Wesco Distribution, Inc.

Benjamin James Albu

Shashi Dande

Raja Vikram Raj Pandya

Ashok Ramesh Bajaj

Merwan Mereby

Kishor Saitwal

Akash Khurana

John J Engel

Rafael Da Matta Navarro

Avinash Wesley

Richard Gigliotti

Juliana Kostrinsky

Trevor Baumel

Edward Cerny

Kris Lindsay

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

Artificial Intelligence (AI) based methods and systems for predicting opportunities for special pricing agreements (SPA) are disclosed herein. An example method includes receiving a user input indicating a stock keeping unit (SKU) and a customer name, and accessing an SPA database to determine (i) a customer type, (ii) a customer address, and (iii) any historical SPAs corresponding to the customer. The example method further includes predicting, by utilizing a machine learning (ML) model, a set of SPA opportunities that each have a respective cost and a respective confidence interval and that satisfy a confidence interval threshold. The example method further includes determining a first SPA opportunity corresponding to a highest respective confidence interval, and a second SPA opportunity corresponding to a lowest cost of each SPA opportunity in the set of SPA opportunities; and transmitting a notification of the first and second SPA opportunities for display to a user.

Machine learning technologies to predict opportunities for special pricing agreements

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Pittsburgh, PA, United States of America

Benjamin James Albu