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：US11816696B2

公开(公告)日：2023-11-14

申请号：US17355907

申请日：2021-06-23

Applicant: Adobe Inc.

Inventor： Pankhri Singhai ,  Sundeep Parsa ,  Piyush Gupta ,  Nupur Kumari ,  Nikaash Puri ,  Mayank Singh ,  Eshita Shah ,  Balaji Krishnamurthy ,  Akash Rupela

IPC: G06Q30/00 ,  G06Q30/0242 ,  G06Q30/0251 ,  G06N20/00 ,  G06N5/00 ,  G05B19/418

CPC classification number: G06Q30/0244 ,  G06N5/00 ,  G06N20/00 ,  G06Q30/0242 ,  G06Q30/0254 ,  G06Q30/0255 ,  G06Q30/0264

Abstract: Machine-learning based multi-step engagement strategy modification is described. Rather than rely heavily on human involvement to manage content delivery over the course of a campaign, the described learning-based engagement system modifies a multi-step engagement strategy, originally created by an engagement-system user, by leveraging machine-learning models. In particular, these leveraged machine-learning models are trained using data describing user interactions with delivered content as those interactions occur over the course of the campaign. Initially, the learning-based engagement system obtains a multi-step engagement strategy created by an engagement-system user. As the multi-step engagement strategy is deployed, the learning-based engagement system randomly adjusts aspects of the sequence of deliveries for some users. Based on data describing the interactions of recipients with deliveries served according to both the user-created and random multi-step engagement strategies, the machine-learning models generate a modified multi-step engagement strategy.

公开(公告)号：US11544336B2

公开(公告)日：2023-01-03

申请号：US16983890

申请日：2020-08-03

Applicant: Adobe Inc.

Inventor： Nikaash Puri ,  Piyush Gupta

IPC: G06F16/9535 ,  G06Q30/02 ,  G06F16/9536 ,  H04L67/306 ,  H04L67/50

Abstract: Collaborative-filtered content recommendations with justification in real-time is described. A recommendation system determines these recommendations, in part, by identifying digital content items of a catalog that are associated with a single attribute used to describe digital content. The attribute used for the identification is based on affinity scores computed for a client device user to which the recommendations are being provided. These affinity scores indicate the client device user's affinity for different attributes used to describe the digital content. Once the digital content items are identified based on the one attribute, the recommendation system is then limited to ranking and selecting from the identified digital content items to provide the recommendations. The recommendation system does not process the entire catalog of digital content items at once to rank and select the items. Due to this, the described recommendation system performs less computing and is therefore faster than conventional recommendation systems.

公开(公告)号：US11450310B2

公开(公告)日：2022-09-20

申请号：US16989012

申请日：2020-08-10

Applicant: ADOBE INC.

Inventor： Nikita Kapoor ,  Jaya Dodeja ,  Nikaash Puri

IPC: G10L15/06 ,  G10L19/04 ,  G10L15/02 ,  G10L15/18 ,  G10L15/16 ,  G10L25/51

Abstract: Systems and methods for spoken language understanding are described. Embodiments of the systems and methods receive audio data for a spoken language expression, encode the audio data using a multi-stage encoder comprising a basic encoder and a sequential encoder, wherein the basic encoder is trained to generate character features during a first training phase and the sequential encoder is trained to generate token features during a second training phase, and decode the token features to generate semantic information representing the spoken language expression.

公开(公告)号：US11188579B2

公开(公告)日：2021-11-30

申请号：US16377424

申请日：2019-04-08

Applicant: ADOBE INC.

Inventor： Dheeraj Bansal ,  Sukriti Verma ,  Pratiksha Agarwal ,  Piyush Gupta ,  Nikaash Puri ,  Vishal Wani ,  Balaji Krishnamurthy

IPC: G06F16/00 ,  G06F16/332 ,  G06F16/58 ,  G06F16/535 ,  G06N20/20 ,  G06N3/08

Abstract: Systems and methods are described for serving personalized content using content tagging and transfer learning. The method may include identifying content elements in an experience pool, where each of the content element is associated with one or more attribute tags, identifying a user profile comprising characteristics of a user, generating a set of user-tag affinity vectors based on the user profile and the corresponding attribute tags using a content personalization engine, generating a user-content affinity score based on the set of user-tag affinity vectors, selecting a content element from the plurality of content elements based on the corresponding user-content affinity score, and delivering the selected content element to the user.

公开(公告)号：US11109084B2

公开(公告)日：2021-08-31

申请号：US16694612

申请日：2019-11-25

Applicant: Adobe Inc.

