PATENT CLAIM ANALYSIS

Application Number: 16097653
Application Type: Utility
Filing Date: 2018-10
Publication Date: 2019-05
Patent Classification: ["382", "128000"]

Abstract:
A system and method are provided for enabling atlas registration in medical imaging, said atlas registration comprising matching a medical atlas  300, 302  to a medical image  320 . The system and method may execute a Reinforcement Learning (RL) algorithm to learn a model for matching the medical atlas to the medical image, wherein said learning is on the basis of a reward function quantifying a degree of match between the medical atlas and the medical image. The state space of the RL algorithm may be determined on the basis of a set of features extracted from i) the atlas data and ii) the image data. As such, a model is obtained for medical atlas registration without the use, or with a reduced use, of heuristics. By using a machine learning based approach, the solution can easily be applied to different atlas matching problems, e.g., to different types of medical atlases and/or medical images.

Claim (Index 2):
The system according to  claim 1 , wherein the processor is configured to learn the set of features to be extracted from the atlas data and the image data using a machine learning algorithm.

Metadata:
- Claim Count in Document: 27.0
- Percentile: 97.0
- Lexical Diversity: 2.03571
- Patent Class: 382.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: True
- Related Applications: ['15224710', '15689411', '15160699', '15386856', '15397638']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3446786070341676
- 35 USC 102 Novelty (BERT): 0.4905766417264391
- Combined Prediction Score: 0.3592684105033947
- Mean Citation Score: 193.94943
- Max Citation Score: 199.92468
- Similarity Product: 137.73436696251153

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 0
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 0

Dataset: test