-
公开(公告)号：US12164517B2
公开(公告)日：2024-12-10
申请号：US18092779
申请日：2023-01-03
Applicant: Adobe Inc.
Inventor： Vibhor Porwal , Yeuk-Yin Chan , Vidit Bhatia , Subrata Mitra , Shaddy Garg , Sergey N Kazarin , Sameeksha Arora , Himanshu Panday , Gautam Pratap Kowshik , Fan Du , Anup Bandigadi Rao , Anil Malkani
IPC: G06F16/245 , G06F16/2453 , G06F16/2458
Abstract: To retrieve information derived from a plurality of separately stored datasets, join structures are identified within the plurality of separately stored datasets. Join structures can include datasets joined by a central dataset, datasets joined by a single key, and datasets joined across a plurality of keys. Each of the join structures corresponds to a query processing schema that defines a sampling technique. When a join query is received as a SQL query, the join query identifies a portion of the plurality of separately stored datasets, from which a join structure is selected and a corresponding query processing schema is identified. The join query is reconstructed to form a reconstructed join query that comprises query processing schema instructions to derive the requested information using the sampling technique defined by the identified query processing schema.
-
公开(公告)号：US12014217B2
公开(公告)日：2024-06-18
申请号：US17538663
申请日：2021-11-30
Applicant: Adobe Inc.
Inventor： Subrata Mitra , Sunav Choudhary , Shaddy Garg , Anuj Jitendra Diwan , Piyush Kumar Maurya , Arpit Aggarwal , Prateek Jain
IPC: G06F9/44 , G06F9/50 , G06F18/214 , G06N20/00
CPC classification number: G06F9/5038 , G06F9/5044 , G06F9/5055 , G06F9/5088 , G06F18/214 , G06N20/00
Abstract: A resource control system is described that is configured to control scheduling of executable jobs by compute instances of a service provider system. In one example, the resource control system outputs a deployment user interface to obtain job information. Upon receipt of the job information, the resource control system communicates with a service provider system to obtain logs from compute instances implemented by the service provider system for the respective executable jobs. The resource control system uses data obtained from the logs to estimate utility indicating status of respective executable jobs and an amount of time to complete the executable jobs by respective compute instances. The resource control system then employs a machine-learning module to generate an action to be performed by compute instances for respective executable jobs.
-
公开(公告)号：US20230168941A1
公开(公告)日：2023-06-01
申请号：US17538663
申请日：2021-11-30
Applicant: Adobe Inc.
Inventor： Subrata Mitra , Sunav Choudhary , Shaddy Garg , Anuj Jitendra Diwan , Piyush Kumar Maurya , Arpit Aggarwal , Prateek Jain
CPC classification number: G06F9/5038 , G06F9/5044 , G06F9/5055 , G06F9/5088 , G06K9/6256 , G06N20/00
Abstract: A resource control system is described that is configured to control scheduling of executable jobs by compute instances of a service provider system. In one example, the resource control system outputs a deployment user interface to obtain job information. Upon receipt of the job information, the resource control system communicates with a service provider system to obtain logs from compute instances implemented by the service provider system for the respective executable jobs. The resource control system uses data obtained from the logs to estimate utility indicating status of respective executable jobs and an amount of time to complete the executable jobs by respective compute instances. The resource control system then employs a machine-learning module to generate an action to be performed by compute instances for respective executable jobs.
-
Patent Agency Ranking
公开(公告)号：US20240303662A1
公开(公告)日：2024-09-12
申请号：US18181018
申请日：2023-03-09
Applicant: ADOBE INC.
Inventor： Raunak Shah , Harshvardhan . , Mohit Kumar , Shambhavi Pardhi , Alakh Dixit , Shaddy Garg , Shiv Kumar Saini , Ramasuri Narayanam
CPC classification number: G06Q20/4016 , G06Q20/389
Abstract: Systems and methods for identifying fraudulent activity in NFT exchanges are described. Embodiments of the present disclosure obtain transaction data for non-fungible tokens (NFTs) and generate a transaction graph based on the transaction data. The transaction graph includes nodes corresponding to blockchain addresses and nodes corresponding to individual NFTs. Embodiments additionally identify a cycle of the transaction graph, predict a fraudulent activity based on the cycle using a machine learning model, and transmit an alert to a user indicating the predicted fraudulent activity.
