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 .
Response to Amendment
This Office action has been issued in response to amendment filed 11/12/2025. Claims 21, 28-31, and 36-37 are amended. Claims 25-27, 33-35, and 38-40 are canceled. Claims 41-49 are new. Claims 21-24, 28-32, 36-37, and 41-49 are pending, and rejected as detailed below.
Response to Arguments
Rejection under 35 USC § 103
Applicant argues that the Cited References do not teach or suggest Amended independent claims 21, 29, and 37. Applicant also argues that the dependent claims 22-24, 28, 30-32, and 36 under 35 U.S.C. § 103 should be allowed based on the deficiencies of the Cited References in relation to amended independent claims 21, 29, and 37.
Applicant’s arguments, as amended herein, with respect to the rejection(s) of claim(s) 21, 29, and 37 under 35 U.S.C. § 103 have been fully considered and are not persuasive as the combination of previously applied references Kislovskiy, Beaurepaire, Dicker, Scofield, Tibbitts, and Allen teaches the amended claims 21, 29, and 37. In particular, the amended independent claim 21, 29, 37, and the corresponding dependent claims are addressed in the instant office action.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 21-24, 29-32, and 37 are rejected under 35 U.S.C. 103 as being unpatentable over Kislovskiy et al. (US 20180341887 A1), and further in view of Beaurepaire (US 20200079396 A1), Dicker (US 20170352125 A1), Scofield (EP 3114668 B1), Tibbitts (US 20140113619 A1), and Allen (US 20190347582 A1).
Regarding claim 21, Kislovskiy teaches (Currently Amended) A computer-implemented method comprising:
classifying a plurality of vehicle operators into a carpooling group based on (Kislovskiy, at least one para. 0032; “Accordingly, an example risk regressor may compute an individualized fractional risk quantity for each path segment on a per vehicle or per driver basis given the vehicle's or driver's attributes”) at least one of (a) one or more past vehicle routes associated with each vehicle operator of the plurality of vehicle operators, or (b) one or more upcoming vehicle routes associated with each vehicle operator of the plurality of vehicle operators;
analyzing vehicle sensor data (Kislovskiy, at least one para. 0154; “In certain examples, the actual control input data from vehicles”) associated with each vehicle operator of the plurality of vehicle operators in the carpooling group (Kislovskiy, at least one para. 0032; “Accordingly, an example risk regressor may compute an individualized fractional risk quantity for each path segment on a per vehicle or per driver basis given the vehicle's or driver's attributes, such as on-board hardware and software, an AV state as determined through vehicle telemetry or diagnostics data, or a driver's safety history, current state, and driving characteristics”), wherein the vehicle sensor data are indicative of at least one of speed data, acceleration data, braking data, cornering data, following distance data, turn signal data, or seatbelt use data (Kislovskiy, at least one para. 0154; “In certain examples, the actual control input data from vehicles (e.g., indicating steering, braking, and acceleration inputs and reaction times for humans) can be collected and directly or indirectly compared with AV software responses”);
identifying, based on the vehicle sensor data, as analyzed, one or more indicia of driving behaviors associated with each vehicle operator of the plurality of vehicle operators in the carpooling group (Kislovskiy, at least one para. 0032; “Accordingly, an example risk regressor may compute an individualized fractional risk quantity for each path segment on a per vehicle or per driver basis given the vehicle's or driver's attributes, such as on-board hardware and software, an AV state as determined through vehicle telemetry or diagnostics data, or a driver's safety history, current state, and driving characteristics”);
determining, based on the one or more indicia of driving behaviors, as identified, a safety score for each vehicle operator of the plurality of vehicle operators in the carpooling group (Kislovskiy, at least one para. 0169; “the transport management system can filter out all AVs from the candidate set of vehicles (1425), and select a most optimal HDV or driver to service the transport request (1430). In doing so, the transport management system can include factors such as distance or time to the pick-up location (1431), the driver state (1432) (e.g., how long the driver has been on-duty or the driver's current driving characteristics), and/or the driver's historical safety rating (1433). the driver's safety rating may be determined from a stored driver's profile, which can include passenger ratings for the driver, any incident reports, and the driver's personal accident or insurance history.”), wherein determining the safety score comprises weighting safe and unsafe driving behaviors differently and calculating the safety score based on at least one of a frequency of the driving behaviors or a streak of safe driving behavior;
performing a comparison of the safety scores associated with the plurality of vehicle operators (Kislovskiy, at least one para. 0064; “the matching engine 255 can utilize the trip classification 254 to filter through a candidate set of vehicles for the transport request, and select an optimal vehicle to ultimately service the transport request”);
identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators, a vehicle operator having a highest safety score in the carpooling group;
selecting, based on the comparison, the vehicle operator having the highest safety score in the carpooling group as a driver for a carpool, wherein the carpool includes one or more of the plurality of vehicle operators in the carpooling group as one or more passengers (Kislovskiy, at least one para. 0064; “the matching engine 255 can utilize the trip classification 254 to filter through a candidate set of vehicles for the transport request, and select an optimal vehicle to ultimately service the transport request”);
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle; and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool.
However, Kislovskiy does not explicitly teach at least one of (a) one or more past vehicle routes associated with each vehicle operator of the plurality of vehicle operators, or (b) one or more upcoming vehicle routes associated with each vehicle operator of the plurality of vehicle operators;
the driving behaviors are directly associated with the received vehicle sensor data,
wherein determining the safety score comprises weighting safe and unsafe driving behaviors differently and calculating the safety score based on at least one of a frequency of the driving behaviors or a streak of safe driving behavior;
identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators, a vehicle operator having a highest safety score in the carpooling group;
the comparison of the safety scores are directly associated with the vehicle operators,
the selection of the optimal vehicle is directly associated with the driving behaviors.
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle; and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool.
Beaurepaire, in the same field of endeavor (Beaurepaire, at least one para. 0085; “FIG. 14 is an example of using a passenger profile to configure a vehicle-related service (e.g., a ride-share service), according to one embodiment. In the example of FIG. 14, a user initiates a ride-share application 1401 on a device 1403, and requests a carpooling ride that is shared among multiple users”) teaches how one or more indicia of safe driving behaviors are identified based on the analysis of the vehicle sensor data (Beaurepaire, at least one para. 0047; “the vehicle behavior module 301 collects or records vehicle sensor data of a vehicle carrying a user as a passenger. The vehicle sensor data indicates or relates to at least one driving behavior of the vehicle. By way of example, driving behavior or style includes but is not limited to physical measurements or parameters that record how the vehicle is being driven. In one embodiment, the at least one driving behavior includes an acceleration pattern, a braking pattern, a distance to a neighboring vehicle, a vehicle speed on a curved section, a vehicle turning behavior (e.g., how quickly a vehicle turns, the size of vehicle gaps through which a turn can be made, etc.), or a combination thereof.”)
wherein determining the safety score comprises weighting safe and unsafe driving behaviors differently and calculating the safety score based on at least one of a frequency of the driving behaviors or a streak of safe driving behavior;
identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators, a vehicle operator having a highest safety score in the carpooling group;
Beaurepaire, also teaches the comparison of the safety scores are directly associated with the vehicle operators (Beaurepaire, at least one para. 0085; “In one embodiment, the passenger profiles of the user and any other candidate passengers can be processed to determine a common denominator driving profile or behavior comfortable to all or a subset of the passengers. This common denominator driving profile can then be compared against the driving profiles of candidate drivers to select or recommend compatible drivers”).
Beaurepaire also teaches the selection of the optimal vehicle is directly associated with the driving behaviors (Beaurepaire, at least one para. 0085; “In one embodiment, the passenger profiles of the user and any other candidate passengers can be processed to determine a common denominator driving profile or behavior comfortable to all or a subset of the passengers. This common denominator driving profile can then be compared against the driving profiles of candidate drivers to select or recommend compatible drivers”).
