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公开(公告)号：US20230259403A1
公开(公告)日：2023-08-17
申请号：US17674578
申请日：2022-02-17
Applicant: Adobe Inc.
Inventor： Atanu R. Sinha , Shiv Kumar Saini , Sapthotharan Krishnan Nair , Saarthak Sandip Marathe , Manupriya Gupta , Brahmbhatt Paresh Anand , Ayush Chauhan
CPC classification number: G06F9/5055 , H04L67/10 , H04L47/826
Abstract: In implementations of systems for cloud-based resource allocation using meters, a computing device implements a resource system to receive resource data describing an amount of cloud-based resources reserved for consumption by client devices during a period of time and a total amount of cloud-based resources consumed by the client devices during the period of time. The resource system determines a consumption distribution using each meter included in a set of meters. Each of the consumption distributions allocates a portion of the total amount of the cloud-based resources consumed to each client device of the client devices. A particular meter used to determine a particular consumption distribution is selected based on a Kendall Tau coefficient of the particular consumption distribution. An amount of cloud-based resources to allocate for a future period of time is estimated using the particular meter and an approximate Shapley value.
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公开(公告)号：US12182829B2
公开(公告)日：2024-12-31
申请号：US17849320
申请日：2022-06-24
Applicant: Adobe Inc.
Inventor： Sarthak Chakraborty , Sunav Choudhary , Atanu R. Sinha , Sapthotharan Krishnan Nair , Manoj Ghuhan Arivazhagan , Yuvraj , Atharva Anand Joshi , Atharv Tyagi , Shivi Gupta
IPC: G06Q30/0201 , G06N3/04 , G06Q30/0251
Abstract: A system includes a representation generator subsystem configured to execute a user representation model and a task prediction model to generate a user representation for a user. The user representation model receives user event sequence data comprises a sequence of user interactions with the system. The task prediction model is configured to train the user representation model. The user representation includes a vector of a predetermined size that represents the user event sequence data and is generated by applying the trained user representation model to the user event sequence data. A storage requirement of the user representation is less than a storage space requirement of the user event sequence data. The system includes a data store configured for storing the user representation in a user profile associated with the user.
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Patent Agency Ranking
公开(公告)号：US11544281B2
公开(公告)日：2023-01-03
申请号：US17100618
申请日：2020-11-20
Applicant: Adobe Inc.
Inventor： Subrata Mitra , Nikhil Sheoran , Anup Rao , Tung Mai , Sapthotharan Krishnan Nair , Shivakumar Vaithyanathan , Thomas Jacobs , Ghetia Siddharth , Jatin Varshney , Vikas Maddukuri , Laxmikant Mishra
IPC: G06F16/2458 , G06F16/215 , G06F16/28 , G06F16/22 , G06N20/00 , G06K9/62
Abstract: In some embodiments, a model training system trains a sample generation model configured to generate synthetic data entries for a dataset. The sample generation model includes a prior model for generating an estimated latent vector from a partially observed data entry, a proposal model for generating a latent vector from a data entry of the dataset and a mask corresponding to the partially observed data entry, and a generative model for generating the synthetic data entries from the latent vector and the partially observed data entry. The model training system trains the sample generation model to optimize an objective function that includes a first term determined using the synthetic data entries and a second term determined using the estimated latent vector and the latent vector. The trained sample generation model can be executed on a client computing device to service queries using the generated synthetic data entries.
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公开(公告)号：US20220164346A1
公开(公告)日：2022-05-26
申请号：US17100618
申请日：2020-11-20
Applicant: Adobe Inc.
Inventor： Subrata Mitra , Nikhil Sheoran , Anup Rao , Tung Mai , Sapthotharan Krishnan Nair , Shivakumar Vaithyanathan , Thomas Jacobs , Ghetia Siddharth , Jatin Varshney , Vikas Maddukuri , Laxmikant Mishra
IPC: G06F16/2458 , G06F16/215 , G06F16/28 , G06F16/22 , G06K9/62 , G06N20/00
Abstract: In some embodiments, a model training system trains a sample generation model configured to generate synthetic data entries for a dataset. The sample generation model includes a prior model for generating an estimated latent vector from a partially observed data entry, a proposal model for generating a latent vector from a data entry of the dataset and a mask corresponding to the partially observed data entry, and a generative model for generating the synthetic data entries from the latent vector and the partially observed data entry. The model training system trains the sample generation model to optimize an objective function that includes a first term determined using the synthetic data entries and a second term determined using the estimated latent vector and the latent vector. The trained sample generation model can be executed on a client computing device to service queries using the generated synthetic data entries.
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公开(公告)号：US12086646B2
公开(公告)日：2024-09-10
申请号：US17674578
申请日：2022-02-17
Applicant: Adobe Inc.
Inventor： Atanu R. Sinha , Shiv Kumar Saini , Sapthotharan Krishnan Nair , Saarthak Sandip Marathe , Manupriya Gupta , Brahmbhatt Paresh Anand , Ayush Chauhan
CPC classification number: G06F9/5055 , H04L47/826 , H04L67/10
Abstract: In implementations of systems for cloud-based resource allocation using meters, a computing device implements a resource system to receive resource data describing an amount of cloud-based resources reserved for consumption by client devices during a period of time and a total amount of cloud-based resources consumed by the client devices during the period of time. The resource system determines a consumption distribution using each meter included in a set of meters. Each of the consumption distributions allocates a portion of the total amount of the cloud-based resources consumed to each client device of the client devices. A particular meter used to determine a particular consumption distribution is selected based on a Kendall Tau coefficient of the particular consumption distribution. An amount of cloud-based resources to allocate for a future period of time is estimated using the particular meter and an approximate Shapley value.
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6.发明公开公开(公告)号：US20230419339A1
公开(公告)日：2023-12-28
申请号：US17849320
申请日：2022-06-24
Applicant: Adobe Inc.
Inventor： Sarthak Chakraborty , Sunav Choudhary , Atanu R. Sinha , Sapthotharan Krishnan Nair , Manoj Ghuhan Arivazhagan , Yuvraj , Atharva Anand Joshi , Atharv Tyagi , Shivi Gupta
CPC classification number: G06Q30/0201 , G06N3/04 , G06Q30/0269 , G06Q30/0255
Abstract: A system includes a representation generator subsystem configured to execute a user representation model and a task prediction model to generate a user representation for a user. The user representation model receives user event sequence data comprises a sequence of user interactions with the system. The task prediction model is configured to train the user representation model. The user representation includes a vector of a predetermined size that represents the user event sequence data and is generated by applying the trained user representation model to the user event sequence data. A storage requirement of the user representation is less than a storage space requirement of the user event sequence data. The system includes a data store configured for storing the user representation in a user profile associated with the user.
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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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