Method and system for data optimization

• US 10,515,390 B2
• Filed: 12/28/2016
• Issued: 12/24/2019
• Est. Priority Date: 11/21/2016
• Status: Active Grant

• Current Assignee: NIO Technology Company Limited (Shanghai Anbin Technology Co., Ltd.)
• Original Assignee: Nio Usa Inc.
• Inventors: Pouliot, Christopher F.
• Primary Examiner(s): Sittner, Matthew T
• Application Number:
  US15/393,010
• Publication Number:
  US 20180144369A1
• Time in Patent Office: 1,091 Days
• Field of Search: None
• CPC Class Codes: A61B 5/01 Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
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• CPC Class Codes: B60W 2050/0004 In digital systems, e.g. discrete-time systems involving sampling
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• CPC Class Codes: B60W 2556/55 : using telemetry
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• CPC Class Codes: B60W 2710/20 : Steering systems
• CPC Class Codes: B60W 2756/10 : Involving external transmission of data to or from the vehicle
• CPC Class Codes: B60W 2900/00 : Indexing codes relating to the purpose of, or problem solved of road vehicle drive control systems not otherwise provided for in groups B60W30/00
• CPC Class Codes: B60W 30/08 : Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
• CPC Class Codes: B60W 30/085 : Taking automatic action to adjust vehicle attitude in preparation for collision, e.g. braking for nose dropping
• CPC Class Codes: B60W 30/09 : Taking automatic action to avoid collision, e.g. braking and steering
• CPC Class Codes: B60W 30/095 : Predicting travel path or likelihood of collision
• CPC Class Codes: B60W 30/0953 : the prediction being responsive to vehicle dynamic parameters
• CPC Class Codes: B60W 30/0956 : the prediction being responsive to traffic or environmental parameters
• CPC Class Codes: B60W 40/04 : Traffic conditions
• CPC Class Codes: B60W 40/08 : related to drivers or passengers
• CPC Class Codes: B60W 40/09 : Driving style or behaviour
• CPC Class Codes: B60W 40/105 : Speed
• CPC Class Codes: B60W 50/0097 : Predicting future conditions
• CPC Class Codes: B60W 50/0098 : Details of control systems ensuring comfort, safety or stability not otherwise provided for
• CPC Class Codes: B60W 50/08 : Interaction between the driver and the control system
• CPC Class Codes: B60W 50/082 : Selecting or switching between different modes of propelling
• CPC Class Codes: B60W 60/0027 : using trajectory prediction for other traffic participants
• CPC Class Codes: B62D 15/00 : Steering not otherwise provided for
• CPC Class Codes: B62D 15/0265 : Automatic obstacle avoidance by steering
• CPC Class Codes: G01C 21/3407 : specially adapted for specific applications
• CPC Class Codes: G01C 21/3461 : Preferred or disfavoured areas, e.g. dangerous zones, toll or emission zones, intersections, manoeuvre types, segments such as motorways, toll roads, ferries
• CPC Class Codes: G01C 21/3469 : Fuel consumption; Energy use; Emission aspects
• CPC Class Codes: G01C 21/3484 : Personalized, e.g. from learned user behaviour or user-defined profiles
• CPC Class Codes: G01C 21/3492 : employing speed data or traffic data, e.g. real-time or historical traffic control systems for road vehicles involving transmission of navigation instructions to the vehicle G08G1/0968
• CPC Class Codes: G01C 21/3682 : output of POI information on a road map G01C21/3614, G01C21/3685 take precedence
• CPC Class Codes: G01C 21/3691 : Retrieval, searching and output of information related to real-time traffic, weather, or environmental conditions arrangements for giving variable traffic instructions G08G1/09
• CPC Class Codes: G01C 21/3697 : Output of additional, non-guidance related information, e.g. low fuel level G01C21/3679 takes precedence
