PATENT CLAIM ANALYSIS

Application Number: 15906529
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2019-06
Patent Classification: ["345", "420000"]

Abstract:
Techniques for identifying and labeling distinct objects within  3 -D images of environments in which vehicles operate, to thereby generate training data used to train models that autonomously control and/or operate vehicles, are disclosed. A  3 -D image may be presented from various perspective views (in some cases, dynamically), and/or may be presented with a corresponding  2 -D environment image in a side-by-side and/or a layered manner, thereby allowing a user to more accurately identify groups/clusters of data points within the  3 -D image that represent distinct objects. Automatic identification/delineation of various types of objects depicted within  3 -D images, automatic labeling of identified/delineated objects, and automatic tracking of objects across various frames of a  3 -D video are disclosed. A user may modify and/or refine any automatically generated information. Further, at least some of the techniques described herein are equally applicable to  2 -D images.

Claim (Index 5):
The computer-implemented method of  claim 4 , wherein the usage of the paint user control is as the virtual paint brush, and modifying the second area with the second visual property comprises the user manipulating the virtual paint brush across the second area within the 3-D image.

Metadata:
- Claim Count in Document: 7.0
- Percentile: 88.0
- Lexical Diversity: 1.82955
- Patent Class: 345.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15906443', '15906676', '15906141', '15906610', '15692180']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.7163867494488566
- 35 USC 102 Novelty (BERT): 0.5226599825393009
- Combined Prediction Score: 0.6970140727579011
- Mean Citation Score: 292.17445
- Max Citation Score: 327.44516
- Similarity Product: 222.4109190682745

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

Dataset: test