ML Virtual Assistant for RFQ Quote and Order Processing

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Solution Overview

Problem

Manual processing of product quotations and orders by account managers is time-consuming, repetitive, and error-prone, requiring significant human intervention and handling of RFQs through electronic communication.

Innovation Solution

An account manager virtual assistant system utilizing machine learning techniques, including trained classification and information extraction models, automates the interpretation and processing of RFQs, generating quotes and orders by analyzing electronic messages.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual processing of RFQs is performed by account managers, then human judgment and customer relationship management are maintained, but processing time increases and error rates rise

Engineering Contradiction:

Improveprocessing accuracyVSAvoidprocessing time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent introduces an intermediary system (virtual assistant with ML models) that sits between the customer's RFQ and the account manager. This intermediary automatically extracts information, classifies RFQs, and prepares initial responses, reducing manual processing time while maintaining accuracy through automated validation rules. The account manager remains in the loop for final approval, preserving reliability.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system enables self-service by allowing the virtual assistant to independently handle routine RFQ processing tasks including information extraction, classification, and quote generation. This reduces the account manager's workload to oversight and complex decision-making only, significantly improving processing speed while maintaining accuracy through automated consistency checks.

Inventive Principle:

Principle #25Self-service

2Productivity

If manual RFQ interpretation is performed, then contextual understanding and customer-specific considerations are applied, but repetitive tasks increase workload and error potential

Engineering Contradiction:

Improveprocessing efficiencyVSAvoiderror rate

Core Design Contradiction:

ProductivityVSObject-generated harmful factors

Solution Approach 1:

The patent replaces the mechanical manual process of RFQ interpretation with an automated ML-based system. The virtual assistant uses natural language processing to extract information, classify RFQs, and generate quotes automatically. This substitution eliminates repetitive manual tasks and reduces human error while maintaining productivity through efficient automated workflows.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system incorporates feedback mechanisms where the virtual assistant learns from account manager corrections and customer responses. This continuous feedback loop improves the accuracy of automated processing over time, reducing error rates while maintaining high productivity. The account manager's feedback on automated decisions refines the ML models for better future performance.

Inventive Principle:

Principle #23Feedback

3Ease of operation

If automated processing is implemented, then processing speed and consistency improve, but complexity of the system increases

Engineering Contradiction:

Improveoperational simplicityVSAvoidsystem complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The patent segments the RFQ processing system into distinct functional modules: information extraction module, classification module, quote generation module, and account manager interface module. Each module handles a specific task independently, making the overall complex system manageable through clear separation of concerns. This segmentation improves ease of operation by providing a structured, modular approach while containing system complexity within defined boundaries.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS12548053B2Account manager virtual assistant using machine learning techniques

Publication Date: 2026.02.10 CDW LLC

AI summary

An account manager virtual assistant computing system includes a processor and a memory storing instructions that when executed cause the system to receive a message, process the message using a quote classification machine learning model, generate a response including price information, and process the message using an information extraction machine deep learning model; and transmit the response. A non-transitory computer readable medium includes program instructions that when executed cause a computer to receive a message, process the message using a quote classification machine learning model, generate a response including price information, and process the message using an information extraction machine deep learning model; and transmit the response. An account manager virtual assistant method includes receiving a message, processing the message using a quote classification machine learning model, generating a response including price information, and processing the message using an information extraction machine deep learning model; and transmitting the response.