Machine Learning Email Intent Analysis for Accounts Receivable
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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 inefficiencies and inconsistencies.
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.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review and interpretation of electronic mails is used, then analysts can understand customer intentions, but the process becomes time-consuming and error-prone
Solution Approach 1:
The patent replaces the manual mechanical process of human analysts reviewing and interpreting emails with an automated machine learning-based system. The system uses transformer-based models to automatically extract intent, classify emails into reason codes, and identify named entities, thereby eliminating the time-consuming manual review process while maintaining high accuracy in understanding customer intentions.
Solution Approach 2:
The system enables self-service by automatically processing electronic mails without requiring human intervention for intent analysis. The machine learning models independently perform text extraction, intent classification, and entity recognition, allowing the system to serve itself rather than requiring human analysts to manually process each email.
2Reliability
If manual analysis of electronic mails is performed, then analysts can interpret customer requests, but inconsistencies arise due to varying analyst interpretations
Solution Approach 1:
The patent applies homogeneity by using a unified machine learning model architecture that processes all electronic mails through the same automated pipeline. The transformer-based models ensure consistent intent analysis and classification across all emails, eliminating the variability and inconsistencies that arise from different analysts interpreting similar requests differently.
Solution Approach 2:
The system replaces the complex manual processing mechanism with a standardized automated machine learning pipeline. This substitution ensures that all emails are analyzed using the same computational methods and criteria, thereby achieving consistent and reliable results without the complexity of manual human interpretation.
3Measurement precision
If manual review process is used for each electronic mail, then detailed understanding is achieved, but the process becomes impractical to scale
Solution Approach 1:
The patent replaces the manual review mechanism with an automated machine learning system that can process emails at scale. The transformer-based models maintain detailed understanding of email intent while simultaneously handling large volumes of communications, making the process practical for scaling to handle escalating customer communication volumes.
Solution Approach 2:
The system enables continuous automated processing of electronic mails without interruption or manual intervention. The machine learning models continuously analyze incoming emails, classify them into reason codes, and extract named entities in real-time, providing detailed understanding while maintaining high productivity and scalability.
Data Source
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.