Stakeholder Impact Discovery via Semantic Learning
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Solution Overview
Problem
Current technologies lack the capability to comprehensively discover stakeholders and impacts of product or service design changes, particularly hidden or indirect ones, which limits informed design decisions and regulatory optimization.
Innovation Solution
The development of stakeholder and impact discovery systems that utilize semantic web technologies and statistical machine learning to model relationships between products, people, and environments, allowing for the identification of both known and unknown stakeholders and impacts through data from various sources, including smart environments and sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional data collection methods are used, then data gathering is simple, but stakeholder and impact discovery is incomplete
Solution Approach 1:
The patent merges multiple data sources (smart environments, sensors, social media, online documents) into a unified stakeholder and impact discovery system. This integration allows the system to comprehensively identify both direct and indirect stakeholders and impacts by combining diverse data types through a centralized processing architecture.
Solution Approach 2:
The system employs multi-functional capabilities including data collection from various sources, stakeholder identification, impact analysis, and model maintenance. The platform serves multiple purposes: discovering known stakeholders, uncovering hidden stakeholders, quantifying impacts, and providing continuous model updates through a single integrated system.
2Measurement precision
If comprehensive data from multiple sources is collected, then stakeholder and impact discovery is more accurate, but data processing complexity increases
Solution Approach 1:
The patent segments the data processing workflow into distinct modules: data collection from multiple sources, data cleaning and normalization, stakeholder identification, impact quantification, and model maintenance. This segmentation allows each component to handle specific data types and processing tasks independently, reducing overall processing complexity while maintaining accuracy.
Solution Approach 2:
The system introduces intermediary processing layers including data normalization modules, stakeholder matching algorithms, and impact calculation frameworks. These intermediaries bridge the gap between raw data from diverse sources and the final impact assessment, simplifying the processing of complex multi-source data through standardized transformation layers.
3Measurement precision
If semantic web technologies and machine learning are used, then model accuracy improves, but computational resources required increase
Solution Approach 1:
The patent implements preliminary actions by pre-processing and storing data from smart environments and sensors before they are needed for impact analysis. Historical data is cleaned, normalized, and organized in advance, allowing the machine learning models to operate on pre-prepared data structures, which reduces real-time computational requirements while maintaining high accuracy.
Solution Approach 2:
The system applies partial machine learning models that focus on the most critical stakeholder and impact relationships rather than attempting to analyze all possible data points. The models process only the essential features needed for accurate impact assessment, reducing computational resource consumption while maintaining sufficient model accuracy for effective decision-making.
4Adaptability or versatility
If continuous model updates are implemented, then adaptability to real-world conditions improves, but system maintenance complexity increases
Solution Approach 1:
The patent implements continuous feedback mechanisms where data from smart environments and sensors is continuously collected and fed back into the models for updating and refinement. This feedback loop allows the system to automatically adapt to changing real-world conditions, stakeholder relationships, and impact patterns, improving model adaptability through continuous learning from actual usage data.
Solution Approach 2:
The system performs self-service maintenance by automatically updating its own models using newly collected data without requiring constant human intervention. The machine learning components continuously refine their parameters based on incoming data, and the system self-calibrates its stakeholder and impact models, reducing maintenance complexity while maintaining high adaptability.
Data Source
AI summary
This specification relates to methods, and systems for stakeholder and impact discovery. One of the methods includes receiving a knowledge representation model indicating at least a first stakeholder and a first impact; determining an impact model based at least in part on the knowledge representation model; receiving distributed sensor data; and determining at least a second impact based at least in part on the impact model and the distributed sensor data.