DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/26/2025 has been entered.
Status of Claims
This action is in reply to the amendments filed on 03/26/2025.
Claims 1-7 and 9-21 are currently pending and have been examined.
Claims 1-6, 9-13, 15-16, and 18 have been amended.
Claims 1-7 and 9-21 are currently rejected.
This action is made NON-FINAL.
Response to Arguments
Applicant’s arguments filed 03/26/2025 have been fully considered but they are not fully persuasive.
Regarding the 101 rejection, applicants arguments are not persuasive. Applicant argues that the claims provide meaningful limits on the abstract idea and is tied to a particular machine. In the claims, the servers and vehicle computers are performing the mental process in lieu of a person from gathered data and is not tying the determinations to a specific machine and is only reciting generic computer hardware to perform the mental processes. Therefore the 101 rejections are being maintained below.
Applicant’s arguments with regards to the art rejections have been considered and appear to be directed solely to the instant amendments to the claims. Accordingly, the claims are addressed in the body of the rejections below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-7 and 9-21are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-7 and 9-21 are directed to a system, method, or product, which are/is one of the statutory categories of invention. (Step 1: YES)
The examiner has identified independent system/method/product Claim 1 as the claim that represents the claimed invention for analysis and is similar to independent Claim 6 and Claim 13. Claim 1 recites the limitations of:
determining vehicle degradation values for the available vehicle
selecting a first vehicle over one or more other vehicles
determining, with the server, an updated vehicle degradation value for the first vehicle based on the updated set of vehicle sensor measurements received after providing the destination to the application of the first vehicle;
re-assigning, with the server, the task to a second vehicle by providing, with the server, the destination of the preliminary navigation path to the second vehicle based on the result, wherein a second onboard computing device of the second vehicle generates a second navigation path from a current location of the second vehicle to the destination.
These limitations, under their broadest reasonable interpretation, cover performance of the limitation as mental processes. Determining values and selecting something based off calculated values recites concepts performed in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a concept performed in the human mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. The servers and vehicles in Claim 1 is just applying generic computer components to the recited abstract limitations. The recitation of generic computer components in a claim does not necessarily preclude that claim from reciting an abstract idea. Claims 6 and 13 are also abstract for similar reasons. (Step 2A-Prong 1: YES. The claims recite an abstract idea.)
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of: obtaining vehicle sensor measurements of a plurality of vehicles, obtaining a set of vehicle utilization parameters, and wirelessly providing a destination of the navigation path to an application of the first vehicle. These steps are insignificant pre- and post- solution activity that do not incorporate the mental process into a practical application. Therefore, claims 1, 6, and 13 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application.)
The claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. In the instant application, the vehicles, sensors, and computers/servers. Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. Thus, claims 1, 6, and 13 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more.)
Dependent claims further define the abstract idea that is present in their respective independent claims 1, 6, and 13 and thus correspond to Mental Processes and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims are directed to an abstract idea. Thus, the claims 1-7 and 9-21 are not patent-eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-2, 6, 9, and 13-14is/are rejected under 35 U.S.C. 103 as being unpatentable over Morizumi et. al. (US 2022/0005609), herein Morizumi in view of Liu et. al. (US 2021/0215491), herein Liu and Rakah et. al. (US 2018/0211541), herein Rakah.
Regarding claim 1:
Morizumi teaches:
A method (method for managing shared vehicle [0001]) for selecting a vehicle (assigns the available vehicle to the reservation [0025]) based on utilization parameters (the processor 52 changes the status information of the vehicle A from “In use” to “Unavailable”, which will prohibit any future reservations from being assigned to the vehicle A [0146]) for fulfilling a destination-related task (a vehicle rental service and a car sharing service [0003]) comprising:
vehicle degradation values for a plurality of vehicles (the external server 50 may accumulate the data from the vehicles over the time and monitor a clue of the outbreak of the respiratory disease and/or allergy [0124]) based on (i) a set of vehicle utilization parameters (assessing the respiratory disease symptom of the passenger to generate infection risk information associated with an infection risk of a next passenger expected to ride the vehicle [0013])
determining an updated vehicle degradation value for the first vehicle based on the an updated set of vehicle sensor measurements (the processor 32 generates infection risk information based on the severity of the symptom. In addition or as an alternative, contamination information of the vehicle compartment 24 may be generated. The concentration of aerosols (i.e., the level of contamination) in the compartment 24 of the vehicle 20 depends on not only the frequency of respiratory disease symptoms but also the size or capacity of the compartment 24 of the vehicle 20 as well as the duration of the symptoms. Therefore, the vehicle compartment contamination information should be determined with considering these factors as well. That is, the infection risk information indicates a likelihood (risk) of an infection of a next passenger expected to ride the vehicle, while the vehicle compartment contamination information indicates a necessity of a disinfectant procedure after the vehicle is returned and prior to a next use of the vehicle. [0113]; updating the vehicle compartment contamination information after the disinfectant procedure is completed so as to indicate that the vehicle is clean [0036]);
sending, to the server (the external server 50 utilizes the information to manage an entire fleet schedule [0144]), an indication that the updated vehicle degradation value satisfies a vehicle degradation threshold (The risk degree may be defined in association with the likelihood (risk) of an infection of a next user expected to use the seat. For example, less than 1% may be defined as “low”, 1% or more and less than 20% may be defined as “middle”, and more than 20% may be defined as “high”. The risk degree may also be defined in association with the necessity of a disinfectant procedure prior to a next use of the seat. [0141]); and
re-selecting, with the server, a second vehicle for the task (Since the vehicle A needs to be disinfected prior to the next use and will be unavailable until the disinfectant procedure is completed, this reservation must be changed. Thus, the processor 52 searches an available vehicle in this time slot from the fleet schedule. As the vehicle B is available in this time slot, the processor 52 reassigns the reservation to the vehicle B and move the reservation information from the vehicle A to the vehicle B (S230) [0146])
Liu also teaches:
A method for selecting a vehicle (method for vehicle sharing include a vehicle having sensors [abstract]) based on utilization parameters (parameter values 830 based on sensor signals 812 [0060]) for fulfilling a destination-related task (vehicle sharing arrangements for purchase, lease, or ride sharing of a single vehicle or a group of shared vehicles [0002]) comprising:
vehicle degradation values for a plurality of vehicles (Due to the concern of vehicle maintenance, repairs, depreciation, insurance, and other costs, many vehicle owners hesitate to share their vehicles creating an obstacle to owner acceptance of new vehicle sharing ownership models or car sharing models. While the cost of “wear and tear” on a vehicle may be partially covered by a depreciation or mileage allocation, some types of “wear and tear” associated with a particular user or use may be very difficult to detect and allocate costs accordingly [0021]) based on (i) a set of vehicle utilization parameters (As previously described, some components may have wear and tear monitored based on odometer distance, time, speed or a combination of the two. Some components may function normally, then fail suddenly such as an engine low on oil, while other components wear gradually and predictably, such as tire treads. The wear and tear models used to determine vehicle sharing cost may be adjusted accordingly based on the type of component and types of wear associated with particular vehicle operation. [0064])
Morizumi does not explicitly teach, however Liu teaches:
comprising a preliminary navigation path (processor 106 may perform various calculations associated with determining real-time costs of vehicle wear and tear and display the costs to the user and/or communicate with a cloud-based network to provide data and receive costs associated with vehicle maintenance allocated to a particular use, user, route, weather, road condition etc. [0025]) and, (ii) vehicle sensor measurements indicating odometer measurements of the available plurality of vehicles (The instrument panel adds the wheel rotations to calculate the odometer measurement. The odometer value may be used for component wear estimates that are based on distance driven. [0046]);
providing, with the server, a destination of the preliminary navigation path (Vehicle sharing cost may be determined for a vehicle use, driver, route, road conditions, parking behavior, weather, etc. based on data from vehicle sensors and external sensors to detect ambient and operating conditions. [abstract]; The direction of the sun with respect to the vehicle can be determined by the navigation system from the bearing of the vehicle, longitude, latitude, date and time it receives from the global positioning system using a well-known equation [0079])
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Morizumi to include the teachings as taught by Liu with a reasonable expectation of success. Liu teaches the benefits of “a vehicle sharing method or system includes sensors and a processor programmed to receive sensor data associated with operation of a vehicle by a vehicle sharing user during a trip and calculate a vehicle sharing cost associated with anticipated maintenance of a vehicle component based on the sensor data from the trip, historical information and customer characteristics (e.g. repeated ride-sharing customers, or the years of owning a vehicle). The processor may be further programmed to communicate the vehicle sharing cost for display on a vehicle HMI. The sensor may be embedded within the vehicle, and the processor may receive data from external sensors to detect or determine a particular vehicle use, route, driver behavior, and the like. The processor may be programmed to calculate the vehicle sharing cost by comparing the sensor data to previously stored expected values in a vehicle component wear model, and to determine a route for the trip based on the sensor data from the trip. The vehicle sharing cost may be determined based on historical sensor data associated with the route for the trip. The vehicle sharing cost may be also determined based on historical driving behaviors and customers' characteristics. [Liu, 0003]”. The methods of calculating use cost/depreciation of Liu can be applied to the vehicle tasks as performed by the teachings of Lodhia to provide better cost calculations based on the details of the vehicle use.
Morizumi in view of Liu does not explicitly teach, however Rakah teaches:
providing, with the server, a destination of the preliminary navigation path to an application of a first client device by selecting a first vehicle for a task over one or more other vehicles of the plurality of vehicles based on a first vehicle degradation value for the first vehicle (At step 1111, server 150 may assign a first ridesharing vehicle to pick-up a first group of the plurality of users. For example, the first group may include a first user, a second user, a third user, etc. Some users may be included in the same request (e.g., if a first user and a second user are included in a first request). As explained above with regards to assignment module 920, server 150 may assemble the first group of the plurality of users based on the closeness of starting points of the assembled users, the closeness of the desired destinations of the assembled users, the closeness of the starting points of some of the first group to desired destinations of others of the first group, overlap between predicted routes from the starting points to the desired destinations of some of first group and predicted routes from the starting points to the desired destinations of others of the first group, or the like. [0239]), wherein the first client device performs operations (at least some of the steps of process 400 may be performed by a mobile communications device [0114]) comprising:
re-selecting, with the server, a second vehicle for the task (At step 1125, server 150 may re-assign the first user to the second ridesharing vehicle [0248]) by (l) receiving the indication from the first client device (when the predicted passing time is after the predicted arrival time. Optionally, server 150 may re-assign the first user only when the predicted passing time is within one or more thresholds after the predicted arrival time. For example, server 150 may re-assign the first user if the predicted passing time is less than 5 minutes, 10 minutes, etc. after the predicted arrival time and/or may re-assign the first user if the predicted passing time is more than 1 minute, 2 minutes, etc. after the predicted arrival time. [0248]) and (2) providing, with the server, the destination of the preliminary navigation path to the second vehicle based on the indication (In some embodiments, server 150 may guide the second ridesharing vehicle to the first pick-up location. For example, as explained above with respect to location module 910, server 150 may send a location (e.g., UPS coordinates) and/or an address of the first pick-up location to a second communications device associated with the second ridesharing vehicle and/or send driving directions to the first pick-up location to the second communications device. [0248]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Morizumi in view of Liu to include the teachings as taught by Rakah with a reasonable expectation of success. Rakah teaches the benefits of “software instructions for routing a ridesharing vehicle to pick up and/or transport one or more users. For example, in response to the ride requests received by input data collection module 1801 from the plurality of users headed to differing destinations, vehicle routing module 1803 may send the ridesharing vehicle to pick up the plurality of users headed to the different destinations. That is, vehicle routing module 1803 may direct the ridesharing vehicle along one or more routes through the surrounding environment based on the current state of one or more variables. Further, the route to which the ridesharing vehicle is assigned may be dynamically adjusted during transportation of the plurality of users to redirect the ridesharing vehicle to optimize one or more performance variables. [Rakah, 0366]”.
Regarding claim 2:
Morizumi in view of Liu and Rakah teaches all the limitations of claim 1, upon which this claim is dependent.
