Groupon, Inc.

Sap Se

Bytedance Inc.

Callidus Software, Inc.

Nick Pendar

Shawn Ryan Jeffery

David Alan Johnston

Matthew DeLand

Mark Thomas Daly

Andrew James

Richard Clark Barber

Arjun Rao

Terison Gregory

Rajat Bhattacharjee

Matt DeLand

Liwen Sun

Eric Christopher Hagen

Seyed Farhad Faghihi

Systems and methods are provided for segmenting each image of a first plurality of images, using an image processing segmentation technique, into one or more category of a plurality of predefined categories to generate a set of image segments for the image. A numerical vector representation is generated for each image segment and for each image in a second plurality of images and used to determine a similarity between image segments and images in the second plurality of images. Each image segment in each set of image segment are replaced with an image in the second plurality of images that is similar to the image segment to generate a recommendation catalog including a plurality of sets of recommendation images.

Image segmentation and vectorization system for complementary styling products

Recommendation services typically struggle with sparse data scenarios. A cold generated item start technique can find a warm generated item as a suitable proxy in a matrix representing co-occurrences of generated items. Embeddings can be used to determine distance between items. The technologies are useful for providing recommendations even in scenarios involving little or no transaction data.

Solving sparse data problems in a recommendation system with cold start

Recommendation services typically struggle with sparse data scenarios. A freezing generated item start technique can use a matrix of external generated items to find a linking generated item. Embeddings can be used to determine distance between items. The technologies are useful for providing recommendations even in scenarios involving little or no transaction data.

Solving sparse data problems in a recommendation system with freezing start

A computing system configured to perform artificial intelligence-powered search and evaluation functionalities is disclosed. The computer system can receive a natural language search input entered through a user interface, request a search engine to perform a search based on the natural language search input, receive search results returned by the search engine, and submit, in runtime, structured data contained in the search results to a large language model. The structured data includes a table including one or more records and a plurality of fields. The computing system can prompt, in runtime, the large language model to generate scores for the one or more records and present the structured data and the scores on the user interface.

Artificial intelligence-powered search and evaluation system

In general, embodiments of the present invention provide systems, methods and computer readable media for identifying a new business based on programmatically analyzing content received from online sources and, as a result, discovering one or more references to the business. In embodiments, the system stores historical data representing previously identified new businesses and then uses attributes of those businesses in search queries to receive related content. Additionally or alternatively, the system stores data representing online sources that historically provided content containing references to new businesses and then continues to access those sources for additional content. In embodiments, the system performs content analysis on structured and/or unstructured content. In some embodiments, analysis of content received from a particular online source includes a source-specific algorithm that takes a source-specific representation of the content as input and produces a result indicating the likelihood that the content includes a new business reference.

Discovery of new business openings using web content analysis

Provided herein are systems, methods and computer readable media for classification and tagging of textual data. An example method may include accessing a corpus comprising a plurality of documents, each document having one or more labels indicative of services offered by a merchant, generating a query based on extracted features and the documents, generating a precision score for at least a portion of the generated query and selecting a subset of the generated queries based on an assigned precision score satisfying a precision score threshold, the selected subset of the generated queries configured to provide an indication of one or more labels to be applied to machine readable text. A second example method, utilized for tagging machine readable text with unknown labels, may include assigning a label to textual portions of the machine readable text based on results of the application of the queries.

Method, apparatus, and computer program product for classification and tagging of textual data

In general, embodiments of the present invention provide systems, methods and computer readable media for automated dynamic data quality assessment. One aspect of the subject matter described in this specification includes the actions of receiving a data quality job including a new data sample; and, if the new data sample is determined to be added to a reservoir of data samples, sending a quality verification request to an oracle; receiving a new data sample quality estimate from the oracle; and adding the new data sample and estimate to the reservoir. A second aspect of the subject matter includes the actions of receiving, from a predictive model, a judgment associated with a new data sample; analyzing the new data sample based in part on the judgment to determine whether to send a new data sample quality verification request to an oracle; and, if a new data sample quality estimate is received from the oracle, determining whether to add the new data sample and the judgment to the reservoir.

Automated dynamic data quality assessment

Task/resources are randomly assigned a number of times and a score for each solution of the random assignment is calculated. Using machine learning and artificial intelligence, a subset of the solutions is selected. Assignment of task/resource within the subset may be randomly changed, e.g., a task/resource assignment between two entities, a task/resource within the selected subset may be replaced with another task/resource (without swapping), etc. The additional solutions form a super solution with the selected subset and the score associated with the additional solutions are calculated. The process of selection of assignments, random changes to the assignment and calculating the scores associated with the new solutions is repeated a number of times until a certain condition is met, e.g., a number of iterations, time out, improvement between two iterations is less than a certain threshold, etc. Once the certain condition is satisfied, a solution is selected.

Automatic assignment of hardware/software resources to different entities using machine learning based on determined scores for assignment solutions

In general, embodiments of the present invention provide systems, methods and computer readable media for generating an optimal classifying query set for categorizing and/or labeling textual data based on a query subsumption calculus to determine, given two queries, whether one of the queries subsumes another. In one aspect, a method includes generating a group of determining queries based on analyzing text within a document; receiving a group of classifying queries; and, for each determining query within the group of determining queries, determining whether at least one of the classifying queries is subsumed by the determining query; and updating the group of classifying queries in an instance in which the classifying query is subsumed by the determining query.

Multi-term query subsumption for document classification

Embodiments provide for automated dynamic data quality assessment. A data quality job including a new data sample is received. If the new data sample is determined to be added to a reservoir of data samples, a quality verification request is sent to an oracle. A new data sample quality estimate is received from the oracle. The new data sample and estimate can be added to the reservoir. A judgment associated with a new data sample is received from a predictive model. Whether to send a new data sample quality verification request to an oracle is determined. If a new data sample quality estimate is received from the oracle, whether to add the new data sample and the judgment to the reservoir is determined.

Growing community of inventors

San Ramon, CA, United States of America

Nick Pendar