Patents by Inventor Zhengliang Xue
Zhengliang Xue 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: 20240250177Abstract: A metal oxide thin film transistor is provided, which includes a metal oxide semiconductor layer, including a first semiconductor layer and a second semiconductor layer, the carrier mobility of the first semiconductor layer is higher than that of the second semiconductor layer; the metal oxide semiconductor layer includes a lower surface, an upper surface and a lateral surface, the source electrode is in contact with the lateral surface and the upper surface; the region where the lateral surface contacts the source electrode or the drain electrode includes a first contact region and a second contact region; which have the shape: a first angle between the lower surface of the metal oxide semiconductor layer and the lateral surface of the first contact region is larger than a second angle between the lower surface of the metal oxide semiconductor layer and the lateral surface of the second contact region.Type: ApplicationFiled: March 31, 2022Publication date: July 25, 2024Applicant: BOE TECHNOLOGY GROUP CO., LTD.Inventors: Dapeng XUE, Lizhong WANG, Shuilang DONG, Hehe HU, Nianqi YAO, Guangcai YUAN, Ce NING, Zhengliang LI, Dongfang WANG, Liping LEI, Chen XU, Jie HUANG
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Publication number: 20240220855Abstract: A total demand model can be trained, by machine learning and using historical data. The total demand model can be configured to process current data and output first data indicating a predicted future total demand for a product. A target demand model can be trained. The target demand model can be configured to process the current data and, based on processing the current data, output a plurality of class demand models. Each class demand model can be configured to predict demand, for each of a plurality of future time periods, for a plurality of classes of the product. The class demand models configured to optimize, for each of the plurality of future time periods, a respective set of optimal prices for the respective classes of the product that maximizes total expected revenue for the product over the plurality of classes of the product.Type: ApplicationFiled: December 30, 2022Publication date: July 4, 2024Inventors: Zhengliang Xue, Mo Liu, Shivaram Subramanian, Markus Ettl
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Publication number: 20230073564Abstract: Temporal and spatially integrated forecast modeling includes generating a plurality of forecast models for a plurality of short-term to long-term time periods for a plurality of locations. Temporally integrating the plurality of forecast models sequentially over the plurality of time periods for the plurality of locations and spatially integrating the temporally integrated plurality of forecast models for each location hierarchically over the geographic areas. The forecast models are autoregressive distributed lag models with different explanatory variables for the short-term and long-term forecast models. The temporally integrating includes recursively integrating the plurality of forecast models over the time periods from the short-term to the long-term time periods and the spatially integrating includes recursively integrating the temporally integrated plurality of forecast models hierarchically from larger size geographic areas to smaller size geographic areas.Type: ApplicationFiled: August 27, 2021Publication date: March 9, 2023Inventors: Zhengliang Xue, Bhavna Agrawal, Anuradha Bhamidipaty, Yingjie Li, Shuxin Lin
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Publication number: 20230029218Abstract: A concept associated with a feature used in machine learning model can be determined, the feature extracted from a first data source. A second data source containing the concept can be identified. An additional feature can be generated by performing a natural language processing on the second data source. The feature and the additional feature can be merged. A second machine learning model can be generated, which use the merged feature. A prediction result of the first machine learning model can be compared with a prediction result of the second machine learning model relative to ground truth data, to evaluate effective of the merged feature. Based on the evaluated effectiveness, the feature can be augmented with the merged feature in machine learning.Type: ApplicationFiled: July 20, 2021Publication date: January 26, 2023Inventors: Anuradha Bhamidipaty, Yingjie Li, Shuxin Lin, Zhengliang Xue, Bhavna Agrawal
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Patent number: 11361325Abstract: Systems, methods, and computer-readable media are disclosed for identifying customers having associated opportunities for improved growth and/or profitability with respect to product or service offerings and determining investment solutions that enhance the probability that the customers transition to the higher growth/profitability opportunities. Prior customer transactions are segmented based on segmentation criteria and used to generate a transaction graph. The nodes of the transaction graph represent the segmented transactions and client transaction paths between the nodes represent potential customer life-cycle trajectories. The transaction graph can be used to identify high-value penetration opportunities.Type: GrantFiled: November 6, 2017Date of Patent: June 14, 2022Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Pawan Chowdhary, Markus Ettl, Donald Keefer, Gabriel Toma, Zhengliang Xue
