Salesforce, inc. (20240184855). TRAINING OF PREDICTION NETWORK FOR AUTOMATIC CORRELATION OF INFORMATION simplified abstract
Contents
- 1 TRAINING OF PREDICTION NETWORK FOR AUTOMATIC CORRELATION OF INFORMATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 TRAINING OF PREDICTION NETWORK FOR AUTOMATIC CORRELATION OF INFORMATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Simplified Explanation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about the Technology
- 1.13 Original Abstract Submitted
TRAINING OF PREDICTION NETWORK FOR AUTOMATIC CORRELATION OF INFORMATION
Organization Name
Inventor(s)
Nachiketa Mishra of San Francisco CA (US)
Ziwei Chen of Sodermanland County (SE)
TRAINING OF PREDICTION NETWORK FOR AUTOMATIC CORRELATION OF INFORMATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240184855 titled 'TRAINING OF PREDICTION NETWORK FOR AUTOMATIC CORRELATION OF INFORMATION
Simplified Explanation
Simplified Explanation
The method described in the patent application involves training a prediction network using both machine-generated input and user-generated input. It calculates scores to determine the correlation between the two types of input and adjusts the network parameters accordingly.
- The method receives machine-generated input and user-generated input for training a prediction network.
- It generates a score representing the correlation between the types of input.
- The model analyzes the inputs to categorize them and outputs a confidence score for each input belonging to a category.
- Based on the scores, the method adjusts the parameters of the prediction network.
Potential Applications
This technology could be applied in various fields such as:
- Natural language processing
- Image recognition
- Fraud detection systems
Problems Solved
The technology addresses the following issues:
- Improving the accuracy of prediction models
- Enhancing the correlation between machine-generated and user-generated input
- Optimizing the parameters of prediction networks
Benefits
The benefits of this technology include:
- Increased efficiency in training prediction models
- Enhanced accuracy in categorizing inputs
- Improved performance of prediction networks
Commercial Applications
- Predictive analytics software
- Customer behavior analysis tools
- Personalized recommendation systems
Prior Art
There is prior art related to training prediction networks using machine-generated and user-generated input, but this specific method of correlating inputs and adjusting network parameters based on scores is innovative.
Frequently Updated Research
There may be ongoing research on optimizing prediction networks using a combination of machine-generated and user-generated input, which could further enhance the capabilities of this technology.
Questions about the Technology
How does this method improve the accuracy of prediction models?
This method improves accuracy by correlating machine-generated and user-generated input to categorize them effectively, leading to better parameter adjustments in the prediction network.
What are the potential challenges in implementing this technology in real-world applications?
One potential challenge could be the scalability of the method to handle large volumes of data efficiently while maintaining high accuracy levels.
Original Abstract Submitted
in some embodiments, a method receives machine generated input and user generated input for training a model of a prediction network. a link between a type of machine generated input and a type of user generated input. a first score that represents a correlation between the type of machine generated input and the type of user generated input is generated. the method analyzes the machine generated input and the user generated input using the model of the prediction network to correlate the machine generated input and the user generated input to a category. a second score associated with a confidence that the machine generated input or the user generated input belongs to the category is output. the method adjusts a parameter of the prediction network based on the first score and the second score.