Kislovskiy and Beaurepaire are both considered to be analogous to the claimed invention because both of them are in the same field as the method of efficient carpooling as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the identification of the driving behavior of Kislovskiy with the teaching of Beaurepaire. One of the ordinary skill in the art would have been motivated to make this modification so that the passengers of the carpool can select a driver for the carpool based on a preferred driving style (Beaurepaire, at least one para. 0042; “a “conservative” driver (e.g., operates the vehicle 101 at lower speeds, etc. while driving) might be uncomfortable with such conservative driving and want a more aggressive style when riding as a passenger”).
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle; and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool.
However, the combination of Kislovskiy and Beaurepaire does not explicitly teach at least one of (a) one or more past vehicle routes associated with each vehicle operator of the plurality of vehicle operators, or (b) one or more upcoming vehicle routes associated with each vehicle operator of the plurality of vehicle operators;
wherein determining the safety score comprises weighting safe and unsafe driving behaviors differently and calculating the safety score based on at least one of a frequency of the driving behaviors or a streak of safe driving behavior;
identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators, a vehicle operator having a highest safety score in the carpooling group;
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle; and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool.
Dicker, in the same field of endeavor (Dicker, at least one para. 0011; “A network system is disclosed herein that can provide a ride scheduling feature for a designated application of an on-demand transportation service”) teaches at least one of (a) one or more past vehicle routes associated with each vehicle operator of the plurality of vehicle operators (Dicker, at least one para. 0080; “According to various implementations, the network system 100 determine the candidate drivers based on the driver's claimed rides history in the driver's profile data”), or (b) one or more upcoming vehicle routes associated with each vehicle operator of the plurality of vehicle operators (Dicker, at least one para. 0056; “a trigger 144 for an upcoming scheduled ride 142 can cause the selection engine 135 to perform a search for available transport providers”);
wherein determining the safety score comprises weighting safe and unsafe driving behaviors differently and calculating the safety score based on at least one of a frequency of the driving behaviors or a streak of safe driving behavior;
identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators, a vehicle operator having a highest safety score in the carpooling group;
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle; and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool.
The combination of Kislovskiy, Beaurepaire, and Dicker is considered to be analogous to the claimed invention because all of them are in the same field as the method of efficient carpooling as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the classification of vehicle operators of Kislovskiy with the teaching of Dicker. One of the ordinary skill in the art would have been motivated to make this modification so that the optimal driver can identify for the scheduled ride (Dicker; 0089) and the selection engine is able to find the optimal candidate drivers to the schedule ride (Dicker; 0040).
However, the combination of Kislovskiy, Beaurepaire, and Dicker does not explicitly teach wherein determining the safety score comprises weighting safe and unsafe driving behaviors differently and calculating the safety score based on at least one of a frequency of the driving behaviors or a streak of safe driving behavior;
identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators, a vehicle operator having a highest safety score in the carpooling group;
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle; and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool.
Scofield, in the same field of endeavor (Scofield, at least one para. 0004; “Presented herein are techniques for configuring a navigation device to assist a user who is traveling or intends to travel along a path between a first location and a second location, where the path comprises at least two lanes.”) teaches wherein determining the safety score comprises weighting safe and unsafe driving behaviors differently and calculating the safety score based on at least one of a frequency of the driving behaviors or (Scofield, at least one para. 0030; “As a third example of this first aspect, the techniques presented herein may utilize a variety of sources of information to choose a selected lane 218 for recommendation to the user 102, in addition to the predicted travel duration of the lane 112 while traveling the path 106 between the first location 108 and the second location 110. Such information may include, e.g., a lane preference of the user 102 (e.g., whether the user 102 prefers to operate the vehicle 104 in a particular lane); sensitivities of the user 102 to conditions such as traffic density, speed, speed fluctuation, lane change frequency, costs, and ecological impact; and driving behaviors of the user 102 (e.g., a preferred amount of advance notice before the user 102 is compelled to change lanes 112 in order to adhere to a selected route).”, wherein lane change frequency and the preferred amount of advance notice before the user is compelled to change lanes in order to adhere to a selected route is seen as a frequency of driving behaviors with respect to the safety score);
identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators, a vehicle operator having a highest safety score in the carpooling group;
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle; and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool.
The combination of Kislovskiy, Beaurepaire, Dicker, and Scofield is considered to be analogous to the claimed invention because all of them are in the same field as the method of efficient carpooling as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the safety score of Kislovskiy with the teaching of Scofield. One of the ordinary skill in the art would have been motivated to make this modification thus increasing the safety score when the lane change frequency is low, and decreasing the safety score when the lane change frequency is high. Furthermore, the safety score can also increase when the frequency of lane changing behavior occurs with the preferred amount of advance notice, and the safety score can decrease when the frequency of lane changing behavior occurs without the preferred amount of advance notice.
However, the combination of combination of Kislovskiy, Beaurepaire, Dicker, and Scofield does not explicitly teach a streak of safe driving behavior
identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators, a vehicle operator having a highest safety score in the carpooling group;
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle; and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool.
Tibbitts, in the same field of endeavor (Tibbitts, at least one para. 0003; “the invention generally relates to a method and system for modifying a user's driving behaviors, in particular to a system and method for modifying a user's unsafe driving behaviors.”) teaches a streak of safe driving behavior (Tibbitts, at least one para. 0126; “The game score can also be based on the number of perfect distance segments driven in sequence (a streak), so that the longer the streak, the greater the score, or score multiplier.”)
The combination of Kislovskiy, Beaurepaire, Dicker, Scofield, and Tibbitts is considered to be analogous to the claimed invention because all of them are in the same field as the method of efficient carpooling as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the safety score of Kislovskiy with the teaching of Scofield. One of the ordinary skill in the art would have been motivated to make this modification so that the best driver can be selected to receive rewards and discount (Tibbitts; 0126).
The combination of combination of Kislovskiy, Beaurepaire, Dicker, Scofield, and Tibbitts does not explicitly teach identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators, a vehicle operator having a highest safety score in the carpooling group;
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle; and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool.
Allen, in the same field of endeavor (Allen, at least one para. 0003; “Aspects of the disclosure provide effective, efficient, scalable, and convenient technical solutions that address and overcome the technical problems associated with operating shared vehicle services such as car-sharing services safely and effectively”) teaches identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators (Allen, at least one para. 0008; “the driver safety score. 0035; the training data set may correlate age/gender/zip or other input information with indicators of driver safety that may include accident data, tickets, license suspensions and/or cancellations, the cost of insuring a driver, and the like.”), a vehicle operator having a highest safety score in the carpooling group (Allen, at least one para. 0068; “The shared vehicle support platform 110 may train the one or more driver score models 117 prior to executing the method of FIGS. 2A-2E. The historical outcome data may include indications of safety for a number of drivers. Accordingly, the one or more driver score models 117 may calculate an updated score that estimates safety for a driver of the shared vehicle service.”);
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle (Allen, at least one para. 0057; “The shared vehicle support platform 110 may select one or more best vehicles based on the safety ranking and/or on the repair cost ranking.”); and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool (Allen, at least one para. 0057; “At step 212, the shared vehicle support platform 110 may select one or more best vehicles to offer to the requesting driver. / 0062; The user interface may provide a selection of vehicles (e.g., some or all of the best vehicles), which may be presented in a ranked order as indicated by the shared vehicle support platform 110. The user interface may include information about each vehicle, and the user may select one of the one or more best vehicles.”).
The combination of Kislovskiy, Beaurepaire, Dicker, Scofield, Tibbitts, and Allen is considered to be analogous to the claimed invention because all of them are in the same field as the method of efficient carpooling as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the selection of the vehicle operator of Beaurepaire with the teaching of Allen. One of the ordinary skill in the art would have been motivated to make this modification so that the passengers of the carpool passengers can identify the safest vehicle.