• CPC Class Codes: G01S 13/862 : Combination of radar systems with sonar systems
• CPC Class Codes: G01S 13/865 : Combination of radar systems with lidar systems
• CPC Class Codes: G01S 13/867 : Combination of radar systems with cameras
• CPC Class Codes: G01S 13/87 : Combinations of radar systems, e.g. primary radar and secondary radar
• CPC Class Codes: G01S 15/02 : using reflection of acoustic waves G01S15/66 takes precedence
• CPC Class Codes: G01S 17/89 : for mapping or imaging
• CPC Class Codes: G01S 19/13 : Receivers
• CPC Class Codes: G01S 2007/4977 : including means to prevent or remove the obstruction
• CPC Class Codes: G01S 2013/9316 : combined with communication equipment with other vehicles or with base stations
• CPC Class Codes: G01S 2013/9318 : Controlling the steering
• CPC Class Codes: G01S 2013/93185 : Controlling the brakes
• CPC Class Codes: G01S 2013/9319 : Controlling the accelerator
• CPC Class Codes: G01S 2013/932 : using own vehicle data, e.g. ground speed, steering wheel direction
• CPC Class Codes: G01S 2013/9322 : using additional data, e.g. driver condition, road state or weather data
• CPC Class Codes: G01S 2013/9325 : for inter-vehicle distance regulation, e.g. navigating in platoons
• CPC Class Codes: G01S 2013/93271 : in the front of the vehicles
• CPC Class Codes: G01S 2013/93272 : in the back of the vehicles
• CPC Class Codes: G01S 2013/93273 : on the top of the vehicles
• CPC Class Codes: G01S 2013/93274 : on the side of the vehicles
• CPC Class Codes: G01S 2013/93275 : in the bumper area
• CPC Class Codes: G01S 7/4021 : of receivers
• CPC Class Codes: G01S 7/4043 : including means to prevent or remove the obstruction
• CPC Class Codes: G01S 7/497 : Means for monitoring or calibrating
• CPC Class Codes: G02B 27/0006 : with means to keep optical surfaces clean, e.g. by preventing or removing dirt, stains, contamination, condensation G02B1/18 takes precedence; cleaning in general B08B
• CPC Class Codes: G05D 1/0212 : with means for defining a desired trajectory involving a plurality of land vehicles G05D1/0287
• CPC Class Codes: G05D 1/0276 : using signals provided by a source external to the vehicle involving a plurality of vehicles G05D1/0287; automatically controlling vehicle speed responsive to externally generated signals B60K31/0058
• CPC Class Codes: G05D 1/228 : Command input arrangements located on-board unmanned vehicles
• CPC Class Codes: G05D 1/247 : using signals provided by artificial sources external to the vehicle, e.g. navigation beacons
• CPC Class Codes: G05D 1/617 : Safety or protection, e.g. defining protection zones around obstacles or avoiding hazards arrangements for controlling the position or course of two or more vehicles for avoiding collisions therebetween G05D1/693; arrangements for reacting to or preventing system or operator failure G05D1/80
• CPC Class Codes: G05D 1/646 : Following a predefined trajectory, e.g. a line marked on the floor or a flight path
• CPC Class Codes: G05D 1/81 : Handing over between on-board automatic and on-board manual control
• CPC Class Codes: G06F 16/29 : Geographical information databases
• CPC Class Codes: G06F 16/95 : Retrieval from the web
• CPC Class Codes: G06Q 30/0266 : based on the position of the vehicle
• CPC Class Codes: G06Q 30/0269 : based on user profile or attribute
• CPC Class Codes: G07C 5/0808 : Diagnosing performance data testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005
• CPC Class Codes: G08G 1/161 : Decentralised systems, e.g. inter-vehicle communication
• CPC Class Codes: G08G 1/163 : involving continuous checking
• CPC Class Codes: G08G 1/164 : Centralised systems, e.g. external to vehicles
• CPC Class Codes: G08G 1/165 : for passive traffic, e.g. including static obstacles, trees
• CPC Class Codes: G08G 1/166 : for active traffic, e.g. moving vehicles, pedestrians, bikes

