PATENT CLAIM ANALYSIS

Application Number: 15866217
Application Type: Utility
Filing Date: 2018-01
Publication Date: 2018-07
Patent Classification: ["382", "154000"]

Abstract:
A method for detecting a defect in an object includes: capturing, by one or more depth cameras, a plurality of partial point clouds of the object from a plurality of different poses with respect to the object; merging, by a processor, the partial point clouds to generate a merged point cloud; computing, by the processor, a three-dimensional (3D) multi-view model of the object; detecting, by the processor, one or more defects of the object in the 3D multi-view model; and outputting, by the processor, an indication of the one or more defects of the object.

Claim (Index 23):
The system of  claim 20 , wherein the memory further stores instructions that, when executed by the processor, cause the processor to assign the classification to the object using a convolutional neural network, and\n wherein the convolutional neural network is trained by:\n receiving a plurality of training 3D models of objects and corresponding training classifications; \n computing a plurality of feature vectors from the training 3D models by the convolutional neural network; \n computing parameters of the convolutional neural network; \n computing a training error metric between the training classifications of the training 3D models with outputs of the convolutional neural network configured based on the parameters; \n computing a validation error metric in accordance with a plurality of validation 3D models separate from the training 3D models; \n in response to determining that the training error metric and the validation error metric fail to satisfy a threshold, generating additional 3D models with different defects to generate additional training data; \n in response to determining that the training error metric and the validation error metric satisfy the threshold, configuring the neural network in accordance with the parameters; \n receiving a plurality of test 3D models of objects with unknown classifications; and \n classifying the test 3D models using the configured convolutional neural network.

Metadata:
- Claim Count in Document: 28.0
- Percentile: 86.0
- Lexical Diversity: 2.30612
- Patent Class: 382.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15678075', '15862512', '15675684', '15363835', '15596155']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3194840034814653
- 35 USC 102 Novelty (BERT): 0.5381225478931132
- Combined Prediction Score: 0.3413478579226301
- Mean Citation Score: 248.164944
- Max Citation Score: 358.36118
- Similarity Product: 264.96444919320584

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