Machine Learning Email Intent Analysis for Consistent Receivables Review
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The current manual process for analyzing the intent of electronic mails in accounts receivables management is time-consuming and prone to human error, leading to operational inefficiencies and customer dissatisfaction due to inconsistent interpretations by analysts.
Innovation Solution
A machine learning-based computing system that analyzes electronic mails using metadata extraction, intent classification, named entity recognition, and categorization, employing transformer-based models to automate the process and reduce human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual process is used to analyze email intent, then analysts can interpret and respond to customer requests, but the process becomes time-consuming and prone to human error
Solution Approach 1:
The patent replaces the manual mechanical process of email review with an automated machine learning system. The ML model automatically processes emails, extracts intent, and categorizes them without human intervention, thereby eliminating time consumption while maintaining high accuracy through trained algorithms.
Solution Approach 2:
The system performs self-service by automatically analyzing emails and generating responses without requiring analyst intervention. The ML model independently completes the entire workflow from email receipt to intent classification and response generation, making the system self-sufficient and eliminating manual labor.
2Reliability
If manual process is used to analyze email intent, then analysts can understand customer inquiries, but inconsistencies emerge due to variations in analyst interpretation
Solution Approach 1:
The patent changes the operational parameters from human judgment to algorithmic processing. The ML model uses fixed mathematical computations and trained parameters to classify emails, eliminating the variability inherent in human interpretation. This parameter transformation ensures consistent, reproducible results across all email analyses.
Solution Approach 2:
The system substitutes subjective human judgment with objective machine-based classification. The ML model applies consistent computational rules and trained patterns to determine email intent, replacing the variable human interpretation process with a standardized, repeatable mechanical system that delivers uniform results.
3Adaptability or versatility
If manual process is used to manage account receivables, then analysts can handle diverse responsibilities, but the process becomes increasingly impractical with escalating volume of communications
Solution Approach 1:
The patent implements a universal ML system that can handle diverse email types and complex account receivables scenarios through a single integrated model. The model is trained to recognize and process various email patterns, customer inquiries, and account scenarios, providing multi-functional capability that scales with volume without requiring additional specialized personnel.
Solution Approach 2:
The system replaces manual processing capacity limits with automated computational processing. The ML model can simultaneously analyze large volumes of emails at high speed, overcoming the productivity constraints of human analysts. The mechanical processing system maintains high throughput while adapting to diverse communication types through its trained algorithms.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A machine learning based computing method for analyzing intent of electronic mails, is disclosed. The machine learning based computing method includes steps of: receiving metadata associated with the electronic mails from databases; extracting first textual contents from last received electronic mails stored in electronic mail files by preprocessing the last received electronic mails; analyzing the intent of the electronic mails based on a first machine learning model; classifying the electronic mails into reason codes based on a second machine learning model; extracting information associated with named entities from unstructured and unlabeled electronic mails based on a third machine learning model; grouping the electronic mails into categories based on the intent of the electronic mails, the reason codes, and standardized information associated with the named entities; and providing an output of categorized electronic mails to users on a user interface associated with electronic devices.