Twilio Inc. patent applications on July 25th, 2024
Patent Applications by Twilio Inc. on July 25th, 2024
Twilio Inc.: 2 patent applications
Twilio Inc. has applied for patents in the areas of H04L67/141 (1), H04L9/40 (1), H04L65/1033 (1), H04L65/1045 (1), H04L65/1069 (1) H04L67/141 (1), H04W4/12 (1)
With keywords such as: media, server, communication, client, user, measure, interaction, machine, between, and proxy in patent application abstracts.
Patent Applications by Twilio Inc.
Inventor(s): Brian Tarricone of San Francisco CA (US) for twilio inc., Yu Zhang of San Francisco CA (US) for twilio inc.
IPC Code(s): H04L67/141, H04L9/40, H04L65/1033, H04L65/1045, H04L65/1069, H04L65/1104, H04L65/612, H04L69/24
CPC Code(s): H04L67/141
Abstract: systems and methods for communicating media between a client and a media server. responsive to a communication initiation received by a signaling controller from a client system, the signaling controller invites a media server by providing an invitation to the media server. the media server is bridged with the client system by controlling a media proxy service to establish a media proxy between the client system and the media server by using client media parameters of the first communication initiation and media server media parameters provided by the media server responsive to the invitation. media is communicated between the external client system and the media server by using the established media proxy.
20240251225. PREDICTING USER INTERACTION WITH COMMUNICATIONS_simplified_abstract_(twilio inc.)
Inventor(s): Ankit Jaini of Belmont CA (US) for twilio inc., Ivan Senilov of Tallinn (EE) for twilio inc., Jordan Earnest of Farmington Hills MI (US) for twilio inc., Claire Electra Longo of Denver CO (US) for twilio inc., Jiahui Cai of Denver CO (US) for twilio inc., Chiung-Yi Tseng of Berkeley CA (US) for twilio inc.
IPC Code(s): H04W4/12, G06F11/34, G06N3/04, G06N3/08, H04W24/10
CPC Code(s): H04W4/12
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.
