PATENT CLAIM ANALYSIS

Application Number: 15979547
Application Type: Utility
Filing Date: 2018-05
Publication Date: 2018-09
Patent Classification: ["382", "104000"]

Abstract:
A system and method for procedurally synthesizing a training dataset for training a machine-learning model. In one embodiment, the system includes: (1) a training designer configured to describe variations in content of training images to be included in the training dataset and (2) an image definer coupled to the training designer, configured to generate training image definitions in accordance with the variations and transmit the training image definitions: to a 3D graphics engine for rendering into corresponding training images, and further to a ground truth generator for generating associated ground truth corresponding to the training images, the training images and the associated ground truth comprising the training dataset.

Claim (Index 11):
The system as recited in  claim 8  wherein said first processor generates said training image definitions in a 3D graphics language employable by 3D graphics engine.

Metadata:
- Claim Count in Document: 26.0
- Percentile: 93.0
- Lexical Diversity: 2.10526
- Patent Class: 382.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15043697', '15736129', '13621974', '15051005', '11719634']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3388430394231652
- 35 USC 102 Novelty (BERT): 0.5302107158344666
- Combined Prediction Score: 0.3579798070642954
- Mean Citation Score: 218.082648
- Max Citation Score: 347.81372000000016
- Similarity Product: 262.83623292644506

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