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.
Original patent title: “Method and apparatus for extending neural network”
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. Granted to Samsung Electronics Co Ltd in 2020 with 27 claims and 3 forward citations.
Key facts
Coverage
What does this patent actually cover?
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.
The gap
What does this patent 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.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
What made this novel
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.
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
Adaptive deep learning models for mobile devices
Dynamic neural network architectures in edge computing
On-device AI model optimization
Why it matters
The bigger picture
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.
Filed
May 5, 2015
Granted
November 10, 2020
Market context
Who's building on this
Companies in this space
Samsung Electronics continues to lead in mobile AI research, integrating adaptive learning techniques into their Exynos processors and mobile software. Other major players in the adaptive AI space, such as Google and NVIDIA, also explore dynamic network growth to optimize performance on hardware with limited power.
Market impact
This patent contributes to the broader trend of 'efficient AI,' which is critical for running sophisticated machine learning models on smartphones and IoT devices. By enabling models to evolve, it helps manufacturers provide better user experiences without needing constant cloud connectivity or massive server-side compute resources.
Claim 1 — Plain English
What this patent covers
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.
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.
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.
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
PatentBrief Score
Impact Score
Strong
Citation count
12/40
Early citations
Claim breadth
18/20
Very broad protection
Recency
10/20
Granted 5–10 years ago
Assignee scale
20/20
Major company or institution
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$78K – $250K
Midpoint $156K · 8.9 yr remaining · industry ×1.6
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
The original legal language
Original claims
27 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
CHOI, H. (2020). How to Automatically Expand Neural Networks by Adding New Nodes (U.S. Patent No. 10,832,138). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/10832138/gpt-language-model-pre-training
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
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.
Same assignee
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