Case Analysis System Using Static Dynamic Attribute Segmentation
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Solution Overview
Problem
Conventional automated case-based reasoning systems are limited in effectively searching for similar cases across different domains and attribute sets, leading to inconsistent and inefficient decision-making, as they rely on a static set of attributes that are not extendible to multiple domains and may provide unrelated results.
Innovation Solution
A case analysis system that structures attributes into static and dynamic sets, comparing only common and case-specific attributes to efficiently match new cases with previously processed ones, using similarity scores to shortlist relevant cases and improve accuracy and efficiency across various domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static set of attributes is used for case comparison, then the system is simple to implement, but it cannot effectively search for similar cases across different domains and provides inconsistent results
Solution Approach 1:
The patent segments attributes into two distinct sets: static attributes (domain-specific characteristics) and dynamic attributes (case-specific characteristics). This segmentation allows the system to handle multiple domains effectively by comparing static attributes first to identify relevant domains, then comparing dynamic attributes within those domains. The segmentation resolves the contradiction by enabling domain adaptability without requiring a completely complex restructured system, as the static/dynamic division provides a clear organizational framework.
Solution Approach 2:
The patent introduces dynamic attributes that can vary based on the specific case and domain, in contrast to fixed static attributes. The system dynamically determines which dynamic attributes to compare based on the matched static attributes and domain. This dynamic approach enables the system to adapt to different domains and cases effectively, resolving the contradiction between adaptability and complexity by allowing flexibility only where needed rather than throughout the entire system.
2Measurement precision
If all attributes are compared for every case, then comprehensive matching is achieved, but comparison time increases significantly
Solution Approach 1:
The patent divides the attribute comparison process into two sequential stages: first comparing static attributes to identify relevant domains and cases, then comparing dynamic attributes only for cases that pass the static attribute filter. This segmentation dramatically reduces comparison time by avoiding unnecessary dynamic attribute comparisons for unrelated cases, while maintaining matching accuracy through the two-stage filtering process.
Solution Approach 2:
The patent performs preliminary comparison of static attributes before proceeding to dynamic attribute comparison. This preliminary action filters out irrelevant cases early in the process, so that time-consuming dynamic attribute comparisons are performed only on a small subset of potentially relevant cases. This resolves the contradiction by ensuring comprehensive matching where needed while minimizing overall comparison time through early filtering.
3Reliability
If manual case-based reasoning is performed by skilled individuals, then domain expertise is utilized, but the process is time-consuming and dependent on individual experience
Solution Approach 1:
The patent implements an automated system that performs case-based reasoning independently, comparing attributes and identifying similar cases without human intervention. The system uses predefined static and dynamic attribute sets to automatically match cases, eliminating dependence on individual human experience and memory. This resolves the contradiction by providing consistent, reliable automated decisions at high speed, while the structured attribute approach ensures domain expertise is encoded and applied consistently.
Solution Approach 2:
The patent transforms the decision-making process from a human-cognitive process to an automated computational process by defining cases in terms of measurable attributes (static and dynamic). This parameterization allows the system to reliably compare and match cases based on objective attribute values rather than subjective human judgment, achieving both consistency and speed. The parameter changes enable automated processing while maintaining domain relevance through carefully selected attribute definitions.
Data Source
AI summary
Systems and methods for cases based analysis are described. In one implementation, a new case-based document having static attributes and dynamic attributes is received. Once the static and the dynamic attributes are received, the static attributes of the received new case-based document is compared with the static attributes of one or more previously processed case-based documents. Based on the comparison, a sub-set of case-based documents is generated. For the generated sub-set, the dynamic attributes of the new case-based document are compared with the dynamic attributes of the sub-set of case-based documents. Based on the comparison, previously processed case-based documents, if any, which are found to be similar to the new case-based document are identified.