Analytic Inference Engine for Granular Literature Search
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Solution Overview
Problem
Current cognitive analytics systems are limited in their ability to infer relations between precise dictionary terms, restricting the granular search and retrieval of relevant literature, as they primarily focus on broad categories and lack the capability to share capabilities across multiple analytics, leading to a limited user experience in finding detailed results.
Innovation Solution
An analytic inference engine is implemented to compare and combine cognitive analytic outputs from multiple sources, identifying unique features and generating a composite output that infers cognitive capabilities across multiple cognitive analytics, enabling more granular searchable evidence and enhancing user insights in literature searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cognitive analytics focus on broad categories, then the system is simpler to implement, but the search granularity and retrieval precision are limited
Solution Approach 1:
The patent segments cognitive analytics into multiple independent analytics, each focusing on specific aspects of literature analysis. This allows the system to achieve fine-grained search capabilities through specialized analytics while maintaining manageable complexity by dividing the overall system into modular components that can be developed and maintained independently.
Solution Approach 2:
The patent implements capability sharing across multiple cognitive analytics, where common functionalities are universalized and reused. This enables the system to achieve high measurement precision through specialized analysis while reducing overall system complexity by eliminating redundancy and promoting multi-functional components that serve multiple analytics.
2Reliability
If multiple cognitive analytics are applied independently, then each analytic can be optimized for its specific function, but the ability to infer relations across analytics is limited
Solution Approach 1:
The patent merges the outputs of multiple independent cognitive analytics through an analytic inference engine that combines results and infers relations across them. This approach maintains the reliability of each individual analytic by keeping them optimized for their specific functions while simultaneously achieving adaptability and versatility through the integration layer that enables capability sharing and cross-analytic inference.
3Loss of information
If cognitive analytics output is not combined, then the processing is simpler, but the composite insights and inferred relations are lost
Solution Approach 1:
The patent introduces an analytic inference engine as an intermediary component that combines outputs from multiple cognitive analytics. This mediator preserves information completeness by integrating results from all analytics while managing processing complexity through a dedicated component designed specifically for combination and inference, rather than requiring all analytics to directly interact with each other.
Data Source
AI summary
A mechanism is provided to implement an analytic inference engine for inferring cognitive capabilities across multiple cognitive analytics applied to literature. The analytic inference engine receives cognitive analytic output generated by multiple cognitive analytics applied to a portion of content. Response to the analytic inference engine finding a first offset in a first cognitive analytic output matching a second offset in a second cognitive analytic output, the analytic inference engine identifies unique features in the first cognitive analytic output and the second cognitive analytic output with respect to the matching offset. The analytic inference engine generates a composite analytic output comprising the unique features with respect to the matching offset.