DETAILED ACTION
This Final Office Action is in response Applicant communication filed on
2/13/2026. In Applicant’s amendment, claims 1-3, 5, 7-9, 14-16, 21, 23, 24, 26-28, and 30-32 were amended. Claim 25 is canceled. Claims 33-34 are added.
Claims 1-3, 5, 7-10, 14-16, 20, 22-24, and 26-34 are currently pending and have been rejected as follows.
Response to Amendments
Applicant’s amendments necessitated new grounds of rejection under 35 USC 103.
Response to Arguments
Applicant's prior art arguments have been fully considered but they are not persuasive to overcome the rejection.
Applicant argues on p. 9-10 that Sweeney fails to disclose the AVs performing the claim steps, because in Sweeney, a transportation system 100, which is separate from the AV 189, is what receives requests for transportation and identifies a number of proximate available vehicles relative to the user, which is allegedly different from the method of claim 1 performed by the autonomous vehicle language. Examiner respectfully disagrees. Applicant’s specification at [0063] states “The dispatching may involve the server computing devices 410 sending a signal to the autonomous vehicle 100, in particular to the computing devices 110, via the network 460 identifying the destination location and any intermediate destination location as destination locations for the trip as well as a pickup location for picking up the user 422.” The specification treats an AV directed dispatch signal identifying the pickup location as the operative communication that causes the AV to perform the pickup. Under the broadest reasonable interpretation of the claims in light of the specification, the claim language requires a processor of an AV to receive the signal. The claim language does not require direct receipt of the request from the passenger device.
Applicant argues on p. 12-13 that Sweeney and Lynch do not teach claim 21. Examiner respectfully disagrees. The amended claim requires: wherein the estimated time for the passenger to reach the pickup location is further based on contextual information of the pickup location, wherein the contextual information of the pickup location includes a classification of the pickup location, and wherein the estimated walking speed varies based upon the classification of the pickup location. Sweeney supplies the estimated time calculation in [0013]. Lynch [0020] “The user information supplied from the sensor system 114 can include biometric information, user location, location history information, and user environment information. Biometric information can refer to a physiological status of the user, such as heart rate, walking speed, drowsiness level, or other physical or emotional features of the user … For example, user information may include a user location of the user device 104 (as held or nearby the user), user environment information such as whether the user device 104 is physically located outdoors, indoors, in a highly populated area, in an isolated area, in a user's hand, or not currently visible to the user;” [0032] “For example, a speed or rate at which the user device 204 is moving along the path or route marked by the arrow A may dictate to the vehicle 202 how quickly the user will reach the pickup location 228 (or an alternate pickup location, not shown);” [0034] “In operation 334, the network 120, the vehicle 102, or the user device 104 can receive user information from the user device 104. The user information can include one or more of a user authorization (indicating approval for the vehicle 102 to pick up the user); a user location … biometric information such as whether the user is seated, standing, or walking, the user's heart rate, walking speed or pace at which the user (holding the user device 104) is moving … user environment information such as whether the user device 104 is located outdoors, indoors;” [0037] “The pickup time can be based on distances of the vehicle 202 or the user device 204 from the pickup location 228, a speed at which the user device 204 is moving or the vehicle 202 is moving toward the pickup location 228” Lynch expressly categorizes the user pickup environment as indoors, outdoors, highly populated, isolated, etc. while measuring walking speed and using speed to determine when the user will reach the pickup.
Applicant argues on p. 14 that Sweeney and Lynch do not teach claim 24. Examiner respectfully disagrees. wherein the estimated walking speed is determined based on historical data for pickups at the pickup location. Lynch [0022] “The user profile 118 can store user information related to user preferences or activity, both current and historical. The user information that forms the user profile 118 can include … biometric information, … user environment information, location history information … Location history information can include map information representative of places where the user has traveled and time information representative of timing and patterns of user travel to such places” Lynch stores both historical walking speed and location histories with times and repeated hailing patterns, and uses speed to determine pickup time.