Regarding claim 22, Dicker teaches (Previously Presented) The computer-implemented method of claim 21, wherein the one or more past vehicle routes associated with each vehicle operator of the plurality of vehicle operators is determined at least in part based on an analysis of location data detected by one or more respective sensors associated with each vehicle operator (Dicker, at least one para. 0080; “the network system 100 can determine the candidate set based on the home locations of the drivers (640). For example, the network system 100 can correlate the start location of the scheduled transport request 198 to the home locations of drivers within a certain proximity of the start location.”).
Regarding claim 23, Dicker teaches (Previously Presented) The computer-implemented method of claim 21, wherein the one or more upcoming vehicle routes associated with each vehicle operator of the plurality of vehicle operators is determined based on a respective common origin or a respective common destination information provided by each vehicle operator (Dicker, at least one para. 0080; “the network system 100 can determine the candidate set based on the home locations of the drivers (640). For example, the network system 100 can correlate the start location of the scheduled transport request 198 to the home locations of drivers within a certain proximity of the start location.”, It is understood that the “home locations” can be identified as a common origin and/or a common destination with respect to the driver’s address.).
Regarding claim 24, Dicker teaches (Previously Presented) The computer-implemented method of claim 23, wherein the one or more upcoming vehicle routes are further determined in a mapping application of a respective computing device associated with each vehicle operator (Dicker, at least one para. 0080; “In many examples, the user device 200 of FIG. 2 and the driver device 300 of FIG. 3 can comprise mobile computing device (e.g., smart phone devices) that can execute any number of applications stored thereon. Upon initiating the driver application 332, the driver device 300 can initiate a GPS module 360 to transmit location data 362 to the network system 390. Based on the location data 362, the network system 390 can transmit transport invitations 392 to the driver device 300 for transport requests at proximate locations in relation to the driver's current location.”).
Regarding claim 28, Allen teaches (Currently Amended) The computer-implemented method of claim 21, further comprising selecting the driving vehicle, comprising (Allen, at least one para. 0057; “At step 212, the shared vehicle support platform 110 may select one or more best vehicles to offer to the requesting driver. / 0062; The user interface may provide a selection of vehicles (e.g., some or all of the best vehicles), which may be presented in a ranked order as indicated by the shared vehicle support platform 110. The user interface may include information about each vehicle, and the user may select one of the one or more best vehicles.”):
comparing vehicle safety score of vehicles associated with the vehicle operators in the carpooling group (Allen, at least one para. 0045; “Accordingly, the vehicle score model 116 may calculate a vehicle safety score for each vehicle associated with a shared vehicle service”); and
selecting, based on both the safety scores associated with the vehicle operators in operator of the carpooling group and the vehicle safety scores of the vehicles associated with the vehicle operators in the carpooling group, the driving vehicle for the carpool (Allen, at least one para. 0049; “For example, the shared vehicle support platform 110 may multiply a numeric safety rating for each vehicle times a numeric initial score for the driver to obtain a driver/vehicle safety score for each vehicle. The shared vehicle support platform 110 may then rank the vehicles by the driver/vehicle safety score”).
Regarding claim 29, Kislovskiy teaches (Currently Amended) A computer system for efficient carpooling, the computer system comprising (Kislovskiy, at least one para. 0039; “One or more examples described herein provide that methods, techniques, and actions performed by a computing device are performed programmatically, or as a computer-implemented method”):
one or more processors (Kislovskiy, at least one para. 0042; “one or more examples described herein may be implemented through the use of instructions that are executable by one or more processors”); and
one or more non-transitory memories storing instructions that, when executed by the one or more processors, cause the computer system to perform operations comprising (Kislovskiy, at least one para. 0042; “These instructions may be carried on a non-transitory computer-readable medium. Machines shown or described with figures below provide examples of processing resources and computer-readable mediums on which instructions for implementing examples disclosed herein can be carried and/or executed”):
classifying a plurality of vehicle operators into a carpooling group based on (Kislovskiy, at least one para. 0032; “Accordingly, an example risk regressor may compute an individualized fractional risk quantity for each path segment on a per vehicle or per driver basis given the vehicle's or driver's attributes”) at least one of (a) one or more past vehicle routes associated with each vehicle operator of the plurality of vehicle operators, or (b) one or more upcoming vehicle routes associated with each vehicle operator of the plurality of vehicle operators;
analyzing vehicle sensor data (Kislovskiy, at least one para. 0154; “In certain examples, the actual control input data from vehicles”) associated with each vehicle operator of the plurality of vehicle operators in the carpooling group (Kislovskiy, at least one para. 0032; “Accordingly, an example risk regressor may compute an individualized fractional risk quantity for each path segment on a per vehicle or per driver basis given the vehicle's or driver's attributes, such as on-board hardware and software, an AV state as determined through vehicle telemetry or diagnostics data, or a driver's safety history, current state, and driving characteristics”), wherein the vehicle sensor data are indicative of at least one of speed data, acceleration data, braking data, cornering data, following distance data, turn signal data, or seatbelt use data (Kislovskiy, at least one para. 0154; “In certain examples, the actual control input data from vehicles (e.g., indicating steering, braking, and acceleration inputs and reaction times for humans) can be collected and directly or indirectly compared with AV software responses”);
identifying, based on the vehicle sensor data, as analyzed, one or more indicia of driving behaviors associated with each vehicle operator of the plurality of vehicle operators in the carpooling group (Kislovskiy, at least one para. 0032; “Accordingly, an example risk regressor may compute an individualized fractional risk quantity for each path segment on a per vehicle or per driver basis given the vehicle's or driver's attributes, such as on-board hardware and software, an AV state as determined through vehicle telemetry or diagnostics data, or a driver's safety history, current state, and driving characteristics”);
determining, based on the one or more indicia of driving behaviors, as identified, a safety score for each vehicle operator of the plurality of vehicle operators in the carpooling group (Kislovskiy, at least one para. 0169; “the transport management system can filter out all AVs from the candidate set of vehicles (1425), and select a most optimal HDV or driver to service the transport request (1430). In doing so, the transport management system can include factors such as distance or time to the pick-up location (1431), the driver state (1432) (e.g., how long the driver has been on-duty or the driver's current driving characteristics), and/or the driver's historical safety rating (1433). the driver's safety rating may be determined from a stored driver's profile, which can include passenger ratings for the driver, any incident reports, and the driver's personal accident or insurance history.”), wherein determining the safety score comprises weighting safe and unsafe driving behaviors differently and calculating the safety score based on at least one of a frequency of the driving behaviors or a streak of safe driving behavior;
performing a comparison of the safety scores associated with the plurality of vehicle operators (Kislovskiy, at least one para. 0064; “the matching engine 255 can utilize the trip classification 254 to filter through a candidate set of vehicles for the transport request, and select an optimal vehicle to ultimately service the transport request”);
identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators, a vehicle operator having a highest safety score in the carpooling group;
selecting, based on the comparison, the vehicle operator having the highest safety score in the carpooling group as a driver for a carpool, wherein the carpool includes one or more of the plurality of vehicle operators in the carpooling group as one or more passengers (Kislovskiy, at least one para. 0064; “the matching engine 255 can utilize the trip classification 254 to filter through a candidate set of vehicles for the transport request, and select an optimal vehicle to ultimately service the transport request”);
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle; and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool.
However, Kislovskiy does not explicitly teach at least one of (a) one or more past vehicle routes associated with each vehicle operator of the plurality of vehicle operators, or (b) one or more upcoming vehicle routes associated with each vehicle operator of the plurality of vehicle operators;
the driving behaviors are directly associated with the received vehicle sensor data,
wherein determining the safety score comprises weighting safe and unsafe driving behaviors differently and calculating the safety score based on at least one of a frequency of the driving behaviors or a streak of safe driving behavior;
identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators, a vehicle operator having a highest safety score in the carpooling group;
the comparison of the safety scores are directly associated with the vehicle operators,
the selection of the optimal vehicle is directly associated with the driving behaviors.
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle; and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool.
