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 23):
A non-transitory computer readable medium containing instructions when executed by at least one processor cause a computer system to execute a method for executing privacy-enabled biometric analysis, the method comprising:\n instantiating, a classification component 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; and \n return a label for person identification or an unknown class during prediction, based on input of unclassified Euclidean measurable encrypted feature vectors to the DNN; \n training, the DNN with the Euclidean measurable encrypted feature vectors and label inputs, the act of training including defining the DNN for subsequent prediction operations executed responsive to input of an unknown Euclidean measurable encrypted feature vector; classifying, by the DNN, the Euclidean measurable encrypted feature vector and the label inputs during training; generating, by the DNN, an array of values during prediction in response to the input of unclassified encrypted feature vectors to the DNN; determining, by the DNN, a label or unknown class based on analyzing a position of values within the array and based on analyzing a respective value at the respective position; and returning, by the classification component, the unknown class if there is no associated label and the label if there is an associated label as output based on the analyzing the position of values within the array and on the analyzing 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): 0.4042151827092926
- 35 USC 102 Novelty (BERT): 0.4939462448948929
- Combined Prediction Score: 0.4131882889278526
- Mean Citation Score: 209.17255
- Max Citation Score: 250.79417
- Similarity Product: 197.5059756954372

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