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：US20240345707A1

公开(公告)日：2024-10-17

申请号：US18409638

申请日：2024-01-10

Applicant: Adobe Inc.

Inventor： Yaman Kumar ,  Somesh Singh ,  William Brandon George ,  Timothy Chia-chi Liu ,  Suman Basetty ,  Pranjal Prasoon ,  Nikaash Puri ,  Mihir Naware ,  Mihai Corlan ,  Joshua Marshall Butikofer ,  Abhinav Chauhan ,  Kumar Mrityunjay Singh ,  James Patrick O'Reilly ,  Hyman Chung ,  Lauren Dest ,  Clinton Hansen Goudie-Nice ,  Brandon John Pack ,  Balaji Krishnamurthy ,  Kunal Kumar Jain ,  Alexander Klimetschek ,  Matthew William Rozen

IPC: G06F3/0484 ,  G06F3/0482 ,  G06F18/2415 ,  G06F40/151 ,  G06F40/166 ,  G06T11/20 ,  G06V10/40 ,  G06V10/764

CPC classification number: G06F3/0484 ,  G06F3/0482 ,  G06F18/2415 ,  G06F40/151 ,  G06F40/166 ,  G06T11/206 ,  G06V10/40 ,  G06V10/764 ,  G06T2200/24

Abstract: Content creation techniques are described that leverage content analytics to provide insight and guidance as part of content creation. To do so, content features are extracted by a content analytics system from a plurality of content and used by the content analytics system as a basis to generate a content dataset. Event data is also collected by the content analytics system from an event data source. Event data describes user interaction with respective items of content, including subsequent activities in both online and physical environments. The event data is then used to generate an event dataset. An analytics user interface is then generated by the content analytics system using the content dataset and the event dataset and is usable to guide subsequent content creation and editing.

公开(公告)号：US12008033B2

公开(公告)日：2024-06-11

申请号：US17447908

申请日：2021-09-16

Applicant: ADOBE INC.

Inventor： Yaman Kumar ,  Vinh Ngoc Khuc ,  Vijay Srivastava ,  Umang Moorarka ,  Sukriti Verma ,  Simra Shahid ,  Shirsh Bansal ,  Shankar Venkitachalam ,  Sean Steimer ,  Sandipan Karmakar ,  Nimish Srivastav ,  Nikaash Puri ,  Mihir Naware ,  Kunal Kumar Jain ,  Kumar Mrityunjay Singh ,  Hyman Chung ,  Horea Bacila ,  Florin Silviu Iordache ,  Deepak Pai ,  Balaji Krishnamurthy

IPC: G06F7/02 ,  G06F16/00 ,  G06F16/535 ,  G06F16/54 ,  G06F16/58 ,  G06F16/583 ,  G06N20/00

CPC classification number: G06F16/5866 ,  G06F16/535 ,  G06F16/54 ,  G06F16/583 ,  G06N20/00

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for determining user affinities by tracking historical user interactions with tagged digital content and using the user affinities in content generation applications. Accordingly, the system may track user interactions with published digital content in order to generate user interaction reports whenever a user engages with the digital content. The system may aggregate the interaction reports to generate an affinity profile for a user or audience of users. A marketer may then generate digital content for a target user or audience of users and the system may process the digital content to generate a set of tags for the digital content. Based on the set of tags, the system may then evaluate the digital content in view of the affinity profile for the target user/audience to determine similarities or differences between the digital content and the affinity profile.

公开(公告)号：US12124683B1

公开(公告)日：2024-10-22

申请号：US18409638

申请日：2024-01-10

Applicant: Adobe Inc.

Inventor： Yaman Kumar ,  Somesh Singh ,  William Brandon George ,  Timothy Chia-chi Liu ,  Suman Basetty ,  Pranjal Prasoon ,  Nikaash Puri ,  Mihir Naware ,  Mihai Corlan ,  Joshua Marshall Butikofer ,  Abhinav Chauhan ,  Kumar Mrityunjay Singh ,  James Patrick O'Reilly ,  Hyman Chung ,  Lauren Dest ,  Clinton Hansen Goudie-Nice ,  Brandon John Pack ,  Balaji Krishnamurthy ,  Kunal Kumar Jain ,  Alexander Klimetschek ,  Matthew William Rozen

IPC: G06F3/0484 ,  G06F3/0482 ,  G06F18/2415 ,  G06F40/151 ,  G06F40/166 ,  G06T11/20 ,  G06V10/40 ,  G06V10/764

CPC classification number: G06F3/0484 ,  G06F3/0482 ,  G06F18/2415 ,  G06F40/151 ,  G06F40/166 ,  G06T11/206 ,  G06V10/40 ,  G06V10/764 ,  G06T2200/24

Abstract: Content creation techniques are described that leverage content analytics to provide insight and guidance as part of content creation. To do so, content features are extracted by a content analytics system from a plurality of content and used by the content analytics system as a basis to generate a content dataset. Event data is also collected by the content analytics system from an event data source. Event data describes user interaction with respective items of content, including subsequent activities in both online and physical environments. The event data is then used to generate an event dataset. An analytics user interface is then generated by the content analytics system using the content dataset and the event dataset and is usable to guide subsequent content creation and editing.

