Intent Resolution Using Embeddings, RAG, and LLM Feedback
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Solution Overview
Problem
Existing systems struggle to provide real-time intent analysis and resolution for user queries in a dynamic and efficient manner, particularly in handling impermissible intents and generating tailored responses using generative artificial intelligence.
Innovation Solution
A framework utilizing generative artificial intelligence-enabled systems that convert user queries into embeddings, process them through retrieval augmented generation (RAG) and large language models (LLMs) to generate responses, with real-time feedback loops for improving accuracy and compliance with applicable rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing systems are used for intent analysis, then processing can be performed, but real-time resolution and dynamic efficiency are insufficient
Solution Approach 1:
The patent introduces embeddings as an intermediary representation layer between user queries and intent classification. Queries are converted to embedding vectors that capture semantic meaning, enabling faster comparison and matching against known intents while maintaining accuracy through the mathematical properties of the embedding space
Solution Approach 2:
The system transforms discrete intent categories into continuous embedding vectors, changing the parameter space from categorical to continuous. This enables gradient-based optimization and similarity-based matching, improving both real-time performance and accuracy through vector space operations
2Measurement precision
If generative AI models are used to generate tailored responses, then response relevance improves, but processing requirements and complexity increase
Solution Approach 1:
The response generation process is segmented into distinct stages: query embedding, intent classification, response selection, and generative refinement. Each stage handles a specific aspect of the task, reducing overall system complexity while maintaining high response relevance through specialized processing at each step
Solution Approach 2:
The patent uses embedding vectors as an intermediary that bridges user queries and generative models. This intermediate representation simplifies the input to generative AI, reducing processing complexity while preserving semantic information needed for accurate response generation
3Reliability
If feedback loops are implemented for model updating, then accuracy improves over time, but processing time and resource usage increase
Solution Approach 1:
The system implements feedback loops where user interactions and response outcomes are used to update the embedding models and intent classifications. This continuous learning improves accuracy over time while the automated nature of the feedback processing minimizes additional time loss
Solution Approach 2:
The patent performs preliminary embedding conversion and intent classification before full response generation. This preliminary processing prepares data in advance, reducing the time needed for model updating and enabling faster iterative improvements without significant time penalty
4Measurement precision
If comprehensive intent analysis is performed, then response quality improves, but processing requirements increase
Solution Approach 1:
The system extracts only the essential semantic features of user queries through embedding conversion, rather than processing entire query texts. This extraction of key information maintains intent detection accuracy while significantly reducing computational energy requirements by focusing on condensed vector representations
Data Source
AI summary
Systems and methods provide a framework through which generative artificial intelligence-enabled systems are implemented to provide real-time intent analysis and resolution. In response to user queries communicated by different users, the user queries are converted into different sets of embeddings that are evaluated according to different data sources made available through a Retrieval Augmented Generation (RAG) processor. Based on these different data sources and the different sets of embeddings, the RAG processor and one or more Large Language Models (LLMs) and/or generative artificial intelligence processes dynamically generate relevant responses to these different user queries.