Issue Graph Model for Stakeholder Identification
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Solution Overview
Problem
Current software tools are inadequate for automatically analyzing and integrating structured and unstructured data from various sources to understand complex relationships between policy entities and stakeholders in policymaking processes, leading to inefficiencies and high costs in tracking and managing policymaking data across different governmental levels.
Innovation Solution
The development of a system that uses machine-trained models and automated data aggregation techniques to construct issue-based knowledge graphs, enabling the analysis of electronic structured and unstructured data related to legislative, regulatory, and judicial processes, and identifying stakeholders and their relationships within a policy intelligence platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual analysis of policymaking data is performed, then understanding of policy context and relationships is improved, but time consumption and resource requirements increase significantly
Solution Approach 1:
The patent replaces manual mechanical analysis with automated computational systems. Machine learning models and natural language processing algorithms automatically extract entities, relationships, and contextual information from policy documents, eliminating the need for human analysts to manually review and interpret vast quantities of policymaking data across multiple governmental levels.
Solution Approach 2:
The patent introduces an intermediary automated analysis platform that sits between raw policymaking data and human decision-makers. This platform uses trained machine learning models to process unstructured policy documents, structured databases, and news articles, transforming them into structured knowledge graphs that reveal stakeholder relationships and policy impacts without requiring direct human analysis of the raw data.
2Quantity of substance
If existing software tools are used to track policymaking data, then data collection capability is improved, but integration of structured and unstructured data from multiple sources remains inadequate
Solution Approach 1:
The patent creates a universal platform capable of handling multiple data types and sources simultaneously. The system ingests unstructured policy documents, structured database records, news articles, and social media data through a unified architecture. Machine learning models automatically adapt to different data formats and sources, extracting relevant information and integrating them into a cohesive knowledge representation that captures relationships across all data types.
Solution Approach 2:
The patent combines multiple data sources and types into a composite knowledge structure. Rather than treating structured databases, unstructured policy documents, and news articles as separate entities, the system integrates them into a unified knowledge graph where information from different sources complements and reinforces each other, creating a more comprehensive view of policymaking landscapes.
3Loss of information
If comprehensive analysis of disparate data sources is performed, then identification of stakeholder relationships is improved, but system complexity and manual input requirements increase
Solution Approach 1:
The patent implements self-service automation where the system automatically performs data collection, processing, and analysis without requiring manual intervention. Trained machine learning models autonomously extract entities and relationships from disparate data sources, automatically update knowledge graphs, and generate stakeholder relationship mappings. The system self-adjusts and refines its analysis based on the data it processes, eliminating the need for complex manual configuration and input.
4Productivity
If automated tools are used to monitor policymaking processes, then tracking efficiency is improved, but capability to analyze complex relationships between multiple data types is reduced
Solution Approach 1:
The patent replaces simple automated tracking with sophisticated intelligent analysis systems. Instead of merely collecting and storing data, machine learning models actively analyze complex relationships between stakeholders, policies, and events across multiple data types. The system automatically identifies indirect relationships, infers stakeholder influences, and discovers patterns that would be difficult for humans to detect manually, maintaining high efficiency while enhancing analytical depth.
Data Source
AI summary
A method for identifying stakeholders relative to an issue is disclosed. In one embodiment, the method may include accessing first data associated with a plurality of individuals associated with an organization; generating first nodes representing the plurality of individuals within an issue graph model; accessing second data associated with one or more policies; generating second nodes representing the one or more policies within the issue graph model based on the second data; receiving an indication of a selected agenda issue; generating links within the issue graph model representing relationships between the first nodes and the second nodes; determining importance scores for the first nodes in the issue graph; identifying a node of the plurality of first nodes associated with the at least one selected agenda issue based on the importance scores; and outputting node properties associated with the identified node.