Automated Asset Selection via Likelihood Models
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Solution Overview
Problem
Existing virtual environments for computers are often outdated and insecure due to changing security and operational needs, requiring frequent updates and posing risks from malicious parties, and they lack efficient methods for managing dependencies between applications.
Innovation Solution
A system and method for automatically selecting and creating assets using a deployment computer that receives client requests with established and inquiry parameters, determines suitable models based on likelihood values, and generates client response messages to transmit suitable assets, incorporating artificial intelligence models to manage asset creation and security within virtual environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If virtual environments are manually configured with dependencies and security rules, then initial setup accuracy is improved, but the system becomes outdated quickly and requires frequent updates
Solution Approach 1:
The system enables self-service through automated model selection and asset creation. The deployment computer automatically selects suitable AI models based on likelihood values and generates assets without requiring continuous manual intervention, allowing the system to adapt to changing security needs autonomously
Solution Approach 2:
The system implements dynamics by continuously evaluating and selecting from multiple AI models based on changing conditions. The likelihood value calculation dynamically adjusts model selection based on current security and operational requirements, enabling the system to adapt quickly to new threats and needs
2Manufacturing precision
If multiple AI models are evaluated for each request, then asset creation accuracy is improved, but processing time increases
Solution Approach 1:
The system applies partial action by evaluating multiple models but selecting only the most suitable one based on likelihood values. Rather than exhaustively processing all possible models or parameters, the system identifies and selects the best match efficiently, achieving high accuracy without excessive processing time
Solution Approach 2:
The system uses parameter changes by calculating likelihood values based on varying parameters such as security requirements, operational needs, and model characteristics. This allows the system to dynamically adjust model selection criteria to balance accuracy and processing speed based on current priorities
3Reliability
If security and operational needs are frequently updated, then system security is improved, but asset obsolescence increases
Solution Approach 1:
The system implements dynamics by continuously adapting to changing security and operational needs through automated model reevaluation. When security requirements change, the system automatically selects new appropriate models and creates updated assets, maintaining security reliability while managing asset lifecycle dynamically
Solution Approach 2:
The system applies discarding and recovering by phasing out outdated assets and models that no longer meet security requirements while recovering and deploying new models that address current threats. This continuous renewal process maintains security without permanently losing value through structured asset replacement
Data Source
AI summary
A method includes a deployment computer receiving a client request message comprising a plurality of established parameters and established values for the established parameters, and inquiry parameters and inquiry values for the inquiry parameters from a client computer. For each model of a plurality of models stored in a database, the deployment computer can determine if a model in the plurality of models is a suitable model based on a likelihood value. The deployment computer can then generate one or more client response messages comprising one or more assets created by one or more suitable models. The deployment computer can then transmit the one or more client response messages comprising the one or more assets to the client computer.