Patents by Inventor Yelong Shen
Yelong Shen 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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Publication number: 20240078422Abstract: An optoelectronic computing system includes a first semiconductor die having a photonic integrated circuit (PIC) and a second semiconductor die having an electronic integrated circuit (EIC). The PIC includes optical waveguides, in which input values are encoded on respective optical signals carried by the optical waveguides. The PIC includes an optical copying distribution network having optical splitters. The PIC includes an array of optoelectronic circuitry sections, each receiving an optical wave from one of the output ports of the optical copying distribution network, and each optoelectronic circuitry section includes: at least one photodetector detecting at least one optical wave from the optoelectronic operation. The EIC includes electrical input ports receiving respective electrical values.Type: ApplicationFiled: June 29, 2023Publication date: March 7, 2024Inventors: Huaiyu Meng, Yichen Shen, Yelong Xu, Gilbert Hendry, Longwu Ou, Jingdong Deng, Ronald Gagnon, Cheng-Kuan Lu, Maurice Steinman, Mike Evans, Jianhua Wu
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Patent number: 11704551Abstract: Techniques for iterative query-based analysis of text are described. According to various implementations, a neural network architecture is implemented receives a query for information about text content, and iteratively analyzes the content using the query. During the analysis a state of the query evolves until it reaches a termination state, at which point the state of the query is output as an answer to the initial query.Type: GrantFiled: June 30, 2017Date of Patent: July 18, 2023Assignee: Microsoft Technology Licensing, LLCInventors: Po-Sen Huang, Jianfeng Gao, Weizhu Chen, Yelong Shen
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Publication number: 20220383126Abstract: A computer implemented method obtains neural network-based model base model weight matrices for each of multiple neural network layers. First low-rank factorization matrices are added to corresponding base model weight matrices to form a first domain model. The low-rank factorization matrices are treated as trainable parameters. The first domain model is trained with first domain specific training data without modifying base model weight matrices.Type: ApplicationFiled: May 19, 2021Publication date: December 1, 2022Inventors: Weizhu Chen, Jingfeng HU, Yelong SHEN, Shean WANG, Yabin LIU
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Patent number: 10445650Abstract: A processing unit can successively operate layers of a multilayer computational graph (MCG) according to a forward computational order to determine a topic value associated with a document based at least in part on content values associated with the document. The processing unit can successively determine, according to a reverse computational order, layer-specific deviation values associated with the layers based at least in part on the topic value, the content values, and a characteristic value associated with the document. The processing unit can determine a model adjustment value based at least in part on the layer-specific deviation values. The processing unit can modify at least one parameter associated with the MCG based at least in part on the model adjustment value. The MCG can be operated to provide a result characteristic value associated with test content values of a test document.Type: GrantFiled: November 23, 2015Date of Patent: October 15, 2019Assignee: Microsoft Technology Licensing, LLCInventors: Jianfeng Gao, Li Deng, Xiaodong He, Lin Xiao, Xinying Song, Yelong Shen, Ji He, Jianshu Chen
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Publication number: 20190156220Abstract: Systems and methods for machine comprehension are provided. In example embodiments, a machine accesses a context and a question related to the context. The machine determines a low-level meaning of the question and a low-level meaning of the context, the low-level meaning corresponding to words or phrases. The machine determines a high-level meaning of the question and a high-level meaning of the context, the high-level meaning corresponding to sentences or paragraphs. The machine computes, for each position i in the context, a first probability that an answer to the question starts at the position i. The machine computes, for each position j in the context, a second probability that the answer to the question ends at the position j. The machine determines the answer to the question based on the computed first probabilities and the computed second probabilities.Type: ApplicationFiled: November 22, 2017Publication date: May 23, 2019Inventors: Chenguang Zhu, Hsin-Yuan Huang, Pengcheng He, Weizhu Chen, Yelong Shen, Zheng Chen
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Patent number: 10264081Abstract: Techniques for providing a people recommendation system for predicting and recommending relevant people (or other entities) to include in a conversation based on contextual indicators. In an exemplary embodiment, email recipient recommendations may be suggested based on contextual signals, e.g., project names, body text, existing recipients, current date and time, etc. In an aspect, a plurality of properties including ranked key phrases are associated with profiles corresponding to personal entities. Aggregated profiles are analyzed using first- and second-layer processing techniques. The recommendations may be provided to the user reactively, e.g., in response to a specific query by the user to the people recommendation system, or proactively, e.g., based on the context of what the user is currently working on, in the absence of a specific query by the user.Type: GrantFiled: July 22, 2015Date of Patent: April 16, 2019Assignee: MICROSOFT TECHNOLOGY LICENSING, LLCInventors: Chenlei Guo, Jianfeng Gao, Xinying Song, Byungki Byun, Yelong Shen, Ye-Yi Wang, Brian D. Remick, Edward Thiele, Mohammed Aatif Ali, Marcus Gois, Xiaodong He, Jianshu Chen, Divya Jetley, Stephen Friesen
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Patent number: 10133729Abstract: Systems, methods, and computer-readable media for providing semantically-relevant discovery of solutions are described herein. In some examples, a computing device can receive an input, such as a query. The computing device can process each word of the input sequentially to determine a semantic representation of the input. Techniques and technologies described herein determine a response to the input, such as an answer, based on the semantic representation of the input matching a semantic representation of the response. An output including one or more relevant responses to the request can then be provided to the requestor. Example techniques described herein can apply machine learning to train a model with click-through data to provide semantically-relevant discovery of solutions. Example techniques described herein can apply recurrent neural networks (RNN) and/or long short term memory (LSTM) cells in the machine learning model.Type: GrantFiled: August 28, 2015Date of Patent: November 20, 2018Assignee: Microsoft Technology Licensing, LLCInventors: Xiaodong He, Jianfeng Gao, Hamid Palangi, Xinying Song, Yelong Shen, Li Deng, Jianshu Chen
