Predictive Algorithm Deployment System with Automated Monitoring

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Solution Overview

Problem

The manual process of defining and deploying predictive analytic algorithms in enterprises is time-consuming and error-prone, especially when dealing with complex algorithms and numerous factors, which hinders efficient prediction and performance analysis.

Innovation Solution

A system and method that facilitate the deployment of predictive analytic algorithms by allowing users to adjust algorithm characteristic values, initiate deployment in an enterprise operations workflow, and monitor results, generating alert signals when boundary conditions are exceeded, thereby streamlining the process and improving accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual processes are used to define and deploy predictive analytic algorithms, then flexibility and control are maintained, but the process becomes time-consuming and error-prone

Engineering Contradiction:

Improveaccuracy of algorithm deploymentVSAvoidtime required for deployment

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system enables self-service deployment through automated workflows where the deployment system automatically executes deployment steps, monitors progress, and handles errors without requiring manual intervention at each stage. This reduces deployment time while maintaining reliability through systematic automation.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary actions by pre-configuring deployment parameters, validating algorithm compatibility, and preparing target environments before actual deployment occurs. This upfront preparation reduces errors during deployment and accelerates the overall process.

Inventive Principle:

Principle #10Preliminary action

2Adaptability or versatility

If complex algorithms with numerous factors are implemented manually, then comprehensive analysis is achieved, but the process becomes more error-prone and time-consuming

Engineering Contradiction:

Improvecapability to handle complex algorithmsVSAvoidcomplexity of deployment process

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The deployment process is segmented into discrete, manageable steps including validation, configuration, execution, and monitoring. Each step handles specific aspects of complex algorithm deployment independently, reducing overall process complexity while maintaining the ability to deploy sophisticated algorithms with multiple factors.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system introduces an intermediary deployment management layer that mediates between the complex algorithm definitions and the target execution environment. This intermediary handles the complexity of coordinating multiple factors and parameters, simplifying the user interface while supporting comprehensive algorithmic analysis.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If manual rule definition is used, then customization is possible, but the process lacks efficiency and scalability

Engineering Contradiction:

Improvedeployment efficiencyVSAvoidease of algorithm creation

Core Design Contradiction:

ProductivityVSEase of operation

Solution Approach 1:

The system provides universal deployment capabilities that work across different algorithm types and complexity levels through a standardized interface. This multi-functional approach maintains ease of operation by presenting a consistent user experience while dramatically improving productivity through automated deployment processes that scale efficiently.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS11461706B2System to facilitate predictive analytic algorithm deployment in an enterprise

Publication Date: 2022.10.04 HARTFORD FIRE INSURANCE CO

AI summary

According to some embodiments, an analytics computing environment data store may contain a set of electronic data records, each electronic data record being associated with a predictive analytic algorithm and including an algorithm identifier and a set of algorithm characteristic values. An analytics environment computer may receive an adjustment from a user associated with an enterprise, the adjustment changing at least one of the set of algorithm characteristic values for a predictive analytic algorithm. Deployment of the predictive analytic algorithm may then be initiated in an enterprise operations workflow and at least one result may be generated. The deployed predictive analytic algorithm may then monitor the result and generate an alert signal when the result exceeds a boundary condition.