing feature of the network-based communication service. In response, the system provides the query and at least a portion of the shared content as input to a model, which dynamically constructs a prompt for use as input with a generative language model. The system then presents the prompt as input to the generative language model, receives a response as output from the generative language model, and causes presentation of the response to be presented to the communication session participant.

Network-based communication session copilot

A system for providing a personalized assistant within a network-based communication session includes a processor and a memory storage device storing instructions. The system determines when a first communication session participant joins the network-based communication session after a threshold duration of time subsequent to the start time of the session. Upon determining the first participant has joined, the system obtains content associated with the session and creates request data for a pre-trained generative language model. The request data includes an instruction requesting a predetermined number of suggested utterances not present in the content, each utterance relating to one or more topics corresponding to the content. The system transforms the request data to a command based on a command template and provides the command to the generative language model. The system receives a response from the model, including the predetermined number of suggested utterances, and presents them to the communication session participant in a graphical user interface while the session is in session.

A method includes receiving one or more search terms at a mobile computing device while the mobile computing device is located at a particular location. A search query that includes the one or more search terms and a location history of the mobile computing device is transmitted to a server. The method also includes receiving one or more search results in response to the search query, where the one or more search results include content identified based on a predicted destination of the mobile computing device. An interface identifying the one or more search results is displayed at the mobile computing device.

Mobile search based on predicted location

An enterprise system server, a computer-readable storage medium, and a method for targeted training of inductive multi-organization recommendation models for enterprise applications are described herein. The method includes receiving enterprise application data from remote organization computing systems executing the enterprise application, training per-organization recommendation models for a subset of the organizations, and validating each per-organization recommendation model on enterprise application data corresponding to one or more other organizations. The method also includes calculating a transferability metric for each per-organization recommendation model based on results obtained during validation, determining a specified number of organizations including the best-transferring per-organization recommendation models based on the calculated transferability metrics, and training an inductive multi-organization recommendation model using the enterprise application data from the specified number of organizations. The method further includes utilizing the trained inductive multi-organization recommendation model to provide user recommendations to the remote organization computing systems during execution of the enterprise application.

Targeted training of inductive multi-organization recommendation models for enterprise applications

A computing system, computer-readable storage medium, and method for individual treatment effect (ITE) estimation under high-order interference in hypergraphs are described herein. The method includes accessing, via a processor, a hypergraph dataset including multi-way interactions among nodes within each hyperedge of a corresponding hypergraph, where the hypergraph dataset corresponds to a treatment assignment for each node. The method includes performing representation learning on the hypergraph dataset to control for confounders corresponding to features of each node and to learn a confounder representation for each node. The method also includes modeling a high-order interference representation for each node by propagating the learned confounder representation and the treatment assignment for each node through a hypergraph neural network. The method further includes estimating the ITE for each node under the treatment assignment based on the learned confounder representation and the modeled high-order interference representation for each node.

Individual treatment effect estimation under high-order interference in hypergraphs

A content creation application can include a feature that receives an inline note within a document and communicates the content of the inline note and a user identifier associated with an author of the inline note to an intelligence service. The intelligence service can identify, from the content of the inline note, one or more agents and a request, the identified one or more agents being the author, one or more person agents, one or more bot agents, or a combination thereof. Based on the identified agent (or lack thereof), the intelligence service can generate a message to each of the one or more agents and communicate the message to the each of the one or more agents over a communication channel. A person agent or the author can receive the message and view the message using the appropriate communication application without accessing the original document.

Hybrid intelligence approach to eliciting knowledge for inline notes

Edits on a content item, such as a document, are divided into microtasks. The microtasks associated with a document can be automatically identified based on a workflow or can be identified by a user associated with the content item or an administrator. At a later time, the user can complete the microtasks for a content item using an application associated with their smart phone or tablet. The application may present the microtasks in a game-like environment where the user can compete with other users based on metrics such as number of microtasks completed in a day or fastest completion time. In addition, the user can earn rewards such as badges, coupons, or credits by completing microtasks. In this way, users can use time that would have been wasted playing games to complete their content items, while still experiencing some of the fun and competition associated with the games.

Providing rewards and metrics for completion of microtasks

Techniques provide time-aware ranking, such as ranking of information, files or URL (uniform resource locator) links. For example, time-aware modeling assists in determining user intent of a query to a search engine. In response to the query, results are ranked in a time-aware manner to better match the user intent. The ranking may model query, URL and query-URL pair behavior over time to create time-aware query, URL and query-URL pair models, respectively. Such models may predict behavior of a query-URL pair, such as frequency and timing of clicks to the URL of the pair when the query of the pair is posed to the search engine. Results of a query may be ranked by predicted query-URL behavior. Once ranked, the results may be sent to the user in response to the query.

Time-aware ranking adapted to a search engine application

Techniques and systems are disclosed for providing changed content identification for an online document that is accessed by a user or user agent. A reference point for an online document that a user or user agent is interested in accessing is identified, comprising a stored prior version of the document. The prior version of the document is retrieved, when the user or user agent accesses the online document, such as by using the reference point. Elements of the prior version are compared with elements of a current version of the document, to determine whether there are differences between the versions. If changes are identified between the prior version and the current version, the current version is automatically updated with visual or auditory representations that identify those changes of content.

Identifying changes for online documents

Systems, methods, and computer storage media are provided for analyzing a large amount of social media data from a large population of social media users and constructing correlational data models between one or more events that occur within each user's timeline. Social media posts directed to personal experiences of a large number of social media users are extracted. Event timelines are generated for each of the social media users, based on their personal experiences. The event timelines are analyzed with a particular event of interest to measure correlations between events occurring within the timelines and the particular event of interest. Using the measured correlations, a correlational data model is thereby constructed. The correlational data model may be used for application to decision-making calculations by one or more systems in an active or passive manner.

Modeling actions, consequences and goal achievement from social media and other digital traces

A system is described in which a user can add one or more automated agents as 'friends' in a social network service. In operation, an automated agent observes an information need expressed by the user via the social network service, e.g., in the form of an original message posted to the social network service; determines whether it is appropriate to reply to the information need; uses automated functionality to generate a reply message to the information need (if it is deemed appropriate to reply to the information need); and sends the reply message to a target destination, such as a social network page associated with the user. For example, without limitation, one type of automated agent performs a question-answering function. Another type of automated agent performs a social referral service.

Designating automated agents as friends in a social network service

Some examples include transitioning between an individual mode and a collaborative mode in response to an orientation change of a device. Further, some implementations include identifying data to be shared with one or more other devices (e.g., co-located devices) in the collaborative mode. In some examples, the individual mode may be associated with an individual search, the collaborative mode may be associated with a collaborative search, and the devices may transition between the individual mode and the collaborative mode in response to orientation changes.

Collaborative mobile interaction

Growing community of inventors

Bellevue, WA, United States of America

Jaime Brooks Teevan