Patents by Inventor Longqi Yang

Longqi Yang has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20240428156
    Abstract: A system and method for automatically generating a workplan schedule for a team of employees in an organization includes receiving a request to create a workplan schedule for the team, the team including two or more team members who are employees of the organization and the workplan schedule identifying a work location for the team members. One or more collaborators for one or more of the team members is detected, where the collaborator is an employee of the organization who is not a member of the team and with whom at least one of the team members collaborate.
    Type: Application
    Filed: June 21, 2023
    Publication date: December 26, 2024
    Applicant: Microsoft Technology Licensing, LLC
    Inventors: Chin-Chia HSU, Longqi YANG, Siddharth SURI
  • Publication number: 20240354703
    Abstract: A system and method and for optimizing cross-team information flow in a communication network includes receiving, from a communication application, via a network, a plurality of candidate post items for display to a first user of an organization, each candidate post item being a post item published by another user of the organization and being a post that is accessible to the first user. A communication knowledge network graph which represents communication events that have occurred between users of the organization is then generated where each communication event is represented by a first node that represents a sender, a second node that represents a receiver and an edge that represents the communication event from the sender to the receiver.
    Type: Application
    Filed: April 20, 2023
    Publication date: October 24, 2024
    Applicant: Microsoft Technology Licensing, LLC
    Inventors: Longqi YANG, Mengting WAN, Cao LU, Jennifer Lynay NEVILLE, Kiran TOMLINSON
  • Patent number: 11983649
    Abstract: 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.
    Type: Grant
    Filed: October 26, 2021
    Date of Patent: May 14, 2024
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Kiran Tomlinson, Longqi Yang, Mengting Wan, Cao Lu, Brent Jaron Hecht, Jaime Teevan
  • Patent number: 11710139
    Abstract: 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.
    Type: Grant
    Filed: February 28, 2022
    Date of Patent: July 25, 2023
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Mengting Wan, Jing Ma, Longqi Yang, Brent Jaron Hecht, Jaime Teevan
  • Publication number: 20230128832
    Abstract: 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.
    Type: Application
    Filed: October 26, 2021
    Publication date: April 27, 2023
    Applicant: Microsoft Technology Licensing, LLC
    Inventors: Kiran TOMLINSON, Longqi YANG, Mengting WAN, Cao LU, Brent Jaron HECHT, Jaime TEEVAN
  • Patent number: 11636394
    Abstract: The present concepts relate to a differentiable user-item co-clustering (“DUICC”) model for recommendation and co-clustering. Users' interaction with items (e.g., content) may be centered around information co-clusters—groups of items and users that exhibit common consumption behavior. The DUICC model may learn fine-grained co-cluster structures of items and users based on their interaction data. The DUICC model can then leverage the learned latent co-cluster structures to calculate preference stores of the items for a user. The top scoring items may be presented to the user as recommendations.
    Type: Grant
    Filed: June 25, 2020
    Date of Patent: April 25, 2023
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Longqi Yang, Tobias Benjamin Schnabel, Paul Nathan Bennett, Susan Theresa Dumais
  • Publication number: 20220036265
    Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for enhancing remote work productivity data. In one embodiment, worker data is obtained for a set of target workers and a set of control workers. The target workers are generally identified as working in the office prior to a treatment date and working remote after a treatment date, and the control workers are generally identified as working remote before and after the treatment date. Thereafter, an effect of working remotely is determined using a difference-in-differences model that measures differences in observational changes between the set of target workers and the set of control workers measured before and after the treatment date. The effect of working remotely can be used to generate a generating a productivity score indicating an extent of productivity in relation to the set of target workers.
