PATENT CLAIM ANALYSIS

Application Number: 15912511
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2018-08
Patent Classification: ["706", "020000"]

Abstract:
The present disclosure provides methods for applying artificial neural networks to flow cytometry data generated from biological samples to diagnose and characterize cancer in a subject. The disclosure also provides methods of training, testing, and validating artificial neural networks.

Claim (Index 12):
The method of  claim 6 , wherein multiple artificial neural networks are applied to the biological data sample and used to provide multiple statuses of the condition, wherein the multiple artificial neural networks are trained separately, wherein the multiple artificial neural networks provide an independent status of the condition, wherein the statuses of the condition by the multiple artificial neural networks are analyzed by a master neural network to give a global status category of the biological sample, and wherein the global status category is the most probable category based on a frequency of the statuses of the condition of the biological data sample.

Metadata:
- Claim Count in Document: 4.0
- Percentile: 90.0
- Lexical Diversity: 1.34375
- Patent Class: 706.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15445913', '15532844', '13125054', '11928901', '16085077']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3698847464396522
- 35 USC 102 Novelty (BERT): 0.5696333788787825
- Combined Prediction Score: 0.3898596096835652
- Mean Citation Score: 218.638974
- Max Citation Score: 426.39444
- Similarity Product: 321.45436788647174

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

Dataset: test