Multi-Model Query Response Generation for Consistent Due Diligence Answers
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Solution Overview
Problem
Large institutions face challenges in efficiently and accurately managing and responding to diverse and voluminous inquiries, including structured and unstructured formats, which can be resource-draining and prone to errors, particularly in preparing detailed responses to Due Diligence Questionnaires.
Innovation Solution
A system utilizing multiple AI models to determine user type, retrieve relevant data, and formulate responses in a consistent style, leveraging historical data and caching mechanisms to enhance efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dedicated teams manually review and respond to each questionnaire, then accuracy and attention to detail are improved, but response time and resource costs increase significantly
Solution Approach 1:
The patent segments the questionnaire processing task into multiple independent AI models, each responsible for specific functions: user type classification, data retrieval, response generation, and style formatting. This segmentation enables parallel processing of different questionnaire components, significantly reducing overall response time while maintaining accuracy through specialized model attention to their respective tasks.
Solution Approach 2:
The patent introduces an intermediary AI system that acts as a mediator between the questionnaire input and final responses. This intermediary system processes questions through multiple AI models, manages data retrieval from various sources, and coordinates response generation, thereby reducing manual intervention time while preserving accuracy through automated quality control mechanisms.
2Reliability
If multiple AI models are used to process queries, then response consistency and accuracy are improved, but system complexity increases
Solution Approach 1:
The patent divides the complex processing system into distinct AI model segments, each handling a specific aspect: user type determination, data retrieval, response generation, and style application. This segmentation reduces system complexity by making each component independent and manageable, while the coordinated operation of these segments ensures response consistency across different query types.
Solution Approach 2:
The patent creates a universal AI processing framework that handles multiple query types and user categories through the same multi-model architecture. This universal system maintains consistency by applying the same rigorous processing pipeline to all queries, regardless of user type or question complexity, thereby improving reliability without proportionally increasing complexity.
3Measurement precision
If historical data is leveraged to train models, then response accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary action by pre-training AI models on historical questionnaire data and responses before actual use. This upfront training phase incorporates lessons from past interactions, improving response accuracy for future queries. Once trained, the models can process new questions efficiently without requiring continuous access to the full historical dataset, thereby reducing ongoing computational resource requirements.
Data Source
AI summary
Systems, methods, and devices that relate to generation of responses to query sets are disclosed. In one example aspect, the system uses multiple models to retrieve data relevant to queries and appropriate for the requesting user and to output the data in a certain style. In particular, the system uses a first model to determine a type of user submitting a query, a second model to retrieve data relevant to the query, subject to constraints based on the type of user, and a third model to formulate a response to the query, based on the retrieved data, using a consistent style. In some implementations, another model enables certain users to validate the outputs from one or more of the first, second, and third models. The system can output a compilation of the responses to the queries in a particular manner, style, or presentation that is consistent across all outputs.