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

IPC: G06T11/60 ,  G06T5/00 ,  G06T7/11

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.

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.

公开(公告)号：US20240078572A1

公开(公告)日：2024-03-07

申请号：US17903360

申请日：2022-09-06

Applicant: Adobe Inc.

Inventor： Michele Saad ,  Igor Miniailo

IPC: G06Q30/02 ,  G06Q30/06

CPC classification number: G06Q30/0222 ,  G06Q30/0625

Abstract: Embodiments provide systems, methods, and computer storage media for prediction and computation of electronic shopping carts. In an example embodiment, for each interaction between an e-shopper and an e-commerce application, one or more predicted electronic shopping carts that represent a combination of items the e-shopper is likely to purchase are generated based on current items in the e-shopper's electronic shopping cart and recent interactions with the e-shopper. For some or all of the predicted electronic shopping carts (e.g., those with top predicted confidence levels), corresponding shopping cart computations (e.g., identifying application promotions, determining a price total for the items in the predicted shopping cart) are executed and cached prior to the e-shopping adding the predicted items. As such, a page configured to visualize the predicted electronic shopping cart with a value retrieved from the cached shopping cart computations (e.g., price total for the predicted electronic shopping cart) is generated.

公开(公告)号：US11829940B2

公开(公告)日：2023-11-28

申请号：US18117586

申请日：2023-03-06

Applicant: Adobe Inc.

Inventor： Kirankumar Shiragur ,  Tung Thanh Mai ,  Anup Bandigadi Rao ,  Ryan A. Rossi ,  Georgios Theocharous ,  Michele Saad

IPC: G06Q10/0835 ,  G06Q10/087 ,  G06Q10/047 ,  G06F17/11

CPC classification number: G06Q10/08355 ,  G06F17/11 ,  G06Q10/047 ,  G06Q10/087

Abstract: In implementations of item transfer control systems, a computing device implements a transfer system to receive input data describing types of requested items and corresponding quantities of the types of requested items to receive at each of a plurality of destination sites and types of available items and corresponding quantities of the types of available items that are available at each of a plurality of source sites. The transfer system constructs a flow network having a source node for each of the plurality of the source sites and a destination node for each of the plurality of the destination sites. An integral approximate solution is generated that transfers the corresponding quantities of the types of requested items to each of the plurality of the destination sites using a maximum flow solver and the flow network. The transfer system causes transferences of the corresponding quantities of the types of requested items to each of the plurality of the destination sites based on the integral approximate solution.

公开(公告)号：US20230350963A1

公开(公告)日：2023-11-02

申请号：US17731999

申请日：2022-04-28

Applicant: ADOBE INC.

Inventor： Michele Saad ,  Matthew Cecil Zimmerman

IPC: G06F16/954 ,  G06N20/00 ,  G06F16/903 ,  G06Q30/06

CPC classification number: G06F16/954 ,  G06N20/00 ,  G06F16/90335 ,  G06Q30/0623

Abstract: A system leverages reinforcement learning techniques to determine distribution of items to listing platforms and search ranking rules for each listing platform. Using historical listing data regarding items listed at one or more listing platforms, a machine learning model generates item interaction data, and a reinforcement learning agent is initialized using the item interaction data. The reinforcement learning agent is trained to optimize a function for selecting item distributions and search ranking rules across listing platforms. At each epoch of a series of epochs, the function is used to select an action including a new distribution of items to listing platforms and new search ranking rules to use at each listing platform. After the action from an epoch is implemented, the reinforcement learning agent updates the function, for instance, based on an impact of the action.

公开(公告)号：US20230041594A1

公开(公告)日：2023-02-09

申请号：US17394707

申请日：2021-08-05

Applicant: Adobe Inc.

