公开(公告)号：US11985571B2

公开(公告)日：2024-05-14

申请号：US17449404

申请日：2021-09-29

Applicant: Twilio Inc.

Inventor： Ankit Jaini ,  Ivan Senilov ,  Jordan Earnest ,  Claire Electra Longo ,  Jiahui Cai ,  Chiung-Yi Tseng

IPC: H04W4/12 ,  G06F11/34 ,  G06N3/04 ,  G06N3/08 ,  H04W24/10

CPC classification number: H04W4/12 ,  G06F11/3438 ,  G06N3/04 ,  G06N3/08 ,  H04W24/10

Abstract: 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.

公开(公告)号：US20250168056A1

公开(公告)日：2025-05-22

申请号：US18510982

申请日：2023-11-16

Applicant: TWILIO INC.

Inventor： Michael Lasso ,  Darya Shcharbinskaya ,  Jiahui Cai ,  Alireza Farasat ,  Dmitry Rusanovsky ,  Amit Mahajan ,  Joshua Ramsden-Pogue ,  Mariana Simona Mihai ,  Peter Janovsky

IPC: H04L41/0659 ,  H04L41/0604 ,  H04L43/062 ,  H04L43/16

Abstract: A computing device can identify an anomaly based on metadata associated with network traffic messages corresponding to a particular account. After identifying the anomaly, the computing device can determine a failure score for the network traffic messages representing a failure rate for the message traffic. The computing device can determine a fluctuation score by comparing the network traffic messages in a current time period to a previous time period. The computing device can determine a sparsity score by analyzing the message traffic in a previous period of time. The computing device can generate an anomaly impact score based on the failure score, the fluctuation score, and the sparsity score and assign the anomaly to a severity bin based on the anomaly impact score.

公开(公告)号：US20240251225A1

公开(公告)日：2024-07-25

申请号：US18626612

申请日：2024-04-04

Applicant: Twilio Inc.

Inventor： Ankit Jaini ,  Ivan Senilov ,  Jordan Earnest ,  Claire Electra Longo ,  Jiahui Cai ,  Chiung-Yi Tseng

IPC: H04W4/12 ,  G06F11/34 ,  G06N3/04 ,  G06N3/08 ,  H04W24/10

CPC classification number: H04W4/12 ,  G06F11/3438 ,  G06N3/04 ,  G06N3/08 ,  H04W24/10

Abstract: 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.

公开(公告)号：US20230099888A1

公开(公告)日：2023-03-30

申请号：US17449404

申请日：2021-09-29

Applicant: Twilio Inc.

Inventor： Ankit Jaini ,  Ivan Senilov ,  Jordan Earnest ,  Claire Electra Longo ,  Jiahui Cai ,  Chiung-Yi Tseng

IPC: H04W4/12 ,  H04W24/10 ,  G06F11/34 ,  G06N3/08 ,  G06N3/04

Abstract: 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.