Patents by Inventor Jianchang Mao

Jianchang Mao 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).

  • Patent number: 11023473
    Abstract: A computational search method for retrieving computer information related to a query includes transforming a plurality of candidate answers to candidate answer recurrent binary embedding (RBE) embeddings using a trained RBE model. A query is transformed to a query RBE embedding using the trained RBE model. The query RBE embedding is compared to each candidate answer RBE embedding of a plurality of candidate answer RBE embeddings using a similarity function. The candidate answers are sorted based on the comparisons made using the similarity function, and returning a plurality of the top candidate answers.
    Type: Grant
    Filed: June 25, 2018
    Date of Patent: June 1, 2021
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Ying Shan, Jian Jiao, Jie Zhu, Jianchang Mao
  • Publication number: 20210011919
    Abstract: Computer systems and methods incorporate user annotations (metadata) regarding various pages or sites, including annotations by a querying user and by members of a trust network defined for the querying user into search and browsing of a corpus such as the World Wide Web. A trust network is defined for each user, and annotations by any member of a first user's trust network are made visible to the first user during search and/or browsing of the corpus. Users can also limit searches to content annotated by members of their trust networks or by members of a community selected by the user.
    Type: Application
    Filed: July 24, 2020
    Publication date: January 14, 2021
    Applicant: Slack Technologies, Inc.
    Inventors: Qi LU, Eckart WALTHER, David KU, Chung-Man TAM, Kevin LEE, Zhichen XU, Ali DIAB, Kenneth NORTON, Jianchang MAO
  • Patent number: 10726019
    Abstract: Computer systems and methods incorporate user annotations (metadata) regarding various pages or sites, including annotations by a querying user and by members of a trust network defined for the querying user into search and browsing of a corpus such as the World Wide Web. A trust network is defined for each user, and annotations by any member of a first user's trust network are made visible to the first user during search and/or browsing of the corpus. Users can also limit searches to content annotated by members of their trust networks or by members of a community selected by the user.
    Type: Grant
    Filed: November 13, 2017
    Date of Patent: July 28, 2020
    Assignee: Slack Technologies, Inc.
    Inventors: Qi Lu, Eckart Walther, David Ku, Chung-Man Tam, Kevin Lee, Zhichen Xu, Ali Diab, Kenneth Norton, Jianchang Mao
  • Publication number: 20190251184
    Abstract: A computational search method for retrieving computer information related to a query includes transforming a plurality of candidate answers to candidate answer recurrent binary embedding (RBE) embeddings using a trained RBE model. A query is transformed to a query RBE embedding using the trained RBE model. The query RBE embedding is compared to each candidate answer RBE embedding of a plurality of candidate answer RBE embeddings using a similarity function. The candidate answers are sorted based on the comparisons made using the similarity function, and returning a plurality of the top candidate answers.
    Type: Application
    Filed: June 25, 2018
    Publication date: August 15, 2019
    Applicant: Microsoft Technology Licensing, LLC
    Inventors: Ying SHAN, Jian JIAO, Jie ZHU, Jianchang MAO
  • Patent number: 10089580
    Abstract: Functionality is described herein for generating a model on the basis of user-behavioral data and knowledge data. In one case, the user-behavioral data identifies queries submitted by users, together with selections made by the users in response to the queries. The knowledge data represents relationships among linguistic items, as expressed by one or more structured knowledge resources. The functionality leverages the knowledge data to supply information regarding semantic relationships which may not be adequately captured by the user-behavioral data, to thereby produce a more robust and accurate model (compared to a model produced on the basis of only user-behavioral data). Functionality is also described herein for applying the model, once trained. In one case, the model may correspond to a deep learning model.
