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公开(公告)号：US12169856B2
公开(公告)日：2024-12-17
申请号：US17712406
申请日：2022-04-04
Applicant: ADOBE INC.
Inventor： Michele Saad , Matthew Cecil Zimmerman
IPC: G06F7/00 , G06F16/22 , G06F16/245 , G06F16/28 , G06Q30/0601 , H03M7/30
Abstract: An effective stock keeping unit (SKU) management system encodes catalog data into an embedding per catalog item. An embedding space is created by encoding catalog item data into an embedding per catalog item. The embedding is created by generating an index, where a number of rows represents a number of catalog items and a number of columns represents a number of fields associated with each catalog item. The index is then denormalized using customer groups and transformed by compressing the number of columns, to create the embedding space. In some configuration, a machine learning model is trained using catalog data. In the embedding space, item similarity is encoded by clustering catalog SKUs into groups in the embedding space, by placing similarly related items close to each other in the embedding space. Catalog items are then searched for in the embedding, with the closest clusters searched for a particular catalog item.
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公开(公告)号：US11830099B2
公开(公告)日：2023-11-28
申请号：US17093175
申请日：2020-11-09
Applicant: Adobe Inc.
Inventor： Irgelkha Mejia , Ronald Oribio , Robert Burke , Michele Saad
IPC: G06Q10/10 , G06Q10/06 , G06Q30/06 , G06Q30/02 , G06Q40/08 , G06Q50/26 , G06F16/48 , G06F40/40 , G06F21/62 , G06N3/08 , G06Q10/0635 , G06Q50/00 , G06F3/0482
CPC classification number: G06Q50/265 , G06F16/48 , G06F21/6245 , G06F40/40 , G06N3/08 , G06Q10/0635 , G06Q10/10 , G06Q50/01 , G06F3/0482
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.
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公开(公告)号：US20230259979A1
公开(公告)日：2023-08-17
申请号：US17670753
申请日：2022-02-14
Applicant: ADOBE INC.
Inventor： Irgelkha Mejia , Robert William Burke, JR. , Ronald Eduardo Oribio , Michele Saad
CPC classification number: G06Q30/0269 , G06Q30/0253 , G06N5/022
Abstract: Methods and systems are provided for facilitating identification of sensitive content. In embodiments described herein, a set of sensitive topics is obtained. Each sensitive topic in the set of sensitive topics can include subject matter that may be deemed sensitive to one or more individuals. Thereafter, the set of sensitive topics is expanded to an expanded set of sensitive topics using a first machine learning model. The expanded set of sensitive topics is used to train a second machine learning model to predict potential sensitive content in relation to input content.
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公开(公告)号：US11455485B2
公开(公告)日：2022-09-27
申请号：US16915328
申请日：2020-06-29
Applicant: Adobe Inc.
Inventor： Michele Saad , Lauren Dest
IPC: G06K9/62 , G06N3/02 , G06F16/538 , G06V10/56
Abstract: Methods and systems disclosed herein relate generally to systems and methods for predicting content based on vector data structures generated from image pixels. A content-prediction application accesses a color palette having two or more color-palette categories and selects a first color of the color palette. The content-prediction application generates a first vector based on a set of pixel values that represent the first color of the color palette. The content-prediction application determines a distance metric between the first vector and a second vector, in which the second vector is identified by applying a convolutional neural network model on an image depicting an item that includes a second color. In response to determining that the distance metric is less than a predetermined threshold, the content-prediction application selects the content item corresponding to the second vector.
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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
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.
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Patent Agency Ranking
公开(公告)号：US20220036428A1
公开(公告)日：2022-02-03
申请号：US17494080
申请日：2021-10-05
Applicant: Adobe Inc.
Inventor： Michele Saad
Abstract: A trend setting score that identifies a degree of trend setting exhibited by a user is generated for each of multiple users. This degree of trend setting exhibited by the user is an indication of how well the user identifies trends for items (e.g., consumes items) prior to the items becoming popular. The item consumption of users with high trend setting scores is then used to identify items that are expected to become popular after a lag in time. For a given user, another user with a high trend setting score (also referred to as a trendsetter) and having a high affinity with (e.g., similar item consumption behavior or characteristics) the given user is identified. Recommendations are provided to the given user based on items consumed by the trendsetter prior to the items becoming popular.
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公开(公告)号：US20210406593A1
公开(公告)日：2021-12-30
申请号：US16915328
申请日：2020-06-29
Applicant: Adobe Inc.
Inventor： Michele Saad , Lauren Dest
IPC: G06K9/62 , G06K9/46 , G06F16/538 , G06N3/02
Abstract: Methods and systems disclosed herein relate generally to systems and methods for predicting content based on vector data structures generated from image pixels. A content-prediction application accesses a color palette having two or more color-palette categories and selects a first color of the color palette. The content-prediction application generates a first vector based on a set of pixel values that represent the first color of the color palette. The content-prediction application determines a distance metric between the first vector and a second vector, in which the second vector is identified by applying a convolutional neural network model on an image depicting an item that includes a second color. In response to determining that the distance metric is less than a predetermined threshold, the content-prediction application selects the content item corresponding to the second vector.
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公开(公告)号：US11151755B1
公开(公告)日：2021-10-19
申请号：US16942103
申请日：2020-07-29
Applicant: Adobe Inc.
Inventor： Michele Saad , Lauren Dest
Abstract: Methods and systems disclosed herein relate generally to systems and methods for modifying pixel values of an image to improve the visibility of target pixel patterns. A pixel-simulation application accesses an initial image including an initial set of pixel values. The initial set of pixel values define, in an initial color space, a particular color of pixels that indicate a target pixel pattern. The pixel-simulation application generates, based on the initial set of pixel values, a simulated image including a modified set of pixel values that visually indicate another color of pixels in an intermediate color space. The pixel-simulation application generates a pixel map by identifying a difference between the initial set pixel values of the initial image and the modified set of pixel values of simulated image. The pixel-simulation application generates, for display, an output image based at least in part on the pixel map.
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公开(公告)号：US20240386633A1
公开(公告)日：2024-11-21
申请号：US18197963
申请日：2023-05-16
Applicant: Adobe Inc.
Inventor： Michele Saad , Ajay Jain
Abstract: Certain aspects and features of this disclosure relate to automatic generation of composite images. For example, a method involves producing a representative image corresponding to a composite image based on a presentation context of input objects and segmenting the generated objects from the representative image to extract the generated objects from the representative image. The method also includes generating an inferred disposition of each of the generated objects and transforming each of the input objects to the inferred disposition of a corresponding generated object. The method can also include transmitting, storing, display, or rendering, in response to the transforming, the composite image of the input objects. Certain aspects and features also include computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the method.
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公开(公告)号：US20240249331A1
公开(公告)日：2024-07-25
申请号：US18157854
申请日：2023-01-23
Applicant: ADOBE INC.
Inventor： Ajay Jain , Michele Saad
IPC: G06Q30/0601 , G06Q30/0282
CPC classification number: G06Q30/0627 , G06Q30/0282
Abstract: Systems and methods for product description augmentation are provided. According to one aspect, a method for product augmentation includes performing, by an attribute inference component, a semantic analysis of a product review for a product to obtain review data including an attribute of the product; computing, by a delta component, a difference between the review data and a product description for the product, wherein the difference includes the attribute; and generating, by a generative machine learning model, an augmented product description for the product based on the difference, wherein the augmented product description describes the attribute.
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