ML-Based Public Service Fund Allocation Platform
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Solution Overview
Problem
Governments face inefficiencies and resource wastage in tracking, managing, and performing public services, leading to incomplete and error-prone delivery of services such as snow removal and trash collection, which can result in safety issues for citizens.
Innovation Solution
A crowdsource platform utilizing a machine learning model to crowdsource funds for public services by providing task data to user devices, processing image data from cameras and user devices to determine performance metrics, and allocating funds based on task completion, thereby streamlining resource allocation and improving service delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If governments manually track and manage public services, then resource allocation can be adjusted flexibly, but resource wastage increases and service delivery becomes error-prone
Solution Approach 1:
The patent replaces manual mechanical tracking and management processes with an automated machine learning-based system. The ML model automatically processes images from cameras and user devices to verify task completion, eliminating the need for manual monitoring and reducing resource wastage associated with inefficient manual management.
Solution Approach 2:
The system enables self-service through automated task verification and fund allocation. The machine learning model independently evaluates task completion by processing images without human intervention, and automatically determines fund distribution based on verified performance, reducing the need for manual resource allocation and minimizing wastage.
2Reliability
If governments increase manual monitoring of public service tasks, then service delivery accuracy improves, but resource wastage and operational costs increase
Solution Approach 1:
Manual monitoring activities are replaced with an automated machine learning system that processes images from cameras and user devices. The ML model reliably determines task completion accuracy without requiring human monitors, maintaining service delivery accuracy while eliminating the resource consumption associated with manual monitoring operations.
Solution Approach 2:
The system uses image copies from cameras and user devices to verify task completion instead of requiring physical presence of monitors. The machine learning model analyzes these visual copies to determine whether tasks were performed correctly, maintaining high reliability in service delivery verification without the resource cost of manual monitoring.
3Productivity
If governments use traditional fund allocation methods for public services, then funding can be adjusted based on political priorities, but fund allocation efficiency decreases and service completion rates drop
Solution Approach 1:
The system implements automated feedback loops where the machine learning model continuously evaluates task completion status and fund allocation effectiveness. Based on this feedback, the system automatically adjusts fund distribution to maximize service completion rates, replacing inefficient political decision-making with data-driven automated allocation that directly links funding to verified task completion.
Solution Approach 2:
The fund allocation system dynamically changes allocation parameters based on real-time task completion data processed by the machine learning model. Instead of static political decisions, the system automatically adjusts funding levels and distribution priorities based on verified performance metrics, thereby increasing service completion rates through adaptive, evidence-based parameter optimization.
Data Source
AI summary
A device may provide, to a user device, task data identifying tasks to be performed, and may receive, from the user device, a selection of a particular task from the tasks to be performed. The device may identify cameras associated with a particular task location, and may receive, from the user device, data identifying a location of the user device. The device may determine that the location of the user device matches the particular task location, and may receive, from the user device, task image data identifying images of the particular task location. The device may access, from the cameras, camera data identifying images of the particular task location, and may process the task image data and the camera data, with a machine learning model, to determine performance data associated with performance of the particular task. The device may perform one or more actions based on the performance data.