Automated Email Intent Analyzer for Support Ticket Routing

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Solution Overview

Problem

Traditional electronic mail management systems are inefficient and prone to errors due to manual sorting and assignment of support requests, which are time-consuming and require multiple user inputs, leading to limited throughput and potential informational faults.

Innovation Solution

An automated system that classifies electronic messages by analyzing metadata and message body content using techniques such as decryption, information stripping, tokenization, and machine learning algorithms to determine case types and generate new case events in a case management system, reducing the need for manual intervention and improving processing speed and accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual sorting and assignment methods are used, then system complexity is reduced, but productivity and processing speed deteriorate

Engineering Contradiction:

Improvemessage processing throughputVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent replaces manual mechanical sorting operations with an automated electronic message analysis system that uses natural language processing, machine learning algorithms, and automated classification mechanisms to determine message intent and assign messages to appropriate support functions, thereby dramatically increasing productivity while managing system complexity through software-based solutions

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables messages to be automatically classified and assigned without human intervention by using automated intent analysis algorithms that independently evaluate message content, extract key information, determine case types, and route messages to appropriate handlers, allowing the system to serve itself in the message sorting process

Inventive Principle:

Principle #25Self-service

2Loss of time

If manual sorting methods are used, then device complexity is reduced, but loss of time increases

Engineering Contradiction:

Improvemessage processing timeVSAvoidsystem complexity

Core Design Contradiction:

Loss of timeVSDevice complexity

Solution Approach 1:

The system performs preliminary automated analysis of message content, metadata, and intent before manual review is needed, pre-classifying messages and preparing assignment recommendations in advance, which reduces the time required for subsequent processing and minimizes the need for time-consuming manual sorting operations

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent substitutes time-consuming manual reading and analysis of each message with automated electronic processing using natural language processing algorithms and machine learning models that can rapidly analyze message content, extract intent, and determine appropriate case types, dramatically reducing processing time

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If automated classification is implemented, then productivity improves, but reliability may deteriorate due to algorithmic errors

Engineering Contradiction:

Improvemessage processing throughputVSAvoidclassification accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system incorporates feedback mechanisms where classification results are continuously evaluated, and the machine learning algorithms are trained on feedback from corrected misclassifications and manual review outcomes, allowing the system to learn from errors and continuously improve classification accuracy and reliability over time

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system applies multiple layers of analysis including natural language processing, metadata analysis, and pattern recognition algorithms, using more analysis methods than strictly necessary to ensure high classification accuracy, and can escalate to manual review when confidence thresholds are not met, thereby maintaining reliability while achieving high productivity

Inventive Principle:

Principle #16Partial or excessive action

4Productivity

If manual sorting is used, then ease of operation is maintained, but productivity deteriorates

Engineering Contradiction:

ImprovethroughputVSAvoiduser input requirements

Core Design Contradiction:

ProductivityVSEase of operation

Solution Approach 1:

The automated classification system performs message analysis, intent determination, and assignment recommendation without requiring user input for each message, making the system self-sufficient in the sorting process while maintaining ease of operation through automated decision-making capabilities

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual sorting operations that require user reading, analysis, and decision-making with automated electronic processing systems that use algorithms to perform these functions, eliminating the need for user inputs while dramatically increasing throughput and productivity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11200267B2Mail room intent analyzer

Publication Date: 2021.12.14 AMERICAN EXPRESS TRAVEL RELATED SERVICES CO INC

AI summary

A system for automated classifying of electronic messages is disclosed. The system may receive an electronic message comprising a text including a message body and a metadata. The system may determine a case status based on the metadata and extract a set of events form the message body in response to the case status. The system may determine a case type based on the set of events and a set of case types. The system may generate a new case event in a case management system based on the case type.