Entity Trust Index Generation From Multi-Source Machine Learning
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Solution Overview
Problem
Conventional methods for determining an entity's trustworthiness are subjective and costly, requiring substantial time and resources, and lack real-time capability.
Innovation Solution
A system and method for generating an entity trust index using machine learning models to analyze information from various sources, incorporating performance, innovation, and sustainability metrics, providing a real-time, holistic assessment of an entity's trustworthiness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional survey methods are used to assess trustworthiness, then subjective perceptions can be captured, but the process requires substantial time commitment and drives up costs
Solution Approach 1:
The patent replaces manual survey-based trust assessment with an automated machine learning system that processes objective data from multiple sources. The system uses trained ML models to generate trust indices by analyzing entity information from diverse sources, eliminating the need for time-consuming human surveys while maintaining or improving assessment accuracy through multi-factor analysis.
Solution Approach 2:
The system enables automated self-assessment of entity trustworthiness without requiring external survey administration. The machine learning models automatically collect, process, and analyze data from multiple information sources, generating trust indices without human intervention in the assessment process itself, thereby reducing time commitment while preserving measurement capability.
2Measurement precision
If conventional survey methods are used to assess trustworthiness, then subjective perceptions can be captured, but costs drive up for entities that wish to use the surveys
Solution Approach 1:
The patent replaces expensive manual survey processes with an automated machine learning system that processes objective data from multiple sources. The system uses trained ML models to generate trust indices by analyzing entity information from diverse sources, eliminating the need for costly human survey administration while maintaining or improving assessment accuracy through multi-factor analysis.
Solution Approach 2:
The system provides a universal platform that can assess trustworthiness of any entity across multiple dimensions (performance, innovation, sustainability) using a single integrated machine learning framework. This multi-functional approach eliminates the need for separate surveys for different trust aspects, reducing overall costs while comprehensively capturing trustworthiness through diverse data sources.
3Measurement precision
If comprehensive trust assessments are generated using multiple factors, then holistic measures of trustworthiness can be obtained, but the complexity of processing and combining multiple data sources increases
Solution Approach 1:
The patent segments the complex trust assessment task into distinct components: performance index, innovation index, and sustainability index. Each index is calculated separately using relevant data sources and machine learning models, then combined to form the comprehensive trustworthiness assessment. This segmentation reduces processing complexity by breaking down the holistic measurement into manageable, specialized sub-tasks.
Solution Approach 2:
The system introduces machine learning models as intermediary components that automatically process and synthesize data from multiple diverse sources. These ML models act as mediators that handle the complexity of data integration, normalization, and weighting, transforming raw multi-source data into structured trust indices without requiring manual processing complexity.
4Speed
If real-time trust index generation is implemented, then up-to-date assessments are provided, but the computational resources and processing speed requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models on historical data and pre-establishing data collection pipelines from multiple sources. This preliminary preparation enables the system to generate real-time trust indices by applying pre-trained models to new data without requiring intensive computational resources during the actual assessment, as the heavy lifting of model training and data infrastructure setup has already been completed.
Data Source
AI summary
An entity trust index relating to an entity may indicate a trustworthiness level of the entity. To generate an entity trust index, entity information may be received from one or more information sources. Using the entity information and one or more machine learning models, a performance index, an innovation index, and a sustainability index for the entity may be generated. The entity trust index may be generated based on the performance index, the innovation index, and the sustainability index. One or more actions for the entity that will modify the entity trust index may then be determined.