Federal Spend Data Hierarchical Grouping and Segmentation
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Solution Overview
Problem
Users face difficulties in understanding and utilizing federal spending data due to its flat format, which lacks a meaningful view of federal government spending data, making it hard to identify bidding opportunities, grasp interrelationships, and comprehend the federal award process.
Innovation Solution
A computerized system transforms and enhances raw federal spending data by grouping contracts and solicitations, using machine learning to identify relevant fields, and integrating independently-compiled federal staff organizational data, providing users with a holistic view of federal spending opportunities and awards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If federal spending data is provided in a flat format, then the data structure is simple and easy to store, but users cannot understand or utilize the data effectively
Solution Approach 1:
The patent segments federal spending data into hierarchical groups (agencies, sub-agencies, offices, contracts, modifications) to transform flat data into an organized structure that users can navigate and understand, directly addressing the contradiction between simplicity and understandability
Solution Approach 2:
The system introduces an intermediary processing layer that automatically groups and organizes raw spending data before presentation to users, mediating between the simple storage format and the complex user understanding requirements
2Loss of information
If users are provided with detailed federal spending data, then the information completeness is high, but users lack the knowledge to navigate and interpret the data structure
Solution Approach 1:
By dividing comprehensive spending data into manageable hierarchical segments (agency level, sub-agency level, office level, contract level), the system maintains complete information while reducing the cognitive burden on users to understand the overall structure
Solution Approach 2:
The patent adds a hierarchical dimension to the flat spending data, organizing it across multiple levels (agency→sub-agency→office→contract) to make the data more detectable and understandable without losing information
3Productivity
If the system provides comprehensive federal spending data without processing, then the data processing time is minimal, but users cannot identify bidding opportunities or competitors
Solution Approach 1:
The system performs preliminary grouping and organization of spending data into meaningful hierarchies before users need to access it, so that when users query for opportunities, the data is already structured for easy identification of bidding opportunities and competitors
Solution Approach 2:
The system automatically self-organizes the spending data into hierarchical groups based on federal agency structures, eliminating the need for users to manually process or understand the complex data relationships
Data Source
AI summary
Systems and methods applicable, for instance, to federal spend search engines and user interfaces. Data operations can transform and enhance raw federal spend data. Further, users can be presented with information that can be understood and consumed at a glance.