Analytics Engine Controller for Dynamic Cognitive Issue Archiving
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Solution Overview
Problem
Existing analytics engines are limited in their ability to recognize and address complex problems, as they only suggest solutions for previously encountered specific issues, failing to provide insight into new or combination problems.
Innovation Solution
An analytics engine controller that hypothesizes correlations between multiple problems, verifies these correlations upon resolution, and abstracts issues to generate combination problems, enabling the recognition of interrelated issues and facilitating insight generation through dynamic cognitive issue archiving and resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If analytics engines only suggest solutions for previously encountered specific issues, then the system maintains simple operation and low complexity, but the ability to recognize and address complex combination problems is limited
Solution Approach 1:
The patent segments complex problems into individual component problems that can be analyzed separately. The analytics engine breaks down combination problems into constituent issues, allowing each to be evaluated independently while still contributing to the overall solution of the complex problem.
Solution Approach 2:
The system dynamically adapts its analysis depth and approach based on the complexity of the problem. For simple previously-encountered issues, it provides direct solutions. For complex combination problems, it dynamically generates hypotheses about correlations between component problems and adjusts its analysis accordingly.
2Loss of information
If the analytics engine generates and verifies hypotheses about correlations between problems, then the insight generation and problem recognition improve, but the time and computational resources required increase
Solution Approach 1:
The system performs preliminary actions by generating hypotheses about correlations between problems before full verification is required. These hypotheses are formed based on initial pattern recognition and are then verified through controlled experiments or additional data analysis, allowing the system to proactively identify potential issues.
Solution Approach 2:
The hypothesis verification process incorporates feedback mechanisms where the results of resolving component problems are fed back to confirm or refute the hypothesized correlations. This feedback loop allows the system to learn from outcomes and improve its hypothesis generation over time, reducing the time required for verification in future cases.
3Productivity
If the system creates a searchable archive of abstracted issues for failure prediction, then the productivity and problem resolution efficiency improve, but the device complexity and data management requirements increase
Solution Approach 1:
The system creates abstracted copies of actual problems by removing specific identifying information while preserving the essential characteristics and relationships between issues. These abstracted problem copies are stored in the archive, allowing the system to match new problems against historical patterns without storing or managing sensitive specific data, thereby reducing data management complexity.
Solution Approach 2:
The abstracted issue archive serves multiple functions: it enables failure prediction by pattern matching, provides historical reference for hypothesis generation, and supports the verification process by comparing against known outcomes. This multi-functionality justifies the data management infrastructure while delivering significant productivity benefits across multiple system operations.
Data Source
AI summary
A system and method of managing a system include receiving data indicating problems in the system that includes at least one computer system. The method also includes generating a hypothesis of a correlation between two or more of the problems, and determining whether the hypothesis of the correlation is correct following a resolution of at least one of the two or more of the problems. The two or more of the problems are identified as a new combination problem based on determining that the hypothesis of the correlation is correct.