    Type: Grant
    Filed: August 11, 2014
    Date of Patent: October 2, 2018
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Ying Shan, Jianchang Mao
  • Publication number: 20180067994
    Abstract: Computer systems and methods incorporate user annotations (metadata) regarding various pages or sites, including annotations by a querying user and by members of a trust network defined for the querying user into search and browsing of a corpus such as the World Wide Web. A trust network is defined for each user, and annotations by any member of a first user's trust network are made visible to the first user during search and/or browsing of the corpus. Users can also limit searches to content annotated by members of their trust networks or by members of a community selected by the user.
    Type: Application
    Filed: November 13, 2017
    Publication date: March 8, 2018
    Inventors: Qi Lu, Eckart Walther, David Ku, Chung-Man Tam, Kevin Lee, Zhichen Xu, Ali Diab, Kenneth Norton, Jianchang Mao
  • Publication number: 20180060728
    Abstract: A deep embedding forest-based (DEF) model for improving on-line serving time for classification learning methods and other tasks such as, for example, predicting user selection of search results provided in response to a query or for image, speech or text recognition. Initially, a deep neural network (DNN) model is trained to determine parameters of an embedding layer, a stacking layer, deep layers and a scoring layer thereby reducing high dimensional features. After training the DNN model, the parameters of the deep layers and the scoring layer of the DNN model and discarded and the parameters of the embedding layer and the stacking layer are extracted. The extracted parameters from the DNN model then initialize parameters of an embedding layer and a stacking layer of the DEF model such that only a forest layer of the DEF model is then required to be trained. Output from the DEF model is stored in computer memory.
    Type: Application
    Filed: August 31, 2016
    Publication date: March 1, 2018
    Applicant: Microsoft Technology Licensing, LLC
    Inventors: Ying Shan, Jianchang Mao, Dong Yu, Holakou Rahmanian, Yi Zhang
  • Patent number: 9852187
    Abstract: Computer systems and methods incorporate user annotations (metadata) regarding various pages or sites, including annotations by a querying user and by members of a trust network defined for the querying user into search and browsing of a corpus such as the World Wide Web. A trust network is defined for each user, and annotations by any member of a first user's trust network are made visible to the first user during search and/or browsing of the corpus. Users can also limit searches to content annotated by members of their trust networks or by members of a community selected by the user.
    Type: Grant
    Filed: May 30, 2014
    Date of Patent: December 26, 2017
    Assignee: EXCALIBUR IP, LLC
    Inventors: Qi Lu, Eckart Walther, David Ku, Chung-Man Tam, Kevin Lee, Zhichen Xu, Ali Diab, Kenneth Norton, Jianchang Mao
  • Publication number: 20170116200
    Abstract: The present invention is directed towards systems and methods for trust propagation. The method according to one embodiment comprises calculating a first feature vector for a first user, calculating a second feature for a second user and comparing the first feature vector with the second feature vector to calculate a similarity value. A determination is made as to whether the similarity value falls within a threshold. If the similarity value falls within the threshold, a relationship is recorded between the first user and the second user in a first user profile and a second user profile.
    Type: Application
    Filed: January 6, 2017
    Publication date: April 27, 2017
    Inventors: Pavel Berkhim, Zhichen Xu, Jianchang Mao, Daniel E. Rose, Abe Taha, Farzin Maghoul
  • Patent number: 9589277
    Abstract: Methods, computer systems, and computer storage media are provided for evaluating information retrieval (IR) such as search query results (including advertisements) by a machine learning scorer. In an embodiment, a set of features is derived from a query and a machine learning algorithm is applied to construct a linear model of (query, ads) for scoring by maximizing a relevance metric. In an embodiment, the machine learned scorer is adapted for use with WAND algorithm based ad selection.
    Type: Grant
    Filed: December 31, 2013
    Date of Patent: March 7, 2017
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Bruce Zhang, Jianchang Mao, Yuan Shen
  • Patent number: 9576057
    Abstract: The present invention relates to systems, methods, and user interfaces for browsing a collection of content items saved by a user or by one or more buddies associated with a given user. The method of the present invention comprises saving one or more content items and one or more associated keywords as specified by a user. An interface is generated that displays the one or more saved content items and the one or more associated keywords, as well as the one or more buddies associated with a given user. A user indication of the selection of a given keyword or the selection of a given buddy by the user is received. The one or more displayed content items are filtered according to the selected keyword, buddy, or combination of selected keyword and buddy.