Beaurepaire, in the same field of endeavor (Beaurepaire, at least one para. 0085; “FIG. 14 is an example of using a passenger profile to configure a vehicle-related service (e.g., a ride-share service), according to one embodiment. In the example of FIG. 14, a user initiates a ride-share application 1401 on a device 1403, and requests a carpooling ride that is shared among multiple users”) teaches how one or more indicia of safe driving behaviors are identified based on the analysis of the vehicle sensor data (Beaurepaire, at least one para. 0047; “the vehicle behavior module 301 collects or records vehicle sensor data of a vehicle carrying a user as a passenger. The vehicle sensor data indicates or relates to at least one driving behavior of the vehicle. By way of example, driving behavior or style includes but is not limited to physical measurements or parameters that record how the vehicle is being driven. In one embodiment, the at least one driving behavior includes an acceleration pattern, a braking pattern, a distance to a neighboring vehicle, a vehicle speed on a curved section, a vehicle turning behavior (e.g., how quickly a vehicle turns, the size of vehicle gaps through which a turn can be made, etc.), or a combination thereof.”)
wherein determining the safety score comprises weighting safe and unsafe driving behaviors differently and calculating the safety score based on at least one of a frequency of the driving behaviors or a streak of safe driving behavior;
identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators, a vehicle operator having a highest safety score in the carpooling group;
Beaurepaire, also teaches the comparison of the safety scores are directly associated with the vehicle operators (Beaurepaire, at least one para. 0085; “In one embodiment, the passenger profiles of the user and any other candidate passengers can be processed to determine a common denominator driving profile or behavior comfortable to all or a subset of the passengers. This common denominator driving profile can then be compared against the driving profiles of candidate drivers to select or recommend compatible drivers”).
Beaurepaire also teaches the selection of the optimal vehicle is directly associated with the driving behaviors (Beaurepaire, at least one para. 0085; “In one embodiment, the passenger profiles of the user and any other candidate passengers can be processed to determine a common denominator driving profile or behavior comfortable to all or a subset of the passengers. This common denominator driving profile can then be compared against the driving profiles of candidate drivers to select or recommend compatible drivers”).
Kislovskiy and Beaurepaire are both considered to be analogous to the claimed invention because both of them are in the same field as the method of efficient carpooling as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the identification of the driving behavior of Kislovskiy with the teaching of Beaurepaire. One of the ordinary skill in the art would have been motivated to make this modification so that the passengers of the carpool can select a driver for the carpool based on a preferred driving style (Beaurepaire, at least one para. 0042; “a “conservative” driver (e.g., operates the vehicle 101 at lower speeds, etc. while driving) might be uncomfortable with such conservative driving and want a more aggressive style when riding as a passenger”).
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle; and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool.
However, the combination of Kislovskiy and Beaurepaire does not explicitly teach at least one of (a) one or more past vehicle routes associated with each vehicle operator of the plurality of vehicle operators, or (b) one or more upcoming vehicle routes associated with each vehicle operator of the plurality of vehicle operators;
wherein determining the safety score comprises weighting safe and unsafe driving behaviors differently and calculating the safety score based on at least one of a frequency of the driving behaviors or a streak of safe driving behavior;
identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators, a vehicle operator having a highest safety score in the carpooling group;
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle; and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool.
Dicker, in the same field of endeavor (Dicker, at least one para. 0011; “A network system is disclosed herein that can provide a ride scheduling feature for a designated application of an on-demand transportation service”) teaches at least one of (a) one or more past vehicle routes associated with each vehicle operator of the plurality of vehicle operators (Dicker, at least one para. 0080; “According to various implementations, the network system 100 determine the candidate drivers based on the driver's claimed rides history in the driver's profile data”), or (b) one or more upcoming vehicle routes associated with each vehicle operator of the plurality of vehicle operators (Dicker, at least one para. 0056; “a trigger 144 for an upcoming scheduled ride 142 can cause the selection engine 135 to perform a search for available transport providers”);
wherein determining the safety score comprises weighting safe and unsafe driving behaviors differently and calculating the safety score based on at least one of a frequency of the driving behaviors or a streak of safe driving behavior;
identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators, a vehicle operator having a highest safety score in the carpooling group;
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle; and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool.
The combination of Kislovskiy, Beaurepaire, and Dicker is considered to be analogous to the claimed invention because all of them are in the same field as the method of efficient carpooling as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the classification of vehicle operators of Kislovskiy with the teaching of Dicker. One of the ordinary skill in the art would have been motivated to make this modification so that the optimal driver can identify for the scheduled ride (Dicker; 0089) and the selection engine is able to find the optimal candidate drivers to the schedule ride (Dicker; 0040).
However, the combination of Kislovskiy, Beaurepaire, and Dicker does not explicitly teach wherein determining the safety score comprises weighting safe and unsafe driving behaviors differently and calculating the safety score based on at least one of a frequency of the driving behaviors or a streak of safe driving behavior;
identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators, a vehicle operator having a highest safety score in the carpooling group;
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle; and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool.
Scofield, in the same field of endeavor (Scofield, at least one para. 0004; “Presented herein are techniques for configuring a navigation device to assist a user who is traveling or intends to travel along a path between a first location and a second location, where the path comprises at least two lanes.”) teaches wherein determining the safety score comprises weighting safe and unsafe driving behaviors differently and calculating the safety score based on at least one of a frequency of the driving behaviors or (Scofield, at least one para. 0030; “As a third example of this first aspect, the techniques presented herein may utilize a variety of sources of information to choose a selected lane 218 for recommendation to the user 102, in addition to the predicted travel duration of the lane 112 while traveling the path 106 between the first location 108 and the second location 110. Such information may include, e.g., a lane preference of the user 102 (e.g., whether the user 102 prefers to operate the vehicle 104 in a particular lane); sensitivities of the user 102 to conditions such as traffic density, speed, speed fluctuation, lane change frequency, costs, and ecological impact; and driving behaviors of the user 102 (e.g., a preferred amount of advance notice before the user 102 is compelled to change lanes 112 in order to adhere to a selected route).”, wherein lane change frequency and the preferred amount of advance notice before the user is compelled to change lanes in order to adhere to a selected route is seen as a frequency of driving behaviors with respect to the safety score);
identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators, a vehicle operator having a highest safety score in the carpooling group;
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle; and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool.
The combination of Kislovskiy, Beaurepaire, Dicker, and Scofield is considered to be analogous to the claimed invention because all of them are in the same field as the method of efficient carpooling as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the safety score of Kislovskiy with the teaching of Scofield. One of the ordinary skill in the art would have been motivated to make this modification thus increasing the safety score when the lane change frequency is low, and decreasing the safety score when the lane change frequency is high. Furthermore, the safety score can also increase when the frequency of lane changing behavior occurs with the preferred amount of advance notice, and the safety score can decrease when the frequency of lane changing behavior occurs without the preferred amount of advance notice.
However, the combination of combination of Kislovskiy, Beaurepaire, Dicker, and Scofield does not explicitly teach a streak of safe driving behavior
identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators, a vehicle operator having a highest safety score in the carpooling group;
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle; and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool.
Tibbitts, in the same field of endeavor (Tibbitts, at least one para. 0003; “the invention generally relates to a method and system for modifying a user's driving behaviors, in particular to a system and method for modifying a user's unsafe driving behaviors.”) teaches a streak of safe driving behavior (Tibbitts, at least one para. 0126; “The game score can also be based on the number of perfect distance segments driven in sequence (a streak), so that the longer the streak, the greater the score, or score multiplier.”)
The combination of Kislovskiy, Beaurepaire, Dicker, Scofield, and Tibbitts is considered to be analogous to the claimed invention because all of them are in the same field as the method of efficient carpooling as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the safety score of Kislovskiy with the teaching of Scofield. One of the ordinary skill in the art would have been motivated to make this modification so that the best driver can be selected to receive rewards and discount (Tibbitts; 0126).
The combination of combination of Kislovskiy, Beaurepaire, Dicker, Scofield, and Tibbitts does not explicitly teach identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators, a vehicle operator having a highest safety score in the carpooling group;
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle; and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool.
