PATENT CLAIM ANALYSIS

Application Number: 16121015
Application Type: Utility
Filing Date: 2018-09
Publication Date: 2019-03
Patent Classification: ["706", "025000"]

Abstract:
An efficient technique of machine learning is provided for training a plurality of convolutional neural networks (CNNs) with increased speed and accuracy using a genetic evolutionary model. A plurality of artificial chromosomes may be stored representing weights of artificial neuron connections of the plurality of respective CNNs. A plurality of pairs of the chromosomes may be recombined to generate, for each pair, a new chromosome (with a different set of weights than in either chromosome of the pair) by selecting entire filters as inseparable groups of a plurality of weights from each of the pair of chromosomes (e.g., “filter-by-filter” recombination). A plurality of weights of each of the new or original plurality of chromosomes may be mutated by propagating recursive error corrections incrementally throughout the CNN. A small random sampling of weights may optionally be further mutated to zero, random values, or a sum of current and random values.

Claim (Index 22):
A system for training a plurality of convolutional neural networks (CNNs) using a genetic evolutionary model, each CNN comprising a hierarchy of layers, each layer comprising one or more channels, each channel comprising a plurality of artificial neurons, each CNN further comprising a plurality of filters, each filter representing a group of a plurality of weights of connections between neurons in a pair of channels in respective adjacent layers of the hierarchy, the system comprising:\n one or more memories to store a plurality of artificial chromosomes representing the weights of the neuron connections of the plurality of respective CNNs; and one or more processors configured to:\n mutate a plurality of weights of one of the plurality of chromosomes to form a new chromosome by updating the plurality of weights based on errors computed for the connected neurons, the errors of the neurons computed recursively from the final output layer based on the expected values of a training dataset and propagating those error corrections backward incrementally to lower layers in the CNN, \n update the plurality of artificial chromosomes by adding the new chromosomes and removing at least some of the non-recombined chromosomes, and \n iteratively repeat said update until the updated plurality of artificial chromosomes represent weights of CNNs that have a convergent error.

Metadata:
- Claim Count in Document: 5.0
- Percentile: 97.0
- Lexical Diversity: 1.94186
- Patent Class: 706.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['16041497', '15950738', '14813233', '15820253', '15487091']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3797987468832188
- 35 USC 102 Novelty (BERT): 0.5216142593670656
- Combined Prediction Score: 0.3939802981316035
- Mean Citation Score: 241.011236
- Max Citation Score: 345.56454
- Similarity Product: 267.98289072677846

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 0
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test