Salesforce, inc. (20240303280). TECHNIQUES FOR AUTOMATIC SUBJECT LINE GENERATION simplified abstract

TECHNIQUES FOR AUTOMATIC SUBJECT LINE GENERATION

Organization Name

salesforce, inc.

Inventor(s)

Vera Serdiukova of Mountain View CA (US)

Tong Niu of San Jose CA (US)

Yingbo Zhou of Palo Alto CA (US)

Amrutha Krishnan of San Bruno CA (US)

Abigail Kutruff of New York NY (US)

Allen Hoem of Indianapolis IN (US)

Matthew Wells of Indianapolis IN (US)

Andrew Hoblitzell of Greenwood IN (US)

Swetha Pinninti of Zionsville IN (US)

Brian Brechbuhl of Carmel IN (US)

Annie Zhang of Palo Alto CA (US)

TECHNIQUES FOR AUTOMATIC SUBJECT LINE GENERATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240303280 titled 'TECHNIQUES FOR AUTOMATIC SUBJECT LINE GENERATION

The abstract describes a method for data processing in a content generation service, involving receiving a reference string from a cloud client, generating candidate strings based on similarity metrics calculated using a machine learning model, selecting candidate strings based on these metrics, displaying them to the client, receiving feedback, and selecting at least one candidate string.

• Machine learning model used to calculate similarity metrics
• Training of the model using a dataset of annotated strings
• Filtering of candidate strings based on similarity metrics
• Displaying selected candidate strings to the cloud client
• Receiving feedback on the displayed candidate strings

Potential Applications: - Content generation services - Data processing in cloud computing - Machine learning applications in text analysis

Problems Solved: - Efficient selection of relevant candidate strings - Improved user experience in content generation - Automated data processing in cloud services

Benefits: - Enhanced accuracy in selecting candidate strings - Time-saving in content generation - Improved user interaction with cloud services

Commercial Applications: Title: "Enhanced Content Generation Method for Cloud Services" This technology can be used in various industries such as marketing, e-commerce, and social media platforms to streamline content creation processes and improve user engagement.

Prior Art: Further research can be conducted in the field of machine learning models for text analysis and data processing in cloud computing services.

Frequently Updated Research: Stay updated on advancements in machine learning algorithms for text analysis and data processing in cloud services.

Questions about the technology: 1. How does this method improve the efficiency of content generation in cloud services? 2. What are the potential limitations of using machine learning models for selecting candidate strings in this context?

Original Abstract Submitted

a method for data processing is described. the method includes receiving an indication of a reference string from a cloud client of a content generation service. the method further includes generating multiple candidate strings associated with the reference string based on using a machine learning model to calculate similarity metrics between the reference string and the multiple candidate strings, where the machine learning model is trained using a dataset of annotated strings. the method further includes selecting a quantity of the candidate strings based on filtering the multiple candidate strings according to the similarity metrics. the method further includes causing the quantity of candidate strings to be displayed at the cloud client. the method further includes receiving feedback associated with the quantity of candidate strings and a selection of at least one candidate string displayed at the cloud client.