AI Digital Profiles for Multi-Source Data Aggregation
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Solution Overview
Problem
Existing systems struggle to efficiently aggregate and interpret data from disparate sources to create comprehensive digital profiles of individuals or entities, as data is often scattered across various formats and locations, making automation difficult.
Innovation Solution
A system and methodology utilizing artificial intelligence (AI) to aggregate and interpret data from multiple sources, creating dynamic digital profiles that are continuously updated, and enabling various applications such as personalized matching, real-time profiling, and customizable privacy controls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data aggregation from disparate sources is automated, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent employs an intermediary processing layer that standardizes and normalizes data from disparate sources before further processing. This intermediary component handles format conversion and data reconciliation, reducing the complexity burden on the overall system while maintaining high aggregation productivity.
Solution Approach 2:
The data aggregation system is divided into modular segments including data collection modules, processing modules, and analysis modules. Each segment handles specific tasks independently, which reduces overall system complexity while enabling efficient parallel processing of data from multiple sources.
2Measurement precision
If comprehensive digital profiles are created from multiple data sources, then measurement precision is improved, but loss of information increases
Solution Approach 1:
The system incorporates feedback mechanisms that continuously validate and refine profile data against multiple sources. This feedback loop detects and corrects information distortion, ensuring high measurement precision while minimizing data loss through iterative verification and cross-validation processes.
3Speed
If real-time profile updates are implemented, then speed is improved, but use of energy increases
Solution Approach 1:
The system implements periodic batch processing combined with event-triggered updates rather than continuous real-time processing. Profiles are updated at optimized intervals or only when significant changes occur, maintaining perceived real-time performance while dramatically reducing energy consumption compared to continuous processing.
Data Source
AI summary
Various aspects related to aggregating data associated with a person or entity and interpreting the data with an artificial intelligence tool are disclosed. In one such aspect, a method is provided, which includes creating a digital profile of the person or entity based on an interpretation of the data in which the digital profile includes at least one attribute associated with the person or entity. In another aspect, a method is provided, which includes categorizing the at least one attribute into at least one category. In a further aspect, another method is provided, which includes providing a suggestion to the person or entity in accordance with an inferred desired outcome in which the inferred desired outcome is an inference based on the at least one attribute associated with the person or entity within a context associated with the person or entity.