AI Schema Management Platform for Reducing Computational Overhead
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Solution Overview
Problem
Conventional large language models face challenges such as high computational overhead during training and runtime, response variability and inconsistency, context voids in specialized domains, and the inability to protect sensitive data.
Innovation Solution
A schema management platform that leverages artificial intelligence to generate and execute schemas, providing deterministic sequences of actions for specific contexts. This platform modularizes knowledge into discrete units, enhances flexibility and scalability, and ensures knowledge relevance by allowing individual schemas to be updated.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional large language models are used to provide versatile natural language processing functionalities, then response capability and accessibility are improved, but computational overhead during training and runtime increases significantly
Solution Approach 1:
The patent segments knowledge into discrete, modular units called knowledge units that can be independently stored, retrieved, and updated. This segmentation allows the system to avoid processing entire knowledge bases during inference, reducing computational overhead while maintaining versatile response capabilities through selective knowledge unit retrieval based on query relevance.
2Adaptability or versatility
If conventional large language models are used to handle diverse natural language tasks, then functionality and accessibility are improved, but response consistency and determinism deteriorate
Solution Approach 1:
The patent introduces an intermediary layer consisting of structured knowledge units that mediate between the query input and the response generation. This intermediary layer provides deterministic retrieval mechanisms and structured knowledge representation, ensuring consistent and reliable responses while maintaining the ability to handle diverse natural language tasks through the versatility of the knowledge unit database.
3Quantity of substance
If conventional large language models are trained on enormous amounts of diverse data to achieve broad knowledge coverage, then information accessibility is improved, but context voids in specialized domains persist
Solution Approach 1:
The patent applies local quality by creating specialized knowledge units with context-specific attributes and metadata tailored to different domains. Each knowledge unit is enriched with domain-specific context, relationships, and attributes that ensure comprehensive coverage of specialized topics while maintaining broad knowledge accessibility through the overall knowledge unit database structure.
4Ease of operation
If conventional large language models are used to process natural language queries, then natural language interface capability is improved, but ability to protect sensitive data deteriorates
Solution Approach 1:
The patent extracts sensitive information from the training data and places it into a controlled knowledge unit database that is separate from the model weights. This extraction allows the system to maintain natural language interface capabilities while protecting sensitive data by preventing it from being exposed through model memorization or unintended retrieval, as the knowledge units can be selectively accessed based on authorization and context.
Data Source
AI summary
Disclosed herein are system, method, and computer program product aspects for a schema management platform. An aspect operates by leveraging artificial intelligence for generating and executing schemas. An aspect operates by also providing functionality for generating code for a schema. The schema management platform may be used to assist real-world workflows and procedures performed in particular contexts. In some aspects, the schema management platform can utilize a schema to instruct a multimodal model on how to respond with verifiable subject matter expertise to various types of inputs and use cases. As such, the schema management platform may provide more accurate and reliable results compared to conventional systems.