Generative AI Architecture for Domain-Optimized Data Processing
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Solution Overview
Problem
Current database systems face inefficiencies in accessing and processing data across multiple platforms, leading to duplicative efforts and increased bandwidth and processing power requirements, resulting in outdated data analysis that no longer accurately reflects the conditions it describes.
Innovation Solution
A generative artificial intelligence (AI) architecture is developed to recognize semantic and relational properties of domain-specific data, allowing for the identification of entities, objects, and conditions, and generating action metrics to execute transactions efficiently, thereby optimizing data processing and reducing redundant data access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data compilation is performed across multiple platforms, then data accuracy is maintained, but time consumption and processing delays increase significantly
Solution Approach 1:
The patent replaces manual mechanical data compilation processes with an automated AI system that uses natural language processing and machine learning models to extract, validate, and compile data from multiple platforms simultaneously, eliminating the time-consuming manual effort while maintaining accuracy through automated verification mechanisms
Solution Approach 2:
The system performs preliminary data extraction and validation actions in advance by continuously monitoring and pre-processing data from connected platforms, so that when analysis is needed, the data is already prepared and available, significantly reducing the time required for data compilation while ensuring accuracy through pre-validation
2Quantity of substance
If duplicative data access across multiple platforms is performed, then comprehensive data coverage is achieved, but bandwidth occupancy and processing power requirements increase
Solution Approach 1:
The patent creates and maintains local copies or cached versions of data from multiple platforms, allowing the system to access comprehensive data coverage from local storage rather than repeatedly querying external platforms, thereby reducing bandwidth occupancy and processing power requirements while maintaining complete data availability
Solution Approach 2:
The system merges data access operations by consolidating multiple platform queries into a single integrated data retrieval process, and combines data from different sources into a unified structure, reducing redundant data transfer and processing while achieving comprehensive data coverage through intelligent data integration
Data Source
AI summary
Aspects of this technical solution can identify, based on a query, an entity and an object corresponding to the entity, obtain, via an artificial intelligence (AI) model, an entity object that identifies one or more aspects extrinsic to the entity and linked with the entity, obtain, via the AI model, a condition object that identifies one or more aspects extrinsic to the object and the entity, generate, via the AI model, an action object that identifies an action metric, the AI model receiving as input the entity object and the condition object, and cause, in response to the query and based on the action metric, execution of a transaction including the object and the entity.