Cohort-Based ML Completion Time Estimation for Multi-Stage Processes
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Solution Overview
Problem
Traditional automated time estimation techniques for multi-stage processes lack precision and accuracy due to the failure to account for differences among entities and contextual information, making it difficult to provide precise completion time estimates.
Innovation Solution
A computer-implemented method using machine learning (ML) for cohort-based completion time inferencing, which identifies cohorts with similar attributes to a primary entity and utilizes their completion times to generate more accurate estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional automated completion time estimation methods are used, then the system is simple and easy to implement, but the precision and accuracy of completion time estimates deteriorate
Solution Approach 1:
The patent introduces a machine learning model as an intermediary component that sits between the raw process data and the completion time estimation. This ML model processes entity attributes, process stage information, and historical completion data to generate accurate predictions, resolving the contradiction by adding a specialized intermediate layer that improves precision without requiring complete system redesign
Solution Approach 2:
The system creates a virtual copy of historical cohort data and uses it to train the machine learning model. By copying and analyzing patterns from historical completion data of similar entities, the system can make accurate predictions for new entities without requiring direct observation of each individual's complete process timeline, thus improving precision while managing complexity
2Reliability
If traditional completion time estimation methods are used, then the system requires minimal data processing, but the accuracy of estimates for entities with different attributes deteriorates
Solution Approach 1:
The patent applies local quality by tailoring the completion time estimation to specific local characteristics of each entity and process stage. The machine learning model analyzes entity-specific attributes (such as profession, experience level, educational background) and process-specific factors (stage type, complexity) to generate customized time estimates for each local context, thereby improving reliability for different entities while preserving relevant information
Solution Approach 2:
The system adds new dimensions to the estimation problem by incorporating multiple entity attributes and contextual factors into the machine learning model's input space. Instead of estimating completion time based solely on process stage count, the model operates in a higher-dimensional space that includes entity characteristics, historical performance data, and process attributes, enabling accurate predictions for diverse entities while maintaining information about their specific characteristics
3Measurement precision
If detailed cohort-based ML analysis is performed, then the precision of completion time estimates improves, but the computational resources and processing time required increase
Solution Approach 1:
The system performs preliminary action by pre-processing and storing historical completion data in a structured format that the machine learning model can efficiently query. Historical data about cohort completions is pre-organized by entity attributes and process stages, allowing the model to make rapid inferences without performing exhaustive analysis during each estimation request, thus improving precision while reducing real-time processing time
Solution Approach 2:
The patent replaces mechanical manual analysis of completion data with an automated machine learning inference system. Instead of requiring time-consuming manual review of historical completion patterns for each new entity, the trained ML model performs automated pattern recognition and time prediction, significantly improving precision while reducing the time required for data processing and estimation
Data Source
AI summary
As disclosed herein, machine learning model inferencing is used to make inferences about completion times for processes and stages of processes using a cohort-based approach in which a machine learning model infers a cohort completion time based on a cohort of client entities for which the process or stage has been completed. A primary entity may also be provided with a comparison of a completion time for a secondary entity and an inferred cohort completion time. Also, an estimated completion time for a secondary entity may be based on an inferred cohort completion time. A celebration, reward, or other feedback may be provided based on a comparison of an actual completion time for a process for an entity and an inferred cohort completion time for the process. Tooltips or instruction may be provided to a primary user for stages of a process having lengthy inferred cohort completion times.