Abstract

Embodiments can provide an intelligent vehicle that determines that the vehicle interior comprises multiple occupants; identifies the occupants; based on the driving behavior of the vehicle, creates a composite occupant profile associated with the group of plural occupants, the composite occupant profile comprising the identities of the plural occupants and group preferences for various vendor products or services; and, based on the composite occupant profile and received inputs from a user interface, an automatic vehicle location system, and a plurality of sensors in the vehicle, performs one or more actions, such as: (a) proposing one or more vendor products or services for the group of occupants; (b) publishing the vendor products or services selected by the group of occupants, via a social network, to associated or selected associates of the occupants in the group; and (c) presenting advertisement information from a vendor server associated with the proposed or selected vendor products or services to one or more of the occupants in the group.

Claims

1. A vehicle, comprising:

a vehicle interior for receiving one or more occupants;

a powertrain to transmit power from a power source to an electric motor to propel the vehicle;

a user interface to receive input from an occupant and provide output to the occupant;

a plurality of sensors to sense information related to occupants;

an automatic vehicle location system to determine a current spatial location of the vehicle;

a computer readable medium to store a composite occupant profile; and

a microprocessor, coupled to the user interface, plurality of sensors, automatic vehicle location system, and computer readable medium, to;

determine that the vehicle interior comprises a group of plural occupants;

identify each occupant of the group of plural occupants;

based on individual profiles of the group of plural occupants, create a composite occupant profile that represents the group of plural occupants, the composite occupant profile comprising an identity of each occupant of the group of plural occupants and group preferences for various vendor products or services, wherein each occupant of the group of plural occupants has a corresponding individual occupant profile, each individual occupant profile comprising individual preference values, wherein the composite occupant profile and the group preferences are created by combining, for a selected preference, a preference value for the selected preference retrieved from the individual preference values for each occupant using one or more of a role value for each occupant of the group of plural occupants, an unweighted average of the individual preference values of each occupant of the group of plural occupants for the selected preference, a mode of the individual preference values of each occupant of the group of plural occupants for the selected preference, and a median of the individual preference values of each occupant of the group of plural occupants for the selected preference; and

based on the composite occupant profile and received inputs from the user interface, automatic vehicle location system, and plurality of sensors, one or more of;

(a) propose one or more vendor products or services for the group of plural occupants;

(b) publish the one or more vendor products or services selected by the group of plural occupants, via a social network, to associated or selected associates of one or more of the occupants in the group of plural occupants; and

(c) present advertisement information from a vendor server associated with the proposed or selected one or more vendor products or services to one or more of the occupants in the group of plural occupants.

2. The vehicle of claim 1, wherein the composite occupant profile comprises plural preference fields, wherein a preference value of at least one of the plural preference fields in the composite occupant profile is based on a preference value in an individual occupant profile of an occupant of a different vehicle and not currently an occupant of the vehicle, wherein creating the composite occupant profile is based on a driving behavior of the vehicle, wherein the driving behavior is one or more of current or historical user driving behavior, historical route driven by the vehicle, destination of the vehicle, and waypoints of the vehicle, wherein the driving behavior of the vehicle is attributed to the group of plural occupants, wherein the plurality of sensors comprise a plurality of:

a camera to capture images of the occupants in the vehicle interior, a camera to capture images of objects outside the vehicle, a seat belt sensor to determine a seat belt setting, a seat weight sensor to determine a weight of a seated occupant, a microphone to capture speech of the group of plural occupants, and a portable computing device of at least one of the group of plural occupants to sense interaction of the at least one of the group of plural occupants with the portable computing device, and wherein the composite occupant profile comprises, for each occupant of the group of plural occupants, an age or biometric information for each occupant and, for the group of plural occupants, visitation and/or transaction history with one or more vendors.

