Distributed Event AI for Real-Time Situation Determination

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Solution Overview

Problem

Current decision support systems fail to address cognitive biases in daily life comprehensively and lack behavioral-oriented solutions, focusing instead on specific situations, thus neglecting the fundamental aspect of human judgment and decision-making.

Innovation Solution

The Decision Support Platform employs an AI framework that monitors human behavior in real-time using neural pathways and situational response engines, predicting situational responses and providing personalized decision support through machine learning to mitigate cognitive biases and improve decision-making.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If decision support systems focus on specific situations, then they can provide targeted assistance for particular decisions, but they fail to address cognitive biases comprehensively and neglect the fundamental aspect of human judgment

Engineering Contradiction:

Improvescope of decision supportVSAvoidbehavioral element in decision making

Core Design Contradiction:

Adaptability or versatilityVSLoss of information

Solution Approach 1:

The system monitors multiple data sources including device usage patterns, location data, communication patterns, and physiological signals to comprehensively track user behavior across all situations. This multi-functional monitoring approach captures the behavioral elements that traditional situation-specific systems miss, while still providing targeted decision support interventions.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system continuously monitors user behavior and provides real-time feedback about cognitive biases detected in decision-making patterns. By analyzing the relationship between behavioral data and decision outcomes, the system offers corrective feedback that helps users become aware of and adjust their cognitive biases, thereby preserving the behavioral element in decision making.

Inventive Principle:

Principle #23Feedback

2Measurement precision

If the system monitors human behavior in real-time using multiple data sources, then it can comprehensively understand user behavior and cognitive biases, but the system complexity and data processing requirements increase

Engineering Contradiction:

Improvebehavioral data accuracyVSAvoidsystem architecture

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system is divided into modular components: data collection modules that gather information from various sources, processing modules that analyze specific behavior patterns, and intervention modules that provide targeted support. This segmentation allows the system to handle complex multi-source data through organized, manageable modules rather than a monolithic complex system.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system introduces an intermediary AI layer that processes and integrates data from multiple sources before presenting analyzed behavioral patterns to users. This intermediary processing layer simplifies the complexity by automatically synthesizing multi-source data into coherent behavioral insights, reducing the burden on system architecture.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If the system provides real-time decision support interventions, then it can mitigate cognitive biases and improve decision-making, but it requires continuous monitoring and processing of user data

Engineering Contradiction:

Improvedecision-making efficiencyVSAvoidcomputational resource consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system pre-processes and stores behavioral data patterns as they occur, building a historical baseline of user decision-making behaviors. This preliminary action allows the system to quickly compare current decisions against established patterns without requiring intensive real-time analysis of all historical data, thereby reducing computational energy consumption while maintaining intervention effectiveness.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system focuses computational resources on analyzing only the specific behavioral patterns and decision contexts where cognitive biases are most likely to occur, rather than continuously processing all user data. This selective partial action approach maintains decision-making efficiency while conserving computational energy by avoiding unnecessary processing of irrelevant data.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20260030553A1System and method for real-time artificial intelligence situation determination based on distributed device event data

Publication Date: 2026.01.29 SKOGSTAD DANIEL SADEGHI

AI summary

Various embodiments of methods and systems, including computer programs encoded on computer storage media described herein are directed to real-time situation determination based on distributed event data. According to various embodiments, the system receives event data from one or more computing devices. The system provides a machine learning model configured to use a plurality of interconnected check-point evaluators to evaluate the received event data and determine an occurrence of a situation. The system evaluates event values, via one or more check-point evaluator of the plurality of interconnected check-point evaluators, whether the event values meet criteria for one or more situation indicators. Based on the evaluation of the event values the system determines the occurrence of the situation.