    Type: Application
    Filed: July 29, 2020
    Publication date: February 3, 2022
    Inventors: Longqi YANG, Sonia Patricia JAFFE, David Michael HOLTZ, Siddharth SURI, Shilpi SINHA, Jeffrey Michael WESTON, Connor Michael JOYCE, Neha Parikh SHAH, Kevin Scott SHERMAN, Chia-Jung LEE, Brent Jaron HECHT, Jaime TEEVAN
  • Publication number: 20210406761
    Abstract: The present concepts relate to a differentiable user-item co-clustering (“DUICC”) model for recommendation and co-clustering. Users' interaction with items (e.g., content) may be centered around information co-clusters—groups of items and users that exhibit common consumption behavior. The DUICC model may learn fine-grained co-cluster structures of items and users based on their interaction data. The DUICC model can then leverage the learned latent co-cluster structures to calculate preference stores of the items for a user. The top scoring items may be presented to the user as recommendations.
    Type: Application
    Filed: June 25, 2020
    Publication date: December 30, 2021
    Applicant: Microsoft Technology Licensing, LLC
    Inventors: Longqi Yang, Tobias Benjamin Schnabel, Paul Nathan Bennett, Susan Theresa Dumais
  • Patent number: 10762135
    Abstract: A digital medium environment includes an asset processing application that performs editing of assets. A projection function is trained using pairs of actions pertaining to software edits, and assets resulting from the actions to learn a joint embedding between the actions and the assets. The projection function is used in the asset processing application to recommend software actions to create an asset, and also to recommend assets to demonstrate the effects of software actions. Recommendations are based on ranking distance measures that measure distances between actions representations and asset representations in a vector space.
    Type: Grant
    Filed: November 21, 2016
    Date of Patent: September 1, 2020
    Assignee: Adobe Inc.
    Inventors: Matthew Douglas Hoffman, Longqi Yang, Hailin Jin, Chen Fang
  • Patent number: 10614381
    Abstract: This disclosure involves personalizing user experiences with electronic content based on application usage data. For example, a user representation model that facilitates content recommendations is iteratively trained with action histories from a content manipulation application. Each iteration involves selecting, from an action history for a particular user, an action sequence including a target action. An initial output is computed in each iteration by applying a probability function to the selected action sequence and a user representation vector for the particular user. The user representation vector is adjusted to maximize an output that is generated by applying the probability function to the action sequence and the user representation vector. This iterative training process generates a user representation model, which includes a set of adjusted user representation vectors, that facilitates content recommendations corresponding to users' usage pattern in the content manipulation application.
    Type: Grant
    Filed: December 16, 2016
    Date of Patent: April 7, 2020
    Assignee: Adobe Inc.
    Inventors: Matthew Hoffman, Longqi Yang, Hailin Jin, Chen Fang
  • Publication number: 20180174070
    Abstract: This disclosure involves personalizing user experiences with electronic content based on application usage data. For example, a user representation model that facilitates content recommendations is iteratively trained with action histories from a content manipulation application. Each iteration involves selecting, from an action history for a particular user, an action sequence including a target action. An initial output is computed in each iteration by applying a probability function to the selected action sequence and a user representation vector for the particular user. The user representation vector is adjusted to maximize an output that is generated by applying the probability function to the action sequence and the user representation vector. This iterative training process generates a user representation model, which includes a set of adjusted user representation vectors, that facilitates content recommendations corresponding to users' usage pattern in the content manipulation application.
    Type: Application
    Filed: December 16, 2016
    Publication date: June 21, 2018
    Inventors: MATTHEW HOFFMAN, LONGQI YANG, HAILIN JIN, CHEN FANG
  • Publication number: 20180143988
    Abstract: A digital medium environment includes an asset processing application that performs editing of assets. A projection function is trained using pairs of actions pertaining to software edits, and assets resulting from the actions to learn a joint embedding between the actions and the assets. The projection function is used in the asset processing application to recommend software actions to create an asset, and also to recommend assets to demonstrate the effects of software actions. Recommendations are based on ranking distance measures that measure distances between actions representations and asset representations in a vector space.
    Type: Application
    Filed: November 21, 2016
    Publication date: May 24, 2018
    Applicant: Adobe Systems Incorporated
    Inventors: Matthew Douglas Hoffman, Longqi Yang, Hailin Jin, Chen Fang