Correlation-Based Instrument Discovery for Software Architecture
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Solution Overview
Problem
In the architecture and design phase of software applications, experts' subjective selection of instruments is limited by their knowledge and bias, omitting potential alternatives, and stakeholders lack objective comparisons to determine which instruments best meet specifications, especially due to the dynamic nature of new instruments and lack of metrics for ongoing performance.
Innovation Solution
A correlation-based instruments discovery apparatus and method that aggregates data from internal, external, and social media sources to provide a comprehensive ecosystem for instrument selection, using product specifications satisfaction indices and cost comparisons to recommend the best instruments based on user-defined specifications, facilitating objective comparisons and ongoing evaluations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If expert subjective selection is used, then instrument selection speed is improved, but selection objectivity and comprehensiveness deteriorate
Solution Approach 1:
The patent introduces an intermediary system comprising a data aggregation module and correlation analysis module that objectively matches instrument capabilities with project specifications. This intermediary processes both expert recommendations and instrument data impartially, resolving the contradiction between fast expert selection and objective comprehensive evaluation by automating the matching process while preserving expert input as one of multiple data sources.
Solution Approach 2:
The patent replaces the manual mechanical process of expert subjective selection with an automated computational system that aggregates data from multiple sources including expert recommendations, instrument specifications, and project requirements. The system automatically calculates correlation scores and ranks instruments, eliminating human bias while maintaining the speed advantage of automated processing.
2Measurement precision
If comprehensive data aggregation is performed, then instrument selection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex data aggregation and analysis process into distinct functional modules: a data aggregation module that collects data from multiple sources, a correlation analysis module that computes matching scores, and a recommendation module that ranks instruments. This segmentation reduces system complexity by making each module independently manageable while collectively achieving high selection accuracy through comprehensive data processing.
Solution Approach 2:
The patent creates a universal instrument selection system that handles multiple data types (expert recommendations, instrument specifications, project requirements) and multiple evaluation criteria through a single integrated platform. The system's multi-functional design allows it to aggregate diverse data sources and perform various analysis tasks without requiring separate systems for each function, thereby managing complexity while improving accuracy.
3Measurement precision
If objective comparison metrics are introduced, then instrument evaluation quality is improved, but data processing requirements increase
Solution Approach 1:
The patent extracts only the most relevant features and specifications from the comprehensive instrument and project data using the correlation analysis module. Instead of processing all available data, the system identifies and extracts key matching criteria that directly impact instrument suitability, thereby maintaining high evaluation quality while significantly reducing the volume of data that requires intensive processing.
Data Source
AI summary
According to an example, correlation based instruments discovery may include extracting text from content identified as being related to instruments for implementing machine readable instructions based products. An identified content object graph model having subjects and features may be generated. A product specifications object graph model having subjects and features may be generated based upon product specifications related to a machine readable instructions based product that is to be implemented. The subjects and features in the identified content object graph model that correspond to the subjects and features in the product specifications object graph model may be correlated and scored. Subjects and features from the identified content object graph model that include respective highest scores for matching subjects and features from the product specifications object graph model may be selected to identify a subset of the content that is related to instruments for implementing the machine readable instructions based product.