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).
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Patent number: 11556544Abstract: 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: GrantFiled: July 24, 2020Date of Patent: January 17, 2023Assignee: Slack Technologies, LLCInventors: Qi Lu, Eckart Walther, David Ku, Chung-Man Tam, Kevin Lee, Zhichen Xu, Ali Diab, Kenneth Norton, Jianchang Mao
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Patent number: 11023473Abstract: 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: GrantFiled: June 25, 2018Date of Patent: June 1, 2021Assignee: Microsoft Technology Licensing, LLCInventors: Ying Shan, Jian Jiao, Jie Zhu, Jianchang Mao
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Publication number: 20210011919Abstract: 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: ApplicationFiled: July 24, 2020Publication date: January 14, 2021Applicant: Slack Technologies, Inc.Inventors: Qi LU, Eckart WALTHER, David KU, Chung-Man TAM, Kevin LEE, Zhichen XU, Ali DIAB, Kenneth NORTON, Jianchang MAO
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Patent number: 10726019Abstract: 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: GrantFiled: November 13, 2017Date of Patent: July 28, 2020Assignee: Slack Technologies, Inc.Inventors: Qi Lu, Eckart Walther, David Ku, Chung-Man Tam, Kevin Lee, Zhichen Xu, Ali Diab, Kenneth Norton, Jianchang Mao
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Publication number: 20190251184Abstract: 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: ApplicationFiled: June 25, 2018Publication date: August 15, 2019Applicant: Microsoft Technology Licensing, LLCInventors: Ying SHAN, Jian JIAO, Jie ZHU, Jianchang MAO
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Patent number: 10089580Abstract: 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: GrantFiled: August 11, 2014Date of Patent: October 2, 2018Assignee: Microsoft Technology Licensing, LLCInventors: Ying Shan, Jianchang Mao
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Publication number: 20180067994Abstract: 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: ApplicationFiled: November 13, 2017Publication date: March 8, 2018Inventors: Qi Lu, Eckart Walther, David Ku, Chung-Man Tam, Kevin Lee, Zhichen Xu, Ali Diab, Kenneth Norton, Jianchang Mao
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Publication number: 20180060728Abstract: 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: ApplicationFiled: August 31, 2016Publication date: March 1, 2018Applicant: Microsoft Technology Licensing, LLCInventors: Ying Shan, Jianchang Mao, Dong Yu, Holakou Rahmanian, Yi Zhang
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Patent number: 9852187Abstract: 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: GrantFiled: May 30, 2014Date of Patent: December 26, 2017Assignee: EXCALIBUR IP, LLCInventors: Qi Lu, Eckart Walther, David Ku, Chung-Man Tam, Kevin Lee, Zhichen Xu, Ali Diab, Kenneth Norton, Jianchang Mao
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Publication number: 20170116200Abstract: 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: ApplicationFiled: January 6, 2017Publication date: April 27, 2017Inventors: Pavel Berkhim, Zhichen Xu, Jianchang Mao, Daniel E. Rose, Abe Taha, Farzin Maghoul
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Patent number: 9589277Abstract: 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: GrantFiled: December 31, 2013Date of Patent: March 7, 2017Assignee: Microsoft Technology Licensing, LLCInventors: Bruce Zhang, Jianchang Mao, Yuan Shen
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Patent number: 9576029Abstract: 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: GrantFiled: April 9, 2013Date of Patent: February 21, 2017Assignee: EXCALIBUR IP, LLCInventors: Pavel Berkhim, Zhichen Xu, Jianchang Mao, Daniel E. Rose, Abe Taha, Farzin Maghoul
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Patent number: 9576057Abstract: 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: GrantFiled: January 2, 2013Date of Patent: February 21, 2017Assignee: YAHOO! INC.Inventors: Kenneth Norton, Chung-Man Tam, Jianchang Mao, Zhichen Xu, Adrienne Bassett, Ashley Hall, Nathan Arnold
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Patent number: 9477654Abstract: 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: GrantFiled: April 1, 2014Date of Patent: October 25, 2016Assignee: Microsoft CorporationInventors: Xiaodong He, Jianfeng Gao, Li Deng, Qiang Lou, Yunhong Zhou, Guowei Liu, Gregory T. Buehrer, Jianchang Mao, Yelong Shen, Ruofei Zhang
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Publication number: 20160042296Abstract: 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: ApplicationFiled: August 11, 2014Publication date: February 11, 2016Inventors: Ying Shan, Jianchang Mao
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Publication number: 20150278200Abstract: 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: ApplicationFiled: April 1, 2014Publication date: October 1, 2015Applicant: Microsoft CorporationInventors: Xiaodong He, Jianfeng Gao, Li Deng, Qiang Lou, Yunhong Zhou, Guowei Liu, Gregory T. Buehrer, Jianchang Mao, Yelong Shen, Ruofei Zhang
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Publication number: 20150199435Abstract: 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: ApplicationFiled: January 2, 2013Publication date: July 16, 2015Applicant: YAHOO! INC.Inventors: Kenneth Norton, Chung-Man Tam, Jianchang Mao, Zhichen Xu, Adrienne Bassett, Ashley Hall, Nathan Arnold
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Publication number: 20150186938Abstract: 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: ApplicationFiled: December 31, 2013Publication date: July 2, 2015Applicant: MICROSOFT CORPORATIONInventors: Ruofei Zhang, Jianchang Mao, Yuan Shen
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Patent number: 8856028Abstract: 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: GrantFiled: January 24, 2011Date of Patent: October 7, 2014Assignee: Yahoo! Inc.Inventors: Jian Yang, Jianchang Mao
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Publication number: 20140289232Abstract: 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: ApplicationFiled: May 30, 2014Publication date: September 25, 2014Applicant: Yahoo! Inc.Inventors: Qi Lu, Eckart Walther, David Ku, Chung-Man Tam, Kevin Lee, Zhichen Xu, Ali Diab, Kenneth Norton, Jianchang Mao