PATENT CLAIM ANALYSIS

Application Number: 15942226
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2019-10
Patent Classification: ["382", "157000"]

Abstract:
In order for the feature extractors to operate with sufficient accuracy, a high degree of training is required. In this situation, a neural network implementing the feature extractor may be trained by providing it with images having known correspondence. A 3D model of a city may be utilized in order to train a neural network for location detection. 3D models are sophisticated and allow manipulation of viewer perspective and ambient features such as day/night sky variations, weather variations, and occlusion placement. Various manipulations may be executed in order to generate vast numbers of image pairs having known correspondence despite having variations. These image pairs with known correspondence may be utilized to train the neural network to be able to generate feature maps from query images and identify correspondence between query image feature maps and reference feature maps. This training can be accomplished without requiring the capture of real images with known correspondence. Capture of real images with known correspondence is cumbersome, time and resource-intensive, and difficult to manage.

Claim (Index 1):
A method for training a neural network implementation of a feature extractor and a correspondence matcher comprising the steps of:\n set rendering parameters in a 3D model wherein said rendering parameters include at least position and orientation; rendering a frame according to said rendering parameters to establish a current image; processing ray-tracing information of said frame to solve a ground-truth correspondence map of said current image with a neighbor image; incrementing said rendering parameters; and providing said ground-truth correspondence map as a first training input to said neural network.

Metadata:
- Claim Count in Document: 1.0
- Percentile: 90.0
- Lexical Diversity: 1.95699
- Patent Class: 382.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15374010', '15155818', '15138821', '10155948', '15092421']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2689038973245615
- 35 USC 102 Novelty (BERT): 0.479356455227572
- Combined Prediction Score: 0.2899491531148626
- Mean Citation Score: 162.465678
- Max Citation Score: 169.88301
- Similarity Product: 126.2923421990347

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

Dataset: test