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 2):
The method of  claim 1 , comprising repeating step (a)(2) at least one time to generate sibling samples, wherein the sibling samples are at least two sub-samples generated from the biological data sample, wherein the sibling samples comprise a first sibling sample and a second sibling sample, wherein the first sibling sample comprises a number (N) of measurements of the biological sample and a second sibling sample, wherein the second sibling sample comprises at least N/4 measurements of the biological 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): 0.3482268572146449
- 35 USC 102 Novelty (BERT): 0.5564338227640901
- Combined Prediction Score: 0.3690475537695895
- Mean Citation Score: 218.638974
- Max Citation Score: 426.39444
- Similarity Product: 209.4784841584396

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