Twilio inc. (20240251225). PREDICTING USER INTERACTION WITH COMMUNICATIONS simplified abstract
Contents
PREDICTING USER INTERACTION WITH COMMUNICATIONS
Organization Name
Inventor(s)
Ankit Jaini of Belmont CA (US)
Jordan Earnest of Farmington Hills MI (US)
Claire Electra Longo of Denver CO (US)
Chiung-Yi Tseng of Berkeley CA (US)
PREDICTING USER INTERACTION WITH COMMUNICATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240251225 titled 'PREDICTING USER INTERACTION WITH COMMUNICATIONS
Simplified Explanation: The patent application describes a system where a machine learning model is trained using annotated communication data to predict user interaction with future communications.
Key Features and Innovation:
- Machine learning model trained with annotated communication data
- Prediction of user interaction with future communications
- Ability to alter communications based on predicted user interaction
- Automatic sending of communications based on predicted user interaction exceeding a threshold
Potential Applications: This technology could be used in marketing campaigns, customer service interactions, and personalized messaging systems.
Problems Solved: This technology addresses the challenge of predicting user interaction with communications and optimizing messaging strategies accordingly.
Benefits:
- Improved user engagement
- Enhanced communication effectiveness
- Time and cost savings in communication strategies
Commercial Applications: Potential commercial applications include targeted advertising, customer relationship management, and automated messaging systems for businesses.
Prior Art: Prior research in machine learning models for predicting user behavior in various contexts could be relevant to this technology.
Frequently Updated Research: Stay updated on advancements in machine learning algorithms for predicting user behavior and communication optimization.
Questions about the Technology: 1. How does this technology improve user engagement compared to traditional communication strategies? 2. What are the potential limitations of using machine learning models to predict user interaction with communications?
Original Abstract Submitted
a machine learning model may be trained using annotated communications data. each communication (e.g., a short messaging system (sms) message or email) is annotated with a measure of user interaction. the machine learning model is thus trained to predict a measure of user interaction for future communications. before sending future communications, at least a portion of the communication is provided to the trained machine learning model to predict the expected measure of user interaction with the communication. in response to the prediction, the sender of the communication may alter the communication. the system may automatically send the communication if the predicted measure of user interaction exceeds a predetermined threshold and only prompt the user if the predicted measure of user interaction does not exceed the predetermined threshold.
