International business machines corporation (20240095516). NEURAL NETWORK TRAINING USING EXCHANGE DATA simplified abstract
Contents
- 1 NEURAL NETWORK TRAINING USING EXCHANGE DATA
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 NEURAL NETWORK TRAINING USING EXCHANGE DATA - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
NEURAL NETWORK TRAINING USING EXCHANGE DATA
Organization Name
international business machines corporation
Inventor(s)
Raghuveer Prasad Nagar of Kota (IN)
Prashant Pillai of Bangalore (IN)
Suvojyoti Sinha Ray of Hooghly (IN)
Pradeep Kumar Katherapally of Yemmiganur (IN)
NEURAL NETWORK TRAINING USING EXCHANGE DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240095516 titled 'NEURAL NETWORK TRAINING USING EXCHANGE DATA
Simplified Explanation
The abstract describes a computer-implemented process for training a neural network, which involves evaluating return data against a threshold, validating the data, generating insights, and using corrective actions to train the neural network.
- Data received from a return channel is evaluated against a threshold.
- If the threshold is satisfied, the return data is validated and processed to generate insights.
- A corrective action is generated based on the insights and used to train the neural network.
- The threshold is updated using the neural network.
Potential Applications
This technology could be applied in various fields such as healthcare for diagnosing diseases, finance for fraud detection, and manufacturing for quality control.
Problems Solved
This technology helps in improving the accuracy and efficiency of neural networks by incorporating feedback and corrective actions into the training process.
Benefits
The benefits of this technology include enhanced performance of neural networks, increased accuracy in decision-making, and improved adaptability to changing data patterns.
Potential Commercial Applications
- "Enhancing Neural Network Training Process for Improved Performance and Accuracy"
Possible Prior Art
There may be prior art related to the use of feedback and corrective actions in training neural networks, but further research is needed to identify specific examples.
What are the specific industries that could benefit from this technology?
Industries such as healthcare, finance, and manufacturing could benefit from this technology by improving their data analysis processes and decision-making capabilities.
How does this technology compare to traditional neural network training methods?
This technology offers a more dynamic and adaptive approach to training neural networks by incorporating feedback and corrective actions, leading to improved performance and accuracy.
Original Abstract Submitted
a computer-implemented process for training a neural network includes the following operations. return data received from a return channel is evaluated against a threshold. based upon the threshold being satisfied, the return data is validated, and the return data is cognitive processed to generate a return insight. using the neural network and based upon the return insight, a corrective action is generated. the neural network is trained using feedback generated based upon the corrective action. the threshold is then updated using the neural network.