Applicant argues on p. 14-16 that Tanaka does not teach claim 7. This argument is moot in light of the newly applied art.
Applicant argues on p. 16-17 that Sweeney, Lynch, Tanaka, and Tanabe do not disclose claims 9-10. Examiner respectfully disagrees. Regarding claim 9, Lynch [0026] “the user location 226 is an office or room of a building nearby the parking lot associated with the vehicle location 224 and is shown in dotted line;” [0047] “In this example, the first user location 426 is an office or room of a building nearby the parking lot associated with the vehicle location 424;” [0037] “Returning to FIG. 2 for a description of a detailed example, the pickup location 228 can be based on the vehicle location 224, the user location 226, other user information, such as whether the user is carrying a heavy purchase or the user enjoys walking a certain route or a certain number of steps to reach a fitness goal before arriving at the pickup location 228, and other vehicle information such as traffic patterns around the vehicle 202” Lynch identifies an office and store as pickup locations and uses contextual user information in selecting the pickup.Regarding claim 10, Tanabe [0069] “When the user taps the transportation selection button 308 indicating that airplane is selected as the public transportation, the flight route list is displayed on the boarding route list 310, the list of arrival airports of the flight selected from the boarding route list 310 is displayed on the planned alighting place list 312, and a list of the respective flights to the arrival airport selected from the planned alighting place list 312 is displayed on the boarding operating vehicle list 314, respectively … the boarding location to the share ride vehicle is preliminarily fixed at the corresponding airport. The arrival time or estimated arrival time of the user at the boarding location may be calculated as the time derived from adding the spare time to the arrival time or the estimated arrival time of the airplane at the airport” Tanabe expressly treats the pickup/boarding location as an airport and adds spare time to the passengers estimated arrival at that pickup. Tanabe accounts for a location specific delay before the passenger reaches the pickup.
Applicant argues on p. 17-18 that Shoval does not disclose claims 14-16 because Shoval does not use weather, a current time of day, or congestion conditions to calculate or adjust the estimate time for the passenger to reach the pickup location. Examiner respectfully submits that Shoval supplies the context for Sweeney’s calculation/optimization.
Applicant argues on p. 18-19 that Peters does not disclose claim 28 because Peters adds a cost to the route when a cab arrives on time but has to wait for the passenger while the claim adds a cost to a route when a vehicle arrives before the estimated time for the passenger to arrive. Examiner respectfully disagrees. The claim states when it is determined that the route would result in the autonomous vehicle reaching the pickup location earlier than the estimated time for the passenger to reach the pickup location, adding, by the one or more processors of the autonomous vehicle, a cost to the route. Under the BRI, the claim is adding a cost to the route when the vehicle has to wait for the passenger to reach the vehicle, as is disclosed by Peters.
Newly added claims are mapped accordingly below.
Claim Rejections - 35 USC § 103
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.
