Generative AI Request Classification With Vector Validation Feedback
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Solution Overview
Problem
Generative AI models face challenges in generalizing to unseen data, posing privacy risks, requiring complex hardware, and being computationally intensive, which complicates their deployment and efficiency in real-world applications, especially in handling large volumes of repetitive tasks in document creation and analysis.
Innovation Solution
A processor-based system that receives user requests, classifies them into solution patterns, segments metadata, generates vector representations, and validates generative AI solutions using large language models, continuously updating these models with user feedback for improved accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generative AI models are deployed to handle diverse tasks, then task handling capability is improved, but computational resource requirements and complexity increase
Solution Approach 1:
The system segments the complex generative AI task into multiple specialized components: a classification module that categorizes user requests into solution patterns, a vector representation module that converts data to numerical vectors, and a validation module that verifies outputs. This segmentation allows each component to be optimized independently, reducing overall system complexity while maintaining versatility.
Solution Approach 2:
The patent introduces intermediary elements including a solution pattern library that mediates between user requests and AI generation, and a validation module that acts as an intermediary check between generated content and final output. These intermediaries simplify the interaction complexity by providing structured interfaces and validation layers.
2Productivity
If existing generative AI models are used, then solution generation capability is improved, but privacy and security risks increase due to synthetic data generation
Solution Approach 1:
The system implements a feedback mechanism where the validation module continuously evaluates generated solutions against privacy and security criteria. User feedback on generated solutions is incorporated to refine the classification and generation processes, enabling the system to learn from and adapt to privacy concerns while maintaining productivity.
Solution Approach 2:
The validation module performs preliminary checks on generated content before output, proactively identifying and preventing potential privacy violations or security risks. This preliminary anti-action approach catches issues before they can cause harm, allowing the system to maintain high productivity while mitigating risks.
3Measurement precision
If generative AI models are trained on large datasets, then accuracy is improved, but training time and computational cost increase
Solution Approach 1:
The system performs preliminary classification of user requests into solution patterns before generating responses. This preliminary action allows the system to reuse previously validated solution patterns for similar requests, avoiding redundant training and generation processes while maintaining high accuracy for common task types.
Solution Approach 2:
The patent employs copying by maintaining a library of validated solution patterns that can be reused across multiple similar requests. Instead of generating solutions from scratch each time, the system copies and adapts proven solutions, significantly reducing training time and computational cost while preserving accuracy through the validation framework.
4Manufacturing precision
If manual document creation processes are used, then data accuracy is improved, but productivity decreases due to repetitive manual tasks
Solution Approach 1:
The system enables self-service by automatically classifying user requests, selecting appropriate solution patterns, generating solutions, and validating outputs without requiring manual intervention at each step. This automation maintains data accuracy through structured validation while dramatically improving productivity by eliminating repetitive manual document creation tasks.
Data Source
AI summary
Systems and methods for generating generative AI based solution are disclosed. A system receives a request for generating the generative AI (GenAI) based solution. The system classifies the received request into solution patterns and performs actions corresponding to at least one of the solution patterns. The system extracts a metadata from the received request and the actions based on the type of GenAI based solution to be generated. Further, the system segments the extracted metadata into data segments and generates a vector representation of the data segments. The system generates the GenAI based solution corresponding to the received request based on the generated vector representation. The system validates the GenAI based solution large language model (LLM). The system continuously updates the LLM and the vector-based machine learning model with the validated GenAI based solution and user feedback. The system outputs the GenAI based solution on a user interface.