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 25):
The system of  claim 22 , wherein the one or more processors are configured to propagate the error corrections to all neurons in the CNN.

Metadata:
- Claim Count in Document: 5.0
- Percentile: 97.0
- Lexical Diversity: 1.94186
- Patent Class: 706.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['16041497', '15950738', '14813233', '15820253', '15487091']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4085715140500884
- 35 USC 102 Novelty (BERT): 0.5265472095290337
- Combined Prediction Score: 0.420369083597983
- Mean Citation Score: 241.011236
- Max Citation Score: 345.56454
- Similarity Product: 223.3026321330786

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