Machine Learning Routing Prediction Using Input Characteristics
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Solution Overview
Problem
Existing routing processes are complex and challenging to determine, especially when new processes are initiated or existing ones need modification, due to the difficulty in identifying the required processing resources, operations, and their sequencing, leading to suboptimal outcomes and resource wastage.
Innovation Solution
A method using input characteristics to train machine learning models, which analyzes sets of inference data to predict routing elements, such as processing resources and operations, by associating characteristic values with labels and using these models to generate inference results, thereby improving accuracy and data utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual methods are used to determine routing processes, then flexibility in process design is maintained, but the complexity and time required to determine routing increases significantly
Solution Approach 1:
The patent replaces manual routing determination processes with an automated machine learning system. The system uses trained models that analyze input characteristics and automatically generate routing recommendations, eliminating the need for manual analysis of complex processing resources, operations, and sequencing.
Solution Approach 2:
The patent transforms the routing determination problem by changing the input parameters from simple descriptions to detailed characteristic values. By using multiple input characteristics (e.g., material properties, component specifications, quantity data), the system achieves more accurate routing predictions without increasing operational complexity.
2Measurement precision
If comprehensive input characteristics are used to train the machine learning model, then prediction accuracy improves, but the amount of data processing and model complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-processing and organizing input characteristics before model training. The system prepares training data with standardized characteristic formats and pre-identified relevant features, which reduces the computational burden during actual model training and inference while maintaining high prediction accuracy.
Solution Approach 2:
The patent creates a universal machine learning model that can handle multiple types of routing determination tasks using the same framework. The model is designed to process various input characteristics (material properties, component data, quantity information) through a unified architecture, reducing overall system complexity despite the comprehensive nature of the inputs.
3Reliability
If more training data is utilized including characteristic values, then the model's inference accuracy improves, but the computational resources required for training increases
Solution Approach 1:
The patent extracts and uses only the most relevant characteristic values from the available training data. By identifying and focusing on key input characteristics that have the greatest impact on routing determination, the system achieves high inference reliability without processing every possible data point, thereby reducing computational energy consumption.
Solution Approach 2:
The patent employs partial action by using a representative subset of training data characteristics rather than processing all available data comprehensively. The system identifies sufficient characteristic values needed to achieve accurate routing predictions without the excessive computational cost of analyzing every possible input parameter.
Data Source
AI summary
Techniques and solutions are provided for determining elements of a routing. A set of inputs is obtained, where the set of inputs includes sets of one or more characteristics for respective inputs of the set of inputs. At least a portion of values for the one or more characteristics are submitted along with a set of labels to train a machine learning model. A set of inference data that includes input values for a set of one or more characteristics for inputs of the set of inference data is analyzed using the machine learning model to provide an inference result. The inference result provides a predicted set of labels associated with a routing element of a routing involving the set of inference data. Using characteristics values can provide more accurate inference results and can allow a greater portion of data to be used as training data.