Making AI Smarter by Focusing on Unsure 'Nodes'
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.
Original patent title: “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. Granted to D5AI in 2025 with 38 claims and 1 forward citation, and it is expected to expire in 2045.
Key facts
Coverage
What does this patent actually cover?
This patent explains a method for training artificial intelligence, specifically neural networks. When training an AI, it looks at individual 'nodes' (like tiny decision-makers within the AI) and checks if they are 'decisive' for a given piece of training data. If a node isn't clearly deciding one way or another – meaning its output doesn't strongly lean towards a specific outcome – the system then amplifies the learning signal for that node. This amplification is done by multiplying the 'partial derivative' (a measure of how much the AI's error would change if the node's output changed slightly) by a factor greater than 1.0. This helps the AI learn faster by focusing on the parts that are struggling to make a clear choice, using common training algorithms like stochastic gradient descent.
The gap
What does this patent NOT cover?
- Training methods that do not identify if a target node is 'not decisive'.
- Methods where the learning signal for an undecided node is not multiplied by a factor greater than 1.0.
- Training that only applies to neural networks with fewer than two layers.
- Methods that do not involve a 'feed-forward' computation phase.
- Methods that do not involve a 'back-propagation' computation phase.
- Training where the 'partial derivative' is not computed for the target node.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
What made this novel
The innovation lies in identifying AI 'nodes' that are ambivalent or uncertain about their decisions on specific data points, and then specifically boosting their learning rate. Instead of treating all nodes equally during training, it intelligently targets the ones that need more guidance.
The Patent Drawing
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
Training large language models
Improving image recognition AI
Developing AI for autonomous vehicles
Optimizing AI in recommendation systems
Why it matters
The bigger picture
As AI models become more complex, efficiently training them is crucial. This patent addresses a core challenge in AI development: ensuring that all parts of the model learn effectively. By focusing on nodes that are 'undecided,' this method aims to speed up the learning process and potentially lead to more robust and accurate AI systems, which are increasingly vital across many industries.
Filed
January 30, 2025
Granted
September 23, 2025
Market context
Who's building on this
Companies in this space
D5AI LLC, the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is likely the primary entity developing this technology. Given the focus on AI training, major AI research labs at companies like Google, Meta, and OpenAI, as well as specialized AI hardware companies, would be interested in similar techniques.
Market impact
This patent could influence the development of more efficient AI training frameworks and hardware. By potentially reducing training time or improving model accuracy, it could lower the cost and increase the accessibility of advanced AI capabilities, impacting sectors from cloud computing to specialized AI applications.
Claim 1 — Plain English
What this patent covers
This patent explains a method for training artificial intelligence, specifically neural networks. When training an AI, it looks at individual 'nodes' (like tiny decision-makers within the AI) and checks if they are 'decisive' for a given piece of training data. If a node isn't clearly deciding one way or another – meaning its output doesn't strongly lean towards a specific outcome – the system then amplifies the learning signal for that node. This amplification is done by multiplying the 'partial derivative' (a measure of how much the AI's error would change if the node's output changed slightly) by a factor greater than 1.0. This helps the AI learn faster by focusing on the parts that are struggling to make a clear choice, using common training algorithms like stochastic gradient descent.
The clever bit
The innovation lies in identifying AI 'nodes' that are ambivalent or uncertain about their decisions on specific data points, and then specifically boosting their learning rate. Instead of treating all nodes equally during training, it intelligently targets the ones that need more guidance.
What it does not cover
- Training methods that do not identify if a target node is 'not decisive'.
- Methods where the learning signal for an undecided node is not multiplied by a factor greater than 1.0.
- Training that only applies to neural networks with fewer than two layers.
- Methods that do not involve a 'feed-forward' computation phase.
- Methods that do not involve a 'back-propagation' computation phase.
- Training where the 'partial derivative' is not computed for the target node.
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
Patent enters public domain
PatentBrief Score
Impact Score
Moderate
Citation count
6/40
Early citations
Claim breadth
20/20
Very broad protection
Recency
20/20
Granted within 5 years
Assignee scale
0/20
Independent or smaller assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →
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
$115K – $369K
Midpoint $230K · 18.6 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
38 claims as filed with the patent office.
Glossary
Key terms defined
- node
- A basic processing unit within a neural network, analogous to a neuron in the brain.
- neural network
- A type of AI inspired by the human brain, made up of interconnected 'nodes' or 'neurons' that process information in layers.
- activation value
- The output value of a node after it processes input data.
- back-propagation
- The process of calculating and assigning error gradients (partial derivatives) back through the neural network layers.
- learned parameters
- The internal settings or weights within a neural network that are adjusted during training to improve performance.
- objective function
- A mathematical function that the neural network tries to minimize during training, representing its error or goal.
- activation function
- A function applied to the output of a node to introduce non-linearity, allowing the network to learn complex patterns.
- partial derivatives
- A mathematical concept used in training AI to measure how a small change in one part of the network affects the overall error.
- optimization algorithm
- A method used to adjust the learned parameters of a neural network to minimize the objective function (e.g., gradient descent).
- feed-forward computation
- The process where input data moves through the neural network layers to produce an output.
- neutral activation value
- A specific output value of an activation function that represents a midpoint or baseline state.
- tanh activation function
- Another common activation function that outputs values between -1 and 1.
- sigmoid activation function
- A common activation function that outputs values between 0 and 1.
Concepts involved
Citations
Patent lineage
Cite this patent
Baker, J. K. (2025). Making AI Smarter by Focusing on Unsure 'Nodes' (U.S. Patent No. 12,423,586). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/12423586/training-nodes-of-a-neural-network-to-be-decisive
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 Making AI Smarter by Focusing on Unsure 'Nodes' cover?
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.
Who owns patent US 12423586?
D5AI owns this patent, granted in 2025.
When does this patent expire?
This patent is expected to expire on January 30, 2045, when the invention enters the public domain.
What is patent US 12423586 cited by?
This patent has been cited by 1 later patents that build on its ideas.
What problem does this patent solve?
As AI models become more complex, efficiently training them is crucial. This patent addresses a core challenge in AI development: ensuring that all parts of the model learn effectively. By focusing on nodes that are 'undecided,' this method aims to speed up the learning process and potentially lead to more robust and accurate AI systems, which are increasingly vital across many industries.
What does this patent NOT cover?
Training methods that do not identify if a target node is 'not decisive'.
Patent monitoring