Wesco Distribution, Inc.

Akash Khurana

Shashi Dande

Raja Vikram Raj Pandya

Merwan Mereby

Kishor Saitwal

Avinash Wesley

Rafael Da Matta Navarro

Ashok Ramesh Bajaj

Benjamin James Albu

John J Engel

Kris Lindsay

Systems, methods, and computer-readable storage media for reinforcing and modifying the search engine behavior using aspects of federated learning. A system can receive, from a plurality of first users at a computer system, a query for a product, and generate a vector representation of the query. The system can then execute a search of an embedding space for the vector representation, resulting in a first ranked list of at least one product, wherein the embedding space comprises vectorized product representations for a plurality of products, and wherein the first ranked list is ranked in order of similarity of the vectorized product representations to the vector representation of the query. As feedback is received from multiple users, and the query is received again, the system can generate a second ranked list which is the first ranked list modified by the feedback of the plurality of first users.

Systems and methods for reinforcing search engine behavior using federated learning

Systems, methods, and computer-readable storage media for reinforcing and modifying the search engine behavior using aspects of federated learning. A system can receive from a user at a first time, a query for a product, then generate a vector representation of the query, and execute a search of an embedding space for the vector representation, resulting in at least one similarity score for at least one product. The system can present to the user search results based on the at least one similarity score, and receive from the user a selected product from within the search results. The system can update, based on the selected product, the at least one similarity score for the selected product, resulting in at least one updated similarity score, where when the query is received again the search of the embedding space for the vector representation produces the at least one updated similarity score.

A computer method for identifying product in a distributor's inventory system that fulfills a product request made via a natural language query. the natural language query is received as a product request including multiple words in sequential order. The words are vectorized into word-vectors that are concatenated and used to generate a query embedding. The query embedding is processed utilizing a trained product category classifier that predicts which product category the requested product belongs. Forward and backward sequence vectors are generated from the sequentially ordered words of the query that are concatenated and processed using a trained model specific to the predicted product category. The sequence vectors represent positional relationships between the words of the natural language query. Thereafter, the system identifies product attribute(s) embodied in the natural language query that each correspond to a predetermined key-characteristic of the category.

Intelligent product matching based on a natural language query

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Houston, TX, United States of America

Akash Khurana