    Type: Grant
    Filed: January 2, 2013
    Date of Patent: February 21, 2017
    Assignee: YAHOO! INC.
    Inventors: Kenneth Norton, Chung-Man Tam, Jianchang Mao, Zhichen Xu, Adrienne Bassett, Ashley Hall, Nathan Arnold
  • Patent number: 9576029
    Abstract: The present invention is directed towards systems and methods for trust propagation. The method according to one embodiment comprises calculating a first feature vector for a first user, calculating a second feature for a second user and comparing the first feature vector with the second feature vector to calculate a similarity value. A determination is made as to whether the similarity value falls within a threshold. If the similarity value falls within the threshold, a relationship is recorded between the first user and the second user in a first user profile and a second user profile.
    Type: Grant
    Filed: April 9, 2013
    Date of Patent: February 21, 2017
    Assignee: EXCALIBUR IP, LLC
    Inventors: Pavel Berkhim, Zhichen Xu, Jianchang Mao, Daniel E. Rose, Abe Taha, Farzin Maghoul
  • Patent number: 9477654
    Abstract: Functionality is described herein for transforming first and second symbolic linguistic items into respective first and second continuous-valued concept vectors, using a deep learning model, such as a convolutional latent semantic model. The model is designed to capture both the local and global linguistic contexts of the linguistic items. The functionality then compares the first concept vector with the second concept vector to produce a similarity measure. More specifically, the similarity measure expresses the closeness between the first and second linguistic items in a high-level semantic space. In one case, the first linguistic item corresponds to a query, and the second linguistic item may correspond to a phrase, or a document, or a keyword, or an ad, etc. In one implementation, the convolutional latent semantic model is produced in a training phase based on click-through data.
    Type: Grant
    Filed: April 1, 2014
    Date of Patent: October 25, 2016
    Assignee: Microsoft Corporation
    Inventors: Xiaodong He, Jianfeng Gao, Li Deng, Qiang Lou, Yunhong Zhou, Guowei Liu, Gregory T. Buehrer, Jianchang Mao, Yelong Shen, Ruofei Zhang
  • Publication number: 20160042296
    Abstract: Functionality is described herein for generating a model on the basis of user-behavioral data and knowledge data. In one case, the user-behavioral data identifies queries submitted by users, together with selections made by the users in response to the queries. The knowledge data represents relationships among linguistic items, as expressed by one or more structured knowledge resources. The functionality leverages the knowledge data to supply information regarding semantic relationships which may not be adequately captured by the user-behavioral data, to thereby produce a more robust and accurate model (compared to a model produced on the basis of only user-behavioral data). Functionality is also described herein for applying the model, once trained. In one case, the model may correspond to a deep learning model.
    Type: Application
    Filed: August 11, 2014
    Publication date: February 11, 2016
    Inventors: Ying Shan, Jianchang Mao
  • Publication number: 20150278200
    Abstract: Functionality is described herein for transforming first and second symbolic linguistic items into respective first and second continuous-valued concept vectors, using a deep learning model, such as a convolutional latent semantic model. The model is designed to capture both the local and global linguistic contexts of the linguistic items. The functionality then compares the first concept vector with the second concept vector to produce a similarity measure. More specifically, the similarity measure expresses the closeness between the first and second linguistic items in a high-level semantic space. In one case, the first linguistic item corresponds to a query, and the second linguistic item may correspond to a phrase, or a document, or a keyword, or an ad, etc. In one implementation, the convolutional latent semantic model is produced in a training phase based on click-through data.
