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 7):
The method of  claim 6 , the method further comprising performing, by the computer, an augmentation process comprising:\n (a)(2) generating a sub-sample, wherein the generating comprises selecting measurements of event features from a subset of the events of interest; and (a)(3) repeating step (a)(2) to generate sibling samples, wherein the sibling samples are two or more sub-samples from the biological data sample, wherein the artificial neural network provides the status of the condition for at least three sibling samples from the same biological data sample to give a global status category of the biological data sample, wherein the global status category comprises the most probable category based on a frequency of the statuses of the condition of the sibling samples.

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): 0.3429488872801432
- 35 USC 102 Novelty (BERT): 0.5667551288881536
- Combined Prediction Score: 0.3653295114409443
- Mean Citation Score: 218.638974
- Max Citation Score: 426.39444
- Similarity Product: 315.05822865029097

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