Salesforce, inc. (20240184661). PREDICTION NETWORK FOR AUTOMATIC CORRELATION OF INFORMATION simplified abstract

PREDICTION NETWORK FOR AUTOMATIC CORRELATION OF INFORMATION

Organization Name

salesforce, inc.

Inventor(s)

Nachiketa Mishra of San Francisco CA (US)

Ziwei Chen of Sodermanland County (SE)

PREDICTION NETWORK FOR AUTOMATIC CORRELATION OF INFORMATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240184661 titled 'PREDICTION NETWORK FOR AUTOMATIC CORRELATION OF INFORMATION

Simplified Explanation

The patent application describes a method that utilizes a trained model for a prediction network to process machine-generated input and correlate it to a set of categories. The input is automatically generated by an application, and the method assigns a score to each category based on the confidence of the input being associated with it. The highest scoring category is selected, and a resolution is output based on user-generated input linked to that category.

• Trained model for prediction network
• Correlation of machine-generated input to categories
• Automatic generation of input by an application
• Scoring of categories based on input confidence
• Selection of highest scoring category for resolution output

1. 1. 1. Potential Applications:

- Personalized recommendations - Content filtering - Automated decision-making systems

1. 1. 1. Problems Solved:

- Streamlining categorization processes - Enhancing user experience with tailored resolutions - Improving efficiency in handling machine-generated input

1. 1. 1. Benefits:

- Increased accuracy in predicting user preferences - Time-saving in providing relevant resolutions - Customized solutions based on user behavior

1. 1. 1. Commercial Applications:
         1. Predictive Resolution System: Revolutionizing customer service by offering personalized solutions based on machine-generated input and user behavior.

1. 1. 1. Prior Art:

No prior art information available at this time.

1. 1. 1. Frequently Updated Research:

Stay updated on advancements in machine learning algorithms for improved categorization and resolution prediction.

1. 1. 1. 1. Questions about Machine Learning Prediction Network:
            1. How does the trained model differentiate between categories in the prediction network?

The trained model uses features extracted from the input data to distinguish patterns and assign scores to each category based on the input's correlation.

1. 1. 1. 1. 1. What are the potential challenges in integrating user-generated input with machine-generated input for resolution prediction?

Integrating user-generated input with machine-generated input may pose challenges in ensuring the accuracy and relevance of the resolutions provided, as the system needs to effectively link the two types of input for optimal results.

Original Abstract Submitted

in some embodiments, a method receives a trained model for a prediction network, wherein the trained model was trained based on machine generated input and user generated input being correlated to a plurality of categories. machine generated input is input into the prediction network. the machine generated input is automatically generated based on an execution of an application. the method correlates the machine generated input to one or more of the plurality of categories using the trained model. a score for a respective category is output based on a confidence of the machine generated input being associated with the category. a category is selected from the plurality of categories based on the respective score of the category. the method outputs a resolution for the machine generated input based on the category. the resolution is determined from user generated input that is associated with the category.