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 17):
The system as described in  claim 10  wherein neither the first computing entity nor the second computing entity can reconstruct the first data set from the shares, thereby preserving privacy of each sequenced genome and phenotype.

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): 0.2044795259746237
- 35 USC 102 Novelty (BERT): 0.4985835675914818
- Combined Prediction Score: 0.2338899301363095
- Mean Citation Score: 164.62925800000005
- Max Citation Score: 187.6243
- Similarity Product: 113.98285015945434

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

Dataset: test