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公开(公告)号：US20230267158A1
公开(公告)日：2023-08-24
申请号：US17675290
申请日：2022-02-18
Applicant: Adobe Inc.
Inventor： Matvey Kapilevich , Margarita R. Savova , Anup Bandigadi Rao , Tung Thanh Mai , Lakshmi Shivalingaiah , Liron Goren Snai , Charles Menguy , Vijeth Lomada , Moumita Sinha , Harleen Sahni
IPC: G06F16/9538 , G06F16/901 , G06F16/28
CPC classification number: G06F16/9538 , G06F16/9024 , G06F16/283 , G06N20/00
Abstract: 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.
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公开(公告)号：US20230206171A1
公开(公告)日：2023-06-29
申请号：US18117586
申请日：2023-03-06
Applicant: Adobe Inc.
Inventor： Kirankumar Shiragur , Tung Thanh Mai , Anup Bandigadi Rao , Ryan A. Rossi , Georgios Theocharous , Michele Saad
IPC: G06Q10/0835 , G06F17/11 , G06Q10/087 , G06Q10/047
CPC classification number: G06Q10/08355 , G06F17/11 , G06Q10/087 , G06Q10/047
Abstract: 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. The transfer system causes transferences of the corresponding quantities of the types of requested items to each of the plurality of the destination sites based on the integral approximate solution.
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Patent Agency Ranking
公开(公告)号：US11636423B2
公开(公告)日：2023-04-25
申请号：US17394707
申请日：2021-08-05
Applicant: Adobe Inc.
Inventor： Kirankumar Shiragur , Tung Thanh Mai , Anup Bandigadi Rao , Ryan A. Rossi , Georgios Theocharous , Michele Saad
IPC: G06Q10/0835 , G06F17/11 , G06Q10/087 , G06Q10/047
Abstract: 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. The transfer system causes transferences of the corresponding quantities of the types of requested items to each of the plurality of the destination sites based on the integral approximate solution.
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公开(公告)号：US11914665B2
公开(公告)日：2024-02-27
申请号：US17675290
申请日：2022-02-18
Applicant: Adobe Inc.
Inventor： Matvey Kapilevich , Margarita R. Savova , Anup Bandigadi Rao , Tung Thanh Mai , Lakshmi Shivalingaiah , Liron Goren Snai , Charles Menguy , Vijeth Lomada , Moumita Sinha , Harleen Sahni
IPC: G06F16/248 , G06F16/9538 , G06F16/28 , G06F16/901 , G06N20/00
CPC classification number: G06F16/9538 , G06F16/248 , G06F16/283 , G06F16/9024 , G06N20/00
Abstract: 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.
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公开(公告)号：US11829940B2
公开(公告)日：2023-11-28
申请号：US18117586
申请日：2023-03-06
Applicant: Adobe Inc.
Inventor： Kirankumar Shiragur , Tung Thanh Mai , Anup Bandigadi Rao , Ryan A. Rossi , Georgios Theocharous , Michele Saad
IPC: G06Q10/0835 , G06Q10/087 , G06Q10/047 , G06F17/11
CPC classification number: G06Q10/08355 , G06F17/11 , G06Q10/047 , G06Q10/087
Abstract: 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. The transfer system causes transferences of the corresponding quantities of the types of requested items to each of the plurality of the destination sites based on the integral approximate solution.
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公开(公告)号：US20230297430A1
公开(公告)日：2023-09-21
申请号：US17696148
申请日：2022-03-16
Applicant: Adobe Inc.
Inventor： Moumita Sinha , Anup Bandigadi Rao , Tung Thanh Mai , Vijeth Lomada , Margarita R. Savova , Sapthotharan Krishnan Nair , Harleen Sahni
CPC classification number: G06F9/5044 , G06F9/5077 , G06K9/6262 , G06K9/6257 , G06N20/00
Abstract: 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.
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公开(公告)号：US20230041594A1
公开(公告)日：2023-02-09
申请号：US17394707
申请日：2021-08-05
Applicant: Adobe Inc.
Inventor： Kirankumar Shiragur , Tung Thanh Mai , Anup Bandigadi Rao , Ryan A. Rossi , Georgios Theocharous , Michele Saad
Abstract: 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. The transfer system causes transferences of the corresponding quantities of the types of requested items to each of the plurality of the destination sites based on the integral approximate solution.
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