3. The vehicle of claim 1, wherein the microprocessor provides information in the composite occupant profile to multiple vendor servers of multiple vendors, wherein the composite occupant profile comprises, for each occupant in the group of plural occupants the role value indicating a role a corresponding occupant plays in a group decision, each occupant of the group of plural occupants having a different role value, wherein, in response to providing the information, the microprocessor receives multiple vendor responses from the multiple vendors, assigns a compliance score to each of the multiple vendor responses, the compliance score indicating a degree of compliance of a respective vendor response with one or more of an occupant preference and an attractiveness of the respective vendor response to a need, specification, or requirement of the composite occupant profile, filters the multiple vendor responses based on the assigned compliance scores, and provides a subset of the multiple vendor responses to at least one occupant of the group of plural occupants.

4. The vehicle of claim 1, wherein the microprocessor directly or indirectly provides vehicle occupant information in the composite occupant profile and current vehicle location to the vendor server, wherein the microprocessor proposes one or more vendor products or services for the group of plural occupants, wherein the microprocessor provides to an occupant of the group of plural occupants a plurality of vendor hours of operation, available product and/or service types, available product and/or service prices, available product and/or service specifications or requirements, and geographical location of a respective vendor, wherein the microprocessor provides to the occupant a geographic data file comprising the geographic location of the respective vendor, wherein the plurality of vendor hours of operation, available product and/or service types, available product and/or service prices, available product and/or service specifications or requirements, and geographical location of the respective vendor are cross-referenced by a function-specific referencing table and/or generic reference features or attributes of the geographic data file, and wherein the proposed one or more vendor products is proposed based on a number of members in the group of plural occupants, ages of the members in the group of plural occupants, and sexes of the members in the group of plural occupants and a need, specification, or availability of a product or service offered by a vendor.

5. The vehicle of claim 1, wherein the microprocessor publishes the one or more vendor products or services selected by the group of plural occupants, via the social network, to associated or selected associates of one or more of the occupants in the group of plural occupants and the publication comprises a plurality of vendor hours of operation, available product and/or service types, available product and/or service prices, available product and/or service specifications or requirements, and geographical location of a respective vendor.

6. The vehicle of claim 1, wherein the microprocessor directly or indirectly provides vehicle occupant information in the composite occupant profile and current vehicle location to the vendor server, wherein the microprocessor presents advertisement information from the vendor server associated with the proposed or selected one or more vendor products or services to one or more of the occupants in the group of plural occupants, wherein the microprocessor provides to an occupant a plurality of vendor hours of operation, available product and/or service types, available product and/or service prices, available product and/or service specifications or requirements, and geographical location of a respective vendor, wherein the microprocessor provides to the occupant a geographic data file comprising the geographic location of the respective vendor, wherein the plurality of vendor hours of operation, available product and/or service types, available product and/or service prices, available product and/or service specifications or requirements, and geographical location of the respective vendor are cross-referenced by a function-specific referencing table and/or generic reference features or attributes of the geographic data file, and wherein the advertisement information is associated with a vendor product or service located within a selected spatial range of a current vehicle location.

7. The vehicle of claim 1, wherein the composite occupant profile comprises plural preference fields, wherein, when the microprocessor determines that a selected preference field in each individual occupant profile of two or more occupants comprise of the group of plural occupants comprises a conflicting value, the microprocessor uses, for the selected preference field value in the composite occupant profile, one or more of a most frequently occurring value for the selected preference, the value of the selected preference field in the individual occupant profile having a highest role ranking and multiple, different values in the individual occupant profiles, each of which is a source of a stimulus or suggestion or recommendation to the two or more occupants of the group of plural occupants.

