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 19):
A method of legalizing self-driving motor vehicles comprises the steps of:\n having government regulatory agencies and/or local traffic law administrative agencies in charge of vehicle license evaluating a first complementary criterion of a first customizing of a self-driving motor vehicle, or evaluating a second complementary criterion of a second customizing of a self-driving motor vehicle, wherein the first complementary criterion of the first customizing of a self-driving motor vehicle comprises:\n obtaining a training collection of a plurality of scenarios and one or more selective operation behaviors of a self-driving motor vehicle in each of the scenarios, comprising designing a training collection or receiving a designed a training collection or combining the designing and the receiving, wherein the scope of scenarios in the training collection covers a legitimate area and the scope of selective vehicle operation behaviors for each of the scenarios in the training collection covers a legitimate area, and the selective vehicle operation behaviors in each of the scenarios is lawful, verifiable and/or verified by simulation and/or road tests; \n obtaining a scenario-user-choice pair in an entry of the user in the scenario-user-choice pair set by presenting to the user one scenario at a time from a training collection of scenarios and one or more selective operating behaviors of a self-driving motor vehicle in the scenario, informing the user about at least partial responsibilities of consequences of the operating according to a user choice of an operating behavior, obtaining a user choice on a preferred operating behavior, and storing the scenario-user-choice pair in an entry of the user in the scenario-user-choice pair data set; or \n receiving scenario-user-choice pairs in the entry of the user in the scenario-user-choice pair set, or combining the obtaining and the receiving, and confirming and/or updating the scenario-user-choice pairs in the entry of the user in a scenario-user-choice pair data set prior to the self-driving motor vehicle being practically used by the user, \n identifying a current user, comprising identifying a current rider, or one of current riders of a self-driving motor vehicle to be a user having acquired data in the entry of the user in a scenario-user-choice pair data set or having acquired data in the entry of the user in a scenario-user-choice pair data set and the entry of the user in a user profile data set of the self-driving motor vehicle prior to the self-driving motor vehicle being practically used, wherein if no rider rides a self-driving motor vehicle, identify a default or designated user to be a current user; \n applying data in the entry of the current user in a scenario-user-choice pair data set of the current user in a user profile data set in operating the self-driving motor vehicle, comprising: finding a match between a current scenario and a scenario in a scenario-user-choice pair in the entry of the current user in a scenario-user-choice pair data set, operating the self-driving motor vehicle according to the user choice in the scenario-user-choice pair if a match being found, and if a current user is a current rider, the current user assumes at least partial responsibilities for consequences of the operating; and \n the second complementary criterion of the second customizing of a self-driving motor vehicle comprises:\n obtaining a training collection of a plurality of scenarios and one or more selective operation behaviors of a self-driving motor vehicle in each of the scenarios, comprising designing a training collection or receiving a designed a training collection or combining the designing and the receiving, wherein the scope of scenarios in the training collection covers a legitimate area and the scope of selective vehicle operation behaviors for each of the scenarios in the training collection covers a legitimate area, and the selective vehicle operation behaviors in each of the scenarios is lawful, verifiable and/or verified by simulation and/or road tests; \n acquiring a scenario-user-choice pair data set of one or more users, comprising: identifying a user, obtaining a scenario-user-choice pair in an entry of the user in the scenario-user-choice pair set by presenting to the user one scenario at a time from a training collection of scenarios and one or more selective operating behaviors of a self-driving motor vehicle in the scenario, obtaining a user choice on a preferred behavior, and storing the scenario-user-choice pair in an entry of the user in the scenario-user-choice pair data set, or \n receiving scenario-user-choice pairs in the entry of the user in the scenario-user-choice pair set, or combining the obtaining and the receiving, and confirming and/or updating the scenario-user-choice pairs in the entry of the user in a scenario-user-choice pair data set prior to the self-driving motor vehicle being practically used by the user, \n identifying a current user, comprising identifying a current rider, or one of current riders of a self-driving motor vehicle to be a user having acquired data in the entry of the user in a scenario-user-choice pair data set or having acquired data in the entry of the user in a scenario-user-choice pair data set and the entry of the user in a user profile data set of the self-driving motor vehicle prior to the self-driving motor vehicle being practically used, wherein if no rider riding a self-driving motor vehicle, identifying a default or designated user to be a current user; \n applying data in the entry of the current user in a scenario-user-choice pair data set of the current user in a user profile data set in operating the self-driving motor vehicle, comprising: finding a match between a current scenario and a scenario in a scenario-user-choice pair in the entry of the current user in a scenario-user-choice pair data set, operating the self-driving motor vehicle according to the user choice in the scenario-user-choice pair if a match being found, and if a current user is a current rider, the current user assumes at least partial responsibilities for consequences of the operating; \n government regulatory agencies and/or local traffic law administrative agencies in charge of vehicle license issuance issue a vehicle license to the self-driving motor vehicle, if the first complementary criterion is met, and if other features and performance of the self-driving motor vehicle driving in scenarios other than in the training collection is also qualified; or government regulatory agencies and/or local traffic law administrative agencies in charge of vehicle license issuance issue a vehicle license and/or a sale permit and/or a service permit to the self-driving motor vehicle, and/or issue a purchase and/or service permit to the user for purchasing and/or using the service of the self-driving motor vehicle, or issue to the user a conditional purchase and/or service permit of the self-driving motor vehicle, if the second complementary criterion is met, and if other features and performance of the self-driving motor vehicle driving in scenarios other than in the training collection is also qualified.

Metadata:
- Claim Count in Document: 5.0
- Percentile: 86.0
- Lexical Diversity: 1.38
- Patent Class: 701.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: True
- Related Applications: ['15662282', '11221027', '15251104', '14563182', '15720775']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3550591220012867
- 35 USC 102 Novelty (BERT): 0.5055649717338988
- Combined Prediction Score: 0.3701097069745479
- Mean Citation Score: 155.855704
- Max Citation Score: 275.33047
- Similarity Product: 211.27534985739945

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