Inventor： Pankhri Singhai ,  Sundeep Parsa ,  Piyush Gupta ,  Nikaash Puri ,  Eshita Shah ,  Balaji Krishnamurthy ,  Nupur Kumari ,  Mayank Singh ,  Akash Rupela

IPC: H04N21/25 ,  H04N21/2668 ,  H04N21/258 ,  H04N21/475 ,  G06N20/00 ,  H04N21/81 ,  G06Q30/02

Abstract: Machine-learning based multi-step engagement strategy generation and visualization is described. Rather than rely heavily on human involvement to create delivery strategies, the described learning-based engagement system generates multi-step engagement strategies by leveraging machine-learning models trained using data describing historical user interactions with content delivered in connection with historical campaigns. Initially, the learning-based engagement system obtains data describing an entry condition and an exit condition for a campaign. Based on the entry and exit condition, the learning-based engagement system utilizes the machine-learning models to generate a multi-step engagement strategy, which describes a sequence of content deliveries that are to be served to a particular client device user (or segment of client device users). Once the multi-step engagement strategies are generated, the learning-based engagement system may also generate visualizations of the strategies that can be output for display.

公开(公告)号：US20190171906A1

公开(公告)日：2019-06-06

申请号：US15831160

申请日：2017-12-04

Applicant: Adobe Inc.

Inventor： Shagun Sodhani ,  Nikaash Puri

IPC: G06K9/62 ,  G06F17/30

CPC classification number: G06K9/6215 ,  G06F16/583 ,  G06F16/5838 ,  G06K9/623

Abstract: In implementations of digital image search based on arbitrary image features, a server computing device maintains an images database of digital images, and includes an image search system that receives a search input as a digital image depicting image features, and receives search criteria of depicted image features in the digital image. The image search system can then determine similar images to the received digital image based on similarity criterion corresponding to the search criteria. A trained image model of the image search system is applied to determine an image feature representation of the received digital image. A feature mask model of the image search system is applied to the image feature representation to determine a masked feature representation of the received digital image. The masked feature representation of the received digital image is compared to a masked feature representation of each respective database image to identify the similar images.

公开(公告)号：US20190163829A1

公开(公告)日：2019-05-30

申请号：US15823331

申请日：2017-11-27

Applicant: Adobe Inc.

Inventor： Nikaash Puri ,  Piyush Gupta

IPC: G06F17/30 ,  G06Q30/02

Abstract: Collaborative-filtered content recommendations with justification in real-time is described. A recommendation system determines these recommendations, in part, by identifying digital content items of a catalog that are associated with a single attribute used to describe digital content. The attribute used for the identification is based on affinity scores computed for a client device user to which the recommendations are being provided. These affinity scores indicate the client device user's affinity for different attributes used to describe the digital content. Once the digital content items are identified based on the one attribute, the recommendation system is then limited to ranking and selecting from the identified digital content items to provide the recommendations. The recommendation system does not process the entire catalog of digital content items at once to rank and select the items. Due to this, the described recommendation system performs less computing and is therefore faster than conventional recommendation systems.

公开(公告)号：US12223002B2

公开(公告)日：2025-02-11

申请号：US17454445

申请日：2021-11-10

Applicant: ADOBE INC.

Inventor： Pinkesh Badjatiya ,  Tanay Anand ,  Simra Shahid ,  Nikaash Puri ,  Milan Aggarwal ,  S Sejal Naidu ,  Sharat Chandra Racha

IPC: G06F16/9536 ,  G06F16/9538 ,  G06F40/20

Abstract: A method of finding online relevant conversing posts, comprises receiving, by a web server serving an online forum, a query post from an inquirer using the online forum, computing a contextual similarity score between each conversing post of a set of conversing posts with a query post, wherein the contextual similarity score is computed between the body of each of conversing posts and of the query post, wherein N1 conversing posts with a highest contextual similarity score are selected; computing a fine grained similarity score between the subject of the query post and of each of the N1 conversing posts, wherein N2 conversing posts with a highest fine grained similarity score are selected; and boosting the fine grained similarity score of the N2 conversing posts based on relevance metrics, wherein N3 highest ranked conversing posts are selected as a list of conversing posts most relevant to the query post.

公开(公告)号：US20240005146A1

公开(公告)日：2024-01-04

申请号：US17855085

申请日：2022-06-30

Applicant: Adobe Inc. ,  Delhi Technological University

Inventor： Tanay Anand ,  Piyush Gupta ,  Pinkesh Badjatiya ,  Nikaash Puri ,  Jayakumar Subramanian ,  Balaji Krishnamurthy ,  Chirag Singla ,  Rachit Bansal ,  Anil Singh Parihar

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/08 ,  G06N3/0445

Abstract: In some embodiments, techniques for extracting high-value sequential patterns are provided. For example, a process may involve training a machine learning model to learn a state-action map that contains high-utility sequential patterns; extracting at least one high-utility sequential pattern from the trained machine learning model; and causing a user interface of a computing environment to be modified based on information from the at least one high-utility sequential pattern.