Trademark Overview
On Tuesday, August 19, 2025, a trademark application was filed for HEMLOCK with the United States Patent and Trademark Office. The USPTO has given the HEMLOCK trademark a serial number of 99344996. The federal status of this trademark filing is PUBLICATION/ISSUE REVIEW COMPLETE as of Tuesday, March 24, 2026. This trademark is owned by CIGNAL LLC. The HEMLOCK trademark is filed in the Computer & Software Products & Electrical & Scientific Products and Computer & Software Services & Scientific Services categories with the following description:
Downloadable computer software for conducting artificial intelligence and machine learning (AI/ML) security and defense using large and small language models, vision models, audio models, and multi-modal models, and other machine learning models, algorithms, and agents, for conducting AI/ML red-teaming using adversarial testing, prompt injection, data poisoning, and model inversion, for conducting blue team AI/ML model defenses utilizing adversarial robustness, input filtering, data noising, and model monitoring, for performing AI/ML training, fine-tuning, retrieval-augmented generation (RAG), inference, and neural rendering, and for conducting AI/ML testing and evaluation, AI/ML model benchmarking and ratings, data science, cybersecurity, cloud security, network security, software testing and evaluation, security compliance management, threat intelligence, anomaly detection, threat hunting, intrusion detection and prevention, incident response, digital forensics, reverse engineering, ...
Software as a service (SAAS) services featuring software for conducting artificial intelligence and machine learning (AI/ML) security and defense using large and small language models, vision models, audio models, and multi-modal models, and other machine learning models, algorithms, and agents, for conducting AI/ML red-teaming using adversarial testing, prompt injection, data poisoning, and model inversion, for conducting blue team AI/ML model defenses utilizing adversarial robustness, input filtering, data noising, and model monitoring, for performing AI/ML training, fine-tuning, retrieval-augmented generation (RAG), inference, and neural rendering, and for conducting AI/ML testing and evaluation, AI/ML model benchmarking and ratings, data science, cybersecurity, cloud security, network security, software testing and evaluation, security compliance management, threat intelligence, anomaly detection, threat hunting, intrusion detection and prevention, incident response, digital forens...