    Type: Application
    Filed: April 1, 2014
    Publication date: October 1, 2015
    Applicant: Microsoft Corporation
    Inventors: Xiaodong He, Jianfeng Gao, Li Deng, Qiang Lou, Yunhong Zhou, Guowei Liu, Gregory T. Buehrer, Jianchang Mao, Yelong Shen, Ruofei Zhang
  • Publication number: 20150199435
    Abstract: The present invention relates to systems, methods, and user interfaces for browsing a collection of content items saved by a user or by one or more buddies associated with a given user. The method of the present invention comprises saving one or more content items and one or more associated keywords as specified by a user. An interface is generated that displays the one or more saved content items and the one or more associated keywords, as well as the one or more buddies associated with a given user. A user indication of the selection of a given keyword or the selection of a given buddy by the user is received. The one or more displayed content items are filtered according to the selected keyword, buddy, or combination of selected keyword and buddy.
    Type: Application
    Filed: January 2, 2013
    Publication date: July 16, 2015
    Applicant: YAHOO! INC.
    Inventors: Kenneth Norton, Chung-Man Tam, Jianchang Mao, Zhichen Xu, Adrienne Bassett, Ashley Hall, Nathan Arnold
  • Publication number: 20150186938
    Abstract: Methods, computer systems, and computer storage media are provided for evaluating information retrieval (IR) such as search query results (including advertisements) by a machine learning scorer. In an embodiment, a set of features is derived from a query and a machine learning algorithm is applied to construct a linear model of (query, ads) for scoring by maximizing a relevance metric. In an embodiment, the machine learned scorer is adapted for use with WAND algorithm based ad selection.
    Type: Application
    Filed: December 31, 2013
    Publication date: July 2, 2015
    Applicant: MICROSOFT CORPORATION
    Inventors: Ruofei Zhang, Jianchang Mao, Yuan Shen
  • Patent number: 8856028
    Abstract: An advertisement impression distribution system is programmed to generate an allocation plan for serving a number of advertisement impressions changeable as a result of one or more events, the allocation plan to allocate a first portion of advertisement impressions to satisfy guaranteed demand and a second portion of advertisement impressions to satisfy non-guaranteed demand.
    Type: Grant
    Filed: January 24, 2011
    Date of Patent: October 7, 2014
    Assignee: Yahoo! Inc.
    Inventors: Jian Yang, Jianchang Mao
  • Publication number: 20140289232
    Abstract: Computer systems and methods incorporate user annotations (metadata) regarding various pages or sites, including annotations by a querying user and by members of a trust network defined for the querying user into search and browsing of a corpus such as the World Wide Web. A trust network is defined for each user, and annotations by any member of a first user's trust network are made visible to the first user during search and/or browsing of the corpus. Users can also limit searches to content annotated by members of their trust networks or by members of a community selected by the user.
    Type: Application
    Filed: May 30, 2014
    Publication date: September 25, 2014
    Applicant: Yahoo! Inc.
    Inventors: Qi Lu, Eckart Walther, David Ku, Chung-Man Tam, Kevin Lee, Zhichen Xu, Ali Diab, Kenneth Norton, Jianchang Mao
  • Patent number: 8788492
    Abstract: Computer systems and methods incorporate user annotations (metadata) regarding various pages or sites, including annotations by a querying user and by members of a trust network defined for the querying user into search and browsing of a corpus such as the World Wide Web. A trust network is defined for each user, and annotations by any member of a first user's trust network are made visible to the first user during search and/or browsing of the corpus. Users can also limit searches to content annotated by members of their trust networks or by members of a community selected by the user.
    Type: Grant
    Filed: March 15, 2005
    Date of Patent: July 22, 2014
    Assignee: Yahoo!, Inc.
    Inventors: Qi Lu, Eckart Walther, David Ku, Chung-Man Tam, Kevin Lee, Zhichen Xu, Ali Diab, Kenneth Norton, Jianchang Mao