PATENT CLAIM ANALYSIS

Application Number: 16020058
Application Type: Utility
Filing Date: 2018-06
Publication Date: 2018-12
Patent Classification: ["702", "019000"]

Abstract:
Computationally-efficient techniques facilitate secure crowdsourcing of genomic and phenotypic data, e.g., for large-scale association studies. In one embodiment, a method begins by receiving, via a secret sharing protocol, genomic and phenotypic data of individual study participants. Another data set, comprising results of pre-computation over random number data, e.g., mutually independent and uniformly-distributed random numbers and results of calculations over those random numbers, is also received via secret sharing. A secure computation then is executed against the secretly-shared genomic and phenotypic data, using the secretly-shared results of the pre-computation over random number data, to generate a set of genome-wide association study (GWAS) statistics. For increased computational efficiency, at least a part of the computation is executed over dimensionality-reduced genomic data. The resulting GWAS statistics are then used to identify genetic variants that are statistically-correlated with a phenotype of interest.

Claim (Index 20):
The method as described in claim  21  further including using the GWAS statistics to identify genetic variants that are statistically-correlated with a phenotype of interest.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 94.0
- Lexical Diversity: 1.88095
- Patent Class: 702.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['14528736', '15382034', '14200520', '15084951', '13007365']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2039592281890878
- 35 USC 102 Novelty (BERT): 0.5017108607258806
- Combined Prediction Score: 0.2337343914427671
- Mean Citation Score: 164.62925800000005
- Max Citation Score: 187.6243
- Similarity Product: 109.88462546126844

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