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 9):
The method of  claim 1  wherein applying comprises applying with the machine-learnt generative network trained to output the second imaging data with the labeled fissures indicating visible fissures of the lungs, and wherein segmenting comprises segmenting the lobar regions with boundaries including the visible fissures as well as inferred lobar boundaries that extend beyond the visible 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): 0.3733222703790573
- 35 USC 102 Novelty (BERT): 0.497901919104373
- Combined Prediction Score: 0.3857802352515889
- Mean Citation Score: 245.78362800000005
- Max Citation Score: 258.20218
- Similarity Product: 198.7070660978449

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