18326124. TECHNIQUES FOR AUTOMATIC SUBJECT LINE GENERATION simplified abstract (Salesforce, Inc.)

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 18326124 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 multiple candidate strings based on similarity metrics calculated using a machine learning model, selecting candidate strings based on these metrics, displaying them to the client, and receiving feedback on the selection.

• Machine learning model used to calculate similarity metrics between reference string and candidate strings
• Training dataset of annotated strings used to train the machine learning model
• Filtering of candidate strings based on similarity metrics to select a quantity to display
• Displaying the selected candidate strings to the cloud client for feedback
• Receiving feedback on the displayed candidate strings

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

Problems Solved: - Efficient selection of candidate strings based on similarity metrics - Improved user experience in content generation services - Automated data processing in cloud environments

Benefits: - Enhanced accuracy in selecting relevant candidate strings - Time-saving in content generation processes - Improved user satisfaction with displayed options

Commercial Applications: Title: "Enhanced Data Processing Method for Content Generation Services" This technology can be applied in various industries such as marketing, e-commerce, and social media platforms to streamline content creation processes, improve user engagement, and enhance overall efficiency in data processing tasks.

Questions about the technology: 1. How does the machine learning model calculate similarity metrics between the reference string and candidate strings? 2. What are the potential implications of this method for the future of content generation services?

Frequently Updated Research: Stay updated on advancements in machine learning models for calculating similarity metrics in data processing tasks to further enhance the efficiency and accuracy of content generation services.

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.