Inventor： Kirankumar Shiragur ,  Tung Thanh Mai ,  Anup Bandigadi Rao ,  Ryan A. Rossi ,  Georgios Theocharous ,  Michele Saad

IPC: G06Q10/08 ,  G06Q10/04 ,  G06F17/11

Abstract: In implementations of item transfer control systems, a computing device implements a transfer system to receive input data describing types of requested items and corresponding quantities of the types of requested items to receive at each of a plurality of destination sites and types of available items and corresponding quantities of the types of available items that are available at each of a plurality of source sites. The transfer system constructs a flow network having a source node for each of the plurality of the source sites and a destination node for each of the plurality of the destination sites. An integral approximate solution is generated that transfers the corresponding quantities of the types of requested items to each of the plurality of the destination sites using a maximum flow solver and the flow network. The transfer system causes transferences of the corresponding quantities of the types of requested items to each of the plurality of the destination sites based on the integral approximate solution.

公开(公告)号：US20220383035A1

公开(公告)日：2022-12-01

申请号：US17880706

申请日：2022-08-04

Applicant: Adobe Inc.

Inventor： Michele Saad ,  Lauren Dest

IPC: G06K9/62 ,  G06N3/02 ,  G06F16/538 ,  G06V10/56 ,  G06V10/82

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.

公开(公告)号：US20220270152A1

公开(公告)日：2022-08-25

申请号：US17180693

申请日：2021-02-19

Applicant: Adobe Inc.

Inventor： Georgios Theocharous ,  Nedim Lipka ,  Michele Saad

IPC: G06Q30/06 ,  G06F40/205 ,  G06F40/284

Abstract: Techniques are provided herein for identifying contrasting items based on a target item and presenting each of the target item and contrasting items together to a user. The target item may be any item that is of interest to the user. The contrasting items are identified using a system that compares features of the items together and also considers historical user data associated with the items. Natural language processes are used to label and identify salient portions of the catalog data for the items. Historical user data between items may be determined based on one or more documented event actions that occur with regards to co-viewing the items in some fashion. Both the historical user data and catalog comparisons between items are combined to determine a similarity score or metric between items. Items having highest similarity scores with the target item within a same cluster or group are presented.

公开(公告)号：US20210304280A1

公开(公告)日：2021-09-30

申请号：US16829410

申请日：2020-03-25

Applicant: Adobe Inc.

Inventor： Michele Saad

IPC: G06Q30/06 ,  G06Q30/02 ,  G06N20/00 ,  G06K9/62 ,  G06F16/903

Abstract: This disclosure describes one or more embodiments of systems, methods, and non-transitory computer-readable media that determine a degree of diversification for item recommendations to a user based on the user's input and generate diverse item recommendations for the user according to the degree of diversification. For instance, the disclosed systems can receive a diversification metric from a client device based on a user interaction with a selectable tool (or another interactive element) in a graphical user interface. From among data segments representing users clustered according to item affinities, the disclosed systems can subsequently use the diversification metric to identify a data segment that is diverse from a reference data segment for the user. The disclosed systems further rank items associated with the diverse data segment to select an anomalous item as an item recommendation for display on the client device.

公开(公告)号：US20240331210A1

公开(公告)日：2024-10-03

申请号：US18128906

申请日：2023-03-30

Applicant: Adobe Inc.

Inventor： Michele Saad ,  Ajay Jain

IPC: G06T11/00 ,  G06N3/045 ,  G06N3/0475 ,  G06N3/094 ,  G06Q30/0601 ,  G06Q50/00

CPC classification number: G06T11/00 ,  G06N3/045 ,  G06N3/0475 ,  G06N3/094 ,  G06Q30/0631 ,  G06Q50/01

Abstract: Some embodiments described herein relate to systems and methods for parameter-based synthetic model generation and recommendations including an image generation module and a recommendation module. The image generation module can receive one or more parameters and, responsive to receive the one or more parameters, generate a parameterized image using a generative machine learning model. The generative ML model may use the parameters as a seed for generating the parameterized image. The recommendation module may generate a first set of recommendations for a user of the client device and receive the one or more parameters. The recommendation module may determine, based on the one or more parameters, a second set of recommendations for the user of the client device. The second set of recommendations may include at least one element from the first set of recommendations.