Allen, in the same field of endeavor (Allen, at least one para. 0003; “Aspects of the disclosure provide effective, efficient, scalable, and convenient technical solutions that address and overcome the technical problems associated with operating shared vehicle services such as car-sharing services safely and effectively”) teaches identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators (Allen, at least one para. 0008; “the driver safety score. 0035; the training data set may correlate age/gender/zip or other input information with indicators of driver safety that may include accident data, tickets, license suspensions and/or cancellations, the cost of insuring a driver, and the like.”), a vehicle operator having a highest safety score in the carpooling group (Allen, at least one para. 0068; “The shared vehicle support platform 110 may train the one or more driver score models 117 prior to executing the method of FIGS. 2A-2E. The historical outcome data may include indications of safety for a number of drivers. Accordingly, the one or more driver score models 117 may calculate an updated score that estimates safety for a driver of the shared vehicle service.”);
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle (Allen, at least one para. 0057; “The shared vehicle support platform 110 may select one or more best vehicles based on the safety ranking and/or on the repair cost ranking.”); and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool (Allen, at least one para. 0057; “At step 212, the shared vehicle support platform 110 may select one or more best vehicles to offer to the requesting driver. / 0062; The user interface may provide a selection of vehicles (e.g., some or all of the best vehicles), which may be presented in a ranked order as indicated by the shared vehicle support platform 110. The user interface may include information about each vehicle, and the user may select one of the one or more best vehicles.”).
The combination of Kislovskiy, Beaurepaire, Dicker, Scofield, Tibbitts, and Allen is considered to be analogous to the claimed invention because all of them are in the same field as the method of efficient carpooling as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the selection of the vehicle operator of Beaurepaire with the teaching of Allen. One of the ordinary skill in the art would have been motivated to make this modification so that the passengers of the carpool passengers can identify the safest vehicle.
Regarding claim 30, Dicker teaches (Currently Amended) The computer system of claim 29, wherein the one or more past vehicle routes associated with each vehicle operator of the plurality of vehicle operators is determined at least in part based on an analysis of location data detected by one or more respective sensors associated with each vehicle operator (Dicker, at least one para. 0080; “the network system 100 can determine the candidate set based on the home locations of the drivers (640). For example, the network system 100 can correlate the start location of the scheduled transport request 198 to the home locations of drivers within a certain proximity of the start location.”).
Regarding claim 31, Dicker teaches (Currently Amended) The computer system of claim 29, wherein the one or more upcoming vehicle routes associated with each vehicle operator of the plurality of vehicle operators is determined based on a respective common origin or a respective common destination information provided by each vehicle operator (Dicker, at least one para. 0080; “the network system 100 can determine the candidate set based on the home locations of the drivers (640). For example, the network system 100 can correlate the start location of the scheduled transport request 198 to the home locations of drivers within a certain proximity of the start location.”, It is understood that the “home locations” can be identified as a common origin and/or a common destination with respect to the driver’s address.).
Regarding claim 32, Dicker teaches (Previously Presented) The computer system of claim 31,wherein the one or more upcoming vehicle routes are further determined in a mapping application of a respective computing device associated with each vehicle operator (Dicker, at least one para. 0080; “In many examples, the user device 200 of FIG. 2 and the driver device 300 of FIG. 3 can comprise mobile computing device (e.g., smart phone devices) that can execute any number of applications stored thereon. Upon initiating the driver application 332, the driver device 300 can initiate a GPS module 360 to transmit location data 362 to the network system 390. Based on the location data 362, the network system 390 can transmit transport invitations 392 to the driver device 300 for transport requests at proximate locations in relation to the driver's current location.”).
Regarding claim 36, Allen teaches (Currently Amended) The computer system of claim 29, further comprising selecting the driving vehicle, comprising (Allen, at least one para. 0057; “At step 212, the shared vehicle support platform 110 may select one or more best vehicles to offer to the requesting driver. / 0062; The user interface may provide a selection of vehicles (e.g., some or all of the best vehicles), which may be presented in a ranked order as indicated by the shared vehicle support platform 110. The user interface may include information about each vehicle, and the user may select one of the one or more best vehicles.”):
comparing vehicle safety score of vehicles associated with the vehicle operators in the carpooling group (Allen, at least one para. 0045; “Accordingly, the vehicle score model 116 may calculate a vehicle safety score for each vehicle associated with a shared vehicle service”); and
selecting, based on both the safety scores associated with the vehicle operators in operator of the carpooling group and the vehicle safety scores of the vehicles associated with the vehicle operators in the carpooling group, the driving vehicle for the carpool (Allen, at least one para. 0049; “For example, the shared vehicle support platform 110 may multiply a numeric safety rating for each vehicle times a numeric initial score for the driver to obtain a driver/vehicle safety score for each vehicle. The shared vehicle support platform 110 may then rank the vehicles by the driver/vehicle safety score”).
Regarding claim 37, Kislovskiy teaches (Currently Amended) A non-transitory computer-readable storage medium having stored thereon a set of instructions for efficient carpooling, when executed by one or more processors, cause the one or more processors to perform operations comprising (Kislovskiy, at least one para. 0039; “One or more examples described herein provide that methods, techniques, and actions performed by a computing device are performed programmatically, or as a computer-implemented method”) and (Kislovskiy, at least one para. 0042; “one or more examples described herein may be implemented through the use of instructions that are executable by one or more processors”):
classifying a plurality of vehicle operators into a carpooling group based on (Kislovskiy, at least one para. 0032; “Accordingly, an example risk regressor may compute an individualized fractional risk quantity for each path segment on a per vehicle or per driver basis given the vehicle's or driver's attributes”) at least one of (a) one or more past vehicle routes associated with each vehicle operator of the plurality of vehicle operators, or (b) one or more upcoming vehicle routes associated with each vehicle operator of the plurality of vehicle operators;
analyzing vehicle sensor data (Kislovskiy, at least one para. 0154; “In certain examples, the actual control input data from vehicles”) associated with each vehicle operator of the plurality of vehicle operators in the carpooling group (Kislovskiy, at least one para. 0032; “Accordingly, an example risk regressor may compute an individualized fractional risk quantity for each path segment on a per vehicle or per driver basis given the vehicle's or driver's attributes, such as on-board hardware and software, an AV state as determined through vehicle telemetry or diagnostics data, or a driver's safety history, current state, and driving characteristics”), wherein the vehicle sensor data are indicative of at least one of speed data, acceleration data, braking data, cornering data, following distance data, turn signal data, or seatbelt use data (Kislovskiy, at least one para. 0154; “In certain examples, the actual control input data from vehicles (e.g., indicating steering, braking, and acceleration inputs and reaction times for humans) can be collected and directly or indirectly compared with AV software responses”);
identifying, based on the vehicle sensor data, as analyzed, one or more indicia of driving behaviors associated with each vehicle operator of the plurality of vehicle operators in the carpooling group (Kislovskiy, at least one para. 0032; “Accordingly, an example risk regressor may compute an individualized fractional risk quantity for each path segment on a per vehicle or per driver basis given the vehicle's or driver's attributes, such as on-board hardware and software, an AV state as determined through vehicle telemetry or diagnostics data, or a driver's safety history, current state, and driving characteristics”);
determining, based on the one or more indicia of driving behaviors, as identified, a safety score for each vehicle operator of the plurality of vehicle operators in the carpooling group (Kislovskiy, at least one para. 0169; “the transport management system can filter out all AVs from the candidate set of vehicles (1425), and select a most optimal HDV or driver to service the transport request (1430). In doing so, the transport management system can include factors such as distance or time to the pick-up location (1431), the driver state (1432) (e.g., how long the driver has been on-duty or the driver's current driving characteristics), and/or the driver's historical safety rating (1433). the driver's safety rating may be determined from a stored driver's profile, which can include passenger ratings for the driver, any incident reports, and the driver's personal accident or insurance history.”), wherein determining the safety score comprises weighting safe and unsafe driving behaviors differently and calculating the safety score based on at least one of a frequency of the driving behaviors or a streak of safe driving behavior;
performing a comparison of the safety scores associated with the plurality of vehicle operators (Kislovskiy, at least one para. 0064; “the matching engine 255 can utilize the trip classification 254 to filter through a candidate set of vehicles for the transport request, and select an optimal vehicle to ultimately service the transport request”);
identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators, a vehicle operator having a highest safety score in the carpooling group;
selecting, based on the comparison, the vehicle operator having the highest safety score in the carpooling group as a driver for a carpool, wherein the carpool includes one or more of the plurality of vehicle operators in the carpooling group as one or more passengers (Kislovskiy, at least one para. 0064; “the matching engine 255 can utilize the trip classification 254 to filter through a candidate set of vehicles for the transport request, and select an optimal vehicle to ultimately service the transport request”);
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle; and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool.
