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 17):
The system of  claim 14  wherein the image processor is further configured to segment lobar regions of the lungs as represented in the image data based on the locations of the fissures.

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): 0.3719367175591154
- 35 USC 102 Novelty (BERT): 0.5048474479477978
- Combined Prediction Score: 0.3852277905979837
- Mean Citation Score: 245.78362800000005
- Max Citation Score: 258.20218
- Similarity Product: 178.40622146342992

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