PATENT CLAIM ANALYSIS

Application Number: 16509091
Application Type: Utility
Filing Date: 2019-07
Publication Date: 2019-10
Patent Classification: ["382", "118000"]

Abstract:
Face model matrix training method, apparatus, and storage medium are provided. The method includes: obtaining a face image library, the face image library including k groups of face images, and each group of face images including at least one face image of at least one person, k>2, and k being an integer; separately parsing each group of the k groups of face images, and calculating a first matrix and a second matrix according to parsing results, the first matrix being an intra-group covariance matrix of facial features of each group of face images, and the second matrix being an inter-group covariance matrix of facial features of the k groups of face images; and training face model matrices according to the first matrix and the second matrix.

Claim (Index 27):
The method according to  claim 21 , further comprising:\n obtaining a high-dimensional feature vector of a target face image and a high-dimensional feature vector of a reference face image; and calculating a similarity between the target face image and the reference face image according to the high-dimensional feature vector of the target face image, the high-dimensional feature vector of the reference face image, and the face model matrices.

Metadata:
- Claim Count in Document: 14.0
- Percentile: 100.0
- Lexical Diversity: 2.67925
- Patent Class: 382.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: True
- Related Applications: ['15703826', '13084406', '13239997', '13355335', '12402761']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3207594731070176
- 35 USC 102 Novelty (BERT): 0.5795805839202255
- Combined Prediction Score: 0.3466415841883384
- Mean Citation Score: 266.923878
- Max Citation Score: 483.66867
- Similarity Product: 422.8153033034742

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