AI & Computing Patent IP

AI IP — Algorithms, Architecture & Hardware

Artificial intelligence patents span a unique multi-layer intersection: software algorithm claims (neural network architectures, training methodologies, loss functions), hardware claims (NPU chip architectures, memory systems for AI workloads, interconnect designs), and application-specific claims (computer vision systems, NLP deployments, autonomous decision systems). Understanding the claim requires understanding all three layers simultaneously.

Large Language Models and Foundation Model IP

The transformer architecture — introduced in "Attention Is All You Need" (Vaswani et al., 2017, Google Brain) — has become the foundation of modern AI. Transformer patents cover attention mechanism variants (sparse attention in Longformer, linear attention in Performer, sliding window attention), positional encoding approaches (sinusoidal, RoPE, ALiBi), normalisation placement (pre-norm vs post-norm), and mixture-of-experts (MoE) scaling — where only a subset of model parameters are active per token, enabling massive parameter counts without proportional compute increase.

Fine-tuning methodology patents cover parameter-efficient approaches: LoRA (Low-Rank Adaptation — inserting trainable rank-decomposition matrices into frozen weight matrices), prefix tuning, prompt tuning, and adapter layers. Retrieval-Augmented Generation (RAG) — combining large language models with external knowledge retrieval systems — is generating rapid patent filing from Microsoft, Google, Anthropic, Cohere, and enterprise AI companies. Reasoning enhancement methods (chain-of-thought prompting, process reward models, MCTS-based inference-time compute scaling) are the newest active patent areas.

AI Hardware — NPU Architecture Patents

Dedicated Neural Processing Unit (NPU) silicon generates patents covering systolic array architecture (2D arrays of multiply-accumulate units with data flow between adjacent cells — the core compute primitive in Google TPU and many other AI accelerators), on-chip SRAM hierarchy for weight and activation storage (minimising off-chip DRAM bandwidth that dominates inference energy), dataflow scheduling (weight-stationary vs output-stationary vs no-local-reuse dataflow — determining which tensor is held stationary in registers), and hardware-software co-design approaches (compiler backends that map operations to specific hardware primitives).

Apple's Neural Engine, Qualcomm's Hexagon NPU, Google's Edge TPU, ARM's Ethos-U series, and NVIDIA's Tensor Cores all hold patent portfolios covering their specific architectural innovations. Key differentiation areas: INT8 and INT4 fixed-point MAC units, LPDDR5X and HBM3 memory interface optimisation, in-memory computing approaches using SRAM or emerging NVM, and chiplet-based disaggregation of large AI accelerators.

Computer Vision and Autonomous Perception

Computer vision patents cover convolutional neural network architecture variants (depthwise separable convolutions in MobileNet enabling on-device inference, EfficientNet compound scaling, Vision Transformer (ViT) and its efficient variants), object detection networks (the YOLO architecture family, DETR transformer-based detection, anchor-free CenterNet), and video understanding (temporal modelling, optical flow estimation, video transformer architectures).

Diffusion Models and Generative AI

Diffusion models — the foundation of Stable Diffusion, DALL-E 3, Midjourney, and Sora — generate patents on denoising score matching algorithms, U-Net architecture with attention for image synthesis, classifier-free guidance, latent diffusion (compressing to latent space before diffusion — Stable Diffusion's key efficiency insight), and video generation temporal consistency mechanisms. The generative AI patent landscape is one of the fastest-growing in all of technology, with Google, Meta, Stability AI, and OpenAI filing aggressively.