Enterprise Process Similarity Identification Using AI
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large enterprises face inefficiencies and redundancy due to the difficulty in managing and identifying similar business processes, leading to inconsistent and improper outcomes when updating processes.
Innovation Solution
A computer system utilizing machine learning, specifically AI techniques like Doc2Vec, BERT, and T5, to analyze and identify similar processes by calculating similarity scores based on metadata attributes, allowing for consolidation and reducing redundant processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning is used to analyze processes, then identification accuracy is improved, but system complexity increases
Solution Approach 1:
The patent introduces a similarity engine as an intermediary component that专门 handles the complex machine learning-based similarity analysis. This mediator sits between the process storage and the user interface, automatically calculating similarity scores using AI models (Doc2Vec, BERT, T5) without requiring users to directly interact with the complex underlying algorithms. The engine processes process metadata, compares it against stored processes, and returns simplified similarity results, thereby managing system complexity while maintaining high identification accuracy.
2Device complexity
If manual process analysis is performed, then system complexity is reduced, but time consumption increases
Solution Approach 1:
The system implements self-service by enabling automatic process similarity identification without requiring manual intervention. The similarity engine autonomously performs the following tasks: extracting metadata from process definitions, applying machine learning models (Doc2Vec for document embeddings, BERT for contextual understanding, T5 for text transformation), calculating similarity scores, and ranking processes. This automated self-service approach eliminates the need for manual process analysis while reducing time consumption significantly, though it does increase system complexity through the incorporation of AI/ML components.
3Productivity
If process consolidation is implemented, then operational efficiency is improved, but risk of inconsistent outcomes increases
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor and evaluate process similarities and consolidation decisions. The system provides feedback to users through the user interface, displaying similarity scores, process comparisons, and recommendations. This feedback loop allows users to review AI-generated similarities, provide corrections, and adjust consolidation decisions. The feedback mechanism ensures operational efficiency through automated identification while maintaining reliability by allowing human verification and correction of consolidation outcomes.
Data Source
AI summary
An example computer system for identifying similar processes can include: one or more processors; and non-transitory computer-readable storage media encoding instructions which, when executed by the one or more processors, causes the computer system to create: a similarity engine programmed to use machine learning to analyze a plurality of processes for an enterprise; a models engine programmed to identify similarities between the plurality of processes using the machine learning; and a display engine programmed to display the similarities between the plurality of processes.