However, Kislovskiy does not explicitly teach at least one of (a) one or more past vehicle routes associated with each vehicle operator of the plurality of vehicle operators, or (b) one or more upcoming vehicle routes associated with each vehicle operator of the plurality of vehicle operators;
the driving behaviors are directly associated with the received vehicle sensor data,
wherein determining the safety score comprises weighting safe and unsafe driving behaviors differently and calculating the safety score based on at least one of a frequency of the driving behaviors or a streak of safe driving behavior;
identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators, a vehicle operator having a highest safety score in the carpooling group;
the comparison of the safety scores are directly associated with the vehicle operators,
the selection of the optimal vehicle is directly associated with the driving behaviors.
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle; and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool.
Beaurepaire, in the same field of endeavor (Beaurepaire, at least one para. 0085; “FIG. 14 is an example of using a passenger profile to configure a vehicle-related service (e.g., a ride-share service), according to one embodiment. In the example of FIG. 14, a user initiates a ride-share application 1401 on a device 1403, and requests a carpooling ride that is shared among multiple users”) teaches how one or more indicia of safe driving behaviors are identified based on the analysis of the vehicle sensor data (Beaurepaire, at least one para. 0047; “the vehicle behavior module 301 collects or records vehicle sensor data of a vehicle carrying a user as a passenger. The vehicle sensor data indicates or relates to at least one driving behavior of the vehicle. By way of example, driving behavior or style includes but is not limited to physical measurements or parameters that record how the vehicle is being driven. In one embodiment, the at least one driving behavior includes an acceleration pattern, a braking pattern, a distance to a neighboring vehicle, a vehicle speed on a curved section, a vehicle turning behavior (e.g., how quickly a vehicle turns, the size of vehicle gaps through which a turn can be made, etc.), or a combination thereof.”)
wherein determining the safety score comprises weighting safe and unsafe driving behaviors differently and calculating the safety score based on at least one of a frequency of the driving behaviors or a streak of safe driving behavior;
identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators, a vehicle operator having a highest safety score in the carpooling group;
Beaurepaire, also teaches the comparison of the safety scores are directly associated with the vehicle operators (Beaurepaire, at least one para. 0085; “In one embodiment, the passenger profiles of the user and any other candidate passengers can be processed to determine a common denominator driving profile or behavior comfortable to all or a subset of the passengers. This common denominator driving profile can then be compared against the driving profiles of candidate drivers to select or recommend compatible drivers”).
Beaurepaire also teaches the selection of the optimal vehicle is directly associated with the driving behaviors (Beaurepaire, at least one para. 0085; “In one embodiment, the passenger profiles of the user and any other candidate passengers can be processed to determine a common denominator driving profile or behavior comfortable to all or a subset of the passengers. This common denominator driving profile can then be compared against the driving profiles of candidate drivers to select or recommend compatible drivers”).
Kislovskiy and Beaurepaire are both considered to be analogous to the claimed invention because both of them are in the same field as the method of efficient carpooling as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the identification of the driving behavior of Kislovskiy with the teaching of Beaurepaire. One of the ordinary skill in the art would have been motivated to make this modification so that the passengers of the carpool can select a driver for the carpool based on a preferred driving style (Beaurepaire, at least one para. 0042; “a “conservative” driver (e.g., operates the vehicle 101 at lower speeds, etc. while driving) might be uncomfortable with such conservative driving and want a more aggressive style when riding as a passenger”).
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle; and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool.
However, the combination of Kislovskiy and Beaurepaire does not explicitly teach at least one of (a) one or more past vehicle routes associated with each vehicle operator of the plurality of vehicle operators, or (b) one or more upcoming vehicle routes associated with each vehicle operator of the plurality of vehicle operators;
wherein determining the safety score comprises weighting safe and unsafe driving behaviors differently and calculating the safety score based on at least one of a frequency of the driving behaviors or a streak of safe driving behavior;
identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators, a vehicle operator having a highest safety score in the carpooling group;
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle; and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool.
Dicker, in the same field of endeavor (Dicker, at least one para. 0011; “A network system is disclosed herein that can provide a ride scheduling feature for a designated application of an on-demand transportation service”) teaches at least one of (a) one or more past vehicle routes associated with each vehicle operator of the plurality of vehicle operators (Dicker, at least one para. 0080; “According to various implementations, the network system 100 determine the candidate drivers based on the driver's claimed rides history in the driver's profile data”), or (b) one or more upcoming vehicle routes associated with each vehicle operator of the plurality of vehicle operators (Dicker, at least one para. 0056; “a trigger 144 for an upcoming scheduled ride 142 can cause the selection engine 135 to perform a search for available transport providers”);
wherein determining the safety score comprises weighting safe and unsafe driving behaviors differently and calculating the safety score based on at least one of a frequency of the driving behaviors or a streak of safe driving behavior;
identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators, a vehicle operator having a highest safety score in the carpooling group;
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle; and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool.
The combination of Kislovskiy, Beaurepaire, and Dicker is considered to be analogous to the claimed invention because all of them are in the same field as the method of efficient carpooling as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the classification of vehicle operators of Kislovskiy with the teaching of Dicker. One of the ordinary skill in the art would have been motivated to make this modification so that the optimal driver can identify for the scheduled ride (Dicker; 0089) and the selection engine is able to find the optimal candidate drivers to the schedule ride (Dicker; 0040).
However, the combination of Kislovskiy, Beaurepaire, and Dicker does not explicitly teach wherein determining the safety score comprises weighting safe and unsafe driving behaviors differently and calculating the safety score based on at least one of a frequency of the driving behaviors or a streak of safe driving behavior;
identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators, a vehicle operator having a highest safety score in the carpooling group;
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle; and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool.
Scofield, in the same field of endeavor (Scofield, at least one para. 0004; “Presented herein are techniques for configuring a navigation device to assist a user who is traveling or intends to travel along a path between a first location and a second location, where the path comprises at least two lanes.”) teaches wherein determining the safety score comprises weighting safe and unsafe driving behaviors differently and calculating the safety score based on at least one of a frequency of the driving behaviors or (Scofield, at least one para. 0030; “As a third example of this first aspect, the techniques presented herein may utilize a variety of sources of information to choose a selected lane 218 for recommendation to the user 102, in addition to the predicted travel duration of the lane 112 while traveling the path 106 between the first location 108 and the second location 110. Such information may include, e.g., a lane preference of the user 102 (e.g., whether the user 102 prefers to operate the vehicle 104 in a particular lane); sensitivities of the user 102 to conditions such as traffic density, speed, speed fluctuation, lane change frequency, costs, and ecological impact; and driving behaviors of the user 102 (e.g., a preferred amount of advance notice before the user 102 is compelled to change lanes 112 in order to adhere to a selected route).”, wherein lane change frequency and the preferred amount of advance notice before the user is compelled to change lanes in order to adhere to a selected route is seen as a frequency of driving behaviors with respect to the safety score);
identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators, a vehicle operator having a highest safety score in the carpooling group;
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle; and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool.
