PATENT CLAIM ANALYSIS

Application Number: 15867946
Application Type: Utility
Filing Date: 2018-01
Publication Date: 2018-05
Patent Classification: ["701", "023000"]

Abstract:
Methods are introduced for customizing and legalizing a self-driving motor vehicle by personalizing and/or disciplining before and during a self-driving motor vehicle is practically used, with human knowledge, preferences and experiences, to provide a more personal service and overcome some hurdles in legalization of self-driving motor vehicles, serving as a bridge in the transition from a human driving world to a personized autonomous freeway.

Claim (Index 13):
The method of  claim 12 , wherein the designing a training collection comprises an embodiment of:\n obtaining a scenario data set from conventional motor vehicle driving records and/or based on simulations and road tests of self-driving motor vehicles; categorizing and/or quantizing the scenario data in the scenario data set; assigning a weight to each scenario in the scenario data set respectively to its appearance probability, level of abruptness or uncertainty, and risk level of involving a user in handling conflicts of interests between traffic rules and laws, safety of a self-driving motor vehicle and/or the user, and/or other parties sharing roadways; finding a combined weight by a weighted average of individual weights; sorting the scenarios data set in descending order according to the value of the combined weights of the scenarios; selecting into the training collection the scenarios from the sorted scenario data set prioritized by a top-down rule according to the value of the combined weights of the scenarios, until to an adjustable first threshold value of the combined weight; obtaining a second data set of operational behaviors in a scenario in the training collection based on conventional vehicle driving records and/or data based on self-driving motor vehicle simulations and/or road tests; removing the unlawful operational behaviors from the second data set; finding a probability of appearance of the operational behaviors; removing operational behaviors having a probability of appearance smaller than an adjustable second threshold from the second data set; obtaining a user group data set based on a psychological behavior model of the driving style and/or moral and/or ethics traits of the users; forming a probability density distribution between extreme selfish at one side and altruism at the other side; or forming a multiple dimensional user psychological behavior probability density distribution; dividing a range of the probability density distribution into a plurality of segments or regions and the probability of each segment or region corresponding to a group of users with similar psychological behavior pattern of driving styles and/or moral and/or ethics traits; removing from user group data set groups of users having a probability smaller than a third adjustable threshold; finding for each group of users a match or matches in the second data set of operational behaviors of the self-driving motor vehicle in the scenario and the matched operational behavior or behaviors are qualified to be the selective operational behaviors in the scenario of the training collection for a user to choose from to form a scenario-user-choice pair; achieving a compromise between the size of the data sets, the granularities of user groups and coverage of the psychological behaviors.

Metadata:
- Claim Count in Document: 5.0
- Percentile: 86.0
- Lexical Diversity: 1.38
- Patent Class: 701.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: True
- Related Applications: ['15662282', '11221027', '15251104', '14563182', '15720775']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3819440522843855
- 35 USC 102 Novelty (BERT): 0.5121391387210452
- Combined Prediction Score: 0.3949635609280515
- Mean Citation Score: 155.855704
- Max Citation Score: 275.33047
- Similarity Product: 195.5759876890957

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

Dataset: test