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 5):
The apparatus of  claim 4 , wherein the instructions are further executable by the processor to cause the apparatus to:\n feed the plurality of output channels of a last iteration of the set of processing operations to a fully connected layer to generate a plurality of connected-layer output channels; divide the plurality of connected-layer output channels into third channel groups, wherein each connected-layer output channel of the plurality of connected-layer output channels is associated with a single channel group of the third channel groups; and perform the feature selection operation for each third channel group to generate a plurality of final channels, wherein each final channel is associated with a respective channel group of the third channel groups and wherein the object in the image is recognized based at least in part on the plurality of final channels.

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): 0.3055205955704032
- 35 USC 102 Novelty (BERT): 0.5133455662371669
- Combined Prediction Score: 0.3263030926370796
- Mean Citation Score: 178.74276799999996
- Max Citation Score: 205.69014
- Similarity Product: 118.70246362944604

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