公开(公告)号：US20230021653A1

公开(公告)日：2023-01-26

申请号：US17955781

申请日：2022-09-29

Applicant: Adobe Inc.

Inventor： Anirban Basu ,  Tathagata Sengupta ,  Kunal Kumar Jain ,  Ashish Kumar

IPC: G06Q30/02 ,  G06F16/9038

Abstract: Keyword bids determined from sparse data are described. Initially, a portfolio optimization platform identifies which keywords included in a portfolio of keywords are low-impression keywords. This platform trains a machine learning model to generate bids for the low-impression keywords with historical data from a search engine. In particular, the platform trains this machine learning model according to an algorithm suited for training with sparse amounts of data, e.g., a temporal difference learning algorithm. In contrast, the platform uses different models, trained according to different algorithms than the low-impression keyword model, to generate bids for keywords determined not to be low-impression keywords. Once the low-impression keyword model is trained offline, the platform deploys the model for use online to generate actual bids for the low-impression keywords and submits them to the search engine. The platform continues to update the low-impression keyword model while deployed according to the sparse-data algorithm.

公开(公告)号：US11861664B2

公开(公告)日：2024-01-02

申请号：US17955781

申请日：2022-09-29

Applicant: Adobe Inc.

Inventor： Anirban Basu ,  Tathagata Sengupta ,  Kunal Kumar Jain ,  Ashish Kumar

IPC: G06Q30/00 ,  G06Q30/0273 ,  G06F16/9038

CPC classification number: G06Q30/0275 ,  G06F16/9038

Abstract: Keyword bids determined from sparse data are described. Initially, a portfolio optimization platform identifies which keywords included in a portfolio of keywords are low-impression keywords. This platform trains a machine learning model to generate bids for the low-impression keywords with historical data from a search engine. In particular, the platform trains this machine learning model according to an algorithm suited for training with sparse amounts of data, e.g., a temporal difference learning algorithm. In contrast, the platform uses different models, trained according to different algorithms than the low-impression keyword model, to generate bids for keywords determined not to be low-impression keywords. Once the low-impression keyword model is trained offline, the platform deploys the model for use online to generate actual bids for the low-impression keywords and submits them to the search engine. The platform continues to update the low-impression keyword model while deployed according to the sparse-data algorithm.

公开(公告)号：US11494810B2

公开(公告)日：2022-11-08

申请号：US16555380

申请日：2019-08-29

Applicant: Adobe Inc.

Inventor： Anirban Basu ,  Tathagata Sengupta ,  Kunal Kumar Jain ,  Ashish Kumar

IPC: G06Q30/00 ,  G06Q30/02 ,  G06F16/9038

Abstract: Keyword bids determined from sparse data are described. Initially, a portfolio optimization platform identifies which keywords included in a portfolio of keywords are low-impression keywords. This platform trains a machine learning model to generate bids for the low-impression keywords with historical data from a search engine. In particular, the platform trains this machine learning model according to an algorithm suited for training with sparse amounts of data, e.g., a temporal difference learning algorithm. In contrast, the platform uses different models, trained according to different algorithms than the low-impression keyword model, to generate bids for keywords determined not to be low-impression keywords. Once the low-impression keyword model is trained offline, the platform deploys the model for use online to generate actual bids for the low-impression keywords and submits them to the search engine. The platform continues to update the low-impression keyword model while deployed according to the sparse-data algorithm.

公开(公告)号：US20210065250A1

公开(公告)日：2021-03-04

申请号：US16555380

申请日：2019-08-29

Applicant: Adobe Inc.

Inventor： Anirban Basu ,  Tathagata Sengupta ,  Kunal Kumar Jain ,  Ashish Kumar

IPC: G06Q30/02 ,  G06F16/9038

Abstract: Keyword bids determined from sparse data are described. Initially, a portfolio optimization platform identifies which keywords included in a portfolio of keywords are low-impression keywords. This platform trains a machine learning model to generate bids for the low-impression keywords with historical data from a search engine. In particular, the platform trains this machine learning model according to an algorithm suited for training with sparse amounts of data, e.g., a temporal difference learning algorithm. In contrast, the platform uses different models, trained according to different algorithms than the low-impression keyword model, to generate bids for keywords determined not to be low-impression keywords. Once the low-impression keyword model is trained offline, the platform deploys the model for use online to generate actual bids for the low-impression keywords and submits them to the search engine. The platform continues to update the low-impression keyword model while deployed according to the sparse-data algorithm.

公开(公告)号：US10783549B2

公开(公告)日：2020-09-22

申请号：US15355673

申请日：2016-11-18

Applicant: Adobe Inc.

Inventor： Moumita Sinha ,  Varun Gupta ,  Tathagata Sengupta ,  Niloy Ganguly ,  Faran Ahmad

IPC: G06Q30/02

Abstract: The present disclosure is directed towards methods and systems for determining a persuasiveness of a content item. The systems and methods receive a content item from a client device and analyze the content item. Analyzing the content item includes analyzing at least one textual element, at least one image element, and at least one layout element of the content item to determine a first persuasion score, a second persuasion score, and a third persuasion score of the elements the content item. The systems and methods also generate a persuasion score of the content item and provide the persuasion score of the content item to the client device.