PATENT CLAIM ANALYSIS

Application Number: 15942254
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2018-08
Patent Classification: ["382", "156000"]

Abstract:
Disclosed are a training method and apparatus for a CNN model, which belong to the field of image recognition. The method comprises: performing a convolution operation, maximal pooling operation and horizontal pooling operation on training images, respectively, to obtain second feature images; determining feature vectors according to the second feature images; processing the feature vectors to obtain category probability vectors; according to the category probability vectors and an initial category, calculating a category error; based on the category error, adjusting model parameters; based on the adjusted model parameters, continuing the model parameters adjusting process, and using the model parameters when the number of iteration times reaches a pre-set number of times as the model parameters for the well-trained CNN model. After the convolution operation and maximal pooling operation on the training images on each level of convolution layer, a horizontal pooling operation is performed. Since the horizontal pooling operation can extract feature images identifying image horizontal direction features from the feature images, such that the well-trained CNN model can recognize an image of any size, thus expanding the applicable range of the well-trained CNN model in image recognition.

Claim (Index 7):
The method according to  claim 1 , wherein\n the plurality of training images are images in a natural scene, and the CNN model to be trained is a language recognition classifier.

Metadata:
- Claim Count in Document: 6.0
- Percentile: 90.0
- Lexical Diversity: 2.4881
- Patent Class: 382.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: True
- Related Applications: ['15486102', '15380120', '15439896', '15749693', '15909350']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3339015248079643
- 35 USC 102 Novelty (BERT): 0.5836058747084621
- Combined Prediction Score: 0.3588719597980142
- Mean Citation Score: 319.16602400000005
- Max Citation Score: 491.963
- Similarity Product: 398.5296469240188

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

Dataset: test