Multi-Tier ML Support Responses Using RAG and Dynamic Workflows
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Solution Overview
Problem
Finance and accounting software user support is hindered by insufficient customization, skill gaps among support staff, and reliance on inadequate automated responses, leading to inefficiencies, prolonged issue resolution, and user dissatisfaction.
Innovation Solution
A machine learning-based system that integrates retrieval augmented generation, issue classification, and dynamic workflow engines to generate responses, categorize issues, and facilitate user interaction for timely support, using a multi-tier approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional automated response systems are used, then response speed is improved, but response quality and personalization deteriorate
Solution Approach 1:
The patent introduces an ML model as an intermediary component between the automated response system and the user. The ML model analyzes user input, retrieves relevant information from knowledge bases, and generates personalized responses, thereby maintaining fast automated response speeds while significantly improving response quality and personalization.
Solution Approach 2:
The system dynamically adjusts response parameters based on user input characteristics. The ML model modifies retrieval strategies, response length, and personalization level according to the specific query context, enabling the system to deliver high-quality personalized responses while maintaining overall fast response times across different user scenarios.
2Ease of operation
If human support staff are used, then response quality is improved, but response time increases
Solution Approach 1:
The patent implements a self-service support system where the ML model autonomously handles user queries by retrieving information from knowledge bases and generating responses without requiring human intervention for routine issues. This enables the system to maintain high response quality while dramatically reducing resolution times for common problems.
Solution Approach 2:
The support system is segmented into different handling levels: the ML model handles routine queries autonomously, while complex issues are escalated to human agents. This segmentation allows the system to achieve fast resolution for most queries while maintaining high quality through human expertise when needed.
3Ease of operation
If support staff with comprehensive expertise are hired, then support quality is improved, but cost increases
Solution Approach 1:
The patent replaces the mechanical system of hiring highly skilled human support staff with an ML-based system that provides equivalent or superior support quality. The ML model, trained on domain-specific knowledge, delivers expert-level responses without the ongoing costs of recruiting, training, and retaining specialized support personnel.
Solution Approach 2:
The system copies and stores expert knowledge in structured knowledge bases that the ML model can query. Instead of relying on human experts to recall and apply knowledge, the system captures expert responses and patterns, allowing any user query to be answered with expert-level quality without requiring actual experts to be present.
4Ease of manufacture
If generic automated responses are used, then implementation cost is reduced, but user satisfaction deteriorates
Solution Approach 1:
The patent implements dynamic parameter adjustment where the ML model modifies response personalization, information retrieval depth, and tone based on user characteristics and query context. This enables the system to deliver customized, satisfying responses across different user scenarios while maintaining cost-effective automated operation.
Solution Approach 2:
The system transitions from static generic responses to dynamic personalized responses. The ML model adapts its behavior in real-time based on user input patterns, historical data, and contextual information, enabling cost-effective automated responses that feel personalized and satisfying to each user.
Data Source
AI summary
A ML-based method and system for automatically generating responses to inputs using a multi-tier technique, is disclosed. Initially, first inputs are obtained from electronic devices of first users. An information is retrieved from external sources based on first inputs, using RAG engine. The responses are generated based on optimized information, using ML model. The ML-based method determines whether queries of first users within first inputs are resolved through the optimized responses. Issues are categorized using ML-based issue classification engine when the queries are not resolved. Dynamic forms are generated with fields based on categorization of issues, using a dynamic workflow engine. Second inputs are obtained and processed to determine whether queries are resolved using a rule-based logic engine. The first users are adapted to interact with second users to resolve the issues through communication channels. The responses are provided as output upon resolving the issues, to first users.