Claims 1-3, 5, 7, 9, 14-16, 21, 24, 26, 27, 29, and 32-33 are rejected under 35 USC 103 as being unpatentable over the teachings of
Sweeney et al, US Publication No. 20180342035 A1, hereinafter Sweeney, in view of
Lynch et al., US Publication No. 20220092718 A1, hereinafter Lynch. As per,
Claims 1, 27, 32
Sweeney teaches
A method of timing pickups of passengers for autonomous vehicles, the method comprising: /
A vehicle operating in an autonomous driving mode, the vehicle comprising: one or more processors configured to: /
A non-transitory computer-readable medium storing instructions, which when executed by one or more processors, cause the one or more processors to: (Sweeney fig. 1 noting the AV; fig. 6 noting the processors; and memory components)
receiving, by one or more processors of an autonomous vehicle, a request to pick up a passenger from a pickup location; (Sweeney [0062] “the on-demand transport system 100 can receive a transport request 171 from a requesting user” note the request received)
identifying, by the one or more processors of the autonomous vehicle, a location of a client computing device associated with the passenger; (Sweeney fig. 3 noting the rider device; [0056] “the rider device 300 can also include a location-based resource, such as a GPS module 360 that transmits location data 362 to the transport system 390;” [0062] “the transport request 171 can indicate the current location 173 of the requesting user 174” note the identified location of the rider based on the current location from the rider device)
determining, by the one or more processors of the autonomous vehicle, an estimated time for the passenger to reach the pickup location based on the identified location of the client computing device, wherein the estimated time for the passenger to reach the pickup location is based, at least partially, […] and an estimated walking speed; and (Sweeney [0013] “the transport system can include a time optimization … that includes an estimated walking time (EWT) for the requesting user to rendezvous with an AV (e.g., an EWT from the user's current location to a specific point on the autonomy grid) … the transport system can identify the optimal pick-up location such that the EWT and the ETA of the selected AV are the nearly the same” noting the passenger’s current location used in the EWT calculation; further note the estimated time that the passenger is expected to reach the pickup location)
determining, by the one or more processors of the autonomous vehicle, a route for the autonomous vehicle to the pickup location based on the estimated time for the passenger to reach the pickup location to avoid waiting by the passenger or the autonomous vehicle; and (Sweeney [0013] “the transport system can weigh between cost, vehicle ETA to rendezvous with the requesting user, and EWT for the requesting user to a pick-up location … the transport system can determine an optimal pick-up location along the autonomy grid based on the current location of the requesting user and a current location of the selected AV” note the determining of the optimal pick-up location based on the estimated walking time of the passenger and estimated arrival time of the AV; [0066] “The ETA optimization can comprise identifying an AV 189 that has an ETA 153 similar to the EWT 152 of the requesting user 174” noting the optimizing including matching the AV ETA to the passenger’s EWT to the pickup location; [0045] “The control system 220 can perform vehicle control actions (e.g., braking, steering, accelerating) and route planning” note the AV performing the route planning)
controlling, by the one or more processors of the autonomous vehicle, the autonomous vehicle along the determined route to the pickup location so that a time when the autonomous vehicle reaches the pickup location is as close as possible to the estimated time for the passenger to reach the pickup location. (Sweeney [0035] “ the rendezvous optimizer 150 can seek to minimize wait time for the requesting user 174, or minimize overall time to pick-up by converging the ETA 153 of the selected AV 189 with the EWT 152 of the requesting user 174;” [0045] “the control system 220 can operate the AV 200 by autonomously operating the steering, acceleration, and braking systems 272, 274, 276 of the AV 200 to a specified destination;” [0053] “the AV control system 220 can include a route planning engine 260 that provides the vehicle control module 255 with a route plan 267 to a given destination, such as a pick-up location” note the rendezvous optimizer minimizing the overall time to pick-up by converging the AV ETA and the EWT of the passenger; further noting the controlling of the AV along the determined route)
Sweeney does not explicitly teach, Lynch however in the analogous art of ridesharing teaches
[…] on contextual information of the identified location […]; (Lynch [0020] “The user information supplied from the sensor system 114 can include biometric information, user location, location history information, and user environment information. … For example, user information may include a user location of the user device 104 (as held or nearby the user), user environment information such as whether the user device 104 is physically located outdoors, indoors” noting the determination if the user device, with the user, is indoors corresponding to contextual information)
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify Sweeney’s ridesharing autonomous vehicle to include contextual information in view of Lynch in an effort to optimize the user pickup experience (see Lynch ¶ [0039] & MPEP 2143G).
Claim 2
Sweeney does not explicitly teach, Lynch however in the analogous art of ridesharing teaches
wherein the estimated time for the passenger to reach the pickup location is further based on a distance between the identified location of the client computing device and the pickup location. (Lynch [0037] “The pickup time can be based on distances of the vehicle 202 or the user device 204 from the pickup location 228”)
The rationale/motivation to combine Sweeney with Lynch persists.
