Adobe, Inc.

Santa Clara

Michele Saad

Lauren Dest

Ronald Eduardo Oribio

Ryan A Rossi

Anup Bandigadi Rao

Georgios Theocharous

Tung Mai

Robert W Burke, Jr

Dustin Ground

Saina Lajevardi

Robert Burke

Kirankumar Shiragur

Baldo Antonio Faieta

Ajinkya Gorakhnath Kale

Eunyee Koh

Jose Ignacio Echevarria Vallespi

Ryan Rozich

Saeid Motiian

Irgelkha Mejia

Philip J Corriveau

Ajay Jain

Roland Geisler

Venkata Naveen Kumar Yadav Marri

Nathan Douglas Baldwin

Xin Wang

Anna Hammond

Matthew May

Arjun Athreya

Andrew Thomson

Oleksandr Paliarush

Bingxuan Liang

Soumya Unnikrishnan

Igor Miniailo

Dylan Nathaniel Warnock

Mikhail Kotov

Ramesh V Jaladi

Ralston A Da Silva

Catherine Chiodo

Matthew Cecil Zimmerman

A search system employs arrival times with associated confidence scores as search facets for identifying items. The search system identifies a plurality of items based on search input. An arrival time and associated confidence score are determined for each item from the plurality of items. Search results are provided for the plurality of items in response to the search input. The search results are provided based at least in part on the arrival times and associated confidence scores for the plurality of items.

Delivery-related search and analytics

One or more aspects of the method, apparatus, and non-transitory computer readable medium include receiving a query relating to an item and a summarization type indicating an emphasis on item similarities or item differences, obtaining, using a search component, descriptions of items relevant to the query, generating input data for a machine learning model based on the descriptions and the summarization type, and generating, using the machine learning model, a summarization of the descriptions based on the input data in response to the query, wherein the summarization emphasizes the item similarities or item differences based on the summarization type.

Search results summarization tuning

Systems and methods for generating geolocation-based images for a target object are provided. A geolocation module receives a set of geolocations associated with a geographic region of interest. Each geolocation of the set of geolocations is mapped to context data associated with the geolocation. A prompt generation module generates multiple prompts based on the set of geolocations and the context data. The prompt generation module comprises a first generative artificial intelligence (AI) model. An image generation module generates multiple synthetic images based on the multiple prompts. The image generation module comprises a second generative AI model. Each synthetic image depicts the target object in a background generated based on a prompt.

Geolocation-based background generation for object images

The present disclosure relates to systems, methods, and non-transitory computer readable media for flexibly and accurately utilizing a machine learning model to intelligently determine and provide interface features for display via client devices located across different time zones or geographic regions. For example, the disclosed systems can utilize a feature visualization machine learning model to generate an arrangement of graphics, an assortment of graphics, or other graphical visualization of one or more interface features in a target time zone (or a target geographic region) based on client device interactions from other (e.g., leading) time zones or geographic regions. In certain embodiments, the disclosed systems also (or alternatively) determine a sequence of geographic regions for rolling out, or surfacing, an interface feature based on similarities between geographic regions and a comparison of performance metrics over multiple candidate sequences.

Machine learning approaches for interface feature rollout across time zones or geographic regions

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 modeling for protection against online disclosure of sensitive data

Methods, systems, and non-transitory computer readable media are disclosed for utilizing machine learning models to extract digital signals from low-results web queries and generate item demand deficiency predictions for digital item lists corresponding to websites. In one or more embodiments, the deficiency identification system identifies a low-results query submitted by client devices navigating a website. The deficiency identification system generates features for the low-results query and the digital item list to generate a deficiency prediction relative to demand indicated by the low-results query. In some embodiments, the deficiency identification system utilizes a deficiency prediction model to process the extracted signals and generate a deficiency confidence score corresponding to the low-results query. Based on the deficiency confidence score, the deficiency identification systemd can generate and provide demand notifications via one or more graphical user interfaces.

Utilizing machine learning models to process low-results web queries and generate web item deficiency predictions and corresponding user interfaces

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.

Parameter-based synthetic model generation and recommendations

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.

Chromatic undertone detection

Methods and systems disclosed herein relate generally to increasing visibility of pixel patterns of an image. The system includes a pattern-detection application accessing an image depicting an object. The pattern-detection application determines a set of colors from the transformed image. The pattern-detection application identifies a set of pixels depicting a particular color of the set of colors. For the set of pixels depicting the particular color, the pattern-detection application converts an initial set of pixel values of the set of pixels at an initial color space to another set of pixel values that define the particular color of the set of pixels in another color space. The pattern-detection application modifies one or more values of the other set of pixel values to generate a modified set of pixel values. The modification includes causing the set of pixels visually indicate a simulated color that is different from the particular color.

Increasing visibility of pixel patterns in images

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.

Effective stock keeping unit (SKU) management system

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.

Electronic shopping cart prediction and caching of electronic shopping cart computations

The present technology provides for facilitating removal of undesired search results. In one embodiment, a search request including a search term(s) to use for performing a search is obtained. Thereafter, a search query is generated to execute the search. The search query includes the search terms and a removal parameter indicating a particular search result to exclude from search results returned in response to the search request. A set of search results are provided for display via a user device. Such a set of search results can be identified based on execution of the search query and exclude the particular search result.

Facilitating search result removal

Embodiments of the technology described herein, provide improved visual search results by combining a visual similarity and a textual similarity between images. In an embodiment, the visual similarity is quantified as a visual similarity score and the textual similarity is quantified as a textual similarity score. The textual similarity is determined based on text, such as a title, associated with the image. The overall similarity of two images is quantified as a weighted combination of the textual similarity score and the visual similarity score. In an embodiment, the weighting between the textual similarity score and the visual similarity score is user configurable through a control on the search interface. In one embodiment, the aggregate similarity score is the sum of a weighted visual similarity score and a weighted textual similarity score.

Text adjusted visual search

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.

Growing community of inventors

Austin, TX, United States of America

Michele Saad