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Patent number: 11295320Abstract: Systems, methods, and computer-readable media are disclosed for identifying customers having associated opportunities for improved growth and/or profitability with respect to product or service offerings and determining investment solutions that enhance the probability that the customers transition to the higher growth/profitability opportunities. Prior customer transactions are segmented based on segmentation criteria and used to generate a transaction graph. The nodes of the transaction graph represent the segmented transactions and client transaction paths between the nodes represent potential customer life-cycle trajectories. The transaction graph can be used to identify high-value penetration opportunities.Type: GrantFiled: June 29, 2017Date of Patent: April 5, 2022Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Pawan Chowdhary, Markus Ettl, Donald Keefer, Gabriel Toma, Zhengliang Xue
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Patent number: 11176492Abstract: A machine learning algorithm is trained to learn to cluster a plurality of original-destination routes in a network for transporting cargo into a plurality of clusters based on similarities of the original-destination routes, and to learn to cluster the plurality of clusters into a plurality of subgroups based on customer behavior. Influencing criteria associated with each of the subgroups may be determined and based on the influencing criteria, a price elasticity curve for each of the subgroups may be generated. Based on the price elasticity curve and current network traffic, cargo transportation price associated with each of the subgroups may be determined.Type: GrantFiled: March 14, 2019Date of Patent: November 16, 2021Assignee: International Business Machines CorporationInventors: Pawan R. Chowdhary, Markus R. Ettl, Roger D. Lederman, Tim Nonner, Ulrich B. Schimpel, Zhengliang Xue, Hongxia Yang
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Patent number: 11100570Abstract: Systems, methods, and computer-readable media are disclosed for identifying product configurations that are alternatives to a requested product configuration, ranking the alternative product configurations based on one or more pricing metrics, and presenting the alternative product configurations to a prospective customer, thereby providing the customer with the option of selecting an alternative product configuration in lieu of the initially requested product configuration.Type: GrantFiled: October 5, 2017Date of Patent: August 24, 2021Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Pawan Chowdhary, Markus R. Ettl, Somnath Mukherjee, Zhengliang Xue
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Patent number: 10789967Abstract: A noise detection method and a noise detection system are provided. The noise detection method includes: obtaining an audio signal; comparing the audio signal with a wave of a noise model to obtain a correlation value; and identifying whether the audio signal is a candidate noise signal based on the correlation value. The method can detect plugging noises effectively.Type: GrantFiled: May 9, 2016Date of Patent: September 29, 2020Assignee: Harman International Industries, IncorporatedInventors: Dong Yang, Zhengliang Xue, Lan Mao
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Patent number: 10692039Abstract: System and method that improves cargo logistics may be presented. For instance, shipping capacity in cargo logistics may be best utilized based on providing pricing and scheduling solutions that are jointly optimized and prices differentiated based on flexibility of service request. Scheduled service and pricing may be transmitted as a signal to control execution of the cargo logistics.Type: GrantFiled: September 20, 2016Date of Patent: June 23, 2020Assignee: International Business Machines CorporationInventors: Pawan R. Chowdhary, Markus R. Ettl, Zhenyu Hu, Roger D. Lederman, Zhengliang Xue
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Publication number: 20190213500Abstract: A machine learning algorithm is trained to learn to cluster a plurality of original-destination routes in a network for transporting cargo into a plurality of clusters based on similarities of the original-destination routes, and to learn to cluster the plurality of clusters into a plurality of subgroups based on customer behavior. Influencing criteria associated with each of the subgroups may be determined and based on the influencing criteria, a price elasticity curve for each of the subgroups may be generated. Based on the price elasticity curve and current network traffic, cargo transportation price associated with each of the subgroups may be determined.Type: ApplicationFiled: March 14, 2019Publication date: July 11, 2019Inventors: Pawan R. Chowdhary, Markus R. Ettl, Roger D. Lederman, Tim Nonner, Ulrich B. Schimpel, Zhengliang Xue, Hongxia Yang
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Patent number: 10332032Abstract: A machine learning algorithm is trained to learn to cluster a plurality of original-destination routes in a network for transporting cargo into a plurality of clusters based on similarities of the original-destination routes, and to learn to cluster the plurality of clusters into a plurality of subgroups based on customer behavior. Influencing criteria associated with each of the subgroups may be determined and based on the influencing criteria, a price elasticity curve for each of the subgroups may be generated. Based on the price elasticity curve and current network traffic, cargo transportation price associated with each of the subgroups may be determined.Type: GrantFiled: November 1, 2016Date of Patent: June 25, 2019Assignee: International Business Machines CorporationInventors: Pawan R. Chowdhary, Markus R. Ettl, Roger D. Lederman, Tim Nonner, Ulrich B. Schimpel, Zhengliang Xue, Hongxia Yang
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Publication number: 20190156851Abstract: A noise detection method and a noise detection system are provided. The noise detection method includes: obtaining an audio signal; comparing the audio signal with a wave of a noise model to obtain a correlation value; and identifying whether the audio signal is a candidate noise signal based on the correlation value. The method can detect plugging noises effectively.Type: ApplicationFiled: May 9, 2016Publication date: May 23, 2019Applicant: HARMAN INTERNATIONAL INDUSTRIES, INCORPORATEDInventors: Dong YANG, Zhengliang XUE, Lan MAO
