# How to Automatically Expand Neural Networks by Adding New Nodes > A method for growing artificial intelligence models by identifying underperforming parts of a network and adding new nodes based on the behavior of existing ones. - **Patent:** US 10832138 - **Original title:** Method and apparatus for extending neural network - **Owner:** Samsung Electronics Co Ltd - **Granted:** 2020 - **Status:** Active - **Times cited:** 3 - **Field:** ai_ml, consumer_electronics, semiconductors ## What it does This patent describes a way to make neural networks smarter by letting them grow dynamically. Instead of building a fixed-size model, the system monitors how often nodes 'fire' (activation frequency) and how consistently they behave (activation entropy). When the network hits a performance plateau, the processor identifies a specific node that needs help and adds a new one to that layer. The new node inherits some of its connection weights from the original node, while the rest are set to initial values, allowing the network to adapt to new data without starting training from scratch. ## What it does NOT cover - Does not cover static neural networks that do not add nodes during or after training. - Does not cover methods of network expansion that rely on random weight initialization for all new nodes. - Does not cover pruning or shrinking techniques that remove nodes without adding new ones. - Does not cover hardware-specific implementations that do not use activation frequency or entropy as selection criteria. ## The clever bit The system uses 'activation entropy'—a measure of how often a node flips between active and inactive states—as a key metric to decide where to expand, rather than just looking at raw performance metrics. ## Real-world examples 1. Adaptive deep learning models for mobile devices 2. Dynamic neural network architectures in edge computing 3. On-device AI model optimization ## Why it matters Training large AI models is computationally expensive and time-consuming. By allowing a network to grow only where it is needed, this method offers a way to improve model accuracy while potentially saving resources compared to training massive, static models from the ground up. It represents a shift toward more efficient, adaptive machine learning architectures. ## Frequently asked questions ### What does How to Automatically Expand Neural Networks by Adding New Nodes cover? A method for growing artificial intelligence models by identifying underperforming parts of a network and adding new nodes based on the behavior of existing ones. ### Who owns patent US 10832138? Samsung Electronics Co Ltd owns this patent, granted in 2020. ### When does this patent expire? This patent is expected to expire on November 10, 2040, when the invention enters the public domain. ### What is patent US 10832138 cited by? This patent has been cited by 3 later patents that build on its ideas. ### What problem does this patent solve? Training large AI models is computationally expensive and time-consuming. By allowing a network to grow only where it is needed, this method offers a way to improve model accuracy while potentially saving resources compared to training massive, static models from the ground up. It represents a shift toward more efficient, adaptive machine learning architectures. ### What does this patent NOT cover? Does not cover static neural networks that do not add nodes during or after training. **Full plain-English explainer:** https://patentbrief.org/patent/us/10832138/gpt-language-model-pre-training **Original patent:** https://patents.google.com/patent/US10832138 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [How to Update AI on Small Devices with Slow Internet](https://patentbrief.org/patent/us/20250363357/systems-and-methods-for-deploying-and-updating-neural-networks-at-the-edge-of-a-) — This patent describes a method for efficiently updating artificial intelligence models on small, internet-connected devices, like smart cameras, by sending only the changes, or 'patches,' instead of the entire updated model, which saves bandwidth. - [Adapting AI Models to Fit Device Resources](https://patentbrief.org/patent/us/20220383078/data-processing-method-and-related-device) — This patent describes how a computer system can automatically shrink a large artificial intelligence model, specifically a "transformer" type, to fit the available computing power of a phone or other device. - [How to Save and Reuse Skills Learned by Artificial Intelligence Hardware](https://patentbrief.org/patent/us/10410117/method-and-a-system-for-creating-dynamic-neural-function-libraries) — A method for capturing the internal settings of a neuromorphic AI chip after it learns a task, allowing that 'skill' to be exported and loaded onto another AI chip. - [Making AI Smarter by Focusing on Unsure 'Nodes'](https://patentbrief.org/patent/us/12423586/training-nodes-of-a-neural-network-to-be-decisive) — This 2025 patent from D5AI LLC describes a way to train AI models more effectively by boosting the learning signal for 'nodes' that aren't making clear decisions on data. - [Making Neural Networks Faster by Skipping Unnecessary Calculations](https://patentbrief.org/patent/us/11429864/dlss-deep-learning-super-sampling) — A method to speed up AI training by keeping data sparse, meaning it ignores zeros to save memory and processing power during both forward and backward passes.