Claim 3
Sweeney teaches
wherein the estimated walking speed is an expected walking speed of the passenger. (Sweeney [0013] “the transport system can include a time optimization … that includes an estimated walking time (EWT) for the requesting user to rendezvous with an AV (e.g., an EWT from the user's current location to a specific point on the autonomy grid)”)
Claim 5
Sweeney teaches
wherein a driving speed of the autonomous vehicle is reduced as the vehicle approaches the pickup location. (Sweeney [0038] “the coordination engine 120 can generate modulation commands 121 that instruct the selected AV 189 to reduce or slow its progression towards the pick-up location 154” note the speed of the vehicle reduced)
Claim 7
Sweeney does not explicitly teach, Lynch however in the analogous art of ridesharing teaches
wherein the contextual information of the identified location includes whether the passenger is located within a building. (Lynch [0020] “The user information supplied from the sensor system 114 can include biometric information, user location, location history information, and user environment information. … For example, user information may include a user location of the user device 104 (as held or nearby the user), user environment information such as whether the user device 104 is physically located outdoors, indoors” noting the determination if the user device, with the user, is indoors)
The rationale/motivation to combine Sweeney with Lynch persists.
Claim 9
Sweeney does not explicitly teach, Lynch however in the analogous art of ridesharing teaches
wherein the contextual information of the identified location includes a classification of the building. (Lynch [0026] “the user location 226 is an office or room of a building nearby the parking lot associated with the vehicle location 224 and is shown in dotted line;” [0047] “In this example, the first user location 426 is an office or room of a building nearby the parking lot associated with the vehicle location 424;” [0037] “Returning to FIG. 2 for a description of a detailed example, the pickup location 228 can be based on the vehicle location 224, the user location 226, other user information, such as whether the user is carrying a heavy purchase or the user enjoys walking a certain route or a certain number of steps to reach a fitness goal before arriving at the pickup location 228, and other vehicle information such as traffic patterns around the vehicle 202” Lynch identifies an office and store as pickup locations and uses contextual user information in selecting the pickup)
The rationale/motivation to combine Sweeney with Lynch persists.
Claim 21
Sweeney still teaches time optimization based on calculating the estimated walk time and estimated arrival time. Sweeney does not explicitly teach, Lynch however in the analogous art of ridesharing teaches
wherein the estimated time for the passenger to reach the pickup location is further based on contextual information of the pickup location, wherein the contextual information of the pickup location includes a classification of the pickup location, and wherein the estimated walking speed varies based upon the classification of the pickup location. (Lynch [0020] “The user information supplied from the sensor system 114 can include biometric information, user location, location history information, and user environment information. Biometric information can refer to a physiological status of the user, such as heart rate, walking speed, drowsiness level, or other physical or emotional features of the user … For example, user information may include a user location of the user device 104 (as held or nearby the user), user environment information such as whether the user device 104 is physically located outdoors, indoors, in a highly populated area, in an isolated area, in a user's hand, or not currently visible to the user;” [0032] “For example, a speed or rate at which the user device 204 is moving along the path or route marked by the arrow A may dictate to the vehicle 202 how quickly the user will reach the pickup location 228 (or an alternate pickup location, not shown);” [0034] “In operation 334, the network 120, the vehicle 102, or the user device 104 can receive user information from the user device 104. The user information can include one or more of a user authorization (indicating approval for the vehicle 102 to pick up the user); a user location … biometric information such as whether the user is seated, standing, or walking, the user's heart rate, walking speed or pace at which the user (holding the user device 104) is moving … user environment information such as whether the user device 104 is located outdoors, indoors;” [0037] “The pickup time can be based on distances of the vehicle 202 or the user device 204 from the pickup location 228, a speed at which the user device 204 is moving or the vehicle 202 is moving toward the pickup location 228” Lynch expressly categorizes the user pickup environment as indoors, outdoors, highly populated, isolated, etc. while measuring walking speed and using speed to determine when the user will reach the pickup)
The rationale/motivation to combine Sweeney with Lynch persists.