The combination of Kislovskiy, Beaurepaire, Dicker, and Scofield is considered to be analogous to the claimed invention because all of them are in the same field as the method of efficient carpooling as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the safety score of Kislovskiy with the teaching of Scofield. One of the ordinary skill in the art would have been motivated to make this modification thus increasing the safety score when the lane change frequency is low, and decreasing the safety score when the lane change frequency is high. Furthermore, the safety score can also increase when the frequency of lane changing behavior occurs with the preferred amount of advance notice, and the safety score can decrease when the frequency of lane changing behavior occurs without the preferred amount of advance notice.
However, the combination of combination of Kislovskiy, Beaurepaire, Dicker, and Scofield does not explicitly teach a streak of safe driving behavior
identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators, a vehicle operator having a highest safety score in the carpooling group;
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle; and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool.
Tibbitts, in the same field of endeavor (Tibbitts, at least one para. 0003; “the invention generally relates to a method and system for modifying a user's driving behaviors, in particular to a system and method for modifying a user's unsafe driving behaviors.”) teaches a streak of safe driving behavior (Tibbitts, at least one para. 0126; “The game score can also be based on the number of perfect distance segments driven in sequence (a streak), so that the longer the streak, the greater the score, or score multiplier.”)
The combination of Kislovskiy, Beaurepaire, Dicker, Scofield, and Tibbitts is considered to be analogous to the claimed invention because all of them are in the same field as the method of efficient carpooling as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the safety score of Kislovskiy with the teaching of Scofield. One of the ordinary skill in the art would have been motivated to make this modification so that the best driver can be selected to receive rewards and discount (Tibbitts; 0126).
The combination of combination of Kislovskiy, Beaurepaire, Dicker, Scofield, and Tibbitts does not explicitly teach identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators, a vehicle operator having a highest safety score in the carpooling group;
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle; and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool.
Allen, in the same field of endeavor (Allen, at least one para. 0003; “Aspects of the disclosure provide effective, efficient, scalable, and convenient technical solutions that address and overcome the technical problems associated with operating shared vehicle services such as car-sharing services safely and effectively”) teaches identifying, based on the comparison of the safety scores associated with the plurality of vehicle operators (Allen, at least one para. 0008; “the driver safety score. 0035; the training data set may correlate age/gender/zip or other input information with indicators of driver safety that may include accident data, tickets, license suspensions and/or cancellations, the cost of insuring a driver, and the like.”), a vehicle operator having a highest safety score in the carpooling group (Allen, at least one para. 0068; “The shared vehicle support platform 110 may train the one or more driver score models 117 prior to executing the method of FIGS. 2A-2E. The historical outcome data may include indications of safety for a number of drivers. Accordingly, the one or more driver score models 117 may calculate an updated score that estimates safety for a driver of the shared vehicle service.”);
identifying a safest vehicle of vehicles associated with the vehicle operators in the carpooling group based on a vehicle safety score for each vehicle (Allen, at least one para. 0057; “The shared vehicle support platform 110 may select one or more best vehicles based on the safety ranking and/or on the repair cost ranking.”); and
selecting the safest vehicle, as identified, as a driving vehicle for the carpool, wherein the driving vehicle is to be operated by the driver, as selected, for the carpool (Allen, at least one para. 0057; “At step 212, the shared vehicle support platform 110 may select one or more best vehicles to offer to the requesting driver. / 0062; The user interface may provide a selection of vehicles (e.g., some or all of the best vehicles), which may be presented in a ranked order as indicated by the shared vehicle support platform 110. The user interface may include information about each vehicle, and the user may select one of the one or more best vehicles.”).
The combination of Kislovskiy, Beaurepaire, Dicker, Scofield, Tibbitts, and Allen is considered to be analogous to the claimed invention because all of them are in the same field as the method of efficient carpooling as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the selection of the vehicle operator of Beaurepaire with the teaching of Allen. One of the ordinary skill in the art would have been motivated to make this modification so that the passengers of the carpool passengers can identify the safest vehicle.
Regarding claim 41, Dicker teaches (New) The non-transitory computer-readable storage medium of claim 37, wherein the one or more past vehicle routes associated with each vehicle operator of the plurality of vehicle operators is determined based at least on an analysis of location data detected by one or more respective sensors associated with each vehicle operator (Dicker, at least one para. 0080; “In still further implementations, the network system 100 can determine the candidate set based on the home locations of the drivers (640). For example, the network system 100 can correlate the start location of the scheduled transport request 198 to the home locations of drivers within a certain proximity of the start location.”).
Regarding claim 42, Dicker teaches (New) The non-transitory computer-readable storage medium of claim 37, wherein the one or more upcoming vehicle routes associated with each vehicle operator of the plurality of vehicle operators is determined based at least on a respective common origin or a respective common destination information provided by each vehicle operator (Dicker, at least one para. 0080; “the network system 100 can determine the candidate set based on the home locations of the drivers (640). For example, the network system 100 can correlate the start location of the scheduled transport request 198 to the home locations of drivers within a certain proximity of the start location.”, It is understood that the “home locations” can be identified as a common origin and/or a common destination with respect to the driver’s address.).
Regarding claim 43, Dicker teaches (New) The non-transitory computer-readable storage medium of claim 42, wherein the one or more upcoming vehicle routes are further determined in a mapping application of a respective computing device associated with each vehicle operator (Dicker, at least one para. 0080; “In many examples, the user device 200 of FIG. 2 and the driver device 300 of FIG. 3 can comprise mobile computing device (e.g., smart phone devices) that can execute any number of applications stored thereon. Upon initiating the driver application 332, the driver device 300 can initiate a GPS module 360 to transmit location data 362 to the network system 390. Based on the location data 362, the network system 390 can transmit transport invitations 392 to the driver device 300 for transport requests at proximate locations in relation to the driver's current location.”).
Regarding claim 44, Beaurepaire teaches (New) The non-transitory computer-readable storage medium of claim 37, wherein a selection of the driving vehicle (Beaurepaire, at least one para. 0085; “FIG. 14 is an example of using a passenger profile to configure a vehicle-related service (e.g., a ride-share service), according to one embodiment. In the example of FIG. 14, a user initiates a ride-share application 1401 on a device 1403, and requests a carpooling ride that is shared among multiple users”) comprises:
comparing vehicle safety scores of vehicles associated with the vehicle operators in the carpooling group; and
selecting, based on both safety scores associated with the vehicle operators in the carpooling group and the vehicle safety scores of the vehicles associated with the vehicle operators in the carpooling group, the driving vehicle for the carpool.
However, Beaurepaire does not explicitly teach comparing vehicle safety scores of vehicles associated with the vehicle operators in the carpooling group; and
selecting, based on both safety scores associated with the vehicle operators in the carpooling group and the vehicle safety scores of the vehicles associated with the vehicle operators in the carpooling group, the driving vehicle for the carpool.
Allen, in the same field of endeavor (Allen, at least one para. 0003; “Aspects of the disclosure provide effective, efficient, scalable, and convenient technical solutions that address and overcome the technical problems associated with operating shared vehicle services such as car-sharing services safely and effectively”) teaches comparing vehicle safety scores of vehicles associated with the vehicle operators in the carpooling group (Allen, at least one para. 0045; “Accordingly, the vehicle score model 116 may calculate a vehicle safety score for each vehicle associated with a shared vehicle service”); and
selecting, based on both safety scores associated with the vehicle operators in the carpooling group and the vehicle safety scores of the vehicles associated with the vehicle operators in the carpooling group, the driving vehicle for the carpool (Allen, at least one para. 0049; “For example, the shared vehicle support platform 110 may multiply a numeric safety rating for each vehicle times a numeric initial score for the driver to obtain a driver/vehicle safety score for each vehicle. The shared vehicle support platform 110 may then rank the vehicles by the driver/vehicle safety score”).
