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 6):
A computer-implemented method of using a trained artificial neural network to determine a status of a condition of a subject, the method comprising:\n (a) performing, by a computer, an analysis of a biological sample from a subject, the analysis comprising:\n (1) obtaining a biological data sample comprising measurements obtained from a flow cytometer instrument of a plurality of event features for a plurality of events of interest from the biological sample; \n (b) determining a status of a condition of the subject by applying, by the computer, an artificial neural network to 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: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15445913', '15532844', '13125054', '11928901', '16085077']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3405703197762347
- 35 USC 102 Novelty (BERT): 0.5714127662393158
- Combined Prediction Score: 0.3636545644225429
- Mean Citation Score: 218.638974
- Max Citation Score: 426.39444
- Similarity Product: 362.8913325253915

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

Dataset: test