PATENT CLAIM ANALYSIS

Application Number: 15914436
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2019-09
Patent Classification: ["713", "186000"]

Abstract:
In one embodiment, a set of feature vectors can be derived from any biometric data, and then using a deep neural network (“DNN”) on those one-way homomorphic encryptions (i.e., each biometrics' feature vector) can determine matches or execute searches on encrypted data. Each biometrics' feature vector can then be stored and/or used in conjunction with respective classifications, for use in subsequent comparisons without fear of compromising the original biometric data. In various embodiments, the original biometric data is discarded responsive to generating the encrypted values. In another embodiment, the homomorphic encryption enables computations and comparisons on cypher text without decryption. This improves security over conventional approaches. Searching biometrics in the clear on any system, represents a significant security vulnerability. In various examples described herein, only the one-way encrypted biometric data is available on a given device. Various embodiments restrict execution to occur on encrypted biometrics for any matching or searching.

Claim (Index 24):
The method of  claim 23 , wherein the method further comprises an act of accepting or extracting, by the classification component, from another neural network Euclidean measurable encrypted feature vectors.

Metadata:
- Claim Count in Document: 65.0
- Percentile: 90.0
- Lexical Diversity: 1.70588
- Patent Class: 713.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15592542', '15494193', '14581418', '15676077', '15793866']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4302833346825925
- 35 USC 102 Novelty (BERT): 0.5094690654416594
- Combined Prediction Score: 0.4382019077584992
- Mean Citation Score: 209.17255
- Max Citation Score: 250.79417
- Similarity Product: 162.2537713584673

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

Dataset: test