PATENT CLAIM ANALYSIS

Application Number: 15872304
Application Type: Utility
Filing Date: 2018-01
Publication Date: 2019-07
Patent Classification: ["382", "131000"]

Abstract:
A generative network is used for lung lobe segmentation or lung fissure localization, or for training a machine network for lobar segmentation or localization. For segmentation, deep learning is used to better deal with a sparse sampling of training data. To increase the amount of training data available, an image-to-image or generative network localizes fissures in at least some of the samples. The deep-learnt network, fissure localization, or other segmentation may benefit from generative localization of fissures.

Claim (Index 11):
The method of  claim 1  wherein segmenting lobar regions comprises segmenting as a function of the labeled fissures, airway locations, and vessel locations.

Metadata:
- Claim Count in Document: 8.0
- Percentile: 86.0
- Lexical Diversity: 1.7551
- Patent Class: 382.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['14643935', '11566323', '14518138', '14690391', '13804542']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3712624117599752
- 35 USC 102 Novelty (BERT): 0.5082310025746479
- Combined Prediction Score: 0.3849592708414425
- Mean Citation Score: 245.78362800000005
- Max Citation Score: 258.20218
- Similarity Product: 168.51512917058946

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