USPTO Patent Grant: Radio Signal Identification System Learning

The USPTO has granted patent US12585953B2 to Virginia Tech Intellectual Properties, Inc. for methods and systems related to machine-learned identification of radio frequency (RF) signals. The patent covers techniques for training and deploying AI models to classify RF signals based on their characteristics and environmental factors.

Samsung Patent for Optimal Neural Network Model Generation

The USPTO has granted Samsung Electronics a patent for a method to generate optimal neural network models. The patent describes a process for identifying and retaining the most effective layers of a neural network to improve efficiency and accuracy.

USPTO Patent Grant: Neural Processing Device and Pruning Method

The USPTO has granted patent US12585948B2 to Rebellions Inc. for a neural processing device and a method for pruning thereof. The patent describes a system that partitions and prunes weight matrices for efficient processing.

USPTO Patent US12585947B2: Modifying Computational Graphs

The USPTO has granted patent US12585947B2 to Google LLC for methods of modifying computational graphs to efficiently handle communication between devices. This patent introduces send and receive nodes to subgraphs, shifting communication coordination away from the backend.

Huawei Patent for Parameter Optimization Method Granted

The USPTO has granted Huawei Technologies Co., Ltd. a patent for a parameter optimization method applicable to neural network models. The patent, filed in 2022, describes a method for adjusting model parameters based on segmented Kronecker factor matrices.

USPTO Patent Grant: Layered Gradient Accumulation for Machine Learning

The USPTO has granted patent US12585929B2 to ServiceNow, Inc. for a method of layered gradient accumulation and modular pipeline parallelism to improve machine learning model training. The patent details a technique for distributing model layers across compute nodes and optimizing data flow during training.

USPTO Patent US12585928B2: Hardware for Neural Network Activation Sparsity

The USPTO has granted patent US12585928B2 to Numenta, Inc. for a hardware accelerator designed to efficiently introduce activation sparsity in neural networks. This innovation aims to improve the performance and efficiency of artificial intelligence hardware.

USPTO Patent Grant: Integrated Memory System for Neural Networks

The USPTO has granted patent US12585927B2 for an integrated memory system for neural networks, developed by The Board of Trustees of the University of Illinois. The patent covers a memory module designed to compute, store, and sample neural network weights, optimizing precision for machine learning data streams.

Microsoft Patent: Neural Network Training Precision Adjustment

The USPTO has granted Microsoft Technology Licensing, LLC, patent US12585926B2 for a method of adjusting numerical precision and topology during neural network training based on performance metrics. This patent covers techniques for optimizing training efficiency and accuracy using block floating-point formats and accelerator hardware.

USPTO Patent Grant: Spike Neural Network Circuit for Error Prevention

The USPTO has granted a patent (US12585925B2) for a spike neural network circuit designed to prevent errors in charge calculation. The patent, assigned to Electronics and Telecommunications Research Institute, was filed on June 7, 2022, and is set to be granted on March 24, 2026.