Dynamic Trust-Based Authorization System for Automated Task Execution
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Solution Overview
Problem
Large ecosystems face challenges in managing dependencies and executing tasks with smaller entities due to administrative overheads, human errors, and the need for multiple human interventions, making automation of trust-based authorizations cumbersome and costly.
Innovation Solution
A dynamic trust-based authorization system (DTBAS) that computes a normalized trust score based on multi-dimensional data from multiple sources, using a weighted decision matrix to dynamically authorize operations, enabling real-time execution of tasks without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual authorization processes are used to manage dependencies between entities, then human oversight and scrutiny can be provided, but administrative overheads, human errors, and delays increase
Solution Approach 1:
The patent replaces manual human authorization processes with an automated computer-based system that uses machine learning models and algorithms to evaluate trust scores and make authorization decisions. This substitution eliminates human errors and administrative overhead while maintaining reliable authorization through systematic computational evaluation of multiple parameters.
Solution Approach 2:
The system enables entities to autonomously receive authorization decisions based on their computed trust scores without requiring manual human intervention. The automated system serves itself by continuously evaluating parameters, updating trust scores, and making real-time authorization decisions, thereby improving productivity while maintaining reliability through consistent algorithmic evaluation.
2Reliability
If multiple human interventions and scrutinizations are implemented for task authorization, then authorization reliability improves, but administrative costs and time delays increase
Solution Approach 1:
The system performs preliminary computation of trust scores and evaluation of multiple parameters before authorization decisions are needed. By pre-establishing the automated evaluation framework and continuously computing trust metrics, the system is ready to make immediate authorization decisions without requiring time-consuming manual scrutiny, thus reducing time loss while maintaining reliability.
Solution Approach 2:
The system implements continuous feedback loops where trust scores are dynamically updated based on ongoing evaluation of parameters and historical data. This feedback mechanism ensures reliable authorization decisions are made rapidly by leveraging accumulated knowledge and patterns, eliminating the need for repeated manual scrutinizations while maintaining high authorization trustworthiness.
3Productivity
If automated trust-based authorization systems are implemented, then productivity and speed of task execution improve, but system complexity increases
Solution Approach 1:
The patent segments the complex authorization system into distinct functional modules including parameter evaluation components, trust score computation engines, and authorization decision-making systems. This segmentation allows each component to handle specific tasks independently, improving overall productivity while managing complexity through modular design that enables independent development and maintenance of each segment.
Solution Approach 2:
The system implements universal multi-functional components that can evaluate multiple parameters and serve different authorization scenarios through a single integrated platform. The trust score computation engine and decision matrix mechanisms are designed to be universally applicable across various entity types and task domains, thereby improving productivity without proportionally increasing system complexity through reuse of core functional elements.
4Measurement precision
If comprehensive multi-dimensional data computation is performed for trust scoring, then authorization accuracy improves, but computational requirements and processing time increase
Solution Approach 1:
The system implements partial computation strategies where not all parameters are evaluated with equal depth for every authorization request. The decision matrix mechanism selectively applies computation intensity based on the specific context and risk level, achieving sufficient trust score accuracy without the excessive computational resource consumption that would result from uniformly evaluating all possible parameters at maximum detail.
Data Source
AI summary
Configurations of a system and a method for a dynamic trust-based authorization system (DTBAS), are described. In one aspect, the DTBAS may implement an execution of operations of multiple engines, models, framework, circuits, code, etc., for dynamically authorizing a request for a service or an operation. The DTBAS, when deployed in a managing entity, may receive a request from a managed entity to execute an operation or task. Based on parameters or attributes associated with the managed entity, the DTBAS may execute operations to evaluate a trust score. Based on the evaluated trust score, the DTBAS may compute a weight decision matrix that may include multiple weights associated with the managed entity. Based on the computed weight decision matrix and the evaluated trust score, the DTBAS may authorize or deny the execution of the requested operation.