Patents by Inventor Baolei Li
Baolei Li 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: 11789175Abstract: A multi-energy static security CT system comprises at least one N-stage image chain structure (5) and a baggage conveying belt (4) provided at an inner side at the bottom of the N-stage image chain structure (5). The N-stage image chain structure (5) and the baggage conveying belt (4) are fixed at a pre-configured positions by means of a machine frame (8). The N-stage image chain structures (5) are sequentially arranged in a forward direction of a baggage channel, and adjacent N-stage image chain structures (5) are offset relative to each other. By exposing radiation sources in the N-stage image chain structure (5) at different times, the static security CT system generates an image having a higher temporal resolution and more energy spectrum levels than an image generated by a spiral CT system. Also provided is an imaging method implemented by means of the static security CT system.Type: GrantFiled: April 30, 2021Date of Patent: October 17, 2023Assignee: NANOVISION TECHNOLOGY (BEIJING) CO., LTDInventors: Baolei Li, Yantao Hu, Yunxiang Li, Zhili Cui, Jian Gao, Jie Luo
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Patent number: 11204968Abstract: In an example embodiment, a platform is provided that utilizes information available to a computer system to feed a neural network. The neural network is trained to determine both the probability that a searcher would select a given potential search result if it was presented to him or her and the probability that a subject of the potential search result would respond to a communication from the searcher. These probabilities are essentially combined to produce a single score that can be used to determine whether to present the searcher with the potential search result and, if so, how high to rank the potential search result among other search results. In a further example embodiment, embeddings used for the input features are modified during training to maximize an objective.Type: GrantFiled: June 21, 2019Date of Patent: December 21, 2021Assignee: Microsoft Technology Licensing, LLCInventors: Dan Liu, Daniel Sairom Krishnan Hewlett, Qi Guo, Wei Lu, Xuhong Zhang, Wensheng Sun, Mingzhou Zhou, Anthony Hsu, Keqiu Hu, Yi Wu, Chenya Zhang, Baolei Li
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Publication number: 20210325563Abstract: A multi-energy static security CT system comprises at least one N-stage image chain structure (5) and a baggage conveying belt (4) provided at an inner side at the bottom of the N-stage image chain structure (5). The N-stage image chain structure (5) and the baggage conveying belt (4) are fixed at a pre-configured positions by means of a machine frame (8). The N-stage image chain structures (5) are sequentially arranged in a forward direction of a baggage channel, and adjacent N-stage image chain structures (5) are offset relative to each other. By exposing radiation sources in the N-stage image chain structure (5) at different times, the static security CT system generates an image having a higher temporal resolution and more energy spectrum levels than an image generated by a spiral CT system. Also provided is an imaging method implemented by means of the static security CT system.Type: ApplicationFiled: April 30, 2021Publication date: October 21, 2021Applicant: NANOVISION TECHNOLOGY (BEIJING) CO., LTDInventors: Baolei LI, Yantao HU, Yunxiang LI, Zhili CUI, Jian GAO, Jie LUO
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Patent number: 10990754Abstract: This disclosure provides for systems and methods that generate personalized electronic messages for members of a networked communication service. The personalized electronic messages are generated according to commonalities between member profiles. In one approach, an electronic message template is referenced based on the commonalities. The electronic message template is populated with member profile attribute values selected from the member profiles. In another approach, a long-short term memory (LSTM) recurrent neural network (RNN) is used to generate the electronic messages. Under this approach, a sequence-to-sequence model is trained using previous electronic messages labeled with one or more member profile attributes and/or member profile attribute values. When provided with one or more member profile attribute values associated with matching member profiles, the LSTM RNN outputs a relevant and appropriate electronic message designed to create an interest in the recipient of the electronic message.Type: GrantFiled: October 31, 2018Date of Patent: April 27, 2021Assignee: Microsoft Technology Licensing, LLCInventors: Bing Zhao, Romer E. Rosales-Delmoral, Baolei Li
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Publication number: 20200401627Abstract: In an example embodiment, a platform is provided that utilizes information available to a computer system to feed a neural network. The neural network is trained to determine both the probability that a searcher would select a given potential search result if it was presented to him or her and the probability that a subject of the potential search result would respond to a communication from the searcher. These probabilities are essentially combined to produce a single score that can be used to determine whether to present the searcher with the potential search result and, if so, how high to rank the potential search result among other search results. In a further example embodiment, embeddings used for the input features are modified during training to maximize an objective.Type: ApplicationFiled: June 21, 2019Publication date: December 24, 2020Inventors: Dan Liu, Daniel Sairom Krishnan Hewlett, Qi Guo, Wei Lu, Xuhong Zhang, Wensheng Sun, Mingzhou Zhou, Anthony Hsu, Keqiu Hu, Yi Wu, Chenya Zhang, Baolei Li
