Generative Data Analysis for Real-Time Signal and Hypersignal Discovery
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Solution Overview
Problem
Conventional data analytics methods struggle with real-time analysis of large datasets containing thousands of records, as they can only detect predetermined signals and are unable to discover de novo insights or issues, making it difficult to extract detailed analyses from customer data.
Innovation Solution
A system and method utilizing generative models to analyze data records, generating summaries for each record and extracting signals and hypersignals through specialized agents, enabling automatic discovery of unknown insights without manual tagging or categorization, and allowing real-time analysis of large volumes of data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional data analytics methods are used to analyze large datasets, then predetermined signals can be detected, but de novo insights or issues cannot be discovered
Solution Approach 1:
The system employs generative AI models that autonomously discover insights without human intervention. The AI agents self-organize to analyze data, generate hypotheses, and extract patterns automatically, eliminating the need for manual tagging or categorization while discovering de novo insights
Solution Approach 2:
The patent replaces conventional mechanical data processing systems with generative AI-based analysis. Instead of using predetermined rule-based detection, the system uses neural networks and transformers to automatically learn and discover patterns, signals, and insights from unstructured data
2Measurement precision
If detailed per-record analysis is performed on large datasets, then comprehensive insights can be extracted, but real-time analysis becomes extremely difficult or impossible
Solution Approach 1:
The system segments the analysis process into multiple stages: first summarizing individual records, then aggregating summaries into batches for population-level analysis. This segmentation allows detailed per-record analysis to be performed efficiently without compromising real-time capabilities
Solution Approach 2:
The system performs preliminary summarization of individual records before conducting population-level analysis. By pre-processing and summarizing data at the record level, the system reduces the complexity of subsequent batch analysis, enabling real-time insights from large datasets
3Extent of automation
If manual tagging or categorization is used for data analysis, then structured insights can be obtained, but automatic discovery of unknown insights is prevented
Solution Approach 1:
The system incorporates feedback mechanisms where AI agents continuously refine their analysis based on the data they process. The generative models adjust their understanding and categorization based on patterns they discover, enabling automatic adaptation to unknown insights while maintaining context
Data Source
AI summary
data analysis method can include: receiving a set of data records from an entity; determining a set of summaries for each data record S200; determining a set of signals based on a batch of summaries across the set of data records S300; and determining a hypersignal based on the set of signals S400. The method can optionally include: determining an analysis based on the set of signals or hypersignals for the entity; and/or generating recommendations for the entity. The method functions to extract population-level signals (e.g., insights) from the content of each data record within large corpuses of detailed data. In variants, the method can extract the signals in real- or near-real time.