Geofenced Citizen Messaging With ML Caching for Relevant Official Polls
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In democratic setups, communication between citizens and elected officials often breaks down after elections, leading to citizens feeling neglected and officials making decisions that may not align with their interests, as conventional social networking platforms allow untargeted message exchange that clutters relevant information.
Innovation Solution
A message exchange platform that allows citizens of a geographical area to post messages to their elected representatives while preventing non-citizens from doing so, using machine learning to identify and cache relevant historical messages, and compressing data for efficient delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional social networking platforms are used for message exchange, then anyone can post messages to anyone, but the communication becomes cluttered and untargeted
Solution Approach 1:
The platform segments the communication space by creating separate channels for different geographical areas. Each official has a dedicated page or channel that receives messages only from citizens of their specific area, separating the message flow into targeted segments rather than allowing unfiltered global communication.
Solution Approach 2:
The system introduces an intermediary filtering mechanism that identifies and selects relevant messages based on geographical criteria. This intermediary layer filters out irrelevant messages from non-citizens while allowing relevant messages from citizens to reach officials, preventing information clutter.
2Loss of information
If messages are filtered to ensure relevance, then computational processing is required, but this increases system complexity
Solution Approach 1:
The system changes the filtering parameter from complex content analysis to simple geographical parameter matching. Instead of analyzing message content to determine relevance, the system uses geographical parameters (citizen-resident status and official representation area) to automatically filter messages, reducing computational complexity while maintaining relevance.
Data Source
AI summary
A message exchange platform including a memory and a processor is disclosed. The memory may store a machine learning model trained on a training dataset including historical message exchange between a plurality of users and a plurality of officials on the platform. The processor may obtain a message including a poll from an official, and analyze, via the machine learning model, the message relative to the historical message exchange between the users and officials on the platform. The processor may further identify, based on the analysis, at least one historical poll result or message exchange similar to the message obtained from the official, and selectively fetch and cache historical messages associated with the identified historical poll result or message exchange. The processor may further identify a user who is citizen of the geographical area associated with the official, and transmit the message and the historical messages to the user.