AI Permission Request Management for Adaptive Workflow Changes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current software applications in information processing systems face challenges in adapting to changes in domain logic, workflows, and integration practices, leading to crashes and resource overburden due to rigid validation and provisioning systems that require extensive rework.
Innovation Solution
An autonomous usage and permission system utilizing artificial intelligence models, including machine learning algorithms, to manage and validate usage and permission requests, accommodating various input formats and changes through feedback correction and learning mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed domain logic and predefined workflows are used in software applications, then software integration and operation are simplified, but the system becomes rigid and cannot adapt to changes, leading to crashes and requiring extensive rework
Solution Approach 1:
The patent applies dynamics by transforming fixed domain logic into dynamic, learnable patterns. The system uses machine learning models that continuously adapt to changing workflows and domain logic without requiring manual reconfiguration. The usage and permission parameters are dynamically generated based on learned patterns from historical data, enabling the system to evolve with changing requirements while maintaining operational simplicity.
Solution Approach 2:
The patent changes parameters by using machine learning to generate usage and permission parameters adaptively rather than using fixed predefined values. The system learns optimal parameter configurations from historical data and automatically adjusts them to accommodate changes in domain logic and workflows, eliminating the need for extensive rework while managing complexity through automated parameter optimization.
2Reliability
If rigid validation and provisioning systems are implemented, then system control and security are improved, but resource overhead increases and the system becomes burdened with extensive rework
Solution Approach 1:
The patent applies self-service by implementing automated validation and provisioning systems that use machine learning to independently assess and approve usage requests without requiring extensive manual review. The system learns from historical validation data to automatically determine permission parameters, maintaining high validation reliability while significantly reducing resource overhead and eliminating the need for extensive rework.
Solution Approach 2:
The patent implements feedback mechanisms where the validation system continuously learns from approved and rejected requests. The machine learning models are trained on historical validation outcomes, enabling the system to improve its validation accuracy over time while reducing false positives that would require manual intervention. This feedback loop maintains reliability while improving resource efficiency by reducing unnecessary rework.
3Reliability
If extensive rework is performed to accommodate changes in domain logic, then software functionality is maintained, but time consumption and resource overhead increase
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on historical domain logic and workflow data. When changes occur, the system can quickly adapt using the pre-established learning framework rather than performing extensive rework from scratch. The models are preliminarily configured to recognize patterns and generate appropriate usage parameters, enabling rapid adaptation to changes while maintaining software functionality.
Solution Approach 2:
The patent substitutes mechanical rework processes with automated machine learning-based adaptation. Instead of manually rewriting and testing software to accommodate changes in domain logic, the system uses ML models to automatically generate updated usage and permission parameters. This substitution eliminates time-consuming manual rework while preserving software functionality through intelligent parameter adjustment.
4Stability of the object's composition
If traditional software integration practices are used, then system stability is maintained, but the system cannot accommodate heterogeneous systems and varying input formats
Solution Approach 1:
The patent applies universality by designing a machine learning-based validation system that can handle multiple input formats and heterogeneous data sources through a unified learning framework. The system learns to recognize and process various input types (structured and unstructured data) using the same core mechanisms, enabling stable integration with diverse systems while maintaining adaptability to new formats and sources.
Data Source
AI summary
A method includes obtaining a request for generating one or more usage and permission parameters associated with a product. The method further includes applying one or more machine learning algorithms to the request to generate data for use in generating the one or more usage and permission parameters. The method also includes applying at least a portion of the generated data to an approval feedback process. The method still further includes generating the one or more usage and permission parameters responsive to the applying steps.