Hierarchy Encoding for Routing Resource and Sequence Prediction
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Solution Overview
Problem
Developing and modifying routing processes for complex systems, such as manufacturing or computing processes, is challenging due to the complexity of determining processing resources, operations, sequencing, and resource allocation, often leading to suboptimal outcomes and inefficiencies.
Innovation Solution
A method for encoding hierarchical information and quantity information for set elements, using machine learning techniques to predict processing resources, operations, and their sequencing, and encoding characteristics of inputs to enhance the accuracy and relevance of training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If routing processes are developed for complex systems with many components, then the routing can handle more components and operations, but the complexity of determining processing resources, operations, sequencing, and resource allocation increases significantly
Solution Approach 1:
The patent segments the complex routing process into distinct modules: (1) receiving a collection of inputs, (2) determining processing resources using a trained machine learning model, (3) determining operations to be performed, and (4) determining a sequence for the operations. This segmentation allows each module to handle specific aspects of the routing process independently, reducing overall complexity while maintaining versatility.
Solution Approach 2:
The patent introduces trained machine learning models as intermediaries between the input data and the routing decisions. These models act as mediators that automatically determine processing resources, operations, and sequencing based on trained patterns, eliminating the need for manual complex routing process design and reducing process complexity.
2Ease of operation
If manual methods are used to determine routing processes, then flexibility in customization is maintained, but the time and computational resources required increase
Solution Approach 1:
The patent applies preliminary action by training machine learning models in advance on historical routing data. Once trained, these models can quickly determine routing processes for new inputs without requiring manual analysis each time. This preliminary training phase enables rapid, automated routing determination while maintaining accuracy based on learned patterns.
Solution Approach 2:
The patent replaces manual mechanical methods of routing process determination with automated machine learning-based systems. The trained models automatically determine processing resources, operations, and sequencing, substituting human expert analysis with computational algorithms that operate faster and require less time.
3Measurement precision
If existing routing data is encoded and used for training, then the accuracy of routing predictions improves, but the computational resources required for encoding and processing increase
Solution Approach 1:
The patent applies partial action by selectively encoding specific features and characteristics of inputs that are most relevant to routing predictions, rather than encoding all possible data. The machine learning models are trained on these encoded features to achieve accurate predictions while minimizing unnecessary computational processing of irrelevant information.
Data Source
AI summary
Techniques and solutions are provided for encoding information for sets, including sets whose elements are arranged in a hierarchy. Values are defined for different levels of a hierarchy, where the values increase or decrease from a root of the hierarchy. A flattened representation of the hierarchy is generated by multiplying element values by a level value for a level at which a respective element is located. Values for parent and leaf nodes are defined, and a flattened representation of the hierarchy is generated by multiple elements values by the parent value or the leaf node value, depending on whether a respective elements is a parent or leaf node. Quantity values for a set of elements are encoded by adding a quantity of a given element to a value assigned to elements of a set definition that are present in a set.