Dynamic Application Provisioning via Inference Engine
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Solution Overview
Problem
Managers and supervisors face challenges in rapidly making informed decisions about which applications to provide to end users due to the complexity of operational situations and varying technological sophistication, leading to a 'take everything you have' approach that is not strategically optimized.
Innovation Solution
A dynamic application provisioning system that uses an inference engine and policy system to automatically predict and deploy task-specific applications and resources based on user type and environment, providing tailored application sets, network configurations, and enhanced security features, while ensuring only authorized access and scalability from small to large enterprises.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If automated dynamic provisioning is implemented, then decision-making time is reduced and response speed is improved, but system complexity increases due to the need for inference engines and policy systems
Solution Approach 1:
The system enables self-service automation where the inference engine and policy system automatically analyze operational situations, determine appropriate applications, and provision them without human intervention. The system serves itself by making autonomous decisions about application provisioning based on real-time data analysis, eliminating the need for manual decision-making while managing the complexity through automated self-management mechanisms.
Solution Approach 2:
The inference engine acts as an intermediary between operational situations and application provisioning decisions. It mediates the complex analysis of operational data, user profiles, and application requirements, translating this complexity into automated provisioning actions. This intermediary layer manages system complexity by encapsulating the decision-making logic and presenting simplified outcomes to the provisioning system.
2Productivity
If task-specific applications are provisioned dynamically, then application relevance and user productivity are improved, but the difficulty of detecting and measuring appropriate applications increases
Solution Approach 1:
The system implements feedback mechanisms where the inference engine continuously monitors operational situations, user interactions with applications, and system performance metrics. This feedback loop enables the system to learn from past provisioning decisions, measure their effectiveness, and adjust future application selections accordingly. The feedback mechanism transforms the difficulty of measuring appropriate applications into a learnable optimization problem that improves productivity over time.
Solution Approach 2:
The system replaces manual mechanical decision-making processes with automated computational analysis. Instead of humans manually evaluating which applications are appropriate for specific tasks, the inference engine uses automated algorithms to analyze operational data, user profiles, and application characteristics, substituting human judgment with computational measurement and selection processes that improve both accuracy and productivity.
3Reliability
If comprehensive security features are added, then system security and protection against threats are improved, but ease of operation decreases due to additional security protocols
Solution Approach 1:
The security system operates autonomously through automated authentication, authorization, and access control mechanisms. Security protocols are executed automatically without requiring user awareness or manual intervention - the system serves itself by managing security clearances, validating user credentials, and controlling application access based on predefined policies. This self-service approach maintains high security while preserving ease of operation for authorized users.
Solution Approach 2:
The security system is segmented into distinct modular components including authentication modules, authorization modules, and access control modules. Each security function is separated into independent segments that can be managed and executed independently. This segmentation allows comprehensive security features to be implemented without creating a monolithic complex system, as each security component operates independently and can be optimized without affecting the entire security infrastructure.
Data Source
AI summary
Disclosed is a system and method for the automatic, dynamic provisioning of applications configured to provide users with applications and network communications specifically designed to support their particular task. The provisioning of such capabilities is based on the type of event the user is responding to, such that every time a particular event occurs, a specific set of applications and other toolsets will be provisioned to that user (e.g., onto their individual mobile communication devices, such as tablets, smartphones, or the like) on an ad-hoc basis tailored to that particular event. An inference engine and policy system are provided to intelligently and automatically predict and securely deploy resources to end users. Such inference engine and policy system automate some facets of the assessment process accounting for the manager's, supervisor's, commander's, etc. intent and proposed courses of action—greatly reducing the amount of time required to make good decisions about which applications and services should be employed for any particular operation.