{
  "patent_number": "US 12423586",
  "country": "US",
  "title": "Making AI Smarter by Focusing on Unsure 'Nodes'",
  "original_title": "Training nodes of a neural network to be decisive",
  "summary": "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.",
  "what_it_does": "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.",
  "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."
  ],
  "filed": "2025-01-30",
  "granted": "2025-09-23",
  "expires": "2045-01-30",
  "status": "active",
  "holder": "D5AI",
  "holder_url": "https://patentbrief.org/company/d5ai",
  "inventors": [
    {
      "name": "James K. Baker",
      "url": "https://patentbrief.org/inventor/james-k-baker"
    }
  ],
  "times_cited": 1,
  "tags": [
    "ai_ml",
    "software",
    "semiconductors",
    "consumer_electronics"
  ],
  "abstract": "Computer-implemented systems and methods improve training of a neural network. Whether a target node is not decisive on a training data item is determined. Upon a determination that the target node is not decisive, a partial derivative of an objective for the target node is multiplied by a factor greater than 1.0 for the training data item. Determining whether the target node is not decisive can comprise determining whether a direction of the derivative is in a direction that would cause an update of learned parameters for the network to increase the difference between the activation value of the first target node for the training data item and a neutral activation value for the target node.",
  "url": "https://patentbrief.org/patent/us/12423586/training-nodes-of-a-neural-network-to-be-decisive",
  "markdown_url": "https://patentbrief.org/patent/us/12423586/training-nodes-of-a-neural-network-to-be-decisive/md",
  "google_patents_url": "https://patents.google.com/patent/US12423586",
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}