Auto-Hypothesis Iteration for Situation-Specific System Forewarning
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Solution Overview
Problem
Current predictive and preventive analytics systems fail to effectively integrate and analyze diverse data sources, such as weather data and business application data, to provide timely forewarnings of potential system changes that may not have immediate effects, leading to compliance violations and operational inefficiencies.
Innovation Solution
A system and method for correlating hypotheses outcomes using relevance scoring, which aggregates and analyzes core and ring data to generate intuition-based forewarnings by identifying changing situations, providing relevance scores, and generating alerts based on updated hypotheses outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If diverse data sources (weather data, business application data, sensor data) are aggregated and analyzed to predict future system states, then predictive accuracy and forewarning capability are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments data into core data (system-specific) and ring data (environmental/contextual), processes them through separate modules (data ingestion, analysis, hypothesis generation), and generates forewarnings based on correlated patterns between segments, reducing overall system complexity while maintaining predictive accuracy
Solution Approach 2:
The patent introduces an intermediary hypothesis generation layer that mediates between raw data and predictive outcomes. Hypotheses are generated as intermediate representations that capture potential future states, allowing the system to handle complex data relationships through structured assumptions rather than direct complex processing
2Loss of time
If real-time data aggregation and analysis is performed to provide timely forewarnings, then response time for preventive actions is improved, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary data aggregation and pattern analysis continuously in the background, pre-processing data to identify emerging patterns before they become critical. This allows the system to generate forewarnings quickly when thresholds are breached without requiring intensive real-time computation during critical moments
Solution Approach 2:
The system uses periodic data ingestion cycles and batch processing to analyze data at intervals rather than continuously, reducing computational resource consumption while maintaining timely forewarning capability. Analysis occurs periodically at defined frequencies rather than in real-time continuous mode
3Loss of information
If human expertise is combined with machine learning to analyze data patterns, then analytical depth and insight quality are improved, but implementation complexity and system development time increase
Solution Approach 1:
The patent merges human expertise (domain knowledge, contextual understanding) with machine learning algorithms in a unified hypothesis generation framework. Human experts provide contextual rules and interpret patterns, while ML algorithms handle data processing and pattern recognition, combining both approaches to achieve deep analytical insights without excessive implementation complexity
Solution Approach 2:
The system incorporates feedback loops where analytical results are validated against domain knowledge and historical data, allowing continuous refinement of hypotheses. This feedback mechanism ensures analytical depth by comparing machine-generated insights with expert judgment, improving accuracy over time without requiring complex one-time implementation
4Adaptability or versatility
If multiple data sources are integrated to provide comprehensive forewarnings, then predictive coverage and completeness are improved, but data integration complexity and regulatory compliance requirements increase
Solution Approach 1:
The system uses a universal data ingestion framework that handles multiple data sources (sensor data, weather data, business application data) through a common processing pipeline. The same core algorithms and hypothesis generation mechanisms work across different data types, providing comprehensive predictive coverage while reducing data integration complexity through standardized processing
Data Source
AI summary
Methods and systems correlating hypotheses outcomes using relevance scoring for intuition based forewarning are disclosed. For one example, an intuition based forewarning method includes collecting and storing core data and surroundings data, wherein the core data includes parameters describing a system and ring data includes parameters describing surroundings of the system. The collected core data and ring data are analyzed to determine one or more changing situations of the system. A relevance score is provided for each determined changing situation of the system based on the analyzed core data and ring data. Each determined situation is correlated with one or more hypotheses outcomes representing a future system state based on the relevance score. The hypotheses may be modified using, for example, auto-hypothesis generation. A system forewarning is generated based on the correlated hypotheses outcomes which can be observed by one or more users.