公开(公告)号：US11914641B2

公开(公告)日：2024-02-27

申请号：US17186625

申请日：2021-02-26

Applicant: ADOBE INC.

Inventor： Pranav Aggarwal ,  Ajinkya Kale ,  Baldo Faieta ,  Saeid Motiian ,  Venkata naveen kumar yadav Marri

IPC: G06F16/583 ,  G06F40/279 ,  G06N3/08 ,  G06F16/51 ,  G06F16/538 ,  G06F16/532 ,  G06V10/56

CPC classification number: G06F16/5838 ,  G06F16/51 ,  G06F16/532 ,  G06F16/538 ,  G06F40/279 ,  G06N3/08 ,  G06V10/56

Abstract: The present disclosure describes systems and methods for information retrieval. Embodiments of the disclosure provide a color embedding network trained using machine learning techniques to generate embedded color representations for color terms included in a text search query. For example, techniques described herein are used to represent color text in a same space as color embeddings (e.g., an embedding space created by determining a histogram of LAB based colors in a three-dimensional (3D) space). Further, techniques are described for indexing color palettes for all the searchable images in the search space. Accordingly, color terms in a text query are directly converted into a color palette and an image search system can return one or more search images with corresponding color palettes that are relevant to (e.g., within a threshold distance from) the color palette of the text query.

公开(公告)号：US11907280B2

公开(公告)日：2024-02-20

申请号：US17090150

申请日：2020-11-05

Applicant: ADOBE INC.

Inventor： Mikhail Kotov ,  Roland Geisler ,  Saeid Motiian ,  Dylan Nathaniel Warnock ,  Michele Saad ,  Venkata Naveen Kumar Yadav Marri ,  Ajinkya Kale ,  Ryan Rozich ,  Baldo Faieta

IPC: G06F17/00 ,  G06F7/00 ,  G06F16/532 ,  G06F16/2457 ,  G06F16/538 ,  G06F16/583

CPC classification number: G06F16/532 ,  G06F16/24578 ,  G06F16/538 ,  G06F16/5846

Abstract: Embodiments of the technology described herein, provide improved visual search results by combining a visual similarity and a textual similarity between images. In an embodiment, the visual similarity is quantified as a visual similarity score and the textual similarity is quantified as a textual similarity score. The textual similarity is determined based on text, such as a title, associated with the image. The overall similarity of two images is quantified as a weighted combination of the textual similarity score and the visual similarity score. In an embodiment, the weighting between the textual similarity score and the visual similarity score is user configurable through a control on the search interface. In one embodiment, the aggregate similarity score is the sum of a weighted visual similarity score and a weighted textual similarity score.

公开(公告)号：US11681737B2

公开(公告)日：2023-06-20

申请号：US16843218

申请日：2020-04-08

Applicant: ADOBE INC.

Inventor： Handong Zhao ,  Ajinkya Kale ,  Xiaowei Jia ,  Zhe Lin

IPC: G06F16/43 ,  G06F16/45 ,  G06F16/438 ,  G06N3/04 ,  G06N3/08

CPC classification number: G06F16/43 ,  G06F16/438 ,  G06F16/45 ,  G06N3/04 ,  G06N3/08

Abstract: The present disclosure relates to a retrieval method including: generating a graph representing a set of users, items, and queries; generating clusters from the media items; generating embeddings for each cluster from embeddings of the items within the corresponding cluster; generating augmented query embeddings for each cluster from the embedding of the corresponding cluster and query embeddings of the queries; inputting the cluster embeddings and the augmented query embeddings to a layer of a graph convolutional network (GCN) to determine user embeddings of the users; inputting the embedding of the given user and a query embedding of the given query to a layer of the GCN to determine a user-specific query embedding; generating a score for each of the items based on the item embeddings and the user-specific query embedding; and presenting the items having the score exceeding a threshold.

公开(公告)号：US20220300696A1

公开(公告)日：2022-09-22

申请号：US17805910

申请日：2022-06-08

Applicant: ADOBE INC.

Inventor： William Frederick Kraus ,  Nathaniel Joseph Grabaskas ,  Ajinkya Kale

IPC: G06F40/109 ,  G06N3/08 ,  G06T11/60 ,  G06F40/237 ,  G06F40/30

Abstract: Embodiments provide systems, methods, and computer storage media for text style suggestions and/or text emphasis suggestions. In an example embodiment, an electronic design application provides a text style suggestion tool that generates text style suggestions to stylize a selected text element based on the context of the design. A text emphasis tool allows a user to select a text element and generate text emphasis suggestions for which words should be emphasized with a different text styling. Various interaction elements allow the user to iterate through the suggestions. For example, a set of style suggestions may be mapped to successive rotational increments around a style wheel, and as the user rotates through the positions on the style wheel, a corresponding text style suggestion is previewed and/or applied.

公开(公告)号：US20220138247A1

公开(公告)日：2022-05-05

申请号：US17090150

申请日：2020-11-05

Applicant: ADOBE INC.

Inventor： Mikhail Kotov ,  Roland Geisler ,  Saeid Motiian ,  Dylan Nathaniel Warnock ,  Michele Saad ,  Venkata Naveen Kumar Yadav Marri ,  Ajinkya Kale ,  Ryan Rozich ,  Baldo Faieta

IPC: G06F16/532 ,  G06F16/583 ,  G06F16/538 ,  G06F16/2457

Abstract: Embodiments of the technology described herein, provide improved visual search results by combining a visual similarity and a textual similarity between images. In an embodiment, the visual similarity is quantified as a visual similarity score and the textual similarity is quantified as a textual similarity score. The textual similarity is determined based on text, such as a title, associated with the image. The overall similarity of two images is quantified as a weighted combination of the textual similarity score and the visual similarity score. In an embodiment, the weighting between the textual similarity score and the visual similarity score is user configurable through a control on the search interface. In one embodiment, the aggregate similarity score is the sum of a weighted visual similarity score and a weighted textual similarity score.

