GENERATIVE ARTIFICIAL INTELLIGENCE POWERED RESPONSE GENERATION, VALIDATION, AND AUGMENTATION

    公开(公告)号:US20250103822A1

    公开(公告)日:2025-03-27

    申请号:US18372462

    申请日:2023-09-25

    Applicant: Adobe Inc.

    Abstract: System and methods for generating, validating, and augmenting question-answer pairs using generative AI are provided. An online interaction server accesses a set of digital content available at a set of designated network locations. The online interaction server further trains a pre-trained large language model (LLM) using the set of digital content to obtain a customized LLM. The online interaction server generates a set of question-answer pairs based on the set of digital content using the customized LLM and validates the set of question-answer pairs by determining if an answer in a question-answer pair is derived from the set of digital content. The online interaction server also selects a digital asset to augment an answer in a validated question-answer pair based on a semantic similarity between the validated question-answer pair and the digital asset.

    Self-supervised hierarchical event representation learning

    公开(公告)号:US11948358B2

    公开(公告)日:2024-04-02

    申请号:US17455126

    申请日:2021-11-16

    Applicant: ADOBE INC.

    CPC classification number: G06V20/41 G06N3/088 G06V20/47 G06V20/44

    Abstract: Systems and methods for video processing are described. Embodiments of the present disclosure generate a plurality of image feature vectors corresponding to a plurality of frames of a video; generate a plurality of low-level event representation vectors based on the plurality of image feature vectors, wherein a number of the low-level event representation vectors is less than a number of the image feature vectors; generate a plurality of high-level event representation vectors based on the plurality of low-level event representation vectors, wherein a number of the high-level event representation vectors is less than the number of the low-level event representation vectors; and identify a plurality of high-level events occurring in the video based on the plurality of high-level event representation vectors.

    PERSONALIZED FORM ERROR CORRECTION PROPAGATION

    公开(公告)号:US20240362941A1

    公开(公告)日:2024-10-31

    申请号:US18140143

    申请日:2023-04-27

    Applicant: Adobe Inc.

    CPC classification number: G06V30/274 G06V30/1444 G06V30/19147 G06V30/414

    Abstract: A corrective noise system receives an electronic version of a fillable form generated by a segmentation network and receives a correction to a segmentation error in the electronic version of the fillable form. The corrective noise system is trained to generate noise that represents the correction and superimpose the noise on the fillable form. The corrective noise system is further trained to identify regions in a corpus of forms that are semantically similar to a region that was subject to the correction. The generated noise is propagated to the semantically similar regions in the corpus of forms and the noisy corpus of forms is provided as input to the segmentation network. The noise causes the segmentation network to accurately identify fillable regions in the corpus of forms and output a segmented version of the corpus of forms having improved fidelity without retraining or otherwise modifying the segmentation network.

    FORM STRUCTURE SIMILARITY DETECTION
    7.
    发明公开

    公开(公告)号:US20240330351A1

    公开(公告)日:2024-10-03

    申请号:US18190686

    申请日:2023-03-27

    Applicant: Adobe Inc.

    CPC classification number: G06F16/383 G06F16/332 G06V30/19147 G06V30/412

    Abstract: Form structure similarity detection techniques are described. A content processing system, for instance, receives a query snippet that depicts a query form structure. The content processing system generates a query layout string that includes semantic indicators to represent the query form structure and generates candidate layout strings that represent form structures from a target document. The content processing system calculates similarity scores between the query layout string and the candidate layout strings. Based on the similarity scores, the content processing system generates a target snippet for display that depicts a form structure that is structurally similar to the query form structure. The content processing system is further operable to generate a training dataset that includes image pairs of snippets depicting form structures that are structurally similar. The content processing system utilizes the training dataset to train a machine learning model to perform form structure similarity matching.

    INTERACTIVE SEARCH EXPERIENCE USING MACHINE LEARNING

    公开(公告)号:US20220245141A1

    公开(公告)日:2022-08-04

    申请号:US17656772

    申请日:2022-03-28

    Applicant: Adobe Inc.

    Abstract: An 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.

    Interactive search experience using machine learning

    公开(公告)号:US11294891B2

    公开(公告)日:2022-04-05

    申请号:US16394853

    申请日:2019-04-25

    Applicant: Adobe Inc.

    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.

    INTERACTIVE SEARCH EXPERIENCE USING MACHINE LEARNING

    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.

    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.

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