User-Specified Private-Data AI Agents for Domain-Specific Outputs
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Solution Overview
Problem
Existing technologies lack the ability to provide customized, contextualized, and domain-specific outputs from private data sets within smaller computing environments, and there is a need for multi-agent artificial intelligence-based processing across multiple operating systems.
Innovation Solution
A framework that utilizes machine learning tools and artificial intelligence models, including transformer-based models and long-term knowledge graphs, to analyze and augment user-specific documents and files, enabling specialized outputs through a privately-hosted environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If transformer-based models and large language models are used for analyzing and augmenting private data sets, then the understanding and augmentation capabilities are improved, but the computing resource requirements and system complexity increase significantly
Solution Approach 1:
The system segments the large language model processing into modular components: a retrieval module that extracts relevant information from private data sets, an augmentation module that enhances the base model with contextualized information, and an execution module that performs domain-specific tasks. This segmentation allows the complex AI capabilities to be delivered through manageable, distributed computing components rather than requiring a single large-scale system.
Solution Approach 2:
The patent introduces an intermediary retrieval-augmented generation system that sits between the private data sets and the language model. This intermediary layer retrieves relevant contextual information from user-designated files and augments the model's inputs, enabling sophisticated analysis without requiring the model itself to directly process entire data sets, thus reducing computational burden while maintaining high understanding capabilities.
2Productivity
If large-scale computing systems are deployed to implement artificial intelligence models, then the analysis and augmentation capabilities are improved, but the cost and resource consumption increase
Solution Approach 1:
The system performs preliminary retrieval and filtering of relevant information from private data sets before presenting it to the language model. By pre-processing and contextualizing data in advance, the system reduces the amount of computation needed during actual model inference, thereby lowering resource consumption while maintaining high productivity in analysis and augmentation tasks.
Solution Approach 2:
Instead of processing entire data sets or using overly powerful models for every task, the system applies partial processing by retrieving only the necessary contextual information relevant to each specific query. This selective approach avoids excessive resource consumption while delivering sufficient analytical capabilities for domain-specific outputs.
3Ease of operation
If basic search functions are used to identify content in private data sets, then the simplicity and ease of operation are maintained, but the customization and extraction capabilities are limited
Solution Approach 1:
The system incorporates feedback loops where the language model analyzes retrieved information and refines subsequent retrieval queries. This iterative feedback process enables the system to progressively improve its content identification and extraction capabilities, moving from basic search to customized, context-aware information retrieval while maintaining ease of operation through natural language interfaces.
Solution Approach 2:
The search and retrieval system is designed to be dynamic rather than static. It adapts its retrieval strategy based on the specific query, the type of domain-specific output required, and the contextual information already gathered. This dynamic behavior enables customized identification and extraction capabilities while preserving user-friendly operation through automatic adaptation.
4Adaptability or versatility
If existing technology is used for content identification, then the compatibility with existing systems is maintained, but the ability to provide contextualized and domain-specific outputs is insufficient
Solution Approach 1:
The patent creates a universal retrieval-augmented generation framework that can handle multiple domain-specific tasks through a single unified system architecture. This multi-functional system can perform content identification, analysis, augmentation, and various domain-specific outputs (such as summarization, question answering, and information extraction) using the same core components, thereby achieving high adaptability without proportionally increasing system complexity.
Data Source
AI summary
A framework provides an approach for utilizing contextualized content from a user's designed set of documents and private data sets to generate customized, contextualized, private, and domain-specific outputs of agents within an artificial intelligence computing environment and a supporting architecture. The agents and artificial intelligence computing environment include augmenting a language model with the contextualized content, and prompting the language model to generate defined, domain-specific outputs. Such agents enable computing systems to execute specific actions identified by a user that are external to the supporting architecture from the defined, domain-specific outputs.