Claim 24
Sweeney still teaches time optimization based on calculating the estimated walk time and estimated arrival time. Sweeney does not explicitly teach, Lynch however in the analogous art of ridesharing teaches
wherein the estimated walking speed is determined based on historical data for pickups at the pickup location. (Lynch [0022] “The user profile 118 can store user information related to user preferences or activity, both current and historical. The user information that forms the user profile 118 can include … biometric information, … user environment information, location history information … Location history information can include map information representative of places where the user has traveled and time information representative of timing and patterns of user travel to such places” Lynch stores both historical walking speed and location histories with times and repeated hailing patterns, and uses speed to determine pickup time)
The rationale/motivation to combine Sweeney with Lynch persists.
Claim 26
Sweeney does not explicitly teach, Lynch however in the analogous art of ridesharing teaches
wherein the estimated time for the passenger to reach the pickup location is determined when the identified location of the client computing device at different points in time indicates that the passenger is moving towards the pickup location. (Lynch [0028] “User environment information may indicate that the user of the user device 204 has turned off lights in the office (the user location 226) or moved the user device 204 from a desktop to a bag. Biometric information may indicate that the user of the user device 204 has been seated for a period of time but is now walking based on changes in heart rate of the user or changes to the user location 226. The user location 226 may have been generally restricted to the office or a suite of offices within the building for a period of time, but is now changing as the user device 204 begins to move along the path or route marked by the arrow A in dashed line in FIG. 2 as the user (e.g., carrying the user device 204 in a pocket or bag) moves away from the user location 226 and towards, for example, an exit or door of the building as shown by the arrow A”)
The rationale/motivation to combine Sweeney with Lynch persists.
Claim 29
Sweeney does not explicitly teach, Lynch however in the analogous art of ridesharing teaches
wherein the route is determined based on at least one of distances, speed limits, or traffic conditions. (Lynch [0015] “vehicle information may include a vehicle location of the vehicle 102, traffic information for vehicular or pedestrian traffic around the vehicle 102;” [0016] “The autonomous control system 108 uses vehicle information and sensor outputs from the sensor system 106 to understand the environment around the vehicle 102 and to plan trajectories for the vehicle 102.” Noting the use of the traffic in planning the trajectories for the vehicle)
The rationale/motivation to combine Sweeney with Lynch persists.
Claim 33
Sweeney does not explicitly teach, Lynch however in the analogous art of ridesharing teaches
wherein the estimated walking speed may be determined based on historical data for pickups at another location having a same classification as the pickup location. (Lynch [0022] “The user profile 118 can store user information related to user preferences or activity, both current and historical. The user information that forms the user profile 118 can include … biometric information, … user environment information, location history information … Location history information can include map information representative of places where the user has traveled and time information representative of timing and patterns of user travel to such places (e.g., the user visits a specific gym on Tuesday and Thursday mornings or the user picks up children from school each weekday afternoon around 4:30 PM)” Lynch stores both historical walking speed and location histories with times and repeated hailing patterns, and uses speed to determine pickup time. Further note the gym, and school classifications)
The rationale/motivation to combine Sweeney with Lynch persists.
Claim 8 is rejected under 35 USC 103 as being unpatentable over the teachings of
Sweeney in view of Lynch in view of
Mohamed et al, US Publication No. 20210103888 A1, hereinafter Mohamed. As per,
Claim 8
Sweeney / Lynch do not explicitly teach, Mohamed however in the analogous art of route planning teaches
wherein the contextual information of the identified location includes a number of stories the building has. (Mohamed fig. 3; [0031] “The features of the delivery destination Qn are pieces of information that influence the arrival time at the delivery destination Qn. Examples of such information may include a geographical feature of the delivery destination Qn, a feature of a building at the delivery destination Qn,… The arrival time at the delivery destination Qn changes in accordance with the features of the delivery destination. As shown in FIG. 3, in at least one embodiment, as an example of the features of the delivery destination Qn, there are described a vehicle movement distance, a walking movement distance, and a number of stories”)
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify Sweeney’s ridesharing autonomous vehicle and Lynch’s traffic conditions to include the estimated time of arrival based on a number of stories in a building in view of Mohamed in an effort to increase estimation accuracy of the arrival time (see Mohamed [0117] & MPEP 2143G).
