PATENT CLAIM ANALYSIS

Application Number: 16010087
Application Type: Utility
Filing Date: 2018-06
Publication Date: 2018-12
Patent Classification: ["433", "024000"]

Abstract:
Provided herein are systems and methods for detecting the eruption state (e.g., tooth type and/or eruption status) of a target tooth. A patient's dentition may be scanned and/or segmented. A target tooth may be identified. Dental features, principal component analysis (PCA) features, and/or other features may be extracted and compared to those of other teeth, such as those obtained through automated machine learning systems. A detector can identify and/or output the eruption state of the target tooth, such as whether the target tooth is a fully erupted primary tooth, a permanent partially erupted/un-erupted tooth, or a fully erupted permanent tooth.

Claim (Index 9):
The computer-implemented method of  claim 1 , wherein determining the one or more tooth eruption indicators of the target tooth based on the PCA features comprises applying machine learning algorithms selected from the group consisting of Decision Tree, Random Forest, Logistic Regression, Support Vector Machine, AdaBOOST, K-Nearest Neighbor (KNN), Quadratic Discriminant Analysis, and Neural Network.

Metadata:
- Claim Count in Document: 47.0
- Percentile: 94.0
- Lexical Diversity: 1.82812
- Patent Class: 433.0
- Transitional Phrase Type: closed
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15676819', '15942341', '14541021', '12772178', '13020583']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.7366074557988963
- 35 USC 102 Novelty (BERT): 0.4910901713847191
- Combined Prediction Score: 0.7120557273574786
- Mean Citation Score: 242.997696
- Max Citation Score: 265.95895
- Similarity Product: 194.4839215843976

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

Dataset: test