Cybersecurity

NETWORK ADJUSTMENT BASED ON MACHINE LEARNING END SYSTEM PERFORMANCE MONITORING FEEDBACK

Rochester Institute of Technology filed USPTO Patent Application US20260100880A1 on September 13, 2023. The invention discloses a system using machine learning to monitor end-system performance on a network and generate instructions to adjust network parameters based on comparing decision performance metrics to specifications. Patent application publications are informational documents that do not grant enforceable rights.

Dell Patent Tracks Multi-Cloud Nodes via Link-Local Network

USPTO published patent application US20260100879A1 assigned to Dell Products L.P. The application covers an information handling system configured to track provisioning of target nodes in multi-cloud environments using link-local addressing via mDNS and DNS Service Discovery protocols. The system advertises a URL endpoint via multicast and receives telemetry information from target nodes during provisioning.

Cryptographic Credential Re-issuance With Public Key Re-randomization

Cryptographic Credential Re-issuance With Public Key Re-randomization

Dynamic Token Security System for Network Environments

The USPTO published patent application US20260100837A1 assigned to Capital One Services, LLC. The application discloses systems and methods for improving security in network environments by dynamically generating access credentials offline that are resilient to impersonation attempts. The system retrieves token templates for remote services and uses multiple object-based storage devices to generate dynamic tokens.

Multi-Key Partial Decryption for Multi-Cloud Security

The USPTO published patent application US20260100834A1 for a multi-key partial decryption system enabling secure multi-cloud data encryption. The invention allows plaintext encryption using a public key, with multiple different private keys distributed to separate computing devices for successive partial decryption operations. Invented by Vishal Jaikishan CHANDWANI, the system enhances security by requiring decryption across multiple platforms using distinct keys.

Intel Corporation Multimodal LLM Audio Video Tokenization Patent Application

USPTO published patent application US20260099522A1 by Intel Corporation inventors Kuba Lopatka, Adam Kupryjanow, and Tomasz Szmelczynski for power-efficient tokenization and long-context storage of audio and video data for multimodal large language models. The application covers systems with specialized subsystems for receiving input signals, generating discrete tokens, and buffering tokens for durations ranging from seconds to hours.

Expert Selection System for MoE Large Language Models

USPTO published patent application US20260099697A1 for an expert selection system in Mixture of Experts (MoE) large language models. The system includes a global selector that manages selection between global and local modes, and a pre-fetcher that pre-loads selected global expert sets from main memory into faster cache memory. Inventors include Usman Sajid, Marie Mai Nguyen, Shuyi Pei, Younghoon Kim, and Rekha Pitchumani. The filing date was October 1, 2025, under application number 19347703.

Syntiant Neuromorphic IC Sensor Processing Patent Application

USPTO published patent application US20260100186A1 assigned to Syntiant, covering sensor-processing systems with neuromorphic integrated circuits for keyword spotting. The application was filed December 11, 2025, with inventors Kurt F. Busch, Jeremiah H. Holleman III, Pieter Vorenkamp, Stephen W. Bailey, and David Christopher Garrett. The patent describes systems using neuromorphic ICs with neural networks to extract features from sensor data and arrive at actionable decisions for keyword spotting applications.

Multi-Language Model-Based Filtering and Labeling of Datasets

The USPTO published patent application US20260100887A1 assigned to Cisco Technology, Inc. The patent covers methods for labeling telemetry data from computer network entities using a combination of N-gram models and large language models. The labeled data is then used to train predictive maintenance models for network infrastructure. The application was filed on July 28, 2025.

Distributed Training of Compressed Machine Learning Models

USPTO published patent application US20260099761A1 for an apparatus enabling distributed training of compressed machine learning models using quantization-based parameter compression. The invention manages compressed ML model parameters across distributed systems by cycling between compression and decompression states, allowing efficient parameter synchronization over networks. The application was filed on October 7, 2024.