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Publication number: 20190108579Abstract: Systems, methods, and computer-readable media are disclosed for identifying product configurations that are alternatives to a requested product configuration, ranking the alternative product configurations based on one or more pricing metrics, and presenting the alternative product configurations to a prospective customer, thereby providing the customer with the option of selecting an alternative product configuration in lieu of the initially requested product configuration.Type: ApplicationFiled: October 5, 2017Publication date: April 11, 2019Inventors: Pawan Chowdhary, Markus R. Ettl, Somnath Mukherjee, Zhengliang Xue
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Publication number: 20190005512Abstract: Systems, methods, and computer-readable media are disclosed for identifying customers having associated opportunities for improved growth and/or profitability with respect to product or service offerings and determining investment solutions that enhance the probability that the customers transition to the higher growth/profitability opportunities. Prior customer transactions are segmented based on segmentation criteria and used to generate a transaction graph. The nodes of the transaction graph represent the segmented transactions and client transaction paths between the nodes represent potential customer life-cycle trajectories.Type: ApplicationFiled: June 29, 2017Publication date: January 3, 2019Inventors: Pawan Chowdhary, Markus Ettl, Donald Keefer, Gabriel Toma, Zhengliang Xue
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Publication number: 20190005514Abstract: Systems, methods, and computer-readable media are disclosed for identifying customers having associated opportunities for improved growth and/or profitability with respect to product or service offerings and determining investment solutions that enhance the probability that the customers transition to the higher growth/profitability opportunities. Prior customer transactions are segmented based on segmentation criteria and used to generate a transaction graph. The nodes of the transaction graph represent the segmented transactions and client transaction paths between the nodes represent potential customer life-cycle trajectories.Type: ApplicationFiled: November 6, 2017Publication date: January 3, 2019Inventors: Pawan Chowdhary, Markus Ettl, Donald Keefer, Gabriel Toma, Zhengliang Xue
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Publication number: 20180121829Abstract: A machine learning algorithm is trained to learn to cluster a plurality of original-destination routes in a network for transporting cargo into a plurality of clusters based on similarities of the original-destination routes, and to learn to cluster the plurality of clusters into a plurality of subgroups based on customer behavior. Influencing criteria associated with each of the subgroups may be determined and based on the influencing criteria, a price elasticity curve for each of the subgroups may be generated. Based on the price elasticity curve and current network traffic, cargo transportation price associated with each of the subgroups may be determined.Type: ApplicationFiled: November 1, 2016Publication date: May 3, 2018Inventors: Pawan R. Chowdhary, Markus R. Ettl, Roger D. Lederman, Tim Nonner, Ulrich B. Schimpel, Zhengliang Xue, Hongxia Yang
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Publication number: 20180082253Abstract: System and method that improves cargo logistics may be presented. For instance, shipping capacity in cargo logistics may be best utilized based on providing pricing and scheduling solutions that are jointly optimized and prices differentiated based on flexibility of service request. Scheduled service and pricing may be transmitted as a signal to control execution of the cargo logistics.Type: ApplicationFiled: September 20, 2016Publication date: March 22, 2018Inventors: Pawan R. Chowdhary, Markus R. Ettl, Zhenyu Hu, Roger D. Lederman, Zhengliang Xue
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Publication number: 20180060925Abstract: Systems, methods, and computer-readable media are disclosed for jointly optimizing one or more parameters associated with multiple different price mechanisms. The price mechanisms may include spot market pricing, fixed-contract pricing, formula-based pricing, or the like, and may vary in duration. The optimized parameters may include an optimized price for each price mechanism that maximizes expected seller profit across all price mechanisms and all buyers.Type: ApplicationFiled: August 31, 2016Publication date: March 1, 2018Inventors: Markus Ettl, Shivaram Subramanian, Zhengliang Xue
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Publication number: 20180005319Abstract: Systems, methods, and computer-readable media are disclosed for optimizing terms of a long-term contract between a buyer and a seller for a product. Various types of forecasting models may be generated and used to determine the optimized terms such as an optimized price for the long-term contract. A risk model may be generated and evaluated to identify market disruptions that indicate that the optimized price should be re-negotiated to distribute the associated risk between the buyer and seller. An example of such a market disruption may be a market price fluctuation that causes a difference between a spot market price for the product and a contract price to meet or exceed a threshold value.Type: ApplicationFiled: June 30, 2016Publication date: January 4, 2018Inventors: Markus Ettl, Yan Shang, Shivaram Subramanian, Zhengliang Xue