PATENT CLAIM ANALYSIS

Application Number: 15965377
Application Type: Utility
Filing Date: 2018-04
Publication Date: 2019-10
Patent Classification: ["701", "027000"]

Abstract:
The present disclosure generally relates to methods and systems for controlling an autonomous vehicle. The vehicle may collect scenario information from one or more sensors mounted on a vehicle. The vehicle may determine a high-level option for a fixed time horizon based on the scenario information. The vehicle may apply a prediction algorithm to the high-level option to mask undesired low-level behaviors for completing the high-level option where a collision is predicted to occur. The vehicle may evaluate a restricted subspace of low-level behaviors using a reinforcement learning system. The vehicle may control the vehicle to perform the high-level option by executing a low-level behavior selected from the restricted subspace. The vehicle may adjust the reinforcement learning system by evaluating a metric of the executed low-level behavior.

Claim (Index 20):
The computer-readable medium of  claim 19 , wherein the code to adjust the reinforcement learning system comprises code to train the DQN with a reward based on the metric if the high-level option is completed within the fixed time and training the DQN with a penalty if the high-level option is not completed within the fixed time.

Metadata:
- Claim Count in Document: 11.0
- Percentile: 91.0
- Lexical Diversity: 2.0303
- Patent Class: 701.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15663245', '11950765', '15698375', '15594020', '15629004']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4448543264774474
- 35 USC 102 Novelty (BERT): 0.5019479980222022
- Combined Prediction Score: 0.4505636936319229
- Mean Citation Score: 171.70881599999996
- Max Citation Score: 178.67969
- Similarity Product: 97.49255870017946

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

Dataset: test