PATENT CLAIM ANALYSIS

Application Number: 16526390
Application Type: Utility
Filing Date: 2019-07
Publication Date: 2019-11
Patent Classification: ["382", "131000"]

Abstract:
Computationally efficient anatomy segmentation through low-resolution multi-atlas label fusion and corrective learning is provided. In some embodiments, an input image is read. The input image has a first resolution. The input image is downsampled to a second resolution lower than the first resolution. The downsampled image is segmented into a plurality of labeled anatomical segments. Error correction is applied to the segmented image to generate an output image. The output image has the first resolution.

Claim (Index 12):
The system of  claim 11 , wherein the corrective learning algorithm comprises a random forest classifier.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 100.0
- Lexical Diversity: 1.80435
- Patent Class: 382.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['16363330', '15253326', '14509342', '15468089', '13974481']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.338208571683469
- 35 USC 102 Novelty (BERT): 0.541410705936381
- Combined Prediction Score: 0.3585287851087602
- Mean Citation Score: 236.646698
- Max Citation Score: 373.38004
- Similarity Product: 362.8192649726511

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test