Patents by Inventor Tung Thanh Mai
Tung Thanh Mai 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: 11914665Abstract: Multi-modal machine-learning model training techniques for search are described that overcome conventional challenges and inefficiencies to support real time output, which is not possible in conventional training techniques. In one example, a search system is configured to support multi-modal machine-learning model training. This includes use of a preview mode and an expanded mode. In the preview mode, a preview segment is generated as part of real time training of a machine learning model. In the expanded mode, the preview segment is persisted as an expanded segment that is used to train and utilize an expanded machine-learning model as part of search.Type: GrantFiled: February 18, 2022Date of Patent: February 27, 2024Assignee: Adobe Inc.Inventors: Matvey Kapilevich, Margarita R. Savova, Anup Bandigadi Rao, Tung Thanh Mai, Lakshmi Shivalingaiah, Liron Goren Snai, Charles Menguy, Vijeth Lomada, Moumita Sinha, Harleen Sahni
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Patent number: 11829940Abstract: In implementations of item transfer control systems, a computing device implements a transfer system to receive input data describing types of requested items and corresponding quantities of the types of requested items to receive at each of a plurality of destination sites and types of available items and corresponding quantities of the types of available items that are available at each of a plurality of source sites. The transfer system constructs a flow network having a source node for each of the plurality of the source sites and a destination node for each of the plurality of the destination sites. An integral approximate solution is generated that transfers the corresponding quantities of the types of requested items to each of the plurality of the destination sites using a maximum flow solver and the flow network.Type: GrantFiled: March 6, 2023Date of Patent: November 28, 2023Assignee: Adobe Inc.Inventors: Kirankumar Shiragur, Tung Thanh Mai, Anup Bandigadi Rao, Ryan A. Rossi, Georgios Theocharous, Michele Saad
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Publication number: 20230297430Abstract: Machine-learning model retargeting techniques are described. In one example, training data is generated by extrapolating feedback data collected from entities. These techniques supports an ability to identify a wider range of thresholds and corresponding entities than those available in the feedback data. This also provides an opportunity to explore additional thresholds than those used in the past through extrapolating operations outside of a range used to define a segment, for which, the feedback data is captured. These techniques also support retargeting of a machine-learning model for a secondary label that is different than a primary label used to initially train the machine-learning model.Type: ApplicationFiled: March 16, 2022Publication date: September 21, 2023Applicant: Adobe Inc.Inventors: Moumita Sinha, Anup Bandigadi Rao, Tung Thanh Mai, Vijeth Lomada, Margarita R. Savova, Sapthotharan Krishnan Nair, Harleen Sahni
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Publication number: 20230267158Abstract: Multi-modal machine-learning model training techniques for search are described that overcome conventional challenges and inefficiencies to support real time output, which is not possible in conventional training techniques. In one example, a search system is configured to support multi-modal machine-learning model training. This includes use of a preview mode and an expanded mode. In the preview mode, a preview segment is generated as part of real time training of a machine learning model. In the expanded mode, the preview segment is persisted as an expanded segment that is used to train and utilize an expanded machine-learning model as part of search.Type: ApplicationFiled: February 18, 2022Publication date: August 24, 2023Applicant: Adobe Inc.Inventors: Matvey Kapilevich, Margarita R. Savova, Anup Bandigadi Rao, Tung Thanh Mai, Lakshmi Shivalingaiah, Liron Goren Snai, Charles Menguy, Vijeth Lomada, Moumita Sinha, Harleen Sahni
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Publication number: 20230206171Abstract: In implementations of item transfer control systems, a computing device implements a transfer system to receive input data describing types of requested items and corresponding quantities of the types of requested items to receive at each of a plurality of destination sites and types of available items and corresponding quantities of the types of available items that are available at each of a plurality of source sites. The transfer system constructs a flow network having a source node for each of the plurality of the source sites and a destination node for each of the plurality of the destination sites. An integral approximate solution is generated that transfers the corresponding quantities of the types of requested items to each of the plurality of the destination sites using a maximum flow solver and the flow network.Type: ApplicationFiled: March 6, 2023Publication date: June 29, 2023Applicant: Adobe Inc.Inventors: Kirankumar Shiragur, Tung Thanh Mai, Anup Bandigadi Rao, Ryan A. Rossi, Georgios Theocharous, Michele Saad
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Patent number: 11636423Abstract: In implementations of item transfer control systems, a computing device implements a transfer system to receive input data describing types of requested items and corresponding quantities of the types of requested items to receive at each of a plurality of destination sites and types of available items and corresponding quantities of the types of available items that are available at each of a plurality of source sites. The transfer system constructs a flow network having a source node for each of the plurality of the source sites and a destination node for each of the plurality of the destination sites. An integral approximate solution is generated that transfers the corresponding quantities of the types of requested items to each of the plurality of the destination sites using a maximum flow solver and the flow network.Type: GrantFiled: August 5, 2021Date of Patent: April 25, 2023Assignee: Adobe Inc.Inventors: Kirankumar Shiragur, Tung Thanh Mai, Anup Bandigadi Rao, Ryan A. Rossi, Georgios Theocharous, Michele Saad
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Publication number: 20230041594Abstract: In implementations of item transfer control systems, a computing device implements a transfer system to receive input data describing types of requested items and corresponding quantities of the types of requested items to receive at each of a plurality of destination sites and types of available items and corresponding quantities of the types of available items that are available at each of a plurality of source sites. The transfer system constructs a flow network having a source node for each of the plurality of the source sites and a destination node for each of the plurality of the destination sites. An integral approximate solution is generated that transfers the corresponding quantities of the types of requested items to each of the plurality of the destination sites using a maximum flow solver and the flow network.Type: ApplicationFiled: August 5, 2021Publication date: February 9, 2023Applicant: Adobe Inc.Inventors: Kirankumar Shiragur, Tung Thanh Mai, Anup Bandigadi Rao, Ryan A. Rossi, Georgios Theocharous, Michele Saad