Claims 10, 22-23, and 34 are rejected under 35 USC 103 as being unpatentable over the teachings of
Sweeney in view of Lynch in view of
Tanabe et al, US Publication No. 20200272965 A1, hereinafter Tanabe. As per,
Claim 10
Sweeney / Lynch do not explicitly teach, Tanabe however in the analogous art of route planning teaches
wherein the classification is one of an airport, a shopping center, an apartment building, or a house. (Tanabe [0069] “When the user taps the transportation selection button 308 indicating that airplane is selected as the public transportation, the flight route list is displayed on the boarding route list 310, the list of arrival airports of the flight selected from the boarding route list 310 is displayed on the planned alighting place list 312, and a list of the respective flights to the arrival airport selected from the planned alighting place list 312 is displayed on the boarding operating vehicle list 314, respectively … the boarding location to the share ride vehicle is preliminarily fixed at the corresponding airport. The arrival time or estimated arrival time of the user at the boarding location may be calculated as the time derived from adding the spare time to the arrival time or the estimated arrival time of the airplane at the airport” Tanabe expressly treats the pickup/boarding location as an airport and adds spare time to the passengers estimated arrival at that pickup)
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify Sweeney’s ridesharing autonomous vehicle and Lynch’s traffic conditions to include the estimated time of arrival based on a classification of a building in view of Tanabe in an effort to reduce waiting time and improve user’s convenience (see Tanabe [0040] & MPEP 2143G).
Claim 22
Sweeney / Lynch do not explicitly teach, Tanabe however in the analogous art of route planning teaches
wherein the classification of the pickup location is one of a mall, a shopping center, or an airport. (Tanabe [0069] “the boarding location to the share ride vehicle is preliminarily fixed at the corresponding airport. The arrival time or estimated arrival time of the user at the boarding location may be calculated as the time derived from adding the spare time to the arrival time or the estimated arrival time of the airplane at the airport”)
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify Sweeney’s ridesharing autonomous vehicle and Lynch’s context to include the pickup location being an airport in view of Tanabe in an effort to reduce waiting time and improve user’s convenience (see Tanabe [0040] & MPEP 2143G).
Claim 23
Sweeney / Tanabe do not explicitly teach, Lynch however in the analogous art of ridesharing teaches
wherein an average walking speed associated with the mall is different than an average walking speed associated with the shopping center or the airport. (Lynch [0022] “Location history information can include map information representative of places where the user has traveled and time information representative of timing and patterns of user travel to such places”)
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify Sweeney’s ridesharing autonomous vehicle and Tanabe’s airport location to include the location dependent walking paces in view of Lynch in an effort to optimize the user pickup experience (see Lynch [0039] & MPEP 2143G).
Claim 34
Sweeney / Lynch do not explicitly teach, Tanabe however in the analogous art of route planning teaches
further comprising: when the autonomous vehicle is expected to reach the pickup location within a predetermined amount of time and the passenger is not expected to reach the pickup location within the predetermined amount of time, […]. (Tanabe [0091] “The grouping section 216 determines whether or not the estimated arrival time will fall within a second predetermined period from the current time (S110). The second predetermined period may be set to be equal to or shorter than the first predetermined period (for example, 1 hour);” [0094] “Meanwhile, in step S110, if the arrival time estimated in step S104 will fall within the second predetermined period from the current time (S106, YES), the vehicle allocation section 220 transmits the generated vehicle allocation information to the terminal device of the provider of the allocated vehicle, and the mobile terminal 122 of the user 112 and the like as members of the user group allocated to the vehicle for notification (S116)” Tanabe controls the vehicle allocation based on whether estimated passenger arrival falls within a predetermined period from current time)
The motivation/rationale to combine Sweeney / Lynch with Tanabe persists.
