Ford global technologies, llc (20240320501). RULE VISUALIZATION simplified abstract
Contents
RULE VISUALIZATION
Organization Name
Inventor(s)
Bilal Hejase of Columbus OH (US)
Teawon Han of San Jose CA (US)
Subramanya Nageshrao of San Jose CA (US)
Baljeet Singh of Fremont CA (US)
Tejaswi Koduri of Sunnyvale CA (US)
RULE VISUALIZATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240320501 titled 'RULE VISUALIZATION
Simplified Explanation:
This patent application describes a computer system that uses a neural network to make predictions based on input data, generate policies, and train decision nodes to make decisions based on the generated force features.
Key Features and Innovation:
- The computer system includes a processor and memory for training a neural network to make predictions.
- Policies are generated based on the input data.
- Force features are generated based on the policies.
- Decision nodes are trained based on force features and a binary vector from the neural network.
- A decision tree is generated based on the decision nodes.
- Decisions can be made by inputting a policy to the decision tree.
- The decision is compared to the prediction, and the neural network is re-trained based on the difference.
Potential Applications: This technology can be applied in various fields such as finance, healthcare, and marketing for decision-making processes based on complex data analysis.
Problems Solved: This technology addresses the need for efficient decision-making based on large datasets and complex patterns.
Benefits:
- Improved accuracy in decision-making processes.
- Enhanced efficiency in analyzing and processing large amounts of data.
- Automation of decision-making tasks.
Commercial Applications: Potential commercial applications include automated trading systems in finance, personalized medicine in healthcare, and targeted advertising in marketing.
Prior Art: Readers can explore prior art related to neural networks, decision trees, and machine learning algorithms to understand the background of this technology.
Frequently Updated Research: Stay updated on advancements in neural network training techniques, decision tree algorithms, and applications of machine learning in various industries.
Questions about the Technology: 1. How does this technology improve decision-making processes compared to traditional methods? 2. What are the potential limitations or challenges of implementing this technology in real-world applications?
Original Abstract Submitted
a computer that includes a processor and a memory, the memory including instructions executable by the processor to train a neural network to input data and output a prediction. a policy can be generated based on the data. force features can be generated based on the policy. decision nodes can be trained based on force features and a binary vector from the trained neural network. a decision tree can be generated based on the decision nodes. a decision can be generated by inputting a policy to the decision tree. the decision can be compared to the prediction and the neural network re-trained based on a difference between the decision and the prediction.