Morizumi further teaches:
and wherein a set of vehicle degradation values comprises the first vehicle degradation value and is associated with the first vehicle (At the step S140, the processor 32 generates infection risk information based on the severity of the symptom. In addition or as an alternative, contamination information of the vehicle compartment 24 may be generated. The concentration of aerosols (i.e., the level of contamination) in the compartment 24 of the vehicle 20 depends on not only the frequency of respiratory disease symptoms but also the size or capacity of the compartment 24 of the vehicle 20 as well as the duration of the symptoms [0113]), the method further comprising:
Rakah further teaches:
predicting a time point for when the set of vehicle degradation values satisfies a threshold based on a subset of records sharing a vehicle category of the second vehicle (send data to the group of the plurality of users indicating appointed pick-up times at the determined pick-up locations; use information received from at least one of the plurality of ridesharing vehicles to predict when the first ridesharing vehicle will arrive to a first pick-up location assigned to a first user; prior to a first pick-up time associated with the first user, estimate that the first ridesharing vehicle is going to be late to the first pick-up location by more than a time threshold; identify a second ridesharing vehicle to be assigned to pick-up the first user; cancel the assignment of the first ridesharing vehicle to the first user while maintaining the assignment of the first ridesharing vehicle to others of the group of the plurality of users; and assign the second ridesharing vehicle to pick up the first user. [0009]);
in response to a determination that the set of vehicle degradation values satisfies the threshold, updating the set of vehicle degradation values based on an additional navigation path (ridesharing management server 150 may decline to re-assign the second ridesharing vehicle even if the updated total waiting time is less than the initial total waiting time. For example, one or more thresholds (e.g., 10 minutes, 15 minutes, or the like) may be applied to a predicted waiting time for an individual user. In this example, ridesharing management server 150 may decline to re-assign the second ridesharing vehicle if the re-assignment would result in a predicted waiting time for a user exceeding the threshold. Accordingly, inconveniences to individual users may be capped in order to encourage such users to become repeat riders and enjoy the advantages of fleet-wide optimization on one or more future trips. [0234]);
selecting a third vehicle based on the set of vehicle degradation values after the updating of the set of vehicle degradation values (comparing the predicted passing time of the second ridesharing vehicle with the arrival time of the first user; and re-assigning the first user to the second ridesharing vehicle when the predicted passing time is after the predicted arrival time [0008]); and
wirelessly providing a destination of the additional navigation path to the third vehicle (ridesharing management server 150 may include software that, when executed by a processor, provides communications with network 140 through communications interface 360 to one or more mobile communications devices 120A-F. In some embodiments, transmission module 2110 may further send to the user, via the communications interface, information that causes a display of walking directions from a starting point to a pick-up location and from a drop-off location to a desired destination. Transmission module 2110 may further send (e.g., via a communications interface) messages to the passengers of a ridesharing vehicle when a route other than the reduced-backtracking route has been selected. [0398]).
Regarding claim 6:
Morizumi teaches:
One or more tangible, non-transitory, machine-readable media storing instructions that, when executed by one or more processors (the memory 42 may store a system program [0095]), effectuating operations comprising:
determining a plurality of values associated with a plurality of vehicles (assigns the available vehicle to the reservation [0025]) based on a set of vehicle utilization parameters (the processor 52 changes the status information of the vehicle A from “In use” to “Unavailable”, which will prohibit any future reservations from being assigned to the vehicle A [0146]) for fulfilling a destination-related task (a vehicle rental service and a car sharing service [0003]) comprising:
vehicle degradation values for a plurality of vehicles (the external server 50 may accumulate the data from the vehicles over the time and monitor a clue of the outbreak of the respiratory disease and/or allergy [0124]) based on a set of vehicle utilization parameters (assessing the respiratory disease symptom of the passenger to generate infection risk information associated with an infection risk of a next passenger expected to ride the vehicle [0013])
determining an updated vehicle degradation value for the first vehicle based on the an updated set of vehicle sensor measurements (the processor 32 generates infection risk information based on the severity of the symptom. In addition or as an alternative, contamination information of the vehicle compartment 24 may be generated. The concentration of aerosols (i.e., the level of contamination) in the compartment 24 of the vehicle 20 depends on not only the frequency of respiratory disease symptoms but also the size or capacity of the compartment 24 of the vehicle 20 as well as the duration of the symptoms. Therefore, the vehicle compartment contamination information should be determined with considering these factors as well. That is, the infection risk information indicates a likelihood (risk) of an infection of a next passenger expected to ride the vehicle, while the vehicle compartment contamination information indicates a necessity of a disinfectant procedure after the vehicle is returned and prior to a next use of the vehicle. [0113]; updating the vehicle compartment contamination information after the disinfectant procedure is completed so as to indicate that the vehicle is clean [0036]);
sending an indication that the updated vehicle degradation value satisfies a vehicle degradation threshold (The risk degree may be defined in association with the likelihood (risk) of an infection of a next user expected to use the seat. For example, less than 1% may be defined as “low”, 1% or more and less than 20% may be defined as “middle”, and more than 20% may be defined as “high”. The risk degree may also be defined in association with the necessity of a disinfectant procedure prior to a next use of the seat. [0141]); and
re-selecting a second vehicle for the task (Since the vehicle A needs to be disinfected prior to the next use and will be unavailable until the disinfectant procedure is completed, this reservation must be changed. Thus, the processor 52 searches an available vehicle in this time slot from the fleet schedule. As the vehicle B is available in this time slot, the processor 52 reassigns the reservation to the vehicle B and move the reservation information from the vehicle A to the vehicle B (S230) [0146])
Liu also teaches:
vehicle degradation values for a plurality of vehicles (Due to the concern of vehicle maintenance, repairs, depreciation, insurance, and other costs, many vehicle owners hesitate to share their vehicles creating an obstacle to owner acceptance of new vehicle sharing ownership models or car sharing models. While the cost of “wear and tear” on a vehicle may be partially covered by a depreciation or mileage allocation, some types of “wear and tear” associated with a particular user or use may be very difficult to detect and allocate costs accordingly [0021]) based on a set of vehicle utilization parameters (As previously described, some components may have wear and tear monitored based on odometer distance, time, speed or a combination of the two. Some components may function normally, then fail suddenly such as an engine low on oil, while other components wear gradually and predictably, such as tire treads. The wear and tear models used to determine vehicle sharing cost may be adjusted accordingly based on the type of component and types of wear associated with particular vehicle operation. [0064])
Morizumi does not explicitly teach, however Liu teaches:
comprising a preliminary navigation path (processor 106 may perform various calculations associated with determining real-time costs of vehicle wear and tear and display the costs to the user and/or communicate with a cloud-based network to provide data and receive costs associated with vehicle maintenance allocated to a particular use, user, route, weather, road condition etc. [0025]) and a vehicle sensor measurements indicating odometer measurements of the available plurality of vehicles (The instrument panel adds the wheel rotations to calculate the odometer measurement. The odometer value may be used for component wear estimates that are based on distance driven. [0046]);
providing a destination of the preliminary navigation path (Vehicle sharing cost may be determined for a vehicle use, driver, route, road conditions, parking behavior, weather, etc. based on data from vehicle sensors and external sensors to detect ambient and operating conditions. [abstract]; The direction of the sun with respect to the vehicle can be determined by the navigation system from the bearing of the vehicle, longitude, latitude, date and time it receives from the global positioning system using a well-known equation [0079])
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Morizumi to include the teachings as taught by Liu with a reasonable expectation of success. Liu teaches the benefits of “a vehicle sharing method or system includes sensors and a processor programmed to receive sensor data associated with operation of a vehicle by a vehicle sharing user during a trip and calculate a vehicle sharing cost associated with anticipated maintenance of a vehicle component based on the sensor data from the trip, historical information and customer characteristics (e.g. repeated ride-sharing customers, or the years of owning a vehicle). The processor may be further programmed to communicate the vehicle sharing cost for display on a vehicle HMI. The sensor may be embedded within the vehicle, and the processor may receive data from external sensors to detect or determine a particular vehicle use, route, driver behavior, and the like. The processor may be programmed to calculate the vehicle sharing cost by comparing the sensor data to previously stored expected values in a vehicle component wear model, and to determine a route for the trip based on the sensor data from the trip. The vehicle sharing cost may be determined based on historical sensor data associated with the route for the trip. The vehicle sharing cost may be also determined based on historical driving behaviors and customers' characteristics. [Liu, 0003]”. The methods of calculating use cost/depreciation of Liu can be applied to the vehicle tasks as performed by the teachings of Lodhia to provide better cost calculations based on the details of the vehicle use.
Morizumi in view of Liu does not explicitly teach, however Rakah teaches:
providing a destination of the preliminary navigation path to an application of a first client device by selecting a first vehicle for a task over one or more other vehicles of the plurality of vehicles based on a first vehicle degradation value for the first vehicle (At step 1111, server 150 may assign a first ridesharing vehicle to pick-up a first group of the plurality of users. For example, the first group may include a first user, a second user, a third user, etc. Some users may be included in the same request (e.g., if a first user and a second user are included in a first request). As explained above with regards to assignment module 920, server 150 may assemble the first group of the plurality of users based on the closeness of starting points of the assembled users, the closeness of the desired destinations of the assembled users, the closeness of the starting points of some of the first group to desired destinations of others of the first group, overlap between predicted routes from the starting points to the desired destinations of some of first group and predicted routes from the starting points to the desired destinations of others of the first group, or the like. [0239]), wherein the first client device performs operations (at least some of the steps of process 400 may be performed by a mobile communications device [0114]) comprising:
re-selecting a second vehicle for the task (At step 1125, server 150 may re-assign the first user to the second ridesharing vehicle [0248]) by (1) receiving the indication from the first client device (when the predicted passing time is after the predicted arrival time. Optionally, server 150 may re-assign the first user only when the predicted passing time is within one or more thresholds after the predicted arrival time. For example, server 150 may re-assign the first user if the predicted passing time is less than 5 minutes, 10 minutes, etc. after the predicted arrival time and/or may re-assign the first user if the predicted passing time is more than 1 minute, 2 minutes, etc. after the predicted arrival time. [0248]) and (2) providing, with the server, the destination of the preliminary navigation path to the second vehicle based on the indication (In some embodiments, server 150 may guide the second ridesharing vehicle to the first pick-up location. For example, as explained above with respect to location module 910, server 150 may send a location (e.g., UPS coordinates) and/or an address of the first pick-up location to a second communications device associated with the second ridesharing vehicle and/or send driving directions to the first pick-up location to the second communications device. [0248]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Morizumi in view of Liu to include the teachings as taught by Rakah with a reasonable expectation of success. Rakah teaches the benefits of “software instructions for routing a ridesharing vehicle to pick up and/or transport one or more users. For example, in response to the ride requests received by input data collection module 1801 from the plurality of users headed to differing destinations, vehicle routing module 1803 may send the ridesharing vehicle to pick up the plurality of users headed to the different destinations. That is, vehicle routing module 1803 may direct the ridesharing vehicle along one or more routes through the surrounding environment based on the current state of one or more variables. Further, the route to which the ridesharing vehicle is assigned may be dynamically adjusted during transportation of the plurality of users to redirect the ridesharing vehicle to optimize one or more performance variables. [Rakah, 0366]”.
Regarding claim 9:
Morizumi in view of Liu and Rakah teaches all the limitations of claim 6, upon which this claim is dependent.
