Contextual Information Extractor for Resource Interaction Decisioning
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Solution Overview
Problem
Current resource interaction opportunity decisioning processes lack direction and relevance due to the absence of consideration for indirect relationships between users and providers and real-time contextual data, leading to low acceptance rates of presented opportunities.
Innovation Solution
A system that includes a contextual information extractor for real-time data collection, an explainability calculator to determine the optimal machine-learning model based on accuracy, peril, and complexity, and a relationship builder to identify indirect relationships between users and providers, ensuring that resource interaction opportunities are presented by relevant providers aligned with the user's context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If random decisioning is used for resource interaction opportunities, then the system is simple to operate, but the acceptance rate by potential users is highly unlikely to be improved
Solution Approach 1:
The system automatically extracts contextual data and determines provider-user relationships without manual intervention. The contextual information extractor and relationship builder operate autonomously to generate directed resource interaction opportunities, eliminating the need for complex manual decisioning while improving acceptance rates through data-driven recommendations.
Solution Approach 2:
The system uses extracted contextual data and relationship information as feedback to continuously improve resource interaction opportunity decisioning. By analyzing user context and provider relationships, the system refines its recommendations over time, increasing acceptance rates while maintaining automated operation.
2Measurement precision
If contextual data extraction and relationship analysis are implemented, then the relevance and accuracy of resource interaction opportunities are improved, but the system complexity increases
Solution Approach 1:
The system is divided into distinct functional modules: a contextual information extractor module for data collection, a relationship builder module for analyzing provider-user connections, and a resource interaction opportunity generator. This segmentation allows each component to specialize in specific tasks, improving accuracy while managing complexity through modular design.
Solution Approach 2:
The contextual information extractor and relationship builder act as intermediaries between raw data and the resource interaction opportunity decisioning process. These intermediary modules process and structure information, making it usable for accurate matching while shielding the core decisioning logic from complexity.
3Reliability
If multiple machine-learning models are executed to determine opportunities, then the decisioning accuracy is improved, but the processing time and computational complexity increase
Solution Approach 1:
The system executes multiple machine-learning models to evaluate resource interaction opportunities, but applies pruning techniques to eliminate clearly inferior options before final selection. This partial evaluation approach maintains high accuracy by considering multiple models while reducing overall processing time by avoiding exhaustive evaluation of all possibilities.
Data Source
AI summary
Embodiments of the invention are directed to accurate resource interaction opportunity decisioning. Contextual information is extracted, in real-time or otherwise, which takes into account the circumstances surrounding the presentation of resource interaction opportunities to the potential user. A decisioning model from amongst a plurality of models is determined based on implementation of an explainability calculator that assesses the accuracy, peril and complexity of the models in relation to the resource interaction opportunity decisions rendered by the models. A relationship builder is implemented that is configured to build a relationship network amongst potential users and resource interaction opportunity providers and subsequently use the indirect relationships provided by the network as a basis for determining which resource interaction opportunity providers should provide resource interaction opportunities to the potential users.