Causality Augmented Information Retrieval for Root Cause Analysis
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Solution Overview
Problem
Current information retrieval systems are unable to provide potential causes for events, such as a currency drop, as they fail to augment information retrieval responses with links and evidence of the causes, limiting their ability to support root cause analysis.
Innovation Solution
The system augments information retrieval responses with a list of potential causes related to the query by performing a root cause analysis using machine learning and artificial intelligence, providing a visualization that includes a query about an event, ranked sentences explaining the potential causes, and links to the documents where these sentences were retrieved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If information retrieval systems provide basic search results, then the system complexity remains low, but the system cannot provide potential causes or support root cause analysis
Solution Approach 1:
The patent introduces an intermediary causality analysis module that sits between the information retrieval system and the user. This module receives search results, performs root cause analysis by identifying causal relationships among events, and presents enriched information including potential causes ranked by likelihood. The intermediary handles the complexity of causality inference internally while presenting simplified results to users, thus resolving the contradiction between providing complete causality information and maintaining system simplicity.
2Measurement precision
If the system performs comprehensive root cause analysis with machine learning, then the quality of causality information improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-computing causality relationships and organizing event data in a structured format before actual queries are made. The causality analysis module is pre-configured with knowledge graphs and causal models that enable rapid inference during query processing. This preliminary preparation allows the system to deliver high-precision causality analysis results quickly, reducing the perceived processing time while maintaining comprehensive analysis capabilities.
3Loss of information
If the system augments information retrieval with multiple causality data elements, then the completeness of information increases, but the information overload may occur
Solution Approach 1:
The patent applies local quality by differentiating the presentation of causality information based on user needs and context. The system ranks potential causes by likelihood and presents them in a hierarchical format, with the most relevant causes displayed prominently and less certain causes available on demand. This selective presentation ensures that users receive complete causality information when needed while avoiding information overload in routine scenarios, thus resolving the contradiction between information completeness and usability.
Data Source
AI summary
An information retrieval response may be augmented, based upon a query, with a plurality of selected causality data relating to the query. The information retrieval response may be generated from an information retrieval system.