Sweeney / Tanabe do not explicitly teach, Lynch however in the analogous art of ridesharing teaches
[…] determining a new route to the pickup location, wherein the new route takes the autonomous vehicle longer to reach the pickup location (Lynch [0041] “If the decision block 338 determines that the user is not ready for a pickup, the process 332 continues to optional decision block 344 denoted as optional using dotted lines. In the optional decision block 344, the network 120, the vehicle 102, or the user device 104 can optionally determine whether a user is ready for the vehicle 102 to shadow the user based on the user information;” [0044] “The vehicle 202 could also be controlled to travel around the block (not shown) or otherwise stage, circle, or remain near to the building where the user device 204 located” Lynch teaches when the user is not ready, the vehicle can choose a shadow route)
The motivation/rationale to combine Sweeney / Tanabe with Lynch persists.
Claims 14-16 and 30-31 are rejected under 35 USC 103 as being unpatentable over the teachings of
Sweeney in view of Lynch in view of
Shoval et al., US 20210295706 A1, hereinafter Shoval. As per,
Claim 14
Sweeney / Lynch do not explicitly teach, Shoval however in the analogous art of ride sharing teaches
wherein the estimated time for the passenger to reach the pickup location is further based on the contextual information of the pickup location, and wherein the contextual information at the pickup location includes includes current weather conditions at the pickup location. (Shoval [0264] “collection module 1510 may determine that a waiting threshold is a certain duration based on data received that it is raining and/or temperatures are low in a geographical region associated with a current location of a user of a first pick-up location”)
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify Sweeney’s ridesharing autonomous vehicle and Lynch’s traffic conditions to include weather conditions in view of Shoval in an effort to improve the overall quality of service in on-demand ride sharing (see Shoval ¶ [0256] & MPEP 2143G).
Claim 15
Sweeney / Lynch do not explicitly teach, Shoval however in the analogous art of ride sharing teaches
wherein the estimated time for the passenger to reach the pickup location is further based on contextual information of the pickup location, and wherein the contextual information at the pickup location includes current time of day at the pickup location. (Shoval [0261] “Data collection module 1510 may also be configured to receive data associated with the received pick location information and current location information of the user …The weather and traffic information may include real-time data or historical data associated with the geographical region and/or or more characteristics of the ride request including a time of day”)
The motivation/rationale to combine Sweeney / Lynch with Shoval persists.
Claim 16
Sweeney / Lynch do not explicitly teach, Shoval however in the analogous art of ride sharing teaches
wherein the estimated time for the passenger to reach the pickup location is further based on contextual information of the pickup location, and wherein the contextual information at the pickup location includes congestion conditions at the pickup location, and wherein the congestion conditions include at least one of pedestrian traffic and vehicular traffic. (Shoval [0156] “assignment module 605 may determine that first ridesharing vehicle 701 will not be able to reach second user 712 at the corresponding estimated pick-up time for second user 712 due to an unanticipated traffic jam caused by a car accident along the route to the pick-up location of second user 712;” [0261] “Data collection module 1510 may also be configured to receive data associated with the received pick location information and current location information of the user data collection module 1510 may receive weather information and/or traffic information. The weather and traffic information may include real-time data” note the vehicular traffic)
The motivation/rationale to combine Sweeney / Lynch with Shoval persists.
Claims 30, 31
Sweeney / Lynch do not explicitly teach, Shoval however in the analogous art of ride sharing teaches
receiving, by the one or more processors of the autonomous vehicle, a request from the client computing device for more time for the passenger to reach the pickup location; and (Shoval [0150] “second user 712 transmits a request to communication module 601 to modify the pick-up time because second user 712 will arrive at the pick-up location late”)
updating, by the one or more processors of the autonomous vehicle, the estimated time for the passenger to reach the pickup location based on the request for more time. (Shoval [0150] “The late arrival of second user 712 at the pick-up location for second user 712 may affect the schedules of other users (e.g., first user 711 and third user 713). Event detection module 604 may consider (or detect) this event as an unanticipated ridesharing event”)
The motivation/rationale to combine Sweeney / Lynch with Shoval persists.
Claim 20 is rejected under