Real-Time Process Completion Likelihood Prediction Using ML
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Solution Overview
Problem
Current predictive models are unable to effectively update their likelihood predictions in real-time based on new data, leading to inefficiencies and delays in identifying and addressing issues such as manufacturing defects, compliance breaches, and health concerns.
Innovation Solution
A method using a trained machine learning model that continuously updates predictions by incorporating real-time data and fixed descriptors, allowing for real-time likelihood determination of process completion, enabling early issue detection and remediation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional predictive models are used, then model simplicity is maintained, but real-time likelihood updates cannot be performed
Solution Approach 1:
The system segments the prediction model into two distinct components: a static predictive model that processes fixed descriptors and a dynamic model that processes real-time status updates. This segmentation allows each component to be optimized independently, with the static model providing baseline predictions and the dynamic model adjusting likelihoods in real-time based on new data, thereby achieving real-time updates without requiring complete model redesign
Solution Approach 2:
The system introduces dynamic updating capability by allowing the predictive model to adjust its output likelihoods in real-time based on incoming status updates. The model transitions from a static prediction to a dynamic assessment that continuously incorporates new information about system state changes, enabling real-time likelihood determination while maintaining the core predictive structure
2Loss of time
If real-time data processing is implemented, then issue detection speed is improved, but computational resources increase
Solution Approach 1:
The system applies partial action by processing only the relevant status updates that occur since the last prediction, rather than reprocessing all historical data. The dynamic model focuses computational resources on integrating new real-time information with the existing predictive state, performing minimal necessary computations to update likelihoods, thereby reducing overall computational energy requirements while maintaining real-time detection capability
3Measurement precision
If comprehensive status updates are collected, then prediction accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system extracts and separates fixed descriptors from dynamic status updates, processing them through different model components. The static predictive model handles fixed descriptors to establish baseline predictions, while the dynamic model extracts and processes only the changing status update information. This extraction approach allows comprehensive data collection while simplifying processing by handling different data types through specialized pathways
Data Source
AI summary
Provided are systems, methods, and programming for facilitating real-time determination of a process completion likelihood. In some embodiments, data including an update to a system, the update occurring at a first time, wherein updates to the system are permitted until an expiration time may be obtained, a set of fixed descriptors of the system may be retrieved and/or received, and a set of status updates describing the system at prior times may be obtained. Each status update of the set of one or more status updates includes at least (i) an update to the first system and (ii) a time that the respective status update occurred. Based on the data, the fixed descriptors, and the status updates, using a trained machine learning model, a failure/success score indicating a likelihood that, at the expiration time, the system satisfies a threshold condition may be computed and stored in memory.