Multi-Stage Process Modeling for Accurate Stage Classification
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Solution Overview
Problem
Existing models for analyzing processes that change over time suffer from inaccuracy due to excessive retraining needs and computational complexity, limiting their practical use.
Innovation Solution
An apparatus and method for multiple stage process modeling using a processor and memory to generate progression outlook profiles, classify current process data, and output recommended actions, incorporating machine learning algorithms to efficiently model and guide progression stages of entities like businesses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing models are used to analyze processes that change over time, then computational efficiency is improved, but measurement precision deteriorates due to model inaccuracy
Solution Approach 1:
The patent divides the process modeling into multiple discrete stages (progression stages) where each stage has its own trained model. This segmentation allows the system to capture different process behaviors at different phases without requiring a single complex model, thereby maintaining accuracy while improving computational efficiency through specialized stage-specific analysis.
Solution Approach 2:
The system dynamically transitions between different progression stages based on process data analysis. By adapting the modeling approach to the current stage of the process, the system maintains high accuracy for time-varying processes while avoiding the computational burden of retraining a single comprehensive model, as each stage uses pre-trained specialized models.
2Adaptability or versatility
If prior programmatic attempts are made to model changing processes, then model coverage is improved, but device complexity increases due to excessive retraining needs and computational complexity
Solution Approach 1:
The patent performs preliminary training of multiple stage-specific models offline before deployment. During runtime, the system simply classifies the current process stage and applies the corresponding pre-trained model, eliminating the need for complex online retraining operations. This preliminary action reduces operational complexity while maintaining comprehensive model coverage across all process stages.
Solution Approach 2:
The system introduces a stage classifier as an intermediary component that determines which progression stage the process is currently in. This intermediary simplifies the overall system architecture by decoupling stage detection from stage-specific analysis, making the system more manageable and less complex while still achieving comprehensive coverage of changing processes.
Data Source
AI summary
An apparatus and method for multiple stage process modeling is provided. The apparatus includes a processor and a memory connected to the processor. The memory containing instructions configuring the a processor to receive process data sets, each process data set representing a progression stage that describes a sequence of activities performed by an entity device, generate, using the process data sets and a machine learning algorithm, a progression outlook profile including progression stage profiles, each progression stage profile representative of a respective progression stage and may generate progression actions describing progression from a first progression stage to a second progression stage based on input data, and a progression stage profile classifier that may use input data and identify a progression stage currently occupied by a process based on input data. The processor may receive process data describing a process to classify received process data to a progression stage profile.