公开(公告)号：US20240289380A1

公开(公告)日：2024-08-29

申请号：US18656332

申请日：2024-05-06

Applicant: Adobe Inc.

Inventor： Yaman Kumar ,  Vinh Ngoc Khuc ,  Vijay Srivastava ,  Umang Moorarka ,  Sukriti Verma ,  Simra Shahid ,  Shirsh Bansal ,  Shankar Venkitachalam ,  Sean Steimer ,  Sandipan Karmakar ,  Nimish Srivastav ,  Nikaash Puri ,  Mihir Naware ,  Kunal Kumar Jain ,  Kumar Mrityunjay Singh ,  Hyman Chung ,  Horea Bacila ,  Florin Silviu Lordache ,  Deepak Pai ,  Balaji Krishnamurthy

IPC: G06F16/58 ,  G06F16/535 ,  G06F16/54 ,  G06F16/583 ,  G06N20/00

CPC classification number: G06F16/5866 ,  G06F16/535 ,  G06F16/54 ,  G06F16/583 ,  G06N20/00

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for determining user affinities by tracking historical user interactions with tagged digital content and using the user affinities in content generation applications. Accordingly, the system may track user interactions with published digital content in order to generate user interaction reports whenever a user engages with the digital content. The system may aggregate the interaction reports to generate an affinity profile for a user or audience of users. A marketer may then generate digital content for a target user or audience of users and the system may process the digital content to generate a set of tags for the digital content. Based on the set of tags, the system may then evaluate the digital content in view of the affinity profile for the target user/audience to determine similarities or differences between the digital content and the affinity profile.

公开(公告)号：US20230085466A1

公开(公告)日：2023-03-16

申请号：US17447908

申请日：2021-09-16

Applicant: ADOBE INC.

Inventor： Yaman Kumar ,  Vinh Ngoc Khuc ,  Vijay Srivastava ,  Umang Moorarka ,  Sukriti Verma ,  Simra Shahid ,  Shirsh Bansal ,  Shankar Venkitachalam ,  Sean Steimer ,  Sandipan Karmakar ,  Nimish Srivastav ,  Nikaash Puri ,  Mihir Naware ,  Kunal Kumar Jain ,  Kumar Mrityunjay Singh ,  Hyman Chung ,  Horea Bacila ,  Florin Silviu Iordache ,  Deepak Pai ,  Balaji Krishnamurthy

IPC: G06F16/58 ,  G06N20/00 ,  G06F16/535 ,  G06F16/583 ,  G06F16/54

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for determining user affinities by tracking historical user interactions with tagged digital content and using the user affinities in content generation applications. Accordingly, the system may track user interactions with published digital content in order to generate user interaction reports whenever a user engages with the digital content. The system may aggregate the interaction reports to generate an affinity profile for a user or audience of users. A marketer may then generate digital content for a target user or audience of users and the system may process the digital content to generate a set of tags for the digital content. Based on the set of tags, the system may then evaluate the digital content in view of the affinity profile for the target user/audience to determine similarities or differences between the digital content and the affinity profile.

公开(公告)号：US11907508B1

公开(公告)日：2024-02-20

申请号：US18133725

申请日：2023-04-12

Applicant: Adobe Inc.

Inventor： Yaman Kumar ,  Somesh Singh ,  William Brandon George ,  Timothy Chia-chi Liu ,  Suman Basetty ,  Pranjal Prasoon ,  Nikaash Puri ,  Mihir Naware ,  Mihai Corlan ,  Joshua Marshall Butikofer ,  Abhinav Chauhan ,  Kumar Mrityunjay Singh ,  James Patrick O'Reilly ,  Hyman Chung ,  Lauren Dest ,  Clinton Hansen Goudie-Nice ,  Brandon John Pack ,  Balaji Krishnamurthy ,  Kunal Kumar Jain ,  Alexander Klimetschek ,  Matthew William Rozen

IPC: G06F3/0484 ,  G06F18/2415 ,  G06V10/40 ,  G06V10/764 ,  G06F3/0482 ,  G06T11/20 ,  G06F40/166 ,  G06F40/151

CPC classification number: G06F3/0484 ,  G06F3/0482 ,  G06F18/2415 ,  G06F40/151 ,  G06F40/166 ,  G06T11/206 ,  G06V10/40 ,  G06V10/764 ,  G06T2200/24

Abstract: Content creation techniques are described that leverage content analytics to provide insight and guidance as part of content creation. To do so, content features are extracted by a content analytics system from a plurality of content and used by the content analytics system as a basis to generate a content dataset. Event data is also collected by the content analytics system from an event data source. Event data describes user interaction with respective items of content, including subsequent activities in both online and physical environments. The event data is then used to generate an event dataset. An analytics user interface is then generated by the content analytics system using the content dataset and the event dataset and is usable to guide subsequent content creation and editing.

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.