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 1):
A privacy-enabled biometric system comprising:\n at least one processor operatively connected to a memory; a classification component executed by the at least one processor, comprising a classification network having a deep neural network (\u201cDNN\u201d), the DNN configured to:\n classify Euclidean measurable encrypted feature vector and label inputs during training, wherein training of the DNN is executed on the Euclidean measurable encrypted feature vectors taken as input to the DNN, the training defining the DNN for subsequent prediction operations on the input of an unknown encrypted feature vector to the DNN; and \n return a label for person identification or an unknown class during prediction, and \n wherein the DNN is further configured to:\n accept as an input at least one unclassified encrypted feature vector that is Euclidean measurable to the DNN during prediction; \n generate an array of values in response to the input of the at least one unclassified encrypted feature vector during prediction; \n determine a label or unknown class based on analyzing a position of values within the array and analyzing a respective value at the respective position; and \n wherein the classification component is further configured to return the unknown class if there is no associated label and the label as output if there is an associated label responsive to the analyzing of the position of values within the array and the analyzing of the respective value at the respective position.

Metadata:
- Claim Count in Document: 65.0
- Percentile: 90.0
- Lexical Diversity: 1.70588
- Patent Class: 713.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15592542', '15494193', '14581418', '15676077', '15793866']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4043211098643646
- 35 USC 102 Novelty (BERT): 0.4934469930450245
- Combined Prediction Score: 0.4132336981824306
- Mean Citation Score: 209.17255
- Max Citation Score: 250.79417
- Similarity Product: 198.83688526575028

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

Dataset: test