Context-Driven Analytics Selection via Semantic Search
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Solution Overview
Problem
The challenge lies in selecting the most suitable predictive analytics model from a vast array of available models to address specific problems, considering the model's validation grounds and information needed for model rebuilding when it becomes stale, while also tracking performance and context, and obtaining feedback to identify staleness.
Innovation Solution
A system and method that route user analytic requests to a registry of models and data sources, applying natural language processing, semantic search, and feedback loops to select and improve model associations over time, using domain-specific ontologies and knowledge bases to construct and rank vectors for model selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a vast array of predictive analytics models is made available to users, then model selection flexibility and adaptability improve, but the complexity of selecting the most suitable model and tracking its performance increases
Solution Approach 1:
The patent introduces an intermediary system comprising natural language processing components, semantic search algorithms, and feedback processing mechanisms that mediate between the user and the vast array of predictive analytics models. This intermediary automatically interprets user context, searches for suitable models, and manages model selection and tracking, thereby resolving the contradiction by maintaining high adaptability while reducing selection complexity through automation.
2Measurement precision
If context-driven model selection is implemented to ensure model relevance, then prediction accuracy improves, but the time and computational resources required for model selection increase
Solution Approach 1:
The patent applies preliminary action by pre-processing user inputs through natural language processing to extract context, pre-computing semantic representations, and pre-organizing model metadata in the registry. This allows the system to quickly match user needs with suitable models without performing exhaustive searches at selection time, thereby maintaining high prediction accuracy while reducing model selection time through advance preparation.
3Reliability
If feedback loops are implemented to identify model staleness and improve model associations, then model reliability improves, but the system complexity and processing overhead increase
Solution Approach 1:
The patent implements feedback loops that automatically collect performance data from model executions, process user feedback, and update the model registry to identify staleness and improve associations. The system processes feedback by comparing actual performance against expected performance, automatically retraining or replacing stale models, and refining the semantic search indices. This resolves the contradiction by maintaining high reliability through continuous feedback while managing complexity through automated feedback processing mechanisms.
4Ease of operation
If natural language processing and semantic search are used to associate user requests with models, then ease of operation improves, but computational energy consumption and processing time increase
Solution Approach 1:
The patent applies partial action by implementing multi-stage processing where the system first performs lightweight keyword matching and filtering, then applies more computationally intensive natural language processing and semantic search only to the reduced set of candidate models. This approach maintains ease of operation by providing comprehensive semantic understanding where needed while reducing overall computational energy consumption by avoiding full NLP processing for all models in the registry.
Data Source
AI summary
In an approach for routing a user analytic request to a registry of analytics models and data sources and operationalizing the user analytic request, a processor receives a user analytic request. A processor applies natural language processing to the user analytic request. A processor associates a first set of vectors and a second set of vectors from the user analytic request to one or more analytics models and data sources by utilizing a plurality of domain-specific ontologies and a plurality of knowledge bases. A processor performs a semantic search for one or more concepts in the one or more analytics models and data sources. A processor receives the one or more concepts found in the one or more analytics models and data sources. A processor selects an analytics model to process the user analytic request. A processor executes the analytics model. A processor outputs a result to the user.