HNC's founding 1992 Falcon patent specifies backpropagation gradient descent as a supervised training method

hncfalconficofraud-detectionneural-networksbackpropagationsupervised-learningpatentsdeep-learning-history

Immediately after naming its network architecture as feed-forward (claim-hnc-1992-falcon-patent-names-network-architecture-as-feed-forward), HNC's founding Falcon patent (US 5,819,226) names its preferred training algorithm two sentences later in the same "Network Training" section: "The preferred training method, well known to those skilled in the art, is called 'backpropagation gradient descent optimization', although other well-known neural network training techniques may also be used."

The procedure itself is laid out earlier, in the patent's general "Neural Networks" exposition rather than under the algorithm's name — "The 'training' process, the general techniques of which are well known in the art, involves the following steps: 1) Repeatedly presenting examples of a particular input/output task to the neural network model; 2) Comparing the model output and desired output to measure error; and 3) Modifying model weights to reduce the error. This set of steps is repeated until further iteration fails to decrease the error. Then, the network is said to be 'trained.'" The patent is therefore describing standard supervised backpropagation as prior art and electing it, not claiming it.

The patent describes training examples as drawn from a labeled fraud database — accounts already known to have had fraudulent activity, versus a sampled set of non-fraud accounts — the standard supervised-learning setup: a model trained against known labels, not an unsupervised generative model of transaction structure. Combined with the feed-forward architecture, this is the exact "supervised backpropagation feedforward" characterization observation-falcon-helmholtz-inference-embedding-false-friend had flagged [unverified-mechanism]; it also reinforces that note's discriminative-vs-generative verdict (Falcon scores P(fraud | transaction), not a generative model of hidden causes). It complements claim-fico-2008-10k-describes-falcon-as-neural-network-models, FICO's own FY2008 10-K description of Falcon's mechanism, which stopped short of the training algorithm as "outside a 10-K's scope" — this patent is that patent-level answer.