8. The vehicle of claim 1, wherein the composite profile is created by combining like preference field values in the individual occupant profiles of the group of plural occupants using one or more of (i) predictive analytics comprising one or more of a regression technique, linear regression model, discrete choice model, logistic regression, multinomial logistic regression, probit regression, time series model, survival or duration analysis, classification and regression tree, multivariate adaptive regression spline, machine learning technique, neural network, multilayer perception, radial basis function, support vector machine, Nave Bayes, k-nearest neighbor, and geospatial predictive modeling and (ii) behavioral segmentation, in which the microprocessor divides occupants of multiple different vehicles into groups or classes based on selected parameters of behavior using one or more of an objective and non-objective segmentation methodology to maximize a difference among segments with regards to each selected behavior parameter.

9. A method, comprising:

determining, by a microprocessor, that a vehicle interior comprises a group of plural occupants;

identifying, by the microprocessor, each occupant of the group of plural occupants;

based on individual profiles of the group of plural occupants, creating, by the microprocessor, a composite occupant profile that represents the group of plural occupants, the composite occupant profile comprising an identity of each occupant of the group of plural occupants and group preferences for various vendor products or services, wherein each occupant of the group of plural occupants has a corresponding individual occupant profile, each individual occupant profile comprising individual preference values, wherein the composite occupant profile and the group preferences are created by combining, for a selected preference, a preference value for the selected preference retrieved from the individual preference values for each occupant using one or more of a role value for each occupant of the group of plural occupants, an unweighted average of the individual preference values of each occupant of the group of plural occupants for the selected preference, a mode of the individual preference values of each occupant of the group of plural occupants for the selected preference, and a median of the individual preference values of each occupant of the group of plural occupants for the selected preference; and

based on the composite occupant profile and received inputs from a user interface, an automatic vehicle location system, and a plurality of sensors in the vehicle, one or more of;

(a) proposing, by the microprocessor, one or more vendor products or services for the group of plural occupants;

(b) publishing, by the microprocessor, the one or more vendor products or services selected by the group of plural occupants, via a social network, to associated or selected associates of one or more of the occupants in the group; and

(c) presenting, by the microprocessor, advertisement information from a vendor server associated with the proposed or selected one or more vendor products or services to one or more of the occupants in the group of plural occupants.

10. The method of claim 9, wherein the composite occupant profile comprises plural preference fields, wherein a preference value of at least one of the plural preference fields in the composite occupant profile is based on a preference value in an individual occupant profile of an occupant of a different vehicle and not currently an occupant of the vehicle, wherein creating the composite occupant profile is based on a driving behavior of the vehicle, wherein the driving behavior is one or more of current or historical user driving behavior, historical route driven by the vehicle, destination of the vehicle, and waypoints of the vehicle, wherein the driving behavior of the vehicle is attributed to the group of plural occupants, wherein the plurality of sensors comprise a plurality of:

a camera to capture images of the occupants in the vehicle interior, a camera to capture images of objects outside the vehicle, a seat belt sensor to determine a seat belt setting, a seat weight sensor to determine a weight of a seated occupant, a microphone to capture speech of the group of plural occupants, and a portable computing device of at least one of the group of plural occupants to sense interaction of the at least one of the group of plural occupants with the portable computing device, and wherein the composite occupant profile comprises, for each occupant of the group of plural occupants, an age or biometric information for each occupant and, for the group of plural occupants, visitation and/or transaction history with one or more vendors.

11. The method of claim 9, further comprising:

providing, by the microprocessor, information in the composite occupant profile to multiple vendor servers of multiple vendors, wherein the composite occupant profile comprises, for each occupant in the group of plural occupants, the role value indicating a role a corresponding occupant plays in a group decision, each occupant of the group of plural occupants having a different role value;

receiving, by the microprocessor and in response to providing the information, multiple vendor responses from the multiple;

assigning, by the microprocessor, a compliance score to each of the multiple vendor responses received, the compliance score indicating a degree of compliance of the respective vendor response with one or more of an occupant preference and an attractiveness of the respective vendor response to a need, specification or requirement of the composite occupant profile;

filtering, by the microprocessor, the multiple vendor responses based on the assigned compliance scores; and

providing, by the microprocessor and based on the filtering, a subset of the multiple vendor responses to at least one occupant of the group of plural occupants.

