17948129. NEURAL NETWORK TRAINING USING EXCHANGE DATA simplified abstract (International Business Machines Corporation)
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 17948129 titled 'NEURAL NETWORK TRAINING USING EXCHANGE DATA
Simplified Explanation
The computer-implemented process described in the abstract involves training a neural network by evaluating return data against a threshold, validating the data, processing it to generate insights, generating corrective actions based on these insights, and updating the threshold using the neural network.
- Return data is evaluated against a threshold.
- Validated return data is cognitively processed to generate insights.
- Corrective actions are generated based on the insights.
- The neural network is trained using feedback from the corrective actions.
- The threshold is updated using the neural network.
Potential Applications
This technology could be applied in various fields such as healthcare, finance, marketing, and manufacturing for predictive analytics, anomaly detection, and optimization processes.
Problems Solved
This technology helps in improving the accuracy and efficiency of neural network training by incorporating feedback from cognitive processing of return data.
Benefits
The benefits of this technology include enhanced performance of neural networks, improved decision-making capabilities, and increased automation of processes.
Potential Commercial Applications
"Enhancing Neural Network Training with Cognitive Processing" could be used in industries such as healthcare for patient diagnosis, finance for fraud detection, marketing for customer segmentation, and manufacturing for quality control.
Possible Prior Art
One possible prior art for this technology could be the use of machine learning algorithms for training neural networks based on feedback mechanisms.
What are the specific steps involved in the cognitive processing of return data to generate insights?
The specific steps involved in the cognitive processing of return data to generate insights include evaluating the data against predefined criteria, analyzing patterns or trends, identifying anomalies or outliers, and extracting meaningful information for decision-making.
How does the neural network update the threshold based on the feedback received from the corrective actions?
The neural network updates the threshold by adjusting the parameters or weights of the network based on the feedback received from the corrective actions. This process helps in fine-tuning the network to improve its performance and accuracy over time.
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.
