User-Modified Hypothesis Execution for Event Pattern Accuracy
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Solution Overview
Problem
Current methods for developing hypotheses based on recurring patterns of events can lead to inaccurate or false conclusions, as they may include irrelevant or coincidental data, resulting in faulty actions being executed.
Innovation Solution
A computationally implemented method and system that presents a hypothesis identifying relationships between event types to users, allowing for modifications and enabling execution of actions based on modified hypotheses, ensuring relevance and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated hypothesis generation based on recurring patterns is used, then productivity is improved, but reliability deteriorates due to inclusion of irrelevant or coincidental data
Solution Approach 1:
The system implements feedback by presenting generated hypotheses to users for validation and refinement. Users can confirm, reject, or modify hypotheses, creating a feedback loop that improves hypothesis quality while maintaining automated generation efficiency. This resolves the contradiction by allowing automated processing to maintain productivity while human feedback ensures reliability.
Solution Approach 2:
The system introduces an intermediary layer between automated pattern recognition and final hypothesis execution. This intermediary presents hypotheses to users for review, acting as a mediator that filters out irrelevant or coincidental patterns while preserving genuinely useful hypotheses. This maintains productivity through automation while improving reliability through human oversight.
2Reliability
If user modification capability is added to hypotheses, then reliability is improved, but device complexity increases
Solution Approach 1:
The system segments the hypothesis management process into distinct components: automated generation, user review/modification, and execution. This segmentation allows each component to be independently optimized - automation handles pattern recognition efficiently while users handle validation, reducing overall system complexity while improving reliability.
Solution Approach 2:
The system enables users to self-service by allowing them to directly modify and refine hypotheses according to their domain knowledge. This self-service capability improves reliability by incorporating human expertise without requiring complex automated validation systems, thereby improving accuracy without proportionally increasing system complexity.
3Productivity
If automated action execution based on patterns is implemented, then productivity is improved, but harmful factors increase due to execution of faulty actions
Solution Approach 1:
The system applies preliminary anti-action by implementing a review and validation step before executing actions. Rather than directly executing all pattern-based decisions, the system preemptively identifies and blocks potentially faulty actions through user review, preventing harmful outcomes before they occur while maintaining efficient execution of validated actions.
Solution Approach 2:
The system uses feedback to prevent harmful actions by requiring user validation before execution. This feedback mechanism allows the system to maintain fast automated processing for valid patterns while blocking incorrect actions through user oversight, thereby maintaining productivity while eliminating harmful factors.
Data Source
AI summary
A computationally implemented method includes, but is not limited to: presenting to a user a hypothesis identifying at least a relationship between a first event type and a second event type; receiving from the user one or more modifications to modify the hypothesis; and executing one or more actions based, at least in part, on a modified hypothesis resulting, at least in part, from the reception of the one or more modifications. In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure.