PATENT CLAIM ANALYSIS

Application Number: 15890009
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2019-08
Patent Classification: ["701", "023000"]

Abstract:
An approach is provided for machine learning of physical dividers. The approach, for instance, involves retrieving map data, sensor data, or a combination thereof for a segment of a road. The approach also involves retrieving ground truth data for the segment of the road. The ground truth data, for instance, indicates a true presence or a true absence of the physical divider on the segment of the road. The approach further involves processing the map data, the sensor, or a combination thereof and the ground truth data to train a machine learning model to predict the physical divider using the map data, the sensor data, or a combination thereof as an input. The approach further involves using the trained machine learning model to a generate a physical divider overlay of a map representation of a road network.

Claim (Index 19):
The non-transitory computer-readable storage medium of  claim 16 , wherein the trained machine learning model is instantiated at a local component of a vehicle traveling the road network, and wherein the local component uses the trained machine learning model to provide a local prediction of the physical divider.

Metadata:
- Claim Count in Document: 4.0
- Percentile: 88.0
- Lexical Diversity: 3.0
- Patent Class: 701.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15597999', '14578956', '15842444', '16313058', '15464398']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4482147878972828
- 35 USC 102 Novelty (BERT): 0.4807850590515642
- Combined Prediction Score: 0.451471815012711
- Mean Citation Score: 174.051352
- Max Citation Score: 207.404
- Similarity Product: 155.10207487010956

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

Dataset: test