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Publication number: 20200311613Abstract: Herein are techniques for configuring, integrating, and operating trainable tensor transformers that each encapsulate an ensemble of trainable machine learning (ML) models. In an embodiment, a computer-implemented trainable tensor transformer uses underlying ML models and additional mechanisms to assemble and convert data tensors as needed to generate output records based on input records and inferencing. The transformer processes each input record as follows. Input tensors of the input record are converted into converted tensors. Each converted tensor represents a respective feature of many features that are capable of being processed by the underlying trainable models. The trainable models are applied to respective subsets of converted tensors to generate an inference for the input record. The inference is converted into a prediction tensor. The prediction tensor and input tensors are stored as output tensors of a respective output record for the input record.Type: ApplicationFiled: March 29, 2019Publication date: October 1, 2020Inventors: Yiming Ma, Jun Jia, Yi Wu, Xuhong Zhang, Leon Gao, Baolei Li, Bee-Chung Chen, Bo Long
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Publication number: 20200134009Abstract: This disclosure provides for systems and methods that generate personalized electronic messages for members of a networked communication service. The personalized electronic messages are generated according to commonalities between member profiles. In one approach, an electronic message template is referenced based on the commonalities. The electronic message template is populated with member profile attribute values selected from the member profiles. In another approach, a long-short term memory (LSTM) recurrent neural network (RNN) is used to generate the electronic messages. Under this approach, a sequence-to-sequence model is trained using previous electronic messages labeled with one or more member profile attributes and/or member profile attribute values. When provided with one or more member profile attribute values associated with matching member profiles, the LSTM RNN outputs a relevant and appropriate electronic message designed to create an interest in the recipient of the electronic message.Type: ApplicationFiled: October 31, 2018Publication date: April 30, 2020Inventors: Bing Zhao, Romer E. Rosales-Delmoral, Baolei Li
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Patent number: 10600003Abstract: Techniques for auto-tuning anomaly detection are provided. In one technique, training data is stored that comprises training instances, each of which comprises a severity-duration pair and a label that indicates whether the severity-duration pair represents an anomaly. A model is trained based on a first subset of the training data. A second subset of the training data is identified where each training instance includes a positive label that indicates that that training instance represents an anomaly. Based on the second subset of the training data, the model generates multiple scores, each of which corresponds to a different training instance. A minimum score is identified that ensures a particular recall rate of the model. In response to receiving a particular severity-duration pair, the model generates a particular score for the particular severity-duration pair. A notification of an anomaly is generated if the particular score is greater than the minimum score.Type: GrantFiled: June 30, 2018Date of Patent: March 24, 2020Assignee: Microsoft Technology Licensing, LLCInventors: Kexin Nie, Yang Yang, Baolei Li
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Publication number: 20200005193Abstract: Techniques for auto-tuning anomaly detection are provided. In one technique, training data is stored that comprises training instances, each of which comprises a severity-duration pair and a label that indicates whether the severity-duration pair represents an anomaly. A model is trained based on a first subset of the training data. A second subset of the training data is identified where each training instance includes a positive label that indicates that that training instance represents an anomaly. Based on the second subset of the training data, the model generates multiple scores, each of which corresponds to a different training instance. A minimum score is identified that ensures a particular recall rate of the model. In response to receiving a particular severity-duration pair, the model generates a particular score for the particular severity-duration pair. A notification of an anomaly is generated if the particular score is greater than the minimum score.Type: ApplicationFiled: June 30, 2018Publication date: January 2, 2020Inventors: Kexin Nie, Yang Yang, Baolei Li
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Publication number: 20190266497Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a knowledge graph containing a set of job transitions for members of an online network. Next, the system receives a career path query for a member of the online network. The system then uses member features for the member and parameters of the career path query to identify one or more paths in the knowledge graph that match the career path query.Type: ApplicationFiled: February 23, 2018Publication date: August 29, 2019Applicant: Microsoft Technology Licensing, LLCInventors: Yiping Yuan, Baolei Li, Romer E. Rosales-Delmoral