Company patents
CAMBRICON TECHNOLOGIES CORPORATION LIMITED
CAMBRICON TECHNOLOGIES CORPORATION LIMITED's patent strategy reveals a surprising and consistent decline across its entire portfolio, including its core focus on Machine Learning & AI, which accounts for 84.5% of its patents but has seen a -33.3% YoY decline in 2025 and a -75.0% decline so far in 2026. This broad-based reduction, with categories like Operating Systems & Program Control and Multi-Chip & 3D Assemblies experiencing significant drops of -63.6% and -50.0% respectively in 2025, suggests a notable shift away from active patenting across its key computing and semiconductor interests.
Patent Trend by Technology Area
Yearly patent publications since 2023
Product themes
Product-level themes inferred from filings since 2023, with category chips showing where each theme appears. Select a theme to filter the patents below.
84 US filings (since 2023) · 12 categories · 13 themes
Novel hardware designs and processing pipelines tailored for specific computational tasks, such as graphics rendering, neural network operations, or matrix transformations, often involving custom circuits, memory arrays, or data flow mechanisms.
Specialized hardware, architectural designs, and computational methods to improve the speed, efficiency, and security of artificial intelligence and machine learning model execution, particularly for inference and data processing.
Methods and apparatus for improving the visual fidelity, resolution, or compression efficiency of video signals, often through advanced processing, up-scaling, or neural network-based filters.
Methods and systems for efficiently reducing the size of digital data, often employing adaptive techniques, neural networks, or temporal modeling, to achieve high compression ratios while preserving data quality. Includes entropy coding.
Developing and applying machine learning algorithms that leverage quantum computing principles, such as quantum circuits or autoencoders, for tasks like simulation or data processing.
Utilizing machine learning, particularly deep learning, to analyze medical data such as images, sensor readings, or physiological signals for disease prediction, diagnosis, or treatment assessment.
Techniques employed within compilers or related tools to analyze program code, identify entities for compilation, and optimize execution on target hardware, including reconfigurable systems, to improve performance or resource efficiency.
Techniques for combining and analyzing information from multiple distinct data modalities (e.g., text, image, video, audio, sensor data) to derive richer insights or improve system performance and decision-making.
Techniques for generating human-like text or other content using large pre-trained models, often involving prompt engineering, speculative decoding, or multi-modal inputs for content creation.
Methods for training machine learning models across multiple decentralized devices or servers while keeping data localized, often involving aggregation of model parameters and secure communication.
Systems and methods for automating the lifecycle of machine learning models, including pipeline deployment, model management, versioning, and configuring for different inference environments.
Hardware and control techniques for optimizing memory access latency, ensuring data integrity, and managing storage resources efficiently. This includes error correction, read/write voltage control, and intelligent data placement or in-memory computation.
Methods and systems for efficiently allocating computing resources, balancing workloads, and managing power states to improve performance, reduce energy consumption, or enhance reliability in computing platforms.
Patents
Showing 1-10 of 11
Video Quality & Encoding Optimization