Twilio Inc. patent applications on 2025-05-22
Patent Applications by Twilio Inc. on May 22nd, 2025
Twilio Inc.: 2 patent applications
Twilio Inc. has applied for patents in the areas of G06Q10/0633 (Workflow analysis, 1), H04L41/0661 ({by reconfiguring faulty entities}, 1)
With keywords such as: system, specialized, disclosure, presents, multi-agent, utilizing, large, language, models, llms in patent application abstracts.
Top Inventors:
- Wesley Medford of San Francisco CA US (1 patents)
- Michael Lasso of San Francisco CA US (1 patents)
- Darya Shcharbinskaya of San Francisco CA US (1 patents)
- Jiahui Cai of San Francisco CA US (1 patents)
- Alireza Farasat of San Francisco CA US (1 patents)
Patent Applications by Twilio Inc.
Abstract: the disclosure presents a multi-agent ai system utilizing specialized large language models (llms) to automate and enhance software development workflows. this system integrates a memory-augmented generative pre-trained transformer (memgpt) agent for dynamic context management, a critic agent for semi-adversarial quality feedback, and other specialized agents for task delegation and execution. the memgpt agent interacts with an embedding storage to manage extended contextual information, enabling the system to handle complex software projects with enhanced accuracy and efficiency. this innovative approach significantly reduces manual intervention, streamlines the development process, and improves software quality, offering a robust solution to the challenges of modern software development environments.
20250168056. INTELLIGENT ANOMALY DETECTION RECOMMENDATION SYSTEMS (Twilio .)
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.