Relationship Analysis System Generating Trust Metrics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current relationship-analysis systems are limited by their reliance on human supervision, are cumbersome and expensive to manage, and fail to leverage advancements in distributed computer systems and pattern recognition technologies, such as machine learning, to effectively analyze relationships and provide actionable insights.
Innovation Solution
A semi-automated, distributed, interactive relationship-analysis system that collects both objective and subjective observations from participants, using video-enabled devices and physiological sensors, and processes this data to generate trust metrics through computational analysis, enabling automated evaluation and management of relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human-supervised relationship-analysis systems are used, then relationship analysis can be performed with expert judgment, but the systems are cumbersome and expensive to manage and maintain
Solution Approach 1:
The patent replaces human experts (mechanical/systemic intervention) with automated computational systems that use machine learning algorithms and pattern recognition to analyze relationship data. This substitution eliminates the need for human supervision while maintaining or improving analysis accuracy through consistent, scalable automated processing of observational data.
Solution Approach 2:
The system enables self-service relationship analysis by automatically collecting data from sensors and devices, processing it through embedded algorithms, and generating insights without requiring external human expertise. The automated system serves itself by continuously learning from new data and improving its analytical capabilities over time.
2Adaptability or versatility
If traditional relationship-analysis systems are used, then expert judgment can be applied, but they fail to leverage advancements in distributed computer systems and pattern recognition technologies
Solution Approach 1:
The patent creates a universal platform that integrates multiple technologies including distributed computing systems, machine learning algorithms, pattern recognition, and various sensor types. This multi-functional system can handle diverse relationship analysis tasks across different domains, leveraging advancements in multiple technological fields simultaneously to improve both adaptability and productivity.
3Productivity
If automated systems are implemented, then scalability and cost-effectiveness improve, but the system must process and analyze complex interaction data effectively
Solution Approach 1:
The patent segments the complex interaction data analysis into manageable components by collecting data from multiple independent sensors and devices, processing each data type through specialized algorithms, and then integrating the results. This segmentation allows the system to scale efficiently while handling complex multi-dimensional relationship data through modular processing architecture.
Data Source
AI summary
The current document is directed to a relationship-analysis system. The currently disclosed relationship-analysis system collects objective and subjective observations of participants, and their relationship, in an interaction or transaction. The objective and subjective observations are combined to generate an observation data set that is processed by a computational relationship-analysis system. The analysis produces a variety of different types of results, including trust metrics, and stores the results in memory and/or mass-storage for control of downstream analysis, reporting, and actions. Trust metrics provide a basis for carrying out numerous types of downstream actions and for generating recommendations and evaluations by various types of relationship-evaluation and relationship-management systems that employ the relationship-analysis system.