User-Modified Hypothesis Execution for Event Pattern Accuracy
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Solution Overview
Problem
Existing methods for developing hypotheses based on event patterns may lead to inaccurate or faulty conclusions due to the inclusion of irrelevant or coincidental data, resulting in incorrect actions being executed.
Innovation Solution
A computationally implemented method that presents a hypothesis to a user identifying relationships between event types, allows user modifications, and executes actions based on the modified hypothesis, ensuring relevance and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated hypothesis generation based on event patterns is implemented, then productivity is improved, but reliability deteriorates due to inclusion of irrelevant or coincidental data
Solution Approach 1:
The system presents generated hypotheses to users for validation and feedback. Users can confirm, reject, or modify hypotheses, creating a feedback loop that improves the reliability of automated hypothesis generation while maintaining productivity. The system learns from user feedback to refine future hypothesis generation.
Solution Approach 2:
The system introduces an intermediary review step where users act as mediators between automated hypothesis generation and final action execution. This intermediary layer filters out irrelevant or coincidental data patterns that automated systems might miss, improving reliability without eliminating the efficiency benefits of automation.
2Reliability
If user modification of hypotheses is allowed, then reliability is improved through user expertise, but device complexity increases
Solution Approach 1:
The system allows users to modify specific aspects of hypotheses locally rather than requiring complete redesign. Users can adjust individual parameters or conditions of a hypothesis while maintaining the overall structure, reducing the complexity burden while preserving reliability improvements from user expertise.
3Productivity
If automated actions are executed based on generated hypotheses, then productivity is improved, but reliability worsens due to incorrect conclusions
Solution Approach 1:
The system performs preliminary validation of hypotheses by presenting them to users before executing actions. This preliminary check prevents incorrect actions from being executed based on flawed hypotheses, improving reliability while maintaining the overall productivity benefits of automated action execution for validated hypotheses.
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.