PATENT CLAIM ANALYSIS

Application Number: 15957859
Application Type: Utility
Filing Date: 2018-04
Publication Date: 2018-08
Patent Classification: ["382", "159000"]

Abstract:
A system trains a facial recognition modeling system using an extremely large data set of facial images, by distributing a plurality of facial recognition models across a plurality of nodes within the facial recognition modeling system. The system optimizes a facial matching accuracy of the facial recognition modeling system by increasing a facial image set variance among the plurality of facial recognition models. The system selectively matches each facial image within the extremely large data set of facial images with at least one of the plurality of facial recognition models. The system reduces the time associated with training the facial recognition modeling system by load balancing the extremely large data set of facial images across the plurality of facial recognition models while improving the facial matching accuracy associated with each of the plurality of facial recognition models.

Claim (Index 4):
The computer program product of  claim 2  wherein the computer readable program code configured to increase the facial matching model accuracy associated with each of the plurality of facial recognition models is further configured to:\n calculate an eigenvector distance between a facial image within the extremely large data set of facial images and a most closely matching facial image within each of the plurality of facial recognition models; and \n determine the least closely matching facial image associated with a maximum eigenvector distance between the facial image and each of the most closely matching facial images.

Metadata:
- Claim Count in Document: 15.0
- Percentile: 91.0
- Lexical Diversity: 2.95833
- Patent Class: 382.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15228767', '15957884', '12017131', '10610494', '14074615']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3826792239760773
- 35 USC 102 Novelty (BERT): 0.5715417957476209
- Combined Prediction Score: 0.4015654811532317
- Mean Citation Score: 312.1595220000001
- Max Citation Score: 470.8783
- Similarity Product: 395.9805092214823

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

Dataset: test