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 8):
The method according to  claim 1 , further comprising:\n acquiring, by the information processing apparatus, initial model parameters of the CNN model to be trained, the initial model parameters including initial convolution kernels and initial bias matrixes of convolution layers of respective levels, and an initial weight matrix and an initial bias vector of a fully connected layer; on the convolution layer of each level, performing, by the information processing apparatus, convolution operation and maximal pooling operation on each of the plurality of training images to obtain a first feature image of each of the plurality of training images on the convolution layer of each level by using the initial convolution kernel and initial bias matrix of the convolution layer of each level; and performing, by the information processing apparatus, a horizontal pooling operation on the first feature image of each of the plurality of training images on the convolution layer of at least one of the levels to obtain a second feature image of each of the plurality of training images on the convolution layer of each level.

Metadata:
- Claim Count in Document: 6.0
- Percentile: 90.0
- Lexical Diversity: 2.4881
- Patent Class: 382.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: True
- Related Applications: ['15486102', '15380120', '15439896', '15749693', '15909350']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3043531746937939
- 35 USC 102 Novelty (BERT): 0.5910630794602516
- Combined Prediction Score: 0.3330241651704397
- Mean Citation Score: 319.16602400000005
- Max Citation Score: 491.963
- Similarity Product: 451.223962030828

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