Project Comparator Subsystem for Similarity Metric Generation
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Solution Overview
Problem
Current project-management applications lack efficient and accurate methods for identifying similar projects stored in databases, relying on keyword-based searching or database queries which are tedious, error-prone, and produce large, poorly defined result sets.
Innovation Solution
A computational system that compares electronic-data representations of projects using project-feature comparators and feature-similarity aggregators to produce a similarity metric, employing vector-based similarity metrics for text, numeric, and graphical data types, and utilizing a project search engine for pairwise project comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If keyword-based searching or database queries are used to identify similar projects, then the search process is simple to implement, but the results are large, poorly defined, and require extensive review and analysis
Solution Approach 1:
The patent replaces manual keyword-based searching and database querying (mechanical information retrieval processes) with an automated computational system that uses machine learning models and algorithms to automatically compare project features, extract similarities, and rank results. This substitution eliminates the need for manual review of large result sets while maintaining ease of implementation through automated processing.
Solution Approach 2:
The patent introduces an intermediary computational layer between the database and the user. This intermediary system automatically processes project data, extracts relevant features, computes similarity metrics, and presents refined results. This intermediary eliminates the need for users to directly handle large, unprocessed result sets from traditional searching methods.
2Ease of manufacture
If keyword-based searching or database queries are used to identify similar projects, then the search process is simple to implement, but the accuracy of identifying truly similar projects is poor
Solution Approach 1:
The patent replaces simple keyword matching and basic database query operations with sophisticated computational algorithms including machine learning models, feature extraction techniques, and similarity metric calculations. This substitution dramatically improves the accuracy of identifying truly similar projects while maintaining ease of implementation through automated processing.
Solution Approach 2:
The patent transforms the search process from simple keyword matching to a multi-parameter comparison system that evaluates multiple project features simultaneously. By changing from a single-parameter (keyword) approach to multi-parameter analysis (project features, attributes, characteristics), the system achieves higher precision in identifying similar projects while remaining easy to implement.
3Adaptability or versatility
If traditional project management tools are used, then the basic project tracking functionality is sufficient, but new and improved functionality is continuously needed
Solution Approach 1:
The patent creates a universal project management system that performs multiple functions: traditional project tracking, automated similarity detection, project comparison, and intelligent recommendation. This multi-functional system consolidates various capabilities into a single platform, improving adaptability and versatility without proportionally increasing complexity.
Solution Approach 2:
The patent implements self-service capabilities where the system automatically performs project comparison, similarity analysis, and result ranking without requiring manual configuration or complex user setup. The computational system serves itself by automatically extracting features, computing metrics, and presenting results, thereby improving functionality while keeping the user interface simple.
Data Source
AI summary
A project-comparator subsystem generates a similarity metric for input electronically-represented projects input to the project-comparator subsystem. A project search engine receives data that represents a first electronically-represented project, identifies, by pairwise comparison of the stored electronically-represented projects with the first electronically-represented project using the project-comparator subsystem, a stored electronically-represented project with greatest similarity to the first electronically-represented project, and outputs an indication of the identified stored electronically-represented project.