PATENT CLAIM ANALYSIS

Application Number: 16267546
Application Type: Utility
Filing Date: 2019-02
Publication Date: 2019-06
Patent Classification: ["340", "542000"]

Abstract:
For patients who exhibit or may exhibit primary or comorbid disease, pharmacological phenotypes may be predicted through the collection of panomic data over a period of time. A machine learning engine may generate a statistical model based on training data from training patients to predict pharmacological phenotypes, including drug response and dosing, drug adverse events, disease and comorbid disease risk, drug-gene, drug-drug, and polypharmacy interactions. Then the model may be applied to data for new patients to predict their pharmacological phenotypes, and enable decision making in clinical and research contexts, including drug selection and dosage, changes in drug regimens, polypharmacy optimization, monitoring, etc., to benefit from additional predictive power, resulting in adverse event and substance abuse avoidance, improved drug response, better patient outcomes, lower treatment costs, public health benefits, and increases in the effectiveness of research in pharmacology and other biomedical fields.

Claim (Index 1):
A computer-implemented method for identifying pharmacological phenotypes, the method executed by one or more processors programmed to perform the method, the method comprising:\n identifying a plurality of single nucleotide polymorphisms (SNPs) correlated with a particular set of pharmacological phenotypes; comparing the plurality of SNPs to a database of SNPs to identify additional SNPs that are linked to the plurality of SNPs, wherein the plurality of SNPs and additional SNPs are included in a set of permissive candidate variants; performing, by one or more processors, a bioinformatics analysis to filter the set of permissive candidate variants into a subset of intermediate candidate variants based on at least one of: regulatory function, variant dependence, a presence of target gene relationships for the permissive candidate variants, or whether the permissive candidate variants are non-synonymous coding variants with a minor allele frequency; performing, by the one or more processors, a network analysis to filter the subset of intermediate candidate variants into candidate variants of SNPs, genes associated with the SNPs, or networks associated with the SNPs which are causally related to the particular set of pharmacological phenotypes; obtaining a biological sample of a patient; comparing the biological sample to the SNPs which are causally related to the particular set of pharmacological phenotypes; and providing, by the one or more processors, an indication of one or more pharmacological phenotypes for the patient for display to a health care provider based on the comparison.

Metadata:
- Claim Count in Document: 28.0
- Percentile: 99.0
- Lexical Diversity: 1.75532
- Patent Class: 340.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15977347', '12054072', '10229119', '11371511', '12484163']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.6509294127439715
- 35 USC 102 Novelty (BERT): 0.625058914839223
- Combined Prediction Score: 0.6483423629534967
- Mean Citation Score: 272.30126400000006
- Max Citation Score: 570.68585
- Similarity Product: 523.0467637664138

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

Dataset: test