PATENT CLAIM ANALYSIS

Application Number: 16041497
Application Type: Utility
Filing Date: 2018-07
Publication Date: 2019-04
Patent Classification: ["706", "025000"]

Abstract:
A device, system, and method is provided for storing a sparse neural network. A plurality of weights of the sparse neural network may be obtained. Each weight may represent a unique connection between a pair of a plurality of artificial neurons in different layers of a plurality of neuron layers. A minority of pairs of neurons in adjacent neuron layers are connected in the sparse neural network. Each of the plurality of weights of the sparse neural network may be stored with an association to a unique index. The unique index may uniquely identify a pair of artificial neurons that have a connection represented by the weight. Only non-zero weights may be stored that represent connections between pairs of neurons (and zero weights may not be stored that represent no connections between pairs of neurons).

Claim (Index 15):
A method for efficiently storing a sparse convolutional neural network, the method comprising:\n obtaining a sparse convolutional neural network comprising a plurality of neuron channels in one or more neuron layers, each neuron channel comprising a plurality of artificial neurons, the sparse convolutional neural network represented by a plurality of convolutional filters, each convolutional filter comprising a plurality of weights representing a unique connection between the neurons of an input channel of an input layer and the neurons of a convolutional channel of a convolutional layer, wherein a minority of pairs of channels in adjacent neuron layers are connected by convolutional filters in the sparse neural network; storing each of the plurality of convolutional filters of the sparse neural network with an association to a unique index, the unique index uniquely identifying a pair of channels that have a connection represented by the weights of the convolutional filter, wherein only convolutional filters with non-zero weights are stored that represent connections between channels and convolutional filters with all zero weights are not stored that represent no connections between channels.

Metadata:
- Claim Count in Document: 77.0
- Percentile: 95.0
- Lexical Diversity: 2.39344
- Patent Class: 706.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['12158134', '14449101', '15055161', '14699778', '14513497']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3893625602144759
- 35 USC 102 Novelty (BERT): 0.4920126128367171
- Combined Prediction Score: 0.3996275654767001
- Mean Citation Score: 238.614632
- Max Citation Score: 249.7984
- Similarity Product: 188.0541136829376

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