Markov Chain Model for Digital Workflow Optimization

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Solution Overview

Problem

Professional software workflows often become outdated due to changes in UI technology, design, and devices, leading to inefficient user interactions, as existing methods lack comprehensive analysis and optimization capabilities to adapt workflows in real-time.

Innovation Solution

A computer-implemented method using a Markov chain model to analyze and optimize user interactions in digital workflows, generating recommendations for modifications that enhance performance based on user preferences and device modalities, enabling real-time updates and improvements.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If workflows are updated to reflect new UI technology and design, then adaptability is improved, but complexity of implementation increases

Engineering Contradiction:

Improveworkflow adaptabilityVSAvoidimplementation complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system continuously monitors user interactions with the workflow and uses this feedback to automatically identify optimization opportunities. The Markov chain model processes interaction data to determine when workflow modifications would improve performance, creating a closed-loop system that adapts workflows based on actual usage patterns without requiring manual analysis of each interaction.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The workflow system performs self-optimization by automatically analyzing its own operation data through the Markov chain model. The system generates its own modification recommendations based on monitored interactions, eliminating the need for external manual analysis and reducing implementation complexity while maintaining high adaptability.

Inventive Principle:

Principle #25Self-service

2Measurement precision

If comprehensive analysis of user interactions is performed, then optimization accuracy is improved, but processing time increases

Engineering Contradiction:

Improveoptimization accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system transforms detailed user interaction data into a simplified Markov chain model that captures essential transition probabilities between workflow states. This parameter transformation allows the system to maintain high optimization accuracy by preserving the probabilistic relationships in user behavior while reducing the computational complexity and processing time required for analysis.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The Markov chain model extracts and isolates the critical probabilistic transitions from the complex user interaction data. By separating the essential workflow transition information from the full interaction history, the system achieves accurate optimization predictions with significantly reduced processing requirements compared to analyzing all interaction details directly.

Inventive Principle:

Principle #2Taking out (Extraction)

3Productivity

If workflow modifications are implemented based on analysis, then performance is improved, but risk of introducing errors increases

Engineering Contradiction:

Improveworkflow performanceVSAvoidmodification reliability

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system monitors user interactions continuously and uses this feedback to validate proposed modifications before implementation. The Markov chain model simulates potential workflow changes by analyzing transition probabilities, allowing the system to predict outcomes and adjust recommendations to ensure reliable improvements while maintaining workflow stability.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary analysis and simulation of potential workflow modifications using the Markov chain model before actual implementation. By pre-evaluating the probabilistic outcomes of proposed changes, the system can identify and eliminate potentially erroneous modifications in advance, ensuring that only reliable performance-improving changes are implemented.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250005483A1Markov chain model based analysis and optimization of intelligent digital workflows

Publication Date: 2025.01.02 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

A computer-implemented method includes detecting user interactions in a workflow via workflow user interfaces of user devices. The method further includes modeling the user interactions in the digital workflow based on the user interactions in a Markov chain model of the user interactions. The method further includes analyzing the Markov chain model of the user interactions with reference to performance goals of the digital workflow, to determine prospective modifications to the user interactions that would increase performance of the user interactions as indicated by the performance goals. The method further includes generating workflow modification recommendations based on the prospective modifications to the user interactions. The method further includes outputting the workflow modification recommendations to the user devices. The method further includes receiving a confirmation to select one of the workflow modification recommendations. The method further includes implementing a modification to the workflow based on the selected workflow modification recommendation.