公开(公告)号：US20220100791A1

公开(公告)日：2022-03-31

申请号：US17544689

申请日：2021-12-07

Applicant: Adobe Inc.

Inventor： Ajinkya Kale ,  Baldo Faieta ,  Benjamin Leviant ,  Fengbin Chen ,  Francois Guerin ,  Kate Sousa ,  Trung Bui ,  Venkat Barakam ,  Zhe Lin

IPC: G06F16/48 ,  G06K9/62 ,  G06F16/2457 ,  G06F16/43

Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for determining multi-term contextual tags for digital content and propagating the multi-term contextual tags to additional digital content. For instance, the disclosed systems can utilize search query supervision to determine and associate multi-term contextual tags (e.g., tags that represent a specific concept based on the order of the terms in the tag) with digital content. Furthermore, the disclosed systems can propagate the multi-term contextual tags determined for the digital content to additional digital content based on similarities between the digital content and additional digital content (e.g., utilizing clustering techniques). Additionally, the disclosed systems can provide digital content as search results based on the associated multi-term contextual tags.

公开(公告)号：US20210326371A1

公开(公告)日：2021-10-21

申请号：US16849885

申请日：2020-04-15

Applicant: Adobe Inc.

Inventor： Trung Bui ,  Yu Gong ,  Tushar Dublish ,  Sasha Spala ,  Sachin Soni ,  Nicholas Miller ,  Joon Kim ,  Franck Dernoncourt ,  Carl Dockhorn ,  Ajinkya Kale

IPC: G06F16/33 ,  G06N20/00 ,  G06N5/04 ,  G06F40/30

Abstract: Techniques and systems are described for performing semantic text searches. A semantic text-searching solution uses a machine learning system (such as a deep learning system) to determine associations between the semantic meanings of words. These associations are not limited by the spelling, syntax, grammar, or even definition of words. Instead, the associations can be based on the context in which characters, words, and/or phrases are used in relation to one another. In response to detecting a request to locate text within an electronic document associated with a keyword, the semantic text-searching solution can return strings within the document that have matching and/or related semantic meanings or contexts, in addition to exact matches (e.g., string matches) within the document. The semantic text-searching solution can then output an indication of the matching strings.

公开(公告)号：US20210303784A1

公开(公告)日：2021-09-30

申请号：US16836462

申请日：2020-03-31

Applicant: Adobe Inc.

Inventor： Oliver Brdiczka ,  Ajinkya Kale ,  Piyush Chandra ,  Tracy King ,  Abhishek Gupta ,  Sourabh Goel ,  Nitin Garg ,  Deepika Naryani ,  Feroz Ahmad ,  Vikas Sagar

IPC: G06F40/20 ,  G06F40/40

Abstract: The present disclosure relates to systems for identifying instances of natural language input, determining intent classifications associated with instances of natural language input, and generating responses based on the determined intent classifications. In particular, the disclosed systems intelligently identify and group instances of natural language input based on characteristics of the user input. Additionally, the disclosed systems determine intent classifications for the instances of natural language input based message queuing in order to delay responses to the user input in ways that increase accuracy of the responses, while retaining a conversational aspect of the ongoing chat. Moreover, in one or more embodiments, the disclosed systems generate responses utilizing natural language.

公开(公告)号：US12223439B2

公开(公告)日：2025-02-11

申请号：US17190668

申请日：2021-03-03

Applicant: ADOBE INC.

Inventor： Xin Yuan ,  Zhe Lin ,  Jason Wen Yong Kuen ,  Jianming Zhang ,  Yilin Wang ,  Ajinkya Kale ,  Baldo Faieta

IPC: G06N5/04 ,  G06N3/08 ,  G06N20/00

Abstract: Systems and methods for multi-modal representation learning are described. One or more embodiments provide a visual representation learning system trained using machine learning techniques. For example, some embodiments of the visual representation learning system are trained using cross-modal training tasks including a combination of intra-modal and inter-modal similarity preservation objectives. In some examples, the training tasks are based on contrastive learning techniques.

公开(公告)号：US11816162B2

公开(公告)日：2023-11-14

申请号：US16944203

申请日：2020-07-31

Applicant: Adobe Inc.

Inventor： Ritiz Tambi ,  Ajinkya Kale ,  Tracy Holloway King

IPC: G06F16/90 ,  G06F16/9032 ,  G06F40/263 ,  G06F40/242 ,  G06F16/903 ,  G06N20/00 ,  G06F18/214 ,  G06F18/2413

CPC classification number: G06F16/90324 ,  G06F16/90344 ,  G06F18/2155 ,  G06F18/24147 ,  G06F40/242 ,  G06F40/263 ,  G06N20/00

Abstract: Systems and methods are disclosed for search query language identification. One method comprises generating a seed dictionary comprising a plurality of labeled dictionary terms and receiving a plurality of unlabeled sample query terms. The plurality of unlabeled sample query terms are compared to the plurality of labeled dictionary terms at a first time, and a first set of labeled sample query terms are generated by labeling at least a subset of the plurality of unlabeled sample query terms based on the first comparison. Remaining unlabeled sample query terms are then compared with the first set of labeled sample query terms at a second time, and a second set of labeled sample query terms are generated by labeling the remaining unlabeled sample query terms based on the second comparison. The first and second sets of labeled sample query terms are provided to a machine learning model configured for query language prediction.