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    Chromatic undertone detection
    2.
    发明授权
    Chromatic undertone detection 有权

    公开(公告)号：US12243288B2

    公开(公告)日：2025-03-04

    申请号：US17704030

    申请日：2022-03-25

    Applicant: Adobe Inc.

    Inventor： Michele Saad ,  Ronald Oribio ,  Robert W. Burke, Jr. ,  Irgelkha Mejia

    IPC: G06V10/764 ,  G06T3/10 ,  G06T7/10 ,  G06T7/90 ,  G06V10/56 ,  G06V10/60 ,  G06V10/75 ,  G06V10/774

    Abstract: Certain aspects and features of this disclosure relate to chromatic undertone detection. For example, a method involves receiving an image file and producing, using a color warmth classifier, an image warmth profile from the image file. The method further involves applying a surface-image-trained machine-learning model to the image warmth profile to produce an inferred undertone value for the image file. The method further involves comparing, using a recommendation module, and the inferred undertone value, an image color value to a plurality of pre-existing color values corresponding to a database of production images, and causing, in response to the comparing, interactive content including the at least one production image selection from the database of production images to be provided on a recipient device.

    MACHINE LEARNING TECHNIQUES FOR MITIGATING AGGREGATE EXPOSURE OF IDENTIFYING INFORMATION
    3.
    发明申请
    MACHINE LEARNING TECHNIQUES FOR MITIGATING AGGREGATE EXPOSURE OF IDENTIFYING INFORMATION 有权

    公开(公告)号：US20220286438A1

    公开(公告)日：2022-09-08

    申请号：US17195349

    申请日：2021-03-08

    Applicant: Adobe Inc.

    Inventor： Robert W. Burke, JR. ,  Ronald Oribio

    IPC: H04L29/06 ,  G06N3/04 ,  G06N3/08

    Abstract: Systems and methods mitigate aggregate exposure of identifying information using machine learning. A privacy monitoring system identifies entities and corresponding entity types by applying a set of domain-specific neural networks, each trained to recognize a particular entity type, to media data extracted from two or more content items associated with a user. The privacy monitoring system computes a privacy score indicating a cumulative privacy risk for potential exposure of identifying information associated with the user from the two or more content items by identifying connections between the identified entities. The connections between the entities are weighted according to the entity types and contribute to the privacy score. A reporting subsystem outputs an indication of a recommended action for mitigating the cumulative privacy risk.

    MACHINE LEARNING MODELING FOR PROTECTION AGAINST ONLINE DISCLOSURE OF SENSITIVE DATA
    5.
    发明申请
    MACHINE LEARNING MODELING FOR PROTECTION AGAINST ONLINE DISCLOSURE OF SENSITIVE DATA 有权

    公开(公告)号：US20220148113A1

    公开(公告)日：2022-05-12

    申请号：US17093175

    申请日：2020-11-09

    Applicant: Adobe Inc.

    Inventor： Irgelkha Mejia ,  Ronald Oribio ,  Robert Burke ,  Michele Saad

    IPC: G06Q50/26 ,  G06N3/08 ,  G06F16/48 ,  G06Q10/10 ,  G06Q50/00 ,  G06Q10/06 ,  G06F21/62 ,  G06F40/40

    Abstract: Systems and methods use machine learning models with content editing tools to prevent or mitigate inadvertent disclosure and dissemination of sensitive data. Entities associated with private information are identified by applying a trained machine learning model to a set of unstructured text data received via an input field of an interface. A privacy score is computed for the text data by identifying connections between the entities, the connections between the entities contributing to the privacy score according to a cumulative privacy risk, the privacy score indicating potential exposure of the private information. The interface is updated to include an indicator distinguishing a target portion of the set of unstructured text data within the input field from other portions of the set of unstructured text data within the input field, wherein a modification to the target portion changes the potential exposure of the private information indicated by the privacy score.

    MACHINE LEARNING COLLABORATION TECHNIQUES
    6.
    发明申请
    MACHINE LEARNING COLLABORATION TECHNIQUES 有权

    公开(公告)号：US20240420212A1

    公开(公告)日：2024-12-19

    申请号：US18335921

    申请日：2023-06-15

    Applicant: Adobe Inc.

    Inventor： Robert W. Burke, JR. ,  Ronald Oribio ,  Michele Saad ,  Irgelkha Mejia

    IPC: G06Q30/0601 ,  G06F40/20

    Abstract: A feedback management subsystem receives, from a first user, first text comprising commentary on an item. The feedback management subsystem receives, from the first user, instructions to request commentary on the item from a second user. Responsive to receiving the instructions to request commentary from the second user, a communication subsystem transmits a notification to the second user. The feedback management subsystem receives, from the second user, second text comprising commentary on the item. A first machine learning model performs sentiment analysis to identify sentiments of the first text and the second text. A recommendation subsystem identifies prior actions of the first user and associated sentiments of the second user. A second machine learning model identifies a second item based on the prior actions of the first user and the sentiments of the second user. The recommendation subsystem provides output to the first user recommending the second item.

    Machine learning techniques for mitigating aggregate exposure of identifying information
    7.
    发明授权
    Machine learning techniques for mitigating aggregate exposure of identifying information 有权

    公开(公告)号：US12047355B2

    公开(公告)日：2024-07-23

    申请号：US17195349

    申请日：2021-03-08

    Applicant: Adobe Inc.

    Inventor： Robert W. Burke, Jr. ,  Ronald Oribio

    IPC: H04L9/40 ,  G06N3/04 ,  G06N3/08

    CPC classification number: H04L63/0428 ,  G06N3/04 ,  G06N3/08

    Abstract: Systems and methods mitigate aggregate exposure of identifying information using machine learning. A privacy monitoring system identifies entities and corresponding entity types by applying a set of domain-specific neural networks, each trained to recognize a particular entity type, to media data extracted from two or more content items associated with a user. The privacy monitoring system computes a privacy score indicating a cumulative privacy risk for potential exposure of identifying information associated with the user from the two or more content items by identifying connections between the identified entities. The connections between the entities are weighted according to the entity types and contribute to the privacy score. A reporting subsystem outputs an indication of a recommended action for mitigating the cumulative privacy risk.

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