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公开(公告)号：US20240338612A1
公开(公告)日：2024-10-10
申请号：US18745654
申请日：2024-06-17
Applicant: Apple Inc.
Inventor： Moises Goldszmidt , Anatoly D. Adamov , Juan C. Garcia , Julia R. Reisler , Timothy S. Paek , Vishwas Kulkarni , Yu-Chung Hsiao , Pavan Chitta
IPC: G06N20/20 , G06F18/21 , G06F18/214 , G06F18/2415 , G06N20/00
CPC classification number: G06N20/20 , G06F18/214 , G06F18/217 , G06F18/2415 , G06N20/00
Abstract: Systems and methods are disclosed for triggering an update to a machine-learning model upon detecting that a distribution of particular (e.g., recently collected) input data set is sufficiently different from a distribution training input data set used to train the model. The distributions may be determined to be sufficiently different when a classifier can identify to which distribution individual data elements belong (e.g., to at least a predetermined degree). An update to the machine-learning model can include morphing weights used by the model and/or retraining the model.
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Patent Agency Ranking
公开(公告)号：US11562297B2
公开(公告)日：2023-01-24
申请号：US16875825
申请日：2020-05-15
Applicant: Apple Inc.
Inventor： Moises Goldszmidt , Anatoly D. Adamov , Juan C. Garcia , Julia R. Reisler , Timothy S. Paek , Vishwas Kulkarni , Yu-Chung Hsiao , Pavan Chitta
Abstract: Systems and methods are disclosed for triggering an update to a machine-learning model upon detecting that a distribution of particular (e.g., recently collected) input data set is sufficiently different from a distribution training input data set used to train the model. The distributions may be determined to be sufficiently different when a classifier can identify to which distribution individual data elements belong (e.g., to at least a predetermined degree). An update to the machine-learning model can include morphing weights used by the model and/or retraining the model.
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公开(公告)号：US12020133B2
公开(公告)日：2024-06-25
申请号：US18082476
申请日：2022-12-15
Applicant: Apple Inc.
Inventor： Moises Goldszmidt , Anatoly D. Adamov , Juan C. Garcia , Julia R. Reisler , Timothy S. Paek , Vishwas Kulkarni , Yu-Chung Hsiao , Pavan Chitta
IPC: G06N20/20 , G06F18/21 , G06F18/214 , G06F18/2415 , G06N20/00
CPC classification number: G06N20/20 , G06F18/214 , G06F18/217 , G06F18/2415 , G06N20/00
Abstract: Systems and methods are disclosed for triggering an update to a machine-learning model upon detecting that a distribution of particular (e.g., recently collected) input data set is sufficiently different from a distribution training input data set used to train the model. The distributions may be determined to be sufficiently different when a classifier can identify to which distribution individual data elements belong (e.g., to at least a predetermined degree). An update to the machine-learning model can include morphing weights used by the model and/or retraining the model.
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公开(公告)号：US20230124380A1
公开(公告)日：2023-04-20
申请号：US18082476
申请日：2022-12-15
Applicant: Apple Inc.
Inventor： Moises Goldszmidt , Anatoly D. Adamov , Juan C. Garcia , Julia R. Reisler , Timothy S. Paek , Vishwas Kulkarni , Yu-Chung Hsiao , Pavan Chitta
IPC: G06N20/00 , G06F18/214 , G06F18/21 , G06F18/2415
Abstract: Systems and methods are disclosed for triggering an update to a machine-learning model upon detecting that a distribution of particular (e.g., recently collected) input data set is sufficiently different from a distribution training input data set used to train the model. The distributions may be determined to be sufficiently different when a classifier can identify to which distribution individual data elements belong (e.g., to at least a predetermined degree). An update to the machine-learning model can include morphing weights used by the model and/or retraining the model.
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公开(公告)号：US20210224687A1
公开(公告)日：2021-07-22
申请号：US16875825
申请日：2020-05-15
Applicant: Apple Inc.
Inventor： Moises Goldszmidt , Anatoly D. Adamov , Juan C. Garcia , Julia R. Reisler , Timothy S. Paek , Vishwas Kulkarni , Yu-Chung Hsiao , Pavan Chitta
Abstract: Systems and methods are disclosed for triggering an update to a machine-learning model upon detecting that a distribution of particular (e.g., recently collected) input data set is sufficiently different from a distribution training input data set used to train the model. The distributions may be determined to be sufficiently different when a classifier can identify to which distribution individual data elements belong (e.g., to at least a predetermined degree). An update to the machine-learning model can include morphing weights used by the model and/or retraining the model.
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公开(公告)号：US10445429B2
公开(公告)日：2019-10-15
申请号：US15867480
申请日：2018-01-10
Applicant: Apple Inc.
Inventor： Mubarak Ali Seyed Ibrahim , Juan C. Garcia , Rushin N. Shah , Nicholas K. Treadgold , Justin J. Brinegar , Gagan Aneja , Alan Qian
Abstract: Systems and processes for natural language processing using vocabularies with compressed serialized tries are described in the present disclosure. In one example process, natural language input is received. The natural language input is parsed, using a vocabulary, to determine a corresponding user intent. The parsing includes using a data structure of the vocabulary to map a first word of the natural language input to first semantic information and a second word of the natural language input to second semantic information. The data structure includes pointers that map to a same semantic data object of the vocabulary. The first semantic information and the second semantic information are determined using the same semantic data object. The user intent is determined based on the first semantic information and the second semantic information. Performance of a task corresponding to the determined user intent is initiated.
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