User-Specific Process Schedule Modeling for Complex SME Workflows
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Solution Overview
Problem
Existing tools struggle to accurately interpret and integrate large quantities of data for complex subject-matter expert (SME) processes, leading to errors, inefficiencies, and poor comprehension, especially when data changes over time or involves multiple sources.
Innovation Solution
A process analysis computing system generates user-specific process schedule data by extracting process and user factors from input data, determining initial schedule data, and modifying it based on user experience and risk levels to provide tailored visualization and recommendation data, controlling access to resources based on dependency and completeness of milestone stages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If detailed information about a very large quantity of SME process data is provided, then the completeness of process description is improved, but the difficulty of human comprehension increases
Solution Approach 1:
The patent segments the large quantity of SME process data into discrete process stages, each with specific inputs, outputs, and execution criteria. This segmentation transforms an overwhelming mass of information into manageable, structured units that can be individually understood and tracked, resolving the contradiction between completeness and comprehendibility.
Solution Approach 2:
The patent introduces a computer system as an intermediary between the SME process data and human users. The system automatically extracts, structures, and presents process information, acting as a mediator that handles the complexity of large data quantities while presenting simplified, actionable information to users, thereby maintaining completeness without overwhelming human comprehension.
2Measurement precision
If the quantity of requirements for an SME process is increased to cover all details, then the accuracy of process specification is improved, but the ability of human comprehension is exceeded
Solution Approach 1:
The patent divides complex SME processes into distinct stages with clearly defined requirements for each stage. This segmentation maintains high accuracy in process specification by ensuring all details are captured, while simultaneously reducing perceived complexity by organizing requirements into manageable, stage-specific units rather than presenting them as an overwhelming whole.
Solution Approach 2:
The patent applies local quality by providing detailed, accurate information specifically where needed in each process stage, rather than uniformly distributing complexity throughout. Each stage receives the precise level of detail and requirement specification appropriate to its nature, maintaining overall accuracy while preventing any single area from becoming overwhelming.
3Reliability
If interactions and dependencies among stages are fully modeled, then the accuracy of process representation is improved, but the complexity for potential implementations increases
Solution Approach 1:
The patent segments the process model into discrete stages with explicit dependency relationships. This segmentation accurately represents interactions and dependencies while reducing implementation complexity by allowing each stage to be developed, tested, and managed independently, with clear interfaces defined through the dependency relationships.
Solution Approach 2:
The patent establishes all stage dependencies and interactions in advance during the process modeling phase. By preliminarily defining these relationships, the system creates an accurate process representation that guides implementation, allowing developers to understand required sequences and interactions before actual implementation begins, thereby reducing overall complexity.
4Adaptability or versatility
If manual organization and execution of SME processes is performed, then flexibility in handling unique cases is maintained, but errors and inefficiencies increase
Solution Approach 1:
The patent enables the system to automatically extract, structure, and organize SME process data without requiring manual intervention for routine tasks. This self-service capability maintains flexibility by adapting to different SME processes while significantly reducing human errors associated with manual data entry and organization, as the system consistently applies the same extraction and structuring rules.
Data Source
AI summary
An process analysis computing system receives input data that identifies a subject-matter expert (SME) process, such as an SME process that requires detailed information regarding a very large quantity of information about the SME process. In the process analysis computing system, a scenario selection engine extracts process factors and user factors from the input data. Based on the process factors and user factors, the scenario selection engine determines initial schedule data describing event stages and milestone stages included in the SME process, and also generates user-specific process schedule data based on a modification of the initial schedule data. A user-specific execution engine in the process analysis computing system receives the user-specific process schedule data. Based on a milestone stage dependency in the user-specific process schedule data, the process analysis computing system controls access to a requested resource associated with the SME process.