Beaurepaire and Allen are both considered to be analogous to the claimed invention because both of them are in the same field as the method of efficient carpooling as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the selection of the vehicle operator of Beaurepaire with the teaching of Allen. One of the ordinary skill in the art would have been motivated to make this modification so that the driver can be selected based upon the safety score of the vehicle to accommodate dynamic conditions such as sunny weather and rainy weather (Allen; 0050)
Regarding claim 45, Beaurepaire teaches (New) The non-transitory computer-readable storage medium of claim 37, wherein classifying the plurality of vehicle operators into the carpooling group further comprises: inferring the one or more upcoming vehicle routes based on an organizational affiliation of each vehicle operator indicating regular trips to a common destination (Beaurepaire, at least one para. 0094; “In one embodiment, the vehicle is further selected based on contextual data associated with the user, the vehicle, an expected route, a trip objective, or a combination thereof. For example, contextual parameters about the user can include but are not limited to height or stature, family size, degree of mobility, disabilities, etc. Contextual parameters about the vehicle can include but are not limited to brand, color, fuel efficiency, options, etc. Contextual parameters about the expected route can include but are not limited to functional class of road links on the route, speed limits on the route, road type (e.g., paved streets, off-road, etc.), urban vs. rural, etc. Contextual parameters about the trip objective include but are not limited to destination (e.g., work, home), planned activity (e.g., shopping, tourism, work, etc.).”, wherein work can be seen as common destination).
Regarding claim 48, Allen teaches (New) The non-transitory computer-readable storage medium of claim 37, wherein the operations further comprise: notifying each vehicle operator in the carpooling group of a list of members of the carpooling group including contact information for each member, and an indication of a selected driver and the driving vehicle (Allen, at least one para. 0074; “FIG. 5 depicts an illustrative method for matching drivers to shared vehicles in accordance with one or more example embodiments. At step 505, a shared vehicle support platform may receive information indicating a driver requesting a shared vehicle. At step 510, the shared vehicle support platform may estimate one or more driver characteristics based on web browsing information associated with the driver. At step 515, the shared vehicle support platform may determine a driver safety score indicating an estimated risk of an accident involving the driver. At step 520, the shared vehicle support platform may select a subset of available vehicles based on the driver safety score. At step 525, the shared vehicle support platform may cause display of a user interface offering the subset of available vehicles to the driver.”).
Regarding claim 49, Kislovskiy teaches (New) The non-transitory computer-readable storage medium of claim 37, wherein the operations further comprise: providing, to at least one vehicle operator in the carpooling group who has opted into an insurance rewards program, an insurance discount based on the safety score of the vehicle operator (Kislovskiy, at least one para. 0169; “The driver's safety rating may be determined from a stored driver's profile, which can include passenger ratings for the driver, any incident reports, and the driver's personal accident or insurance history.”, it is obvious that insurance discount is awarded to safe driving insurance history, such as accident forgiveness program and good driver record).
Claim(s) 46 is rejected under 35 U.S.C. 103 as being unpatentable over Kislovskiy et al. (US 20180341887 A1), Beaurepaire (US 20200079396 A1), Dicker (US 20170352125 A1), Scofield (EP 3114668 B1), Tibbitts (US 20140113619 A1), and Allen (US 20190347582 A1), and further in view of Brahme (US 20150254581 A1).
Regarding claim 46, Kislovskiy teaches (New) The non-transitory computer-readable storage medium of claim 37 (Kislovskiy, at least one para. 0039; “One or more examples described herein provide that methods, techniques, and actions performed by a computing device are performed programmatically, or as a computer-implemented method”) and (Kislovskiy, at least one para. 0042; “one or more examples described herein may be implemented through the use of instructions that are executable by one or more processors”), wherein the operations further comprise:
selecting a meeting point for the carpooling group when origins or destinations of the vehicle operators in the carpooling group are proximate but not identical, and notifying each vehicle operator of the meeting point and a departure time via a user interface of a computing device associated with the vehicle operator.
Kislovskiy does not explicitly teach selecting a meeting point for the carpooling group when origins or destinations of the vehicle operators in the carpooling group are proximate but not identical, and notifying each vehicle operator of the meeting point and a departure time via a user interface of a computing device associated with the vehicle operator.
Brahme, in the same field of endeavor (Brahme, at least one para. 0001; “The present disclosure is generally applicable in the field of ridesharing”) teaches selecting a meeting point for the carpooling group when origins or destinations of the vehicle operators in the carpooling group are proximate but not identical, and notifying each vehicle operator of the meeting point and a departure time (Brahme, at least one para. 0066; “The method of clause 1 wherein, when the driver indicates through a computer program the intention to start the driver trip, the computer program signals matching riders that a driver trip that meets the rider's travel requirement is about to begin or has begun, where each rider is a match if the origin of the rider is within a configured proximity of at least one stop of the driver trip and the destination of the rider is within a configured proximity of at least one subsequent stop of the driver trip and the time difference between the time at which the driver starts the driver trip and the time at which the rider enters the rider trip is within a configured time interval.”) via a user interface of a computing device associated with the vehicle operator (Brahme, at least one para. 0116; “A processor may be part of a computer system that also has a user interface port that communicates with a user interface, and which receives commands entered by a user, has at least one memory (e.g., hard drive or other comparable storage, and random access memory) that stores electronic information including a program that operates under control of the processor and with communication via the user interface port, and a video output that produces its output via any kind of video output format.”).
Kislovskiy and Brahme are considered to be analogous to the claimed invention because both of them are in the same field as the method of efficient carpooling as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the computing device of Kislovskiy with the teaching of Brahme. One of the ordinary skill in the art would have been motivated to make this modification so that an event is used to indicate the driver's arrival at the stop or the rider's presence at the stop, the driver's arrival at the stop, or the rider's presence at the stop (Brahme; 0070-71).
Claim(s) 47 is rejected under 35 U.S.C. 103 as being unpatentable over Kislovskiy et al. (US 20180341887 A1), Beaurepaire (US 20200079396 A1), Dicker (US 20170352125 A1), Scofield (EP 3114668 B1), Tibbitts (US 20140113619 A1), and Allen (US 20190347582 A1), and further in view of Wengreen (US 20200124425 A1).
Regarding claim 47, Kislovskiy teaches (New) The non-transitory computer-readable storage medium of claim 37 (Kislovskiy, at least one para. 0039; “One or more examples described herein provide that methods, techniques, and actions performed by a computing device are performed programmatically, or as a computer-implemented method”) and (Kislovskiy, at least one para. 0042; “one or more examples described herein may be implemented through the use of instructions that are executable by one or more processors”), wherein the operations further comprise:
providing, to at least one vehicle operator in the carpooling group, a compact transportation device for traveling to a meeting point, wherein the compact transportation device is configured to be folded for storage in the driving vehicle.
Kislovskiy does not explicitly teach providing, to at least one vehicle operator in the carpooling group, a compact transportation device for traveling to a meeting point, wherein the compact transportation device is configured to be folded for storage in the driving vehicle.
Wengreen, in the same field of endeavor (Wengreen, at least one para. 0048; “In some embodiments, the vehicle management system directs the vehicle and a prospective rider towards a pick-up location that is located away from both the vehicle and the rider.”) teaches providing, to at least one vehicle operator in the carpooling group, a compact transportation device for traveling to a meeting point, wherein the compact transportation device is configured to be folded for storage in the driving vehicle (Wengreen, at least one para. 0130; “In some embodiments, the first walking route is replaced by a first commuting route. The rider can use any means (e.g., cars, buses, helicopters, planes, trains, skateboards, motorcycles, electric scooters, bicycles, any other transportation means) to move along the first commuting route to arrive at the pick-up location.”, it is obvious that scooters are configured as foldable transportation device).
Kislovskiy and Wengreen are considered to be analogous to the claimed invention because both of them are in the same field as the method of efficient carpooling as the claimed invention. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have modified the computing device of Kislovskiy with the teaching of Wengreen. One of the ordinary skill in the art would have been motivated to make this modification so that alternative transportation device (scooter) that is used to travel to the meeting point can be easily fit within the storage compartment of the vehicle and not obstruct the other passengers.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/U.P.C./Examiner, Art Unit 3665 /CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665