-
公开(公告)号：US20230367772A1
公开(公告)日：2023-11-16
申请号：US17741811
申请日：2022-05-11
Applicant: Adobe Inc.
Inventor： Subrata Mitra , Yash Gadhia , Tong Yu , Shaddy Garg , Nikhil Sheoran , Arjun Kashettiwar , Anjali Yadav
IPC: G06F16/2455 , G06F16/2457 , G06F16/2458 , G06F16/2453 , G06K9/62
CPC classification number: G06F16/2455 , G06F16/2457 , G06F16/2474 , G06F16/24542 , G06K9/6262
Abstract: Some techniques described herein relate to utilizing a machine-learning (ML) model to select respective samples for queries of a query sequence. In one example, a method includes receiving a query in a query sequence, where the query is directed toward a dataset. Samples are available as down-sampled versions of the dataset. The method further include applying an agent to select, for the query, a sample from among the samples of the dataset. The agent includes an ML model trained, such as via intent-based reinforcement learning, to select respective samples for queries. The query is then executed against the sample to output a response.
-
公开(公告)号：US12079217B2
公开(公告)日：2024-09-03
申请号：US17741811
申请日：2022-05-11
Applicant: Adobe Inc.
Inventor： Subrata Mitra , Yash Gadhia , Tong Yu , Shaddy Garg , Nikhil Sheoran , Arjun Kashettiwar , Anjali Yadav
IPC: G06F16/2455 , G06F16/2453 , G06F16/2457 , G06F16/2458 , G06F18/21
CPC classification number: G06F16/2455 , G06F16/24542 , G06F16/2457 , G06F16/2474 , G06F18/217
Abstract: Some techniques described herein relate to utilizing a machine-learning (ML) model to select respective samples for queries of a query sequence. In one example, a method includes receiving a query in a query sequence, where the query is directed toward a dataset. Samples are available as down-sampled versions of the dataset. The method further include applying an agent to select, for the query, a sample from among the samples of the dataset. The agent includes an ML model trained, such as via intent-based reinforcement learning, to select respective samples for queries. The query is then executed against the sample to output a response.
-
公开(公告)号：US20230306318A1
公开(公告)日：2023-09-28
申请号：US17656263
申请日：2022-03-24
Applicant: ADOBE INC.
Inventor： Shaddy Garg , Shubham Agarwal , Sumit Bisht , Chahat Jain , Ashritha Gonuguntla , Nikhil Sheoran , Shiv Kumar Saini
Abstract: A method and system for outage forecasting are described. One or more aspects of the method and system include receiving, by a machine learning model, time series data for a service metric of a computer network; generating, by the machine learning model, probability distribution information for the service metric based on the time series data, wherein the probability distribution information is generated using a machine learning model that is trained using a distribution loss and a classification loss; and generating, by a forecasting component, outage forecasting information for the computer network based on the probability distribution information.
-
公开(公告)号：US20220108334A1
公开(公告)日：2022-04-07
申请号：US17060723
申请日：2020-10-01
Applicant: ADOBE INC.
Inventor： AYUSH CHAUHAN , Aditya Anand , Sunny Dhamnani , Shaddy Garg , Shiv Kumar Saini
Abstract: Systems and methods for data analytics are described. The systems and methods include receiving attribute data for at least one user, identifying a plurality of precursor events causally related to an observable target interaction with the at least one user, wherein at least one of the precursor events comprises a marketing event, predicting a probability for each of the precursor events based on the attribute data using a neural network trained with a first loss function comparing individual level training data for the observable target interaction, and performing the marketing event directed to the at least one user based at least in part on the predicted probabilities.
-
-
-
-
-
-
-
Search Results
8
records
(took 0.013 seconds)
