PATENT CLAIM ANALYSIS

Application Number: 15869342
Application Type: Utility
Filing Date: 2018-01
Publication Date: 2019-07
Patent Classification: ["382", "118000"]

Abstract:
Methods, systems, and devices for object recognition are described. Generally, the described techniques provide for a compact and efficient convolutional neural network (CNN) model for facial recognition. The proposed techniques relate to a light model with a set of layers of convolution and one fully connected layer for feature representation. A new building block of for each convolution layer is proposed. A maximum feature map (MFM) operation may be employed to reduce channels (e.g., by combining two or more channels via maximum feature selection within the channels). Depth-wise separable convolution may be employed for computation reduction (e.g., reduction of convolution computation). Batch normalization may be applied to normalize the output of the convolution layers and the fully connected layer (e.g., to prevent overfitting). The described techniques provide a compact and efficient CNN model which can be used for efficient and effective face recognition.

Claim (Index 12):
The method of  claim 9 , wherein the set of processing operations is performed a plurality of times, and wherein the plurality of input channels of a second iteration of the set of processing operations is based at least in part on the plurality of output channels of a first iteration of the set of processing operations.

Metadata:
- Claim Count in Document: 7.0
- Percentile: 86.0
- Lexical Diversity: 2.03659
- Patent Class: 382.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15206150', '15224289', '15697015', '15698887', '15256874']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3046052556145451
- 35 USC 102 Novelty (BERT): 0.5176855622350914
- Combined Prediction Score: 0.3259132862765997
- Mean Citation Score: 178.74276799999996
- Max Citation Score: 205.69014
- Similarity Product: 111.78296819618224

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

Dataset: test