12. The method of claim 9, wherein the microprocessor directly or indirectly provides vehicle occupant information in the composite occupant profile and current vehicle location to the vendor server, wherein the microprocessor proposes one or more vendor products or services for the group of plural occupants, wherein the microprocessor provides to an occupant of the group of plural occupants a plurality of vendor hours of operation, available product and/or service types, available product and/or service prices, available product and/or service specifications or requirements, and geographical location of a respective vendor, wherein the microprocessor provides to the occupant a geographic data file comprising the geographic location of the respective vendor, wherein the plurality of vendor hours of operation, available product and/or service types, available product and/or service prices, available product and/or service specifications or requirements, and geographical location of the respective vendor are cross-referenced by a function-specific referencing table and/or generic reference features or attributes of the geographic data file, and wherein the proposed one or more vendor products is proposed based on a number of members in the group of plural occupants, ages of the members in the group of plural occupants, and sexes of the members in the group of plural occupants and a need, specification, or availability of a product or service offered by a vendor.

13. The method of claim 9, further comprising:

publishing, by the microprocessor, the one or more vendor products or services selected by the group of plural occupants, via the social network, to associated or selected associates of one or more of the occupants in the group of plural occupants and the publication comprises a plurality of vendor hours of operation, available product and/or service types, available product and/or service prices, available product and/or service specifications or requirements, and geographical location of a respective vendor.

14. The method of claim 9, further comprising:

providing, by the microprocessor directly or indirectly, vehicle occupant information in the composite occupant profile and current vehicle location to the vendor server;

presenting, by the microprocessor, advertisement information from the vendor server associated with the proposed or selected one or more vendor products or services to one or more of the occupants in the group of plural occupants;

providing, by the microprocessor, to an occupant a plurality of vendor hours of operation, available product and/or service types, available product and/or service prices, available product and/or service specifications or requirements, and geographical location of a respective vendor; and

providing, by the microprocessor, to the occupant a geographic data file comprising the geographic location of the respective vendor, wherein the plurality of vendor hours of operation, available product and/or service types, available product and/or service prices, available product and/or service specifications or requirements, and geographical location of the respective vendor are cross-referenced by a function-specific referencing table and/or generic reference features or attributes of the geographic data file, and wherein the advertisement information is associated with a vendor product or service located within a selected spatial range of a current vehicle location.

15. The method of claim 9, further comprising:

determining, by the microprocessor, that a selected preference field in each individual occupant profile of two or more occupants of the group of plural occupants comprises a conflicting value;

using, by the microprocessor, for the selected preference field value in the composite occupant profile, one or more of a most frequently occurring value for the selected preference, the value of the selected preference in the individual occupant profile having a highest role ranking and multiple, different values in the individual occupant profiles, each of which is a source of a stimulus or suggestion or recommendation to the two or more occupants of the group of plural occupants.

16. The method of claim 9, wherein the composite occupant profile is created by the microprocessor combining like preference field values in the individual occupant profiles of the group of plural occupants using one or more of (i) predictive analytics comprising one or more of a regression technique, linear regression model, discrete choice model, logistic regression, multinomial logistic regression, probit regression, time series model, survival or duration analysis, classification and regression tree, multivariate adaptive regression spline, machine learning technique, neural network, multilayer perception, radial basis function, support vector machine, Naï

ve Bayes, k-nearest neighbor, and geospatial predictive modeling and (ii) behavioral segmentation, in which the microprocessor divides occupants of multiple different vehicles into groups or classes based on selected parameters of behavior using one or more of an objective and non-objective segmentation methodology to maximize a difference among segments with regards to each selected behavior parameter.