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    INTERACTIVE SEARCH EXPERIENCE USING MACHINE LEARNING
    31.
    发明申请
    INTERACTIVE SEARCH EXPERIENCE USING MACHINE LEARNING 审中-公开

    公开(公告)号：US20200341976A1

    公开(公告)日：2020-10-29

    申请号：US16394853

    申请日：2019-04-25

    Applicant: Adobe Inc.

    Inventor： Milan Aggarwal ,  Balaji Krishnamurthy

    IPC: G06F16/242 ,  H04L12/58 ,  G06N20/00

    Abstract: Techniques are disclosed for providing an interactive search session. The interactive search session is implemented using an artificial intelligence model. For example, when the artificial intelligence model receives a search query from a user, the model selects an action from a plurality of actions based on the search query. The selected action queries the user for more contextual cues about the search query (e.g., may enquire about use of the search results, may request to refine the search query, or otherwise engage the user in conversation to better understand the intent of the search). The interactive search session may be in the form, for example, of a chat session between the user and the system, and the chat session may be displayed along with the search results (e.g., in a separate section of display). The interactive search session may enable the system to better understand the user's search needs, and accordingly may help provide more focused search results.

    Classifying Structural Features of a Digital Document by Feature Type using Machine Learning
    33.
    发明申请
    Classifying Structural Features of a Digital Document by Feature Type using Machine Learning 审中-公开

    公开(公告)号：US20200302016A1

    公开(公告)日：2020-09-24

    申请号：US16359402

    申请日：2019-03-20

    Applicant: Adobe Inc.

    Inventor： Milan Aggarwal ,  Balaji Krishnamurthy

    IPC: G06F17/27 ,  G06F16/93 ,  G06N3/08 ,  G06N3/04 ,  G10L15/02

    Abstract: Classifying structural features of a digital document by feature type using machine learning is leveraged in a digital medium environment. A document analysis system is leveraged to extract structural features from digital documents, and to classifying the structural features by respective feature types. To do this, the document analysis system employs a character analysis model and a classification model. The character analysis model takes text content from a digital document and generates text vectors that represent the text content. A vector sequence is generated based on the text vectors and position information for structural features of the digital document, and the classification model processes the vector sequence to classify the structural features into different feature types. The document analysis system can generate a modifiable version of the digital document that enables its structural features to be modified based on their respective feature types.

    Conversational agent for search
    34.
    发明授权
    Conversational agent for search 有权

    公开(公告)号：US10713317B2

    公开(公告)日：2020-07-14

    申请号：US15419497

    申请日：2017-01-30

    Applicant: ADOBE INC.

    Inventor： Balaji Krishnamurthy ,  Shagun Sodhani ,  Aarushi Arora ,  Milan Aggarwal

    IPC: G06F16/9535 ,  G06F16/9032 ,  G06N3/00 ,  G06N20/00 ,  G06F40/30 ,  G06F40/35 ,  G06N3/08 ,  G06N7/00

    Abstract: A conversational agent facilitates conversational searches for users. The conversational agent is a reinforcement learning (RL) agent trained using a user model generated from existing session logs from a search engine. The user model is generated from the session logs by mapping entries from the session logs to user actions understandable by the RL agent and computing conditional probabilities of user actions occurring given previous user actions in the session logs. The RL agent is trained by conducting conversations with the user model in which the RL agent selects agent actions in response to user actions sampled using the conditional probabilities from the user model.

    Graphical Interface for Presentation of Interaction Data Across Multiple Webpage Configurations
    35.
    发明申请
    Graphical Interface for Presentation of Interaction Data Across Multiple Webpage Configurations 审中-公开

    公开(公告)号：US20200159371A1

    公开(公告)日：2020-05-21

    申请号：US16193475

    申请日：2018-11-16

    Applicant: Adobe Inc.

    Inventor： Harpreet Singh ,  Balaji Krishnamurthy ,  Akash Rupela

    IPC: G06F3/0482 ,  G06F17/22

    Abstract: In some embodiments, a configuration management application accesses configuration data for a multi-target website. The configuration management application provides the user interface including a timeline area and a page display area. The timeline area is configured to display timeline entries corresponding to configurations of the multi-target website. Based on a selection of a timeline entry, the page display area is configured to display a webpage configuration corresponding to the selected timeline entry. In addition, the page display area is configured to display graphical annotations indicating interaction metrics for the configured page regions. In some cases, the timeline entries, configurations, and interaction metrics are determined based on a selection of a target segment for the multi-target website.

    Caption Association Techniques
    37.
    发明申请
    Caption Association Techniques 审中-公开

    公开(公告)号：US20190286691A1

    公开(公告)日：2019-09-19

    申请号：US15925059

    申请日：2018-03-19

    Applicant: Adobe Inc.

    Inventor： Shagun Sodhani ,  Kartikay Garg ,  Balaji Krishnamurthy

    IPC: G06F17/24 ,  G06F17/30 ,  G06N3/08

    Abstract: Caption association techniques as part of digital content creation by a computing device are described. The computing device is configured to extract text features and bounding boxes from an input document. These text features and bounding boxes are processed to reduce a number of possible search spaces. The processing may involve generating and utilizing a language model that captures the semantic meaning of the text features to identify and filter static text, and may involve identifying and filtering inline captions. A number of bounding boxes are identified for a potential caption. The potential caption and corresponding identified bounding boxes are concatenated into a vector. The concatenated vector is used to identify relationships among the bounding boxes to determine a single bounding box associated with the caption. The determined association is utilized to generate an output digital document that includes a structured association between the caption and a data entry field.

    UTILIZING IMPLICIT NEURAL REPRESENTATIONS TO PARSE VISUAL COMPONENTS OF SUBJECTS DEPICTED WITHIN VISUAL CONTENT
    38.
    发明申请
    UTILIZING IMPLICIT NEURAL REPRESENTATIONS TO PARSE VISUAL COMPONENTS OF SUBJECTS DEPICTED WITHIN VISUAL CONTENT 有权

    公开(公告)号：US20240378912A1

    公开(公告)日：2024-11-14

    申请号：US18316617

    申请日：2023-05-12

    Applicant: Adobe Inc.

    Inventor： Mausoom Sarkar ,  Nikitha S R ,  Mayur Hemani ,  Rishabh Jain ,  Balaji Krishnamurthy

    IPC: G06V20/70 ,  G06T3/40 ,  G06T7/11 ,  G06V10/46 ,  G06V10/764 ,  G06V10/77 ,  G06V10/774 ,  G06V10/82 ,  G06V40/16

    Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that utilize a local implicit image function neural network to perform image segmentation with a continuous class label probability distribution. For example, the disclosed systems utilize a local-implicit-image-function (LIIF) network to learn a mapping from an image to its semantic label space. In some instances, the disclosed systems utilize an image encoder to generate an image vector representation from an image. Subsequently, in one or more implementations, the disclosed systems utilize the image vector representation with a LIIF network decoder that generates a continuous probability distribution in a label space for the image to create a semantic segmentation mask for the image. Moreover, in some embodiments, the disclosed systems utilize the LIIF-based segmentation network to generate segmentation masks at different resolutions without changes in an input resolution of the segmentation network.

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