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Patent number: 10042961Abstract: Techniques for providing a people recommendation system for predicting and recommending relevant people (or other entities) to include in a conversation. In an exemplary embodiment, a plurality of conversation boxes associated with communications between a user and target recipients, or between other users and recipients, are collected and stored as user history. During a training phase, the user history is used to train encoder and decoder blocks in a de-noising auto-encoder model. During a prediction phase, the trained encoder and decoder are used to predict one or more recipients for a current conversation box composed by the user, based on contextual and other signals extracted from the current conversation box. The predicted recipients are ranked using a scoring function, and the top-ranked individuals or entities may be recommended to the user.Type: GrantFiled: July 28, 2015Date of Patent: August 7, 2018Assignee: MICROSOFT TECHNOLOGY LICENSING, LLCInventors: Yelong Shen, Xinying Song, Jianfeng Gao, Chenlei Guo, Byungki Byun, Ye-Yi Wang, Brian D. Remick, Edward Thiele, Mohammed Aatif Ali, Marcus Gois, Yang Zou, Mariana Stepp, Divya Jetley, Stephen Friesen
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Publication number: 20180101767Abstract: Techniques for iterative query-based analysis of text are described. According to various implementations, a neural network architecture is implemented receives a query for information about text content, and iteratively analyzes the content using the query. During the analysis a state of the query evolves until it reaches a termination state, at which point the state of the query is output as an answer to the initial query.Type: ApplicationFiled: June 30, 2017Publication date: April 12, 2018Applicant: Microsoft Technology Licensing, LLCInventors: Po-Sen Huang, Jianfeng Gao, Weizhu Chen, Yelong Shen
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Publication number: 20170147942Abstract: A processing unit can successively operate layers of a multilayer computational graph (MCG) according to a forward computational order to determine a topic value associated with a document based at least in part on content values associated with the document. The processing unit can successively determine, according to a reverse computational order, layer-specific deviation values associated with the layers based at least in part on the topic value, the content values, and a characteristic value associated with the document. The processing unit can determine a model adjustment value based at least in part on the layer-specific deviation values. The processing unit can modify at least one parameter associated with the MCG based at least in part on the model adjustment value. The MCG can be operated to provide a result characteristic value associated with test content values of a test document.Type: ApplicationFiled: November 23, 2015Publication date: May 25, 2017Inventors: Jianfeng Gao, Li Deng, Xiaodong He, Lin Xiao, Xinying Song, Yelong Shen, Ji He, Jianshu Chen
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Publication number: 20170060844Abstract: Systems, methods, and computer-readable media for providing semantically-relevant discovery of solutions are described herein. In some examples, a computing device can receive an input, such as a query. The computing device can process each word of the input sequentially to determine a semantic representation of the input. Techniques and technologies described herein determine a response to the input, such as an answer, based on the semantic representation of the input matching a semantic representation of the response. An output including one or more relevant responses to the request can then be provided to the requestor. Example techniques described herein can apply machine learning to train a model with click-through data to provide semantically-relevant discovery of solutions. Example techniques described herein can apply recurrent neural networks (RNN) and/or long short term memory (LSTM) cells in the machine learning model.Type: ApplicationFiled: August 28, 2015Publication date: March 2, 2017Inventors: Xiaodong He, Jianfeng Gao, Hamid Palangi, Xinying Song, Yelong Shen, Li Deng, Jianshu Chen
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Publication number: 20160323398Abstract: Techniques for providing a people recommendation system for predicting and recommending relevant people (or other entities) to include in a conversation based on contextual indicators. In an exemplary embodiment, email recipient recommendations may be suggested based on contextual signals, e.g., project names, body text, existing recipients, current date and time, etc. In an aspect, a plurality of properties including ranked key phrases are associated with profiles corresponding to personal entities. Aggregated profiles are analyzed using first- and second-layer processing techniques. The recommendations may be provided to the user reactively, e.g., in response to a specific query by the user to the people recommendation system, or proactively, e.g., based on the context of what the user is currently working on, in the absence of a specific query by the user.Type: ApplicationFiled: July 22, 2015Publication date: November 3, 2016Inventors: Chenlei Guo, Jianfeng Gao, Xinying Song, Byungki Byun, Yelong Shen, Ye-Yi Wang, Brian D. Remick, Edward Thiele, Mohammed Aatif Ali, Marcus Gois, Xiaodong He, Jianshu Chen, Divya Jetley, Stephen Friesen
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Publication number: 20160321283Abstract: Techniques for providing a people recommendation system for predicting and recommending relevant people (or other entities) to include in a conversation. In an exemplary embodiment, a plurality of conversation boxes associated with communications between a user and target recipients, or between other users and recipients, are collected and stored as user history. During a training phase, the user history is used to train encoder and decoder blocks in a de-noising auto-encoder model. During a prediction phase, the trained encoder and decoder are used to predict one or more recipients for a current conversation box composed by the user, based on contextual and other signals extracted from the current conversation box. The predicted recipients are ranked using a scoring function, and the top-ranked individuals or entities may be recommended to the user.Type: ApplicationFiled: July 28, 2015Publication date: November 3, 2016Inventors: Yelong Shen, Xinying Song, Jianfeng Gao, Chenlei Guo, Byungki Byun, Ye-Yi Wang, Brian D. Remick, Edward Thiele, Mohammed Aatif Ali, Marcus Gois, Yang Zou, Mariana Stepp, Divya Jetley, Stephen Friesen
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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: 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