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 20):
The non-transitory computer-readable medium of  claim 16 , wherein the instructions to apply the first convolutional operation to the two-dimensional array of pixels are executable by the processor to:\n apply a plurality of convolution kernels to the two-dimensional array of pixels to generate a plurality of initial input channels, each convolution kernel having a same size; apply a batch normalization operation to the plurality of initial input channels to generate a plurality of normalized input channels, wherein the batch normalization operation adjusts a mean of one or more initial input channels, a variance of one or more initial input channels, or both; divide the plurality of normalized input channels into initial channel groups, wherein each normalized input channel of the plurality of normalized input channels is associated with a single channel group of the initial channel groups; and perform the feature selection operation for each initial channel group to generate the plurality of input channels, wherein each input channel is associated with a respective initial channel group.

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): 0.3090031934185435
- 35 USC 102 Novelty (BERT): 0.4968857987187594
- Combined Prediction Score: 0.3277914539485651
- Mean Citation Score: 178.74276799999996
- Max Citation Score: 205.69014
- Similarity Product: 144.92468254561902

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