AI Email Response Generation with ERP-Verified Client Data
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Solution Overview
Problem
Accounting professionals face significant challenges in managing high volumes of emails, which are time-consuming and prone to miscommunication, leading to missed deadlines and strained relationships with stakeholders.
Innovation Solution
A computer-implemented method using generative AI to analyze incoming emails, identify intent and data identifiers, retrieve relevant client data, and populate response templates with ERP data for approval, thereby automating the email response process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If accountants manually process and respond to high volumes of emails, then communication accuracy can be maintained through careful review, but time consumption increases significantly (exceeding two hours per day)
Solution Approach 1:
The patent introduces an intermediary system comprising NLP models, intent classification modules, and template selection mechanisms that mediate between incoming emails and accountant responses. This intermediary automatically performs initial analysis, categorization, and draft generation, reducing the time accountants spend on manual processing while maintaining accuracy through structured verification steps.
Solution Approach 2:
The email processing workflow is segmented into distinct automated stages: email reception and parsing, intent classification, data extraction, template selection, and response generation. Each segment handles a specific function, allowing parallel processing and reducing overall time consumption while maintaining quality through specialized processing at each stage.
2Reliability
If accountants manually sort and prioritize emails, then communication quality can be maintained through careful attention, but productivity decreases due to the overwhelming volume of correspondence
Solution Approach 1:
The system enables self-service email processing by automatically analyzing incoming messages, classifying intents, extracting relevant data, and generating appropriate response drafts without requiring accountant intervention for each email. This self-automating approach dramatically increases productivity while maintaining quality through built-in verification and approval workflows.
Solution Approach 2:
The patent changes the processing parameters from manual human analysis to automated computational analysis, transforming how emails are sorted and prioritized. The system uses NLP techniques, intent classification algorithms, and automated data extraction to process emails at scale, enabling high productivity while maintaining reliability through structured quality control mechanisms.
3Reliability
If accountants carefully review each email to ensure correct information is conveyed, then miscommunication is reduced, but the complexity of the workflow increases
Solution Approach 1:
The system performs preliminary actions by automatically analyzing emails, classifying intents, extracting data, and generating response drafts before accountant review. This preliminary automated processing reduces the complexity of the overall workflow by handling routine analytical tasks, allowing accountants to focus on verification and final approval, thereby maintaining information accuracy without overwhelming complexity.
4Measurement precision
If more time is dedicated to email response, then communication accuracy improves, but opportunity cost increases as accountants cannot focus on other critical tasks
Solution Approach 1:
The patent replaces the mechanical system of manual email analysis and drafting with an automated computational system using NLP, machine learning models, and template-based generation. This substitution maintains response accuracy through intelligent algorithms while eliminating the opportunity cost by freeing accountants from time-consuming manual tasks, allowing them to focus on higher-value activities.
Data Source
AI summary
A computer implemented method for generating a response to a received electronic message. A generative AI is prompted to analyse the electronic message and to generate intent data indicative of an intent associated with the electronic message and data-identifier data indicative of one or more predetermined data identifiers present in the electronic message. The electronic message is analysed to identify and retrieve client data associated with one or more parties associated with the electronic message. The method includes Verifying the retrieved client data includes data corresponding to the generated data-identifier data. If so, query an ERP system with the verified data-identifier data to extract ERP data corresponding to the predetermined data identifiers of which the data-identifier data is indicative, select a response-message template using the intent data, populate the selected response-message template with the ERP data, and present the populated response-message template to a user for approval.