Liu further teaches:
obtaining a plurality of time series (processor 106 may perform various calculations associated with determining real-time costs of vehicle wear and tear and display the costs to the user and/or communicate with a cloud-based network to provide data and receive costs associated with vehicle maintenance allocated to a particular use, user, route, weather, road condition etc. [0025]), wherein each time series of the plurality of time series is associated with a respective vehicle of the plurality of vehicles (Accurate analysis and assessment of vehicle maintenance and repair costs may encourage more vehicle owners to participate in vehicle sharing programs and more accurately allocate costs to the users in proportion to actual uses of the vehicle. As described in greater detail herein, vehicle sharing prices or charges may be based on real-time driving behaviors, routes driven, road/parking conditions, etc. [0023]);
determining a vehicle status of the first vehicle based on a time series of the plurality of time series (The detection corridor or contour may be determined based on a statistical probability that the data excursion corresponds or is likely to correlate with a particular condition or component failure that requires maintenance or repair. [0087]), wherein the time series is associated with the first vehicle (Model builders 370 may use the data from vehicle sensors/computers 320 to build and calibrate maintenance models and associated pricing models based on the sensor data. Insurers 372 receive data reflecting actual use of vehicles that may be used to determine the current status/condition of insured vehicles, and for pricing of policies based on actual vehicle uses. [0042]);
sending the vehicle status to the first vehicle (A GPS clock determines when and where a braking event occurs based on notification 834. ECU module 814 may store the ambient temperature, barometric pressure, humidity, rain, and similar information received over vehicle network 862 and associated with a particular braking event. The antilock braking system 810 reports when ABS control is triggered indicating a low traction or hard braking event. Wheel height or suspension sensors may also provide date to determine the vehicle weight and weight shift during braking. A brake performance model can then use the data collected by the vehicle sensors to estimate the amount of brake pad and rotor (or drum and lining for drum brakes) wear for each braking event. The events may be logged by the VCS and the wear of brake components estimated with a corresponding price determined for the particular vehicle use or trip. [0063]), wherein a vehicle computer system performs operations comprising:
providing the vehicle status and a set of vehicle sensor measurements to a machine learning model to obtain an output (Based on collected characteristics of events, a machine learning method, such as a support vector machine may be used to classify the events according to: [0048]);
determining whether the output satisfies a threshold (A detection corridor may be defined by a lower threshold 1064 and an upper threshold 1066 with a trigger or flag set when the vehicle data is outside of the corridor, beginning at 1070, for example. [0087]); and
displaying a notification on a visual display of the first vehicle in response to a determination that the output satisfies the threshold (The sensor or the models may also trigger an alert that is sent to the lenders to perform some maintenance work related to specific or accumulated wear and tear. One or more extended service plans may also be priced accordingly and offered or recommended to the vehicle owner/lender. [0047]).
Regarding claim 13:
Morizumi teaches:
A system (shared vehicles managing system 10 [0096]) comprising:
one or more processors (processor 32 [0095]); and
memory storing computer program instructions that, when executed by the one or more processors (the memory 42 may store a system program [0095]), cause the one or more processors to effectuate operations comprising:
determining a plurality of values associated with a plurality of vehicles (assigns the available vehicle to the reservation [0025]) based on a set of vehicle utilization parameters (the processor 52 changes the status information of the vehicle A from “In use” to “Unavailable”, which will prohibit any future reservations from being assigned to the vehicle A [0146]) for fulfilling a destination-related task (a vehicle rental service and a car sharing service [0003]) comprising:
vehicle degradation values for a plurality of vehicles (the external server 50 may accumulate the data from the vehicles over the time and monitor a clue of the outbreak of the respiratory disease and/or allergy [0124]) based on a set of vehicle utilization parameters (assessing the respiratory disease symptom of the passenger to generate infection risk information associated with an infection risk of a next passenger expected to ride the vehicle [0013])
determining an updated vehicle degradation value for the first vehicle based on the an updated set of vehicle sensor measurements (the processor 32 generates infection risk information based on the severity of the symptom. In addition or as an alternative, contamination information of the vehicle compartment 24 may be generated. The concentration of aerosols (i.e., the level of contamination) in the compartment 24 of the vehicle 20 depends on not only the frequency of respiratory disease symptoms but also the size or capacity of the compartment 24 of the vehicle 20 as well as the duration of the symptoms. Therefore, the vehicle compartment contamination information should be determined with considering these factors as well. That is, the infection risk information indicates a likelihood (risk) of an infection of a next passenger expected to ride the vehicle, while the vehicle compartment contamination information indicates a necessity of a disinfectant procedure after the vehicle is returned and prior to a next use of the vehicle. [0113]; updating the vehicle compartment contamination information after the disinfectant procedure is completed so as to indicate that the vehicle is clean [0036]);
sending an indication that the updated vehicle degradation value satisfies a vehicle degradation threshold (The risk degree may be defined in association with the likelihood (risk) of an infection of a next user expected to use the seat. For example, less than 1% may be defined as “low”, 1% or more and less than 20% may be defined as “middle”, and more than 20% may be defined as “high”. The risk degree may also be defined in association with the necessity of a disinfectant procedure prior to a next use of the seat. [0141]); and
re-selecting a second vehicle for the task (Since the vehicle A needs to be disinfected prior to the next use and will be unavailable until the disinfectant procedure is completed, this reservation must be changed. Thus, the processor 52 searches an available vehicle in this time slot from the fleet schedule. As the vehicle B is available in this time slot, the processor 52 reassigns the reservation to the vehicle B and move the reservation information from the vehicle A to the vehicle B (S230) [0146])
Liu also teaches:
vehicle degradation values for a plurality of vehicles (Due to the concern of vehicle maintenance, repairs, depreciation, insurance, and other costs, many vehicle owners hesitate to share their vehicles creating an obstacle to owner acceptance of new vehicle sharing ownership models or car sharing models. While the cost of “wear and tear” on a vehicle may be partially covered by a depreciation or mileage allocation, some types of “wear and tear” associated with a particular user or use may be very difficult to detect and allocate costs accordingly [0021]) based on a set of vehicle utilization parameters (As previously described, some components may have wear and tear monitored based on odometer distance, time, speed or a combination of the two. Some components may function normally, then fail suddenly such as an engine low on oil, while other components wear gradually and predictably, such as tire treads. The wear and tear models used to determine vehicle sharing cost may be adjusted accordingly based on the type of component and types of wear associated with particular vehicle operation. [0064])
Morizumi does not explicitly teach, however Liu teaches:
comprising a preliminary navigation path (processor 106 may perform various calculations associated with determining real-time costs of vehicle wear and tear and display the costs to the user and/or communicate with a cloud-based network to provide data and receive costs associated with vehicle maintenance allocated to a particular use, user, route, weather, road condition etc. [0025]) and a vehicle sensor measurements indicating odometer measurements of the available plurality of vehicles (The instrument panel adds the wheel rotations to calculate the odometer measurement. The odometer value may be used for component wear estimates that are based on distance driven. [0046]);
providing a destination of the preliminary navigation path (Vehicle sharing cost may be determined for a vehicle use, driver, route, road conditions, parking behavior, weather, etc. based on data from vehicle sensors and external sensors to detect ambient and operating conditions. [abstract]; The direction of the sun with respect to the vehicle can be determined by the navigation system from the bearing of the vehicle, longitude, latitude, date and time it receives from the global positioning system using a well-known equation [0079])
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Morizumi to include the teachings as taught by Liu with a reasonable expectation of success. Liu teaches the benefits of “a vehicle sharing method or system includes sensors and a processor programmed to receive sensor data associated with operation of a vehicle by a vehicle sharing user during a trip and calculate a vehicle sharing cost associated with anticipated maintenance of a vehicle component based on the sensor data from the trip, historical information and customer characteristics (e.g. repeated ride-sharing customers, or the years of owning a vehicle). The processor may be further programmed to communicate the vehicle sharing cost for display on a vehicle HMI. The sensor may be embedded within the vehicle, and the processor may receive data from external sensors to detect or determine a particular vehicle use, route, driver behavior, and the like. The processor may be programmed to calculate the vehicle sharing cost by comparing the sensor data to previously stored expected values in a vehicle component wear model, and to determine a route for the trip based on the sensor data from the trip. The vehicle sharing cost may be determined based on historical sensor data associated with the route for the trip. The vehicle sharing cost may be also determined based on historical driving behaviors and customers' characteristics. [Liu, 0003]”. The methods of calculating use cost/depreciation of Liu can be applied to the vehicle tasks as performed by the teachings of Lodhia to provide better cost calculations based on the details of the vehicle use.
Morizumi in view of Liu does not explicitly teach, however Rakah teaches:
providing a destination of the preliminary navigation path to an application of a first client device by selecting a first vehicle for a task over one or more other vehicles of the plurality of vehicles based on a first vehicle degradation value for the first vehicle (At step 1111, server 150 may assign a first ridesharing vehicle to pick-up a first group of the plurality of users. For example, the first group may include a first user, a second user, a third user, etc. Some users may be included in the same request (e.g., if a first user and a second user are included in a first request). As explained above with regards to assignment module 920, server 150 may assemble the first group of the plurality of users based on the closeness of starting points of the assembled users, the closeness of the desired destinations of the assembled users, the closeness of the starting points of some of the first group to desired destinations of others of the first group, overlap between predicted routes from the starting points to the desired destinations of some of first group and predicted routes from the starting points to the desired destinations of others of the first group, or the like. [0239]), wherein the first client device performs operations (at least some of the steps of process 400 may be performed by a mobile communications device [0114]) comprising:
re-selecting a second vehicle for the task (At step 1125, server 150 may re-assign the first user to the second ridesharing vehicle [0248]) by (1) receiving the indication from the first client device (when the predicted passing time is after the predicted arrival time. Optionally, server 150 may re-assign the first user only when the predicted passing time is within one or more thresholds after the predicted arrival time. For example, server 150 may re-assign the first user if the predicted passing time is less than 5 minutes, 10 minutes, etc. after the predicted arrival time and/or may re-assign the first user if the predicted passing time is more than 1 minute, 2 minutes, etc. after the predicted arrival time. [0248]) and (2) providing, with the server, the destination of the preliminary navigation path to the second vehicle based on the indication (In some embodiments, server 150 may guide the second ridesharing vehicle to the first pick-up location. For example, as explained above with respect to location module 910, server 150 may send a location (e.g., UPS coordinates) and/or an address of the first pick-up location to a second communications device associated with the second ridesharing vehicle and/or send driving directions to the first pick-up location to the second communications device. [0248]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Morizumi in view of Liu to include the teachings as taught by Rakah with a reasonable expectation of success. Rakah teaches the benefits of “software instructions for routing a ridesharing vehicle to pick up and/or transport one or more users. For example, in response to the ride requests received by input data collection module 1801 from the plurality of users headed to differing destinations, vehicle routing module 1803 may send the ridesharing vehicle to pick up the plurality of users headed to the different destinations. That is, vehicle routing module 1803 may direct the ridesharing vehicle along one or more routes through the surrounding environment based on the current state of one or more variables. Further, the route to which the ridesharing vehicle is assigned may be dynamically adjusted during transportation of the plurality of users to redirect the ridesharing vehicle to optimize one or more performance variables. [Rakah, 0366]”.
Regarding claim 14:
Morizumi in view of Liu and Rakah teaches all the limitations of claim 13, upon which this claim is dependent.
Liu further teaches:
obtaining a time series of scores associated with the first vehicle (processor 106 may perform various calculations associated with determining real-time costs of vehicle wear and tear and display the costs to the user and/or communicate with a cloud-based network to provide data and receive costs associated with vehicle maintenance allocated to a particular use, user, route, weather, road condition etc. [0025]); and
determining the value associated with the first vehicle based on the time series using a time series model (For wear and tear or damage that may be related to time rather than distance, (e.g. exposure of paint to sunlight) x may represent time rather than distance. Further, damage models may be based on mixed inputs such as distance and time, or time and light intensity, or historical driving behaviors or any other vehicle or environment data depending on the particular implementation. [0050]).
Claim(s) 3 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Morizumi et. al. (US 2022/0005609), herein Morizumi in view of Liu et. al. (US 2021/0215491), herein Liu, Rakah et. al. (US 2018/0211541), herein Rakah, and Lodhia (US 2022/0351104), herein Lodhia.
Regarding claim 3:
Morizumi in view of Liu and Rakah teaches all the limitations of claim 1, upon which this claim is dependent.
Morizumi in view of Liu and Rakah does not explicitly teach, however Lodhia teaches:
obtaining a set of locations (The dispatch service selects the vehicle based at least in part on a cost model (e.g., cost function) that is used to determine a cost of assigning and routing vehicles for trips. In some embodiments, the dispatch service selects the vehicle based at least in part on a total travel distance from a current position of the vehicle to an origin (e.g. pick-up location) of the trip request 120 and/or a total travel distance from the origin to a destination (e.g., drop-off location) of the trip request 120. [0041]) and set of entities (A trip request 120 includes a request to transport two people, one passenger without a wheelchair and one seat with a wheelchair … the require passenger requirements are: 1) ID: “Jane”, required capacity. {seat: 1} and 2) ID: “John,” required capacity {seat: 2}, {wheelchair: 1}. [0038]) via a set of user inputs from a client computer device (the fleet manager 503 interfaces with riders/consumers 504 (e.g., via a mobile application on the rider's smartphone or other device) [0075]; The method 700 also includes receiving (720) a request to complete a task (e.g., trip request 120 shown in FIG. 1C and trip request 220 shown in FIG. 2D) [0083]);
obtaining a transportation weight parameter based on a count of the set of entities (A trip request 120 includes a request to transport two people, one passenger without a wheelchair and one seat with a wheelchair. The capacity required to transport the passenger is one passenger, and the capacity required to transport the passenger with a wheelchair is two seats passenger and one wheelchair (described above with respect to FIG. 1B). Thus, the required capacity 122 to complete the trip request is two passengers and one wheelchair. For example, the require passenger requirements are: 1) ID: “Jane”, required capacity. {seat: 1} and 2) ID: “John,” required capacity {seat: 2}, {wheelchair: 1}. [0038]); and
obtaining the preliminary navigation path based on the set of locations (The first server system 500 includes a routing engine 510 that provides routes, distances, and estimated times of arrival for autonomous vehicles 508 and non-autonomous vehicles 506. In some embodiments, a different instance of the routing engine is instantiated for each fleet. [0071]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Morizumi in view of Liu and Rakah to include the teachings as taught by Lodhia with a reasonable expectation of success. Lodhia teaches “flexible modeling of resource and vehicle capacity to improve dispatching and routing for on-demand transportation of people and on-demand delivery of goods [Lodhia, 0006]”.
Regarding claim 20:
Morizumi in view of Liu and Rakah teaches all the limitations of claim 13, upon which this claim is dependent.
Morizumi in view of Liu and Rakah does not explicitly teach, however Lodhia teaches:
obtaining a set of user inputs from a client computer device, wherein the set of user inputs comprises a first vehicle feature set and a numeric value representing a number of people (In some embodiments, the available capacity may be determined based at least in part on expected loads during the trip. For example, for ride-share trips where part of the trip is already scheduled or dispatched, an available capacity for a second trip is determined based on the number of passengers being picked up at a first pick-up location corresponding to a first trip. [0042]);
determining a second vehicle feature set based on the numeric value The second resource type is different from the first resource type ((e.g., resource type 114-1 is a passenger and resource type 114-2 is a wheelchair). [0091]); and
determining a vehicle feature superset based on the first vehicle feature set and the second vehicle feature set (A trip request 120 includes a request to transport two people, one passenger without a wheelchair and one seat with a wheelchair. The capacity required to transport the passenger is one passenger, and the capacity required to transport the passenger with a wheelchair is two seats passenger and one wheelchair (described above with respect to FIG. 1B). Thus, the required capacity 122 to complete the trip request is two passengers and one wheelchair. For example, the require passenger requirements are: 1) ID: “Jane”, required capacity. {seat: 1} and 2) ID: “John,” required capacity {seat: 2}, {wheelchair: 1}. [0038]), wherein obtaining the set of utilization parameters comprises determining the set of utilization parameters based on the vehicle feature superset (see fig. 1c; The dispatch service determines an available capacity for vehicles of the fleet in order to determine which vehicles have enough capacity to complete the trip request 120. In this example, the fleet manager identifies vehicle 130-5 as having enough capacity available for at least two passengers and one wheelchair (e.g., enough available capacity to fulfill the trip request 120). All the other vehicles shown (e.g., vehicles 130-1, 130-2, 130-4, and 130-5) do not have enough available capacity to fulfill the trip request 120. [0039]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Morizumi in view of Liu and Rakah to include the teachings as taught by Lodhia with a reasonable expectation of success. Lodhia teaches “flexible modeling of resource and vehicle capacity to improve dispatching and routing for on-demand transportation of people and on-demand delivery of goods [Lodhia, 0006]”.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Morizumi et. al. (US 2022/0005609), herein Morizumi in view of Liu et. al. (US 2021/0215491), herein Liu and Rakah et. al. (US 2018/0211541), herein Rakah, in further view of Lothman et. al. (2022/0301044), herein Lothman.
Regarding claim 4:
Morizumi in view of Liu and Rakah teaches all the limitations of claim 1, upon which this claim is dependent.
Liu further teaches:
obtaining the vehicle records based on the plurality of vehicle categories (Cloud data may include past history for a particular vehicle, driver, route, etc.);
Morizumi in view of Liu and Rakah does not explicitly teach, however Lothman teaches:
selecting a plurality of vehicle categories based on the set of vehicle utilization parameters (To facilitate this end, when booking a rental vehicle, the properties that are important for a user are captured in an intent. The available cars in the fleet are then grouped into booking options. The grouping is done by the properties that are important for the user while properties not important are ignored. [0040]), wherein each respective category of the plurality of vehicle categories is associated with a respective set of features that comprises the set of vehicle utilization parameters (techniques are described for minimizing redundancy in rental vehicle options provided in response to a user query. In this regard, rental vehicles can have many different properties or features. For example, rental vehicles can vary with respect to model, engine, number of seats, color, and various additional features such as including a towbar, keyless entry, four-wheel drive, etc. This innovation solves the problem of exposing a user to available options when booking a vehicle without an overload of information. [0039]);
obtaining the vehicle records based on the plurality of vehicle categories (In accordance with process 700, at 702 the search component 202 can receive a request for a rental vehicle booking from a user identity (e.g., an identity of the person/entity from which the search request was received), the request identifying one or more criteria for the rental vehicle booking (e.g., location, timeslot, etc.). At 704, the search component 202 and/or the scheduling component 228 can identify the available vehicles included in the fleet that satisfy the one or more search criteria. For example, the search component can treat the one or more criteria as required criteria and identify all available vehicles that satisfy the required criteria. Information identifying the available vehicles and their properties generated by and/or provided (e.g., by the scheduling component 228) to the search component 202 is represented in process 700 as available vehicle information 706. [0110]); and
causing a display of identifiers of the plurality of vehicle categories in the application (in addition or alternative to providing a list of recommended available vehicles ordered based on an inferred preference order of the user, the rental vehicle provider server device 600 can refine the list of available vehicle options by grouping vehicles the user would consider “the same” booking options based on what vehicle features or properties of the available vehicles are important to the user. For example, a car might have many properties such as towbar, car model, engine, number of seats, color, etc. Assume two available vehicles of the requested model type are available, one with a towbar and one without. [0102]), wherein the identifiers are displayed in an order based on the set of vehicle degradation values (At 412, the recommendation component 208 can select the highest ranked vehicle as the first-choice recommendation for providing to the user in the search results. At 414, the recommendation component 208 can determine whether the next highest ranked vehicle in the ranked list 410 is the same as the highest ranked vehicle aside from a less favorable value for a dynamic parameter (e.g., cost, distance, etc.). If not, the recommendation component 208 can select the next highest ranked vehicle as the next choice recommendation at 416. However, if at 414 the recommendation component determines the next highest ranked vehicle is the same as the first (e.g., same static parameters) yet has a less favorable value for a dynamic parameter (e.g., a higher cost, a farther distance, etc.), than at 418, the recommendation component 208 can move down the ranked list 410 and find the next highest ranked vehicle that differs from the highest ranked vehicle (e.g., with respect to a static parameter) as the next choice recommendation. The recommendation component 208 can continue this process moving down through the ranked list 410 to generate the ordered list of rental vehicles for recommending to the user in the search results. [0087]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Morizumi in view of Liu and Rakah to include the teachings as taught by Lothman with a reasonable expectation of success. Lothman teaches the benefits of “a system is described that facilitates minimizing redundancy in rental vehicle options provided in response to a user query. The system comprises a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. These computer executable components comprise a search component that receives a request for a rental vehicle booking from a user identity and identifies available vehicles that satisfy one or more criteria for the rental vehicle booking included with the request. The computer executable components further comprise a request interpretation component that determines whether one or more properties of the available vehicles are relevant to the user identity, and a grouping component that groups the available vehicles into one or more booking options based on whether the one or more properties of the available vehicles are relevant to the user identity. The computer executable components further comprise a results component that generates a search result list for presenting to the user identity comprising the one or more booking options. [Lothman, 0005]”.
Claim(s) 5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Morizumi et. al. (US 2022/0005609), herein Morizumi in view of Liu et. al. (US 2021/0215491), herein Liu and Rakah et. al. (US 2018/0211541), herein Rakah, in further view of Lerner et. al. (2022/0204016), herein Lerner and Heizer et. al. (CN108205307), herein Heizer.
Regarding claim 5:
Morizumi in view of Liu and Rakah teaches all the limitations of claim 1, upon which this claim is dependent.
Morizumi in view of Liu and Rakah does not explicitly teach, however Lerner teaches:
obtaining a sequence of sensor measurements of a candidate vehicle (Table 1 lists operating data collected and/or calculated during operation of the vehicle 105 at different timestamps and that can be aggregated data for a specified period of time, as described below. That is, the computer 110 can store the operating data and timestamps at which the data were collected, and the computer 110 can identify a single value representing all of a type of operating data within a specified period of time, e.g., a maximum value, an average value, etc. [0042]), wherein the plurality of vehicles comprises the candidate vehicle (fig. 2, multiple vehicles 105);
adding noise to timestamps of the sequence of sensor measurements without modifying an order of the sequence of sensor measurements by modifying the timestamps based on the set of modification amounts (The server 130 can assign the operating data to one of a plurality of sets 300 based on a period of time during which the respective computers 110 of the vehicles 105 collected the data. A “set” in this context is a collection of data from the vehicles 105 that have respective timestamps within a specified period of time. The server 130 can generate a plurality of sets 300 of operating data, and each set 300 of operating data includes data collected during a different specified period of time from each other set 300 of operating data. These sets 300 of operating data generated within the specific period of time are “time window” data sets 300, and each set 300 of time window data includes operating data for one of the specified period of time. The period of time for each time window set 300 can be determined based on, e.g., traffic patterns on the roadway 200. For example, the period of time for each set can be a six-hour period starting before a commute period on the roadway 200 and ending after the commute period, e.g., from 6:00 AM to 12:00 PM. In another example, the period of time can be a three-hour period directed to the commute period, e.g., 6:00 AM to 9:00 AM, and another period of time can be the following three-hour period that can have less traffic than the commute period, e.g., 9:00 AM to 12:00 PM. The operating data in the time window set 300 can include individual data collected at different timestamps within the specified period of time and/or a single aggregated value for each type of data, e.g., an average distance to a lane marking, a maximum lateral acceleration, etc., as shown in Table 1. [0046]; examiner is interpreting the modifying of timestamps to mean aggregating data into time clusters for ease of data processing while maintaining data sequencing.); and
updating the vehicle sensor measurements based on the sequence of sensor measurements after modifying the timestamps (The server 130 can generate a plurality of sets 300 of operating data, and each set 300 of operating data includes data collected during a different specified period of time from each other set 300 of operating data. These sets 300 of operating data generated within the specific period of time are “time window” data sets 300 [0046]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Morizumi in view of Liu and Rakah to include the teachings as taught by Lerner with a reasonable expectation of success. Lerner teaches the benefits of “The operator data can include timestamps at which the data were collected, and the machine learning program can identify a hazard based on data collected within a specified time period. These time period data, i.e., “time window” data, are more readily analyzed by the machine learning program than data without timestamps. That is, the timestamps allow the machine learning program to identify hazards at locations by filtering out old behavior data during which the hazard may not have been present. Thus, the machine learning program can use the time window operator behavior data to identify hazards for a map used by autonomous vehicles for operation on the roadway. [Lerner, 0031]”.
Morizumi in view of Liu, Rakah, and Lerner does not explicitly teach, however Heizer teaches:
determining a set of modification amounts using a random or pseudo-random process (wherein the measurement time point of said received data by randomly pushing to perform the masking time. [claim 7]);
adding noise to timestamps of the sequence of sensor measurements without modifying an order of the sequence of sensor measurements by modifying the timestamps based on the set of modification amounts (FIG. 7 shows a transmission data from the vehicle 40 to the rear end 45 of the system and performs anonymization in the vehicle 40 under the condition of the system. In this case, the environment sensing device 40 by vehicle to estimate traffic flow. thereby determining the masks needed in time or space, so as to conceal the identity of the vehicle 40 in the anonymous group. anonymous group should be understood as a group in the group, although the action of the individual, the individual is still remain anonymous, namely the group cannot be identified portion. the data is transmitted to the receiving system 41, the ID of the vehicle on all communication layers by pseudo-anonymous unit 42 in the reception system 41 is removed. In particular, the scheme is based on the description of the process. all of the vehicle sensors 46, such as a camera, a radar sensor, an ultrasonic sensor, a temperature sensor or a climate sensor report the measured data to the communication unit 47. Here, if this is not already generated by sensor, then to the data with the timestamp and location stamp. anonymous processing unit 43 receives the data and advantageously changing the timestamp or location stamp, such that the identity of the vehicle 40 in the group of the vehicle is sufficiently hidden. the anonymization described function unit 43 according to FIG. 8 below. the anonymization of the data is sent to the receiving system 41 by means of a mobile radio, wherein the anonymization of data still has vehicle specific, based on connection data of the mobile radio, in the reception system pseudo-anonymous processing unit 42 the data from vehicle specific data isolation and in a pseudo ID to the data. log (Anmeldung) of the vehicle 40 is performed via the group certificate in this situation, the group certificate is the same in all vehicles. aim of this is does not give the unauthorized (preisgeben) under the condition of the ID of the vehicle 40 very difficult to upload. the data is then via the data adaptation unit 48 is adapted to the back end 45 of the interface and correspondingly is transmitted. then analyzing target accordingly to evaluate the detected user data with the data of user. In FIG. 7, the pseudo-anonymous unit 42 is a component part of the receiving system 41 provided by the vehicle manufacturers. However, pseudo-anonymous unit 42 can also be provided by the provider of the external (stellen). Accordingly, user data can be vehicle manufacturer or the vehicle manufacturer independent of the enterprise. [page 7]); and
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Morizumi in view of Liu, Rakah, and Lerner to include the teachings as taught by Heizer with a reasonable expectation of success. Heizer teaches the benefits of “a method for processing data in a control device of the vehicle. in the field of vehicle communication with the backend system, the user-specific data is exchanged, the data enabling the user of personal or its behavior or habit for inference. In order to protect against abuse, the data is anonymous. additionally, for the control device is enabled by a user of the vehicle to activate the data protection mode. is only allowed after a data protection mode activation under the condition that data of a predetermined departure from the vehicle transmission is prohibited or only confirmation at the input of user of the vehicle. using the method described, give the user the following possibility: disabling the special data forwarding. Of course, this does not change: excluding from the order obtained involved data must not getting the consent statement of the user. [Heizer, page 2]”.
Regarding claim 15:
Morizumi in view of Liu teaches all the limitations of claim 13, upon which this claim is dependent.
Liu further teaches:
obtaining a sequence of sensor measurements of the first vehicle (Table 1 lists operating data collected and/or calculated during operation of the vehicle 105 at different timestamps and that can be aggregated data for a specified period of time, as described below. That is, the computer 110 can store the operating data and timestamps at which the data were collected, and the computer 110 can identify a single value representing all of a type of operating data within a specified period of time, e.g., a maximum value, an average value, etc. [0042]);
adding noise to times of the sequence of the sensor measurements without modifying an order of the sequence of the sensor measurements by modifying the timestamps based on the set of modification amounts (The server 130 can assign the operating data to one of a plurality of sets 300 based on a period of time during which the respective computers 110 of the vehicles 105 collected the data. A “set” in this context is a collection of data from the vehicles 105 that have respective timestamps within a specified period of time. The server 130 can generate a plurality of sets 300 of operating data, and each set 300 of operating data includes data collected during a different specified period of time from each other set 300 of operating data. These sets 300 of operating data generated within the specific period of time are “time window” data sets 300, and each set 300 of time window data includes operating data for one of the specified period of time. The period of time for each time window set 300 can be determined based on, e.g., traffic patterns on the roadway 200. For example, the period of time for each set can be a six-hour period starting before a commute period on the roadway 200 and ending after the commute period, e.g., from 6:00 AM to 12:00 PM. In another example, the period of time can be a three-hour period directed to the commute period, e.g., 6:00 AM to 9:00 AM, and another period of time can be the following three-hour period that can have less traffic than the commute period, e.g., 9:00 AM to 12:00 PM. The operating data in the time window set 300 can include individual data collected at different timestamps within the specified period of time and/or a single aggregated value for each type of data, e.g., an average distance to a lane marking, a maximum lateral acceleration, etc., as shown in Table 1. [0046]; examiner is interpreting the modifying of timestamps to mean aggregating data into time clusters for ease of data processing while maintaining data sequencing.); and
wherein determining the plurality of values comprises determining the value of the first vehicle based on the sequence of sensor measurements after modifying the timestamps (The server 130 can generate a plurality of sets 300 of operating data, and each set 300 of operating data includes data collected during a different specified period of time from each other set 300 of operating data. These sets 300 of operating data generated within the specific period of time are “time window” data sets 300 [0046]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Lodhia in view of Liu to include the teachings as taught by Lerner with a reasonable expectation of success. Lerner teaches the benefits of “The operator data can include timestamps at which the data were collected, and the machine learning program can identify a hazard based on data collected within a specified time period. These time period data, i.e., “time window” data, are more readily analyzed by the machine learning program than data without timestamps. That is, the timestamps allow the machine learning program to identify hazards at locations by filtering out old behavior data during which the hazard may not have been present. Thus, the machine learning program can use the time window operator behavior data to identify hazards for a map used by autonomous vehicles for operation on the roadway. [Lerner, 0031]”.
Morizumi in view of Liu, Rakah, and Lerner does not explicitly teach, however Heizer teaches:
determining a set of modification amounts using a random or pseudo-random process (wherein the measurement time point of said received data by randomly pushing to perform the masking time. [claim 7]);
adding noise to timestamps of the sequence of sensor measurements without modifying an order of the sequence of sensor measurements by modifying the timestamps based on the set of modification amounts (FIG. 7 shows a transmission data from the vehicle 40 to the rear end 45 of the system and performs anonymization in the vehicle 40 under the condition of the system. In this case, the environment sensing device 40 by vehicle to estimate traffic flow. thereby determining the masks needed in time or space, so as to conceal the identity of the vehicle 40 in the anonymous group. anonymous group should be understood as a group in the group, although the action of the individual, the individual is still remain anonymous, namely the group cannot be identified portion. the data is transmitted to the receiving system 41, the ID of the vehicle on all communication layers by pseudo-anonymous unit 42 in the reception system 41 is removed. In particular, the scheme is based on the description of the process. all of the vehicle sensors 46, such as a camera, a radar sensor, an ultrasonic sensor, a temperature sensor or a climate sensor report the measured data to the communication unit 47. Here, if this is not already generated by sensor, then to the data with the timestamp and location stamp. anonymous processing unit 43 receives the data and advantageously changing the timestamp or location stamp, such that the identity of the vehicle 40 in the group of the vehicle is sufficiently hidden. the anonymization described function unit 43 according to FIG. 8 below. the anonymization of the data is sent to the receiving system 41 by means of a mobile radio, wherein the anonymization of data still has vehicle specific, based on connection data of the mobile radio, in the reception system pseudo-anonymous processing unit 42 the data from vehicle specific data isolation and in a pseudo ID to the data. log (Anmeldung) of the vehicle 40 is performed via the group certificate in this situation, the group certificate is the same in all vehicles. aim of this is does not give the unauthorized (preisgeben) under the condition of the ID of the vehicle 40 very difficult to upload. the data is then via the data adaptation unit 48 is adapted to the back end 45 of the interface and correspondingly is transmitted. then analyzing target accordingly to evaluate the detected user data with the data of user. In FIG. 7, the pseudo-anonymous unit 42 is a component part of the receiving system 41 provided by the vehicle manufacturers. However, pseudo-anonymous unit 42 can also be provided by the provider of the external (stellen). Accordingly, user data can be vehicle manufacturer or the vehicle manufacturer independent of the enterprise. [page 7]); and
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Morizumi in view of Liu, Rakah, and Lerner to include the teachings as taught by Heizer with a reasonable expectation of success. Heizer teaches the benefits of “a method for processing data in a control device of the vehicle. in the field of vehicle communication with the backend system, the user-specific data is exchanged, the data enabling the user of personal or its behavior or habit for inference. In order to protect against abuse, the data is anonymous. additionally, for the control device is enabled by a user of the vehicle to activate the data protection mode. is only allowed after a data protection mode activation under the condition that data of a predetermined departure from the vehicle transmission is prohibited or only confirmation at the input of user of the vehicle. using the method described, give the user the following possibility: disabling the special data forwarding. Of course, this does not change: excluding from the order obtained involved data must not getting the consent statement of the user. [Heizer, page 2]”.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Morizumi et. al. (US 2022/0005609), herein Morizumi in view of Liu et. al. (US 2021/0215491), herein Liu and Rakah et. al. (US 2018/0211541), herein Rakah, in further view of Kobayashi et. al. (2020/0286020), herein Kobayashi and Lothman et. al. (2022/0301044), herein Lothman.
Regarding claim 7:
Morizumi in view of Liu and Rakah teaches all the limitations of claim 6, upon which this claim is dependent.
Liu further teaches:
wherein the value is a first value that is stored in a first vehicle record associated with the first vehicle (The vehicle computing system (VCS) of one or more vehicles 210-A, 210-B may record information from various vehicle sensors for use in determining maintenance and wear and tear on the vehicles. [0038]), the operations further comprising:
Morizumi in view of Liu and Rakah do not explicitly teach, however Kobayashi teaches:
determining whether the first value satisfies a threshold (determining that a vehicle movement metric of the first vehicle violates a vehicle movement requirement of the requesting MP, and responsive to determining that the vehicle movement metric of the first vehicle violates the vehicle movement requirement of the requesting MP, notifying a violation of the vehicle movement requirement to the server of the requesting MP; that computing a violation amount based on the vehicle movement metric of the first vehicle and the vehicle movement requirement of the requesting MP, and adjusting a travel cost associated with the transportation request based on the violation amount; that computing a violation amount based on the vehicle movement metric of the first vehicle and the vehicle movement requirement of the requesting MP, determining that the violation amount satisfies a violation amount threshold, and responsive to determining that the violation amount satisfies the violation amount threshold, providing a maintenance operation to the first vehicle [0007]);
in response to a determination that the first value satisfies the threshold (the mobility agent 120 may determine whether the violation amount associated with the vehicle movement metric and/or the vehicle feature metric of the first vehicle 103 satisfies a violation amount threshold. For example, in the mobility agent 120, the vehicle manager 252 may compare the violation amount associated with the vehicle metric of the first vehicle 103 to the corresponding violation amount threshold. The violation amount threshold may be based on the average value of the vehicle metric for multiple vehicles 103 that have the same vehicle category data as the first vehicle 103 and operate in good condition. In some embodiments, if the violation amount associated with the vehicle movement metric and/or the vehicle feature metric of the first vehicle 103 satisfies the corresponding violation amount threshold [0116]),
updating a record associated with the first vehicle indicating a status change of the first vehicle based on the first utilization parameter (if the violation amount associated with the vehicle movement metric and/or the vehicle feature metric of the first vehicle 103 satisfies the corresponding violation amount threshold, the vehicle manager 252 may generate a vehicle anomaly notification associated with the first vehicle 103, the vehicle anomaly notification may indicate that the first vehicle 103 is anomalous. In some embodiments, the vehicle anomaly notification may include the vehicle ID of the first vehicle 103, the vehicle movement metric and/or the vehicle feature metric of the first vehicle 103 that satisfies the violation amount threshold, and the violation amount associated with the vehicle movement metric and/or the vehicle feature metric of the first vehicle 103. In block 362, the message processor 250 may transmit the vehicle anomaly notification associated with the first vehicle 103 to the first MP 101 to which the first vehicle 103 belongs. [0116]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Morizumi in view of Liu and Rakah to include the teachings as taught by Kobayashi with a reasonable expectation of success. Kobayashi teaches the benefits of “enabling a first mobility provider (MP) to temporarily utilize a vehicle of a second MP to execute a transportation request that is requested by a user of the first MP. Thus, even when the first MP does not have sufficient transportation capacity to itself execute the transportation request, the first MP can still provide the transportation capability to its user by utilizing the vehicle of the second MP, and thus the user experience with the first MP can be improved. As a further example, the technology described herein enables the first MP that requires additional transportation capacity to utilize the available transportation capacity of the second MP, and therefore the first MP and the second MP can dynamically adapt their transportation capacity as needed. As a result, these MPs can avoid the need to maintain a large number of vehicles without degrading the availability of their transportation service. The MPs can also maximize the overall utilization of the vehicles in their vehicle fleet as these vehicles can be utilized to perform the transportation requests for other MPs. [Kobayashi, 0009]”.
Morizumi in view of Liu, Rakah, and Kobayashi do not explicitly teach, however Lothman teaches:
obtaining a first utilization parameter (properties not important [0040]), wherein the set of vehicle utilization parameters does not comprise the first utilization parameter (To facilitate this end, when booking a rental vehicle, the properties that are important for a user are captured in an intent. The available cars in the fleet are then grouped into booking options. The grouping is done by the properties that are important for the user while properties not important are ignored. [0040]); and
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Morizumi in view of Liu, Rakah, and Kobayashi to include the teachings as taught by Lothman with a reasonable expectation of success. Lothman teaches the benefits of “a system is described that facilitates minimizing redundancy in rental vehicle options provided in response to a user query. The system comprises a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. These computer executable components comprise a search component that receives a request for a rental vehicle booking from a user identity and identifies available vehicles that satisfy one or more criteria for the rental vehicle booking included with the request. The computer executable components further comprise a request interpretation component that determines whether one or more properties of the available vehicles are relevant to the user identity, and a grouping component that groups the available vehicles into one or more booking options based on whether the one or more properties of the available vehicles are relevant to the user identity. The computer executable components further comprise a results component that generates a search result list for presenting to the user identity comprising the one or more booking options. [Lothman, 0005]”.
Claim(s) 10-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Morizumi et. al. (US 2022/0005609), herein Morizumi in view of Liu et. al. (US 2021/0215491), herein Liu and Rakah et. al. (US 2018/0211541), herein Rakah, in further view of Irey (2021/0334865), herein Irey.
Regarding claim 10:
Morizumi in view of Liu and Rakah teaches all the limitations of claim 6, upon which this claim is dependent.
Liu further teaches:
obtaining a maintenance history of the first vehicle (a processor programmed to receive sensor data associated with operation of a vehicle by a vehicle sharing user during a trip and calculate a vehicle sharing cost associated with anticipated maintenance of a vehicle component based on the sensor data from the trip, historical information and customer characteristics (e.g. repeated ride-sharing customers, or the years of owning a vehicle). [0003]);
obtaining a selling price of the first vehicle (The system could predict the vehicle condition and related vehicle residual value that is influenced by the vehicle condition, and identify the related charges to a vehicle sharing user. The system could provide more accurate prediction of vehicle physical condition than currently available systems, which impacts the vehicle residual value, and may be used to determine the vehicle sharing charges. [0065]); and
Irey also teaches:
obtaining a maintenance history of the first vehicle (the vehicle driving data, including vehicle maintenance history and/or insurance claim history, of vehicle 210 indicates vehicle wear in step 303 [0053]);
obtaining a selling price of the first vehicle (In step 304, one or more vehicle grades and/or resale prices may be calculated and/or adjusted based on the driving behaviors determined in step 303 and vehicle performance and condition data received in step 301. [0052]); and
Morizumi in view of Liu and Rakah does not explicitly teach, however Irey teaches:
determining a set of sequences of events based on the maintenance history by populating the set of sequences of events (When calculating or adjusting vehicle grades and/or resale prices based on determined driving behaviors, vehicle maintenance history, driving record, and/or insurance claim history, behaviors, wear and/or claims of greater magnitude (e.g., chronic hard braking, severe tailgating, racing, chronic failure to maintain a regular maintenance schedule, several accidents/claims within a short period of time, etc.) may be weighed more heavily than less severe behaviors, wear and/or claims (e.g., minor tailgating, failure to yield to allow a lane change in traffic, a slightly overdue oil change, a single accident/claim within the insurance policy term, etc.). Additionally, minor driving behaviors, wear and/or claims might not cause any adjustments in vehicle grades and/or resale prices, and some positive and negative behaviors may cancel out so that the vehicle grades and/or resale prices might not be adjusted. In some cases, all occurrences of all determined positive and negative driving behaviors, wear and/or claims may be accumulated and stored over a period of time, such a week, month, year, or for an insurance policy term, and the accumulated set of driving behaviors, wear and/or claims may be used to calculate vehicle grades and/or resale prices, insurance rate adjustments and/or discounts. [0054]), wherein:
an event of the set of sequences of events is selected based on words of the maintenance history (Data stored in the driving data and vehicle grade and/or price database 452 may be organized in any of several different manners. For example, a table in driving data and/or vehicle maintenance database 452 may contain all of the vehicle operation data for a specific vehicle 410, similar to a vehicle event log. Other tables in the driving data and/or vehicle maintenance database 452 may store certain types of data for multiple vehicles. For instance, tables may store specific driving behaviors and interactions (e.g., accidents, tailgating, cutting-off, yielding, racing, defensive avoidances, etc.) for multiples vehicles. Vehicle performance and condition data may also be organized by time and/or place, so that the driving behaviors or interactions between multiples vehicle may be stored or grouped by time and location. [0070]); and
determining values for the plurality of vehicles comprises generating a sequence of scores based on the set of sequences of events (As used herein, a vehicle grade may refer to a measurement of driving behavior history (based on vehicle driving data), vehicle maintenance history, vehicle accident and/or insurance claim history and other vehicle information indicative of vehicle value. A vehicle grade may be a rating generated by an insurance provider, financial institution, or other organization. For example, an insurance provider server may periodically calculate (e.g., determine and/or adjust) vehicle grades for one or more vehicles of the insurance provider's customers, and may use the vehicle grades to perform insurance analyses and determinations (e.g., with regard to resale value, insurance coverage, premiums and deductibles, rewards, etc.). As discussed below, a vehicle grade may be calculated based on driving data collected by vehicle sensor(s) and/or telematics device(s). [0028]), wherein the sequence of scores is associated with the first vehicle (a vehicle grade may refer to a measurement of driving behavior history (based on vehicle driving data) [0028]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Morizumi in view of Liu and Rakah to include the teachings as taught by Irey with a reasonable expectation of success. Irey teaches the benefits of “A driving data analysis and vehicle maintenance server may be configured to receive vehicle driving data corresponding to vehicle operation data of a vehicle, analyze the received vehicle driving data, determine a driving behavior associated with the vehicle, determine recommended vehicle maintenance and calculate a vehicle resale price for the vehicle. [Irey, abstract]”.
Regarding claim 11:
Morizumi in view of Liu, Rakah, and Irey teaches all the limitations of claim 10, upon which this claim is dependent.
Irey further teaches:
selecting a user record listed in association with the first vehicle (The driving analysis and vehicle pricing server 250 may include a driving data and vehicle grade and/or price database 252 and driving analysis and vehicle pricing module 251 to respectively store and analyze driving data received from vehicles and other data sources 253 (e.g., insurance records, including policy information, vehicle accident and/or claim history, etc.; vehicle maintenance records; driving records; etc.). [0042]);
obtaining a transaction record associated with the user record (Driving analysis and vehicle pricing module 414 may be implemented in hardware and/or software configured to receive vehicle performance and condition data from vehicle sensors 411, telematics device 413, and/or other driving data sources. After receiving the vehicle driving data, driving analysis and vehicle pricing module 414 may perform a set of functions to analyze the driving data, determine driving behaviors, determine the need for maintenance, and calculate vehicle grades and/or prices. For example, the driving analysis and vehicle pricing module 414 may include one or more driving behavior analysis, vehicle maintenance, vehicle grade and/or vehicle price calculation algorithms, which may be executed by software running on generic or specialized hardware within the driving analysis and vehicle pricing module 414. The driving analysis and vehicle pricing module 414 in vehicle 410 may use the vehicle performance and condition data received from that vehicle's sensors 411 to determine driving behaviors, determine the need for maintenance and determine and/or adjust vehicle grades and/or prices applicable to vehicle 410. Within the driving analysis and vehicle pricing module 414, a vehicle grade/price calculation function may use the results of the driving analysis, maintenance analysis and vehicle pricing performed by the module 414 to calculate/adjust vehicle grades and/or prices for vehicle 410. [0068]), wherein the transaction record indicates a change in a score of a vehicle record identifying the first vehicle (When calculating or adjusting vehicle grades and/or resale prices based on determined driving behaviors, vehicle maintenance history, driving record, and/or insurance claim history, behaviors, wear and/or claims of greater magnitude (e.g., chronic hard braking, severe tailgating, racing, chronic failure to maintain a regular maintenance schedule, several accidents/claims within a short period of time, etc.) may be weighed more heavily than less severe behaviors, wear and/or claims (e.g., minor tailgating, failure to yield to allow a lane change in traffic, a slightly overdue oil change, a single accident/claim within the insurance policy term, etc.) [0054]), a date associated with the change (examiner notes that vehicle records would inherently include a date/timestamp), and a string associated with the change (examiner notes that data would inherently be categorized by at least driver data, maintenance history, telematics data, insurance records , etc.);
selecting the transaction record based on the string (The driving analysis and vehicle maintenance server 450 may include a driving data and/or vehicle maintenance database 452 and driving analysis and vehicle pricing module 451 to respectively store and analyze driving data received from vehicles and other data sources 453 (e.g., insurance records, including policy information, vehicle accident and/or claim history, etc.; vehicle maintenance records; driving records; etc.). [0069]); and
determining an event of a sequence of events (Data stored in the driving data and vehicle grade and/or price database 252 may be organized in any of several different manners. For example, a table in driving data and vehicle grade and/or price database 252 may contain all of the vehicle operation data for a specific vehicle 210, similar to a vehicle event log [0043]) associated with the first vehicle based on the change in the score and the date associated with the change (certain functionality may be performed in vehicle-based driving analysis and vehicle pricing module 214, while other functionality may be performed by the driving analysis and vehicle pricing module 251 at the driving analysis and vehicle pricing server 250. For instance, vehicle-based driving analysis and vehicle pricing module 214 may continuously receive and analyze driving data for its own vehicle 210, and may determine driving behaviors (e.g., speeding, hard braking, swerving, etc.) for its own vehicle 210. After the vehicle-based driving analysis and vehicle pricing module 214 has determined the driving behaviors, indications of these behaviors may be transmitted to the server 250 so that the driving analysis and vehicle pricing module 251 can perform the vehicle grade and/or resale price calculations and updates based on the driving behaviors and vehicle driving data. For instance, vehicle 210 may detect a negative driving behavior for another vehicle, and may report the negative behavior for the other vehicle to the driving analysis and vehicle pricing server 250, which may access other vehicle and driver information for the other vehicle and may potentially adjust a vehicle grade and/or resale price for the other vehicle based on the driving behaviors reported by vehicle 210. [0062]).
Regarding claim 12:
Morizumi in view of Liu, Rakah, and Irey teaches all the limitations of claim 11, upon which this claim is dependent.
Rakah further teaches:
selecting a plurality of vehicle categories based on the set of vehicle utilization parameters (ach ridesharing management server 150 may handle a certain category of ridesharing services, ridesharing services associated with a certain category of service vehicles, or ridesharing services in a specific geographical region, such that a plurality of ridesharing management servers 150 may collectively provide a dynamic and integrated ridesharing service system [0080])
Irey further teaches:
determining a product identifier based on the transaction record (In step 310, the driving analysis and vehicle pricing module 214 determines whether or not the vehicle grade and/or resale price determined in step 304 is within a predetermined threshold for the particular make, model and year of vehicle 210 (e.g., a particular grade and/or price or a grade and/or price range) stored in the driving data and vehicle grade and/or price database 252. [0060]; examiner is interpreting the product identifier to by an metric that can be associated with the vehicle. i.e. a vehicle grade);
determining whether the product identifier is compatible with the first vehicle (In step 310, the driving analysis and vehicle pricing module 214 determines whether or not the vehicle grade and/or resale price determined in step 304 is within a predetermined threshold for the particular make, model and year of vehicle 210 (e.g., a particular grade and/or price or a grade and/or price range) stored in the driving data and vehicle grade and/or price database 252. [0060]) by:
sending the product identifier and the vehicle category to a second set of servers via an application program (a vehicle-based driving analysis and vehicle pricing module 214 may continuously receive and analyze driving data from nearby vehicles to determine certain driving behaviors (e.g., tailgating, cutting-off, yielding, etc.) so that large amounts of driving data need not be transmitted to the driving analysis and vehicle pricing server 250. However, after a positive or negative driving behavior is determined by the vehicle-based driving analysis and vehicle pricing module 214, the behavior may be transmitted to the server 250, and the driving analysis and vehicle pricing module 251 may determine if a vehicle grade and/or price calculation should be updated based on the determined driving behavior. [0045]) interface;
receiving a response from the second set of servers indicating whether the product identifier is compatible with the first vehicle (and the driving analysis and vehicle pricing module 251 may determine if a vehicle grade and/or price calculation should be updated based on the determined driving behavior. [0045]); and
wherein determining the event comprises determining the event in response to a determination that the product identifier is compatible with the first vehicle based on a numeric value associated with the transaction record (The steps shown in FIG. 3 describe performing an analysis to determine driving behaviors of vehicle 210, calculating or adjusting vehicle 210 grades and/or resale prices based on the determined driving behaviors, determining whether or not the vehicle grade and/or resale price is within an insurance provider threshold, and, if the vehicle grade and/or resale price is within the insurance provider threshold, the insurance provider purchasing the vehicle 210. If the vehicle grade and/or resale price is outside the insurance provider threshold, the insurance provider does not purchase the vehicle 210. [0047]).
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Morizumi et. al. (US 2022/0005609), herein Morizumi in view of Liu et. al. (US 2021/0215491), herein Liu and Rakah et. al. (US 2018/0211541), herein Rakah, in further view of Kumar et. al. (US 2022/0063689), herein Kumar.
Regarding claim 16:
Morizumi in view of Liu and Rakah teaches all the limitations of claim 13, upon which this claim is dependent.
Liu further teaches:
obtaining a sequence of tokens from a record associated with a date using a machine learning model (One function of the wear and tear or damage models generally represented at 414 is to classify various incidents or historical behaviors into several pre-specified event groups so that the cost estimation is easier and more efficient based on a limited or reduced number of event groups. Based on collected characteristics of events, a machine learning method, such as a support vector machine may be used to classify the events according to: {eqn. 1} [0048]);
determining an event code based on the sequence of tokens (For wear and tear or damage that may be related to time rather than distance, (e.g. exposure of paint to sunlight) x may represent time rather than distance. Further, damage models may be based on mixed inputs such as distance and time, or time and light intensity, or historical driving behaviors or any other vehicle or environment data depending on the particular implementation. [0050]); and
associating the event code to the date in the vehicle records, wherein determining the plurality of values comprises determining the value associated with the first vehicle based on the event code (During operation of the vehicle, data is collected from vehicle sensors and associated with corresponding service events for the vehicle as represented at 540. Data may be aggregated across many vehicles and uses to analyze service events and associate costs for a particular type of vehicle use, driver behavior, route, weather, etc. Users may use multiple vehicles over time with different uses, routes, etc. with the historical data used in determining the cost for future vehicle sharing transactions. Correlations are made between map locations and the vehicle to develop the metric used for wear and tear costs based on previous and/or anticipated actual service events as represented at 550. Travel patterns for drivers are then mapped using data such as phone numbers as represented at 560. Mobility maps and wear and tear maps for individual drivers are correlated to determine cost associated with a particular driver based on location, route, driving behavior, etc. as represented at 570. [0056]).
Morizumi in view of Liu and Rakah do not explicitly teach, however Kumar teaches:
obtaining natural language text from a record for the first vehicle, wherein the natural language text is associated with a date (Algorithms and machine learning techniques and systems may be used in computational intelligence systems, computer vision, Natural Language Processing (NLP), recommender systems, reinforcement learning, building graphical models, and the like. In an example, machine learning may be used for vehicle performance and behavior analytics, and the like. [0483]);
by providing associated with a date using a machine learning model with the natural language text (Algorithms and machine learning techniques and systems may be used in computational intelligence systems, computer vision, Natural Language Processing (NLP), recommender systems, reinforcement learning, building graphical models, and the like. In an example, machine learning may be used for vehicle performance and behavior analytics, and the like. [0483]);
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Morizumi in view of Liu and Rakah to include the teachings as taught by Kumar with a reasonable expectation of success. Kumar teaches “a controller configured to obtain one or more of a route parameter or a vehicle parameter from discrete examinations of one or more of a route or a vehicle system. The route parameter is indicative of a health of the route over which the vehicle system travels. The vehicle parameter is indicative of a health of the vehicle system. The discrete examinations of the one or more of the route or the vehicle system separated from each other by one or more of location or time. The controller is configured to examine the one or more of the route parameter or the vehicle parameter to determine whether the one or more of the route or the vehicle system is damaged. The system also includes examination equipment configured to continually monitor the one or more of the route or the vehicle system responsive to determining that the one or more of the route or the vehicle is damaged. [Kumar, abstract]”.
Regarding claim 17:
Morizumi in view of Liu Rakah, and Kumar teaches all the limitations of claim 16, upon which this claim is dependent.
Liu further teaches:
generating a set of vectors (one of many possible examples of wear and tear that would be difficult to detect and price without the use of vehicle sensor data incorporated into a vehicle sharing system according to various embodiments of the present disclosure. Other examples of system or component wear and tear that can be detected and priced accordingly may include using sensor data to determine the road condition and/or parking condition of a vehicle in combination with the information of driving behavior. The system could predict the vehicle condition and related vehicle residual value that is influenced by the vehicle condition, and identify the related charges to a vehicle sharing user. The system could provide more accurate prediction of vehicle physical condition than currently available systems, which impacts the vehicle residual value, and may be used to determine the vehicle sharing charges. [0065]; examiner notes that in order to predict the condition, the system must calculate trends (vectors) corresponding to the systems health values.) corresponding with the sequence of tokens using a first neural network (According to various embodiments of the present disclosure, wear and tear and associated costs can be estimated or determined directly by corresponding sensor signals on specific components, such as brake pad wear sensors, or by training neural network or wear models based on the physics and statistics of vehicle operating events by monitoring vehicle conditions, periodically measuring wear, and inputting the data into a cloud-based web service that can estimate the parameters using known statistical approaches [0064]) and a geographic location associated with the record (the integration of vehicle sensors/computers 320, drivers, and cloud components 360 provides a unique way to collect and utilize both vehicle data and personal data. Vehicle manufacturers do not need to rely on a dedicated device plugged-in to selected vehicles. Rather, vehicles already collect sensor data such as location, velocity, acceleration, sound, temperature, road gradient, brake torque, propulsive torque suspension travel engine coolant and oil temperature, parking location, etc. [0044]); and
determining the event code based on the set of vectors (The detection corridor or contour may be determined based on a statistical probability that the data excursion corresponds or is likely to correlate with a particular condition or component failure that requires maintenance or repair. Vehicle data may begin to deviate from expected values as represented at 1072 due to a component failure that allows water into the brake hydraulic system, for example. [0087]).
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Morizumi et. al. (US 2022/0005609), herein Morizumi in view of Liu et. al. (US 2021/0215491), herein Liu and Rakah et. al. (US 2018/0211541), herein Rakah, in further view of Irey (2021/0334865), herein Irey and Lerner et. al. (2022/0204016), herein Lerner.
Regarding claim 18:
Morizumi in view of Liu and Rakah teaches all the limitations of claim 13, upon which this claim is dependent.
Morizumi in view of Liu and Rakah does not explicitly teach, however Irey teaches:
selecting a user record listed in association with the first vehicle (The driving analysis and vehicle pricing server 250 may include a driving data and vehicle grade and/or price database 252 and driving analysis and vehicle pricing module 251 to respectively store and analyze driving data received from vehicles and other data sources 253 (e.g., insurance records, including policy information, vehicle accident and/or claim history, etc.; vehicle maintenance records; driving records; etc.). [0042]);
receiving a set of inputs from a user identified by the user record (Driving analysis and vehicle pricing module 414 may be implemented in hardware and/or software configured to receive vehicle performance and condition data from vehicle sensors 411, telematics device 413, and/or other driving data sources. After receiving the vehicle driving data, driving analysis and vehicle pricing module 414 may perform a set of functions to analyze the driving data, determine driving behaviors, determine the need for maintenance, and calculate vehicle grades and/or prices. For example, the driving analysis and vehicle pricing module 414 may include one or more driving behavior analysis, vehicle maintenance, vehicle grade and/or vehicle price calculation algorithms, which may be executed by software running on generic or specialized hardware within the driving analysis and vehicle pricing module 414. The driving analysis and vehicle pricing module 414 in vehicle 410 may use the vehicle performance and condition data received from that vehicle's sensors 411 to determine driving behaviors, determine the need for maintenance and determine and/or adjust vehicle grades and/or prices applicable to vehicle 410. Within the driving analysis and vehicle pricing module 414, a vehicle grade/price calculation function may use the results of the driving analysis, maintenance analysis and vehicle pricing performed by the module 414 to calculate/adjust vehicle grades and/or prices for vehicle 410. [0068]), wherein the set of inputs comprises a duration (Vehicle performance and condition data may also be organized by time and/or place, so that the driving behaviors or interactions between multiples vehicle may be stored or grouped by time and location. [0070]);
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Lodhia in view of Liu and Rakah to include the teachings as taught by Irey with a reasonable expectation of success. Irey teaches the benefits of “A driving data analysis and vehicle maintenance server may be configured to receive vehicle driving data corresponding to vehicle operation data of a vehicle, analyze the received vehicle driving data, determine a driving behavior associated with the vehicle, determine recommended vehicle maintenance and calculate a vehicle resale price for the vehicle. [Irey, abstract]”.
Morizumi in view of Liu, Rakah, and Irey does not explicitly teach, however Lerner teaches:
segmenting the duration into a sequence of time segments (The server 130 can assign the operating data to one of a plurality of sets 300 based on a period of time during which the respective computers 110 of the vehicles 105 collected the data. A “set” in this context is a collection of data from the vehicles 105 that have respective timestamps within a specified period of time. The server 130 can generate a plurality of sets 300 of operating data, and each set 300 of operating data includes data collected during a different specified period of time from each other set 300 of operating data. These sets 300 of operating data generated within the specific period of time are “time window” data sets 300, and each set 300 of time window data includes operating data for one of the specified period of time. The period of time for each time window set 300 can be determined based on, e.g., traffic patterns on the roadway 200. For example, the period of time for each set can be a six-hour period starting before a commute period on the roadway 200 and ending after the commute period, e.g., from 6:00 AM to 12:00 PM. In another example, the period of time can be a three-hour period directed to the commute period, e.g., 6:00 AM to 9:00 AM, and another period of time can be the following three-hour period that can have less traffic than the commute period, e.g., 9:00 AM to 12:00 PM. The operating data in the time window set 300 can include individual data collected at different timestamps within the specified period of time and/or a single aggregated value for each type of data, e.g., an average distance to a lane marking, a maximum lateral acceleration, etc., as shown in Table 1. [0046]; examiner is interpreting the modifying of timestamps to mean aggregating data into time clusters for ease of data processing while maintaining data sequencing.);
determining a sequence of utilization parameters based on the set of inputs (Table 1 lists operating data collected and/or calculated during operation of the vehicle 105 at different timestamps and that can be aggregated data for a specified period of time, as described below. That is, the computer 110 can store the operating data and timestamps at which the data were collected, and the computer 110 can identify a single value representing all of a type of operating data within a specified period of time, e.g., a maximum value, an average value, etc. [0042]), by predicting a utilization parameter of the sequence of utilization parameters based on the user record and the set of inputs using on a probabilistic model (a block 515, the machine learning program 305 outputs an identification of a hazard 210 based on the input operating data. As described above, the machine learning program 305 can be a deep neural network 400 that outputs data values that correlate to a predicted hazard 210. Based on outputs from one or more neurons 405 in the DNN 400, the machine learning program 305 can output a predicted hazard 210. [0060]), wherein each utilization parameter of the sequence of utilization parameters is associated with a time segment of the sequence of time segments (The time window sets 300 of operating data can include a current set 300 and historical sets 300. In this context, the “current” set 300 of data includes data for which the specified period of time is a most recent completed period of time, i.e., the period of time that most recently completed relative to a current time. The “historical” sets 300 of data are the sets of data for specified periods of time prior to the current set of data. Using the current and historical sets 300, the server 130 can determine whether a hazard 210 detected during the historical sets 300 remains in the current set 300 and whether the hazard 210 may currently remain on the roadway 200. That is, the server 130 can determine, based on the time window sets 300 of operating data, whether there is a hazard 210 currently on a roadway 200. The current set 300 together with one or more historical sets 300 thus provide data for a plurality of respective time periods. [0047]); and
wherein determining the plurality of values comprises, for each respective time segment of the sequence of time segments (identify one or more hazards 210 based on operating data at specific subsets of time within the time window [0057]), determining a respective value of the set of values associated with the first vehicle based on a respective utilization parameter of the sequence of utilization parameters, wherein the respective utilization parameter is associated with the respective time segment (the machine learning program 305 can include programming with a different machine learning algorithm, e.g., Long Short-Term Memory (LSTM), recurrent neural network algorithms (RNN), variational autoencoders, gradient-boosted trees, etc. The machine learning program 305 can, with the machine learning algorithms, identify one or more hazards 210 based on operating data at specific subsets of time within the time window, and can identify the hazards 210 based on the operating data and specific times at which the operating data were collected. That is, if the time window is defined by a period of time elapsed from 6:00 AM to 12:00 PM, the machine learning program 305 can identify hazards 210 based on operating data occurring at a second time window that takes place within the time window, e.g., from 8:00 AM to 10:00 AM. For example, the operating data from 6:00 AM to 12:00 AM can indicate that a first hazard 210 has occurred during the time window, and the machine learning program 305 can, based on the operating data from 8:00 AM to 10:00 AM, determine that the first hazard 210 has resolved. [0057]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Morizumi in view of Liu, Rakah, and Irey to include the teachings as taught by Lerner with a reasonable expectation of success. Lerner teaches the benefits of “The operator data can include timestamps at which the data were collected, and the machine learning program can identify a hazard based on data collected within a specified time period. These time period data, i.e., “time window” data, are more readily analyzed by the machine learning program than data without timestamps. That is, the timestamps allow the machine learning program to identify hazards at locations by filtering out old behavior data during which the hazard may not have been present. Thus, the machine learning program can use the time window operator behavior data to identify hazards for a map used by autonomous vehicles for operation on the roadway. [Lerner, 0031]”.
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Morizumi et. al. (US 2022/0005609), herein Morizumi in view of Liu et. al. (US 2021/0215491), herein Liu and Rakah et. al. (US 2018/0211541), herein Rakah, in further view of Irey (2021/0334865), herein Irey and Kobayashi et. al. (2020/0286020), herein Kobayashi
Regarding claim 19:
Morizumi in view of Liu and Rakah teaches all the limitations of claim 13, upon which this claim is dependent.
Morizumi in view of Liu and Rakah does not explicitly teach, however Irey teaches:
selecting a user record listed in association with the first vehicle (The driving analysis and vehicle pricing server 250 may include a driving data and vehicle grade and/or price database 252 and driving analysis and vehicle pricing module 251 to respectively store and analyze driving data received from vehicles and other data sources 253 (e.g., insurance records, including policy information, vehicle accident and/or claim history, etc.; vehicle maintenance records; driving records; etc.). [0042]);
obtaining a set of numeric scores stored in association with the user record (Driving analysis and vehicle pricing module 414 may be implemented in hardware and/or software configured to receive vehicle performance and condition data from vehicle sensors 411, telematics device 413, and/or other driving data sources. After receiving the vehicle driving data, driving analysis and vehicle pricing module 414 may perform a set of functions to analyze the driving data, determine driving behaviors, determine the need for maintenance, and calculate vehicle grades and/or prices. For example, the driving analysis and vehicle pricing module 414 may include one or more driving behavior analysis, vehicle maintenance, vehicle grade and/or vehicle price calculation algorithms, which may be executed by software running on generic or specialized hardware within the driving analysis and vehicle pricing module 414. The driving analysis and vehicle pricing module 414 in vehicle 410 may use the vehicle performance and condition data received from that vehicle's sensors 411 to determine driving behaviors, determine the need for maintenance and determine and/or adjust vehicle grades and/or prices applicable to vehicle 410. Within the driving analysis and vehicle pricing module 414, a vehicle grade/price calculation function may use the results of the driving analysis, maintenance analysis and vehicle pricing performed by the module 414 to calculate/adjust vehicle grades and/or prices for vehicle 410. [0068]); and
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Morizumi in view of Liu and Rakah to include the teachings as taught by Irey with a reasonable expectation of success. Irey teaches the benefits of “A driving data analysis and vehicle maintenance server may be configured to receive vehicle driving data corresponding to vehicle operation data of a vehicle, analyze the received vehicle driving data, determine a driving behavior associated with the vehicle, determine recommended vehicle maintenance and calculate a vehicle resale price for the vehicle. [Irey, abstract]”.
Morizumi in view of Liu, Rakah, and Irey does not explicitly teach, however Kobayashi teaches:
determining a threshold based on the set of numeric scores, wherein selecting the first vehicle comprises determining whether the value of the first vehicle satisfies the threshold (determining that a vehicle movement metric of the first vehicle violates a vehicle movement requirement of the requesting MP, and responsive to determining that the vehicle movement metric of the first vehicle violates the vehicle movement requirement of the requesting MP, notifying a violation of the vehicle movement requirement to the server of the requesting MP; that computing a violation amount based on the vehicle movement metric of the first vehicle and the vehicle movement requirement of the requesting MP, and adjusting a travel cost associated with the transportation request based on the violation amount; that computing a violation amount based on the vehicle movement metric of the first vehicle and the vehicle movement requirement of the requesting MP, determining that the violation amount satisfies a violation amount threshold, and responsive to determining that the violation amount satisfies the violation amount threshold, providing a maintenance operation to the first vehicle [0007]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Morizumi in view of Liu, Rakah, and Irey to include the teachings as taught by Kobayashi with a reasonable expectation of success. Kobayashi teaches the benefits of “enabling a first mobility provider (MP) to temporarily utilize a vehicle of a second MP to execute a transportation request that is requested by a user of the first MP. Thus, even when the first MP does not have sufficient transportation capacity to itself execute the transportation request, the first MP can still provide the transportation capability to its user by utilizing the vehicle of the second MP, and thus the user experience with the first MP can be improved. As a further example, the technology described herein enables the first MP that requires additional transportation capacity to utilize the available transportation capacity of the second MP, and therefore the first MP and the second MP can dynamically adapt their transportation capacity as needed. As a result, these MPs can avoid the need to maintain a large number of vehicles without degrading the availability of their transportation service. The MPs can also maximize the overall utilization of the vehicles in their vehicle fleet as these vehicles can be utilized to perform the transportation requests for other MPs. [Kobayashi, 0009]”.
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Morizumi et. al. (US 2022/0005609), herein Morizumi in view of Liu et. al. (US 2021/0215491), herein Liu and Rakah et. al. (US 2018/0211541), herein Rakah, in further view of Ishiguro at. al. (JP 4338011), herein Ishiguro.
Liu further teaches:
predicting an anticipated deceleration thrust value and an anticipated deceleration duration based on the preliminary navigation path (According to various embodiments of the present disclosure, wear and tear and associated costs can be estimated or determined directly by corresponding sensor signals on specific components, such as brake pad wear sensors, or by training neural network or wear models based on the physics and statistics of vehicle operating events by monitoring vehicle conditions, periodically measuring wear, and inputting the data into a cloud-based web service that can estimate the parameters using known statistical approaches. Components or systems may be selected for monitoring and modeling based on types and costs of maintenance and repairs. As previously described, some components may have wear and tear monitored based on odometer distance, time, speed or a combination of the two. Some components may function normally, then fail suddenly such as an engine low on oil, while other components wear gradually and predictably, such as tire treads. The wear and tear models used to determine vehicle sharing cost may be adjusted accordingly based on the type of component and types of wear associated with particular vehicle operation. [0064])
determining a component replacement likelihood (A brake performance model can then use the data collected by the vehicle sensors to estimate the amount of brake pad and rotor (or drum and lining for drum brakes) wear for each braking event. The events may be logged by the VCS and the wear of brake components estimated with a corresponding price determined for the particular vehicle use or trip. [0063])
Rakah further teaches:
predicting an anticipated deceleration thrust value and an anticipated deceleration duration based on the preliminary navigation path (Database 170 may further include traffic data, maps, and toll road information, which may be used for ridesharing service management. Traffic data may include historical traffic data and real-time traffic data regarding a certain geographical region, and may be used to, for example, calculate estimate pick-up and drop-off times, and determine an optimal route for a particular ride. Real-time traffic data may be received from a real-time traffic monitoring system, which may be integrated in or independent from ridesharing management system 100. Maps may include map information used for navigation purposes, for example, for calculating potential routes and guiding the users to a pick-off or drop-off location [0085]); and
Morizumi in view of Liu and Rakah does not explicitly teach, however Ishiguro teaches:
determining a component replacement likelihood (the more frequently the brake operation is performed, the more frequently it is necessary to repair and replace brake device parts such as brake shoes. [0002]) based on the anticipated deceleration thrust value and the anticipated deceleration duration, wherein the first vehicle degradation value comprises the component replacement likelihood (n the brake operation evaluation device of the present invention, at least a brake operation detection means for detecting that the brake operation has been performed by detecting the start of the brake operation and the end of deceleration , and the start of the brake operation . Of the number of times of accumulating the number of times of brake operation for each deceleration range, the number of times of brake operation for each deceleration range, the deceleration detection means for obtaining the deceleration due to the end of deceleration, the number of times of the brake operation is recorded for each predetermined deceleration range , A noise removal cumulative ratio corresponding deceleration calculation means for calculating a deceleration corresponding to a predetermined number of noise removal cumulative ratios, and an evaluation score table prepared in advance so that the evaluation score decreases as the deceleration increases A brake operation evaluation means for evaluating the brake operation by applying to the deceleration corresponding to the noise removal cumulative ratio [0006]).
It would have been obvious to one of ordinary skill in the art at the time of the effective filing date of the claimed invention to have modified Morizumi in view of Liu and Rakah to include the teachings as taught by Ishiguro with a reasonable expectation of success. Isuzu teaches the benefits of “In commercial vehicles, these effects of brake operation are reflected in the transportation cost. Therefore, in order to reduce the transportation cost, it is necessary to keep an appropriate brake operation in mind. [Ishiguro, 0002]”.
Conclusion
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Scott R. Jagolinzer
Examiner
Art Unit 3665
/S.R.J./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665
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