Ontological Vector Hypothesis Ranking for Event Detection
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Solution Overview
Problem
Current methods for identifying events of interest in large volumes of digital data, particularly in national security and food safety surveillance, are inefficient and prone to missing surprises or producing biased results due to reliance on keyword searches and limited sensitivity in monitoring numerical variables.
Innovation Solution
A hypotheses generation and event recognition system that analyzes documents to construct qualitative metrics, establishes baselines, and identifies updates, using ontological vectors and optimization algorithms to rank hypotheses and recognize event signatures, enabling human and machine event recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If keyword searches and conventional data monitoring methods are used, then data processing is straightforward, but event detection efficiency and accuracy deteriorate due to inability to identify surprises and limited sensitivity
Solution Approach 1:
The system transforms data from static keyword matching to dynamic hypothesis generation by changing the parameter of analysis from literal keyword frequency to contextual semantic relationships. This enables the system to detect events based on evolving patterns and relationships rather than fixed keywords, improving both efficiency and accuracy in identifying surprising events.
Solution Approach 2:
The patent replaces conventional mechanical keyword-searching mechanisms with an AI-based hypothesis generation system that uses natural language processing, ontological vectors, and optimization algorithms. This substitution enables the system to automatically generate and rank hypotheses about potential events, eliminating the need for manual keyword tuning and improving detection capabilities.
2Productivity
If manual data review is used, then data quality control is thorough, but processing speed and scalability deteriorate due to inability to handle vast amounts of data
Solution Approach 1:
The system enables self-service data processing by automatically generating hypotheses, ranking them based on multiple criteria, and presenting prioritized results to users. This eliminates the need for manual data screening and allows the system to process vast amounts of data independently, maintaining high processing speed and scalability.
Solution Approach 2:
The patent segments the data analysis process into distinct stages: data collection, hypothesis generation, hypothesis ranking, and result presentation. This segmentation allows each component to be optimized independently and enables the system to handle large data volumes by processing information through multiple filtered stages rather than monolithic analysis.
3Adaptability or versatility
If conventional monitoring methods are used, then system complexity is low, but adaptability to emerging events and surprises deteriorates due to reliance on predefined keywords and patterns
Solution Approach 1:
The system introduces dynamics by continuously generating and updating hypotheses based on current data patterns rather than relying on static predefined keywords. The hypothesis ranking mechanism dynamically adjusts priorities based on emerging patterns, enabling the system to adapt to new events and evolving situations while maintaining manageable complexity through automated processes.
Solution Approach 2:
The patent creates a universal hypothesis generation framework that can handle multiple event types and domains through a single ontological vector system. This multi-functional approach allows the same core technology to detect various types of events (food safety, security, health) by adjusting ontological parameters rather than requiring separate specialized systems for each domain.
4Reliability
If data is cleaned and processed according to consistent methodology, then measurement reliability is improved, but processing time and complexity increase due to data preparation requirements
Solution Approach 1:
The system performs preliminary data organization and structuring through ontological vector representation before hypothesis generation begins. By pre-organizing data into standardized ontological frameworks, the system eliminates time-consuming cleaning operations during the analysis phase, maintaining high reliability through consistent methodology while reducing overall processing time.
Data Source
AI summary
A hypotheses generation and event recognition system that enables event recognition by analyzing documents to construct one or more qualitative metrics (e.g., frequency of keywords, changes in sentiment, occurrence of ontological terms, evolution of topics, etc.), establishing a baseline for the qualitative metric(s), and outputting changes to that baseline for display. In aggregate, those qualitative metrics comprise temporal and/or spatial signals that, when combined, define signatures of events of interest. Accordingly, the user and/or the system may identify an event of interest based on the change in baseline. The system may further provide functionality to generate hypotheses by coding data according to an ontology, populating an ontology space, and using an optimization algorithm to rank points or neighborhoods in the coded ontology space. The system may further store links between ontological terms and qualitative metrics to provide functionality to test generated hypotheses that include those linked ontological terms.