PATENT CLAIM ANALYSIS

Application Number: 15894867
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2018-07
Patent Classification: ["382", "157000"]

Abstract:
Tasks such as object classification from image data can take advantage of a deep learning process using convolutional neural networks. These networks can include a convolutional layer followed by an activation layer, or activation unit, among other potential layers. Improved accuracy can be obtained by using a generalized linear unit (GLU) as an activation unit in such a network, where a GLU is linear for both positive and negative inputs, and is defined by a positive slope, a negative slope, and a bias. These parameters can be learned for each channel or a block of channels, and stacking those types of activation units can further improve accuracy.

Claim (Index 10):
The computer-implemented method of  claim 6 , wherein the GLU further includes at least one of a pooling layer, a fully connected layer, or a softmax layer.

Metadata:
- Claim Count in Document: 3.0
- Percentile: 88.0
- Lexical Diversity: 1.73913
- Patent Class: 382.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['14954646', '15802585', '10837382', '15352821', '15551870']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3289133660697193
- 35 USC 102 Novelty (BERT): 0.5192658145130032
- Combined Prediction Score: 0.3479486109140477
- Mean Citation Score: 177.93588400000004
- Max Citation Score: 313.96484
- Similarity Product: 229.5008706224156

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

Dataset: test