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 16):
A non-transitory computer-readable medium storing code for object recognition, the code comprising instructions executable by a processor to:\n obtain a two-dimensional array of pixels representing an image; apply a first convolutional operation to the two-dimensional array of pixels to generate a plurality of input channels; perform a set of processing operations on the plurality of input channels, the set of processing operations comprising instructions executable by the processor to:\n apply a second convolutional operation to the plurality of input channels to generate a second plurality of input channels; \n divide the second plurality of input channels into channel groups, wherein each input channel of the second plurality of input channels is associated with a single channel group; \n perform a feature selection operation for each channel group to generate a plurality of intermediate channels, wherein each intermediate channel is associated with a respective channel group; and \n apply a third convolutional operation to the plurality of intermediate channels, wherein the third convolutional operation comprises a first operation applied to each intermediate channel to generate a plurality of feature maps followed by a second operation applied across the plurality of feature maps to generate a plurality of output channels; and \n recognize an object in the image based at least in part on the plurality of output channels.

Metadata:
- Claim Count in Document: 7.0
- Percentile: 86.0
- Lexical Diversity: 2.03659
- Patent Class: 382.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15206150', '15224289', '15697015', '15698887', '15256874']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2873584140353211
- 35 USC 102 Novelty (BERT): 0.4790867840740425
- Combined Prediction Score: 0.3065312510391933
- Mean Citation Score: 178.74276799999996
- Max Citation Score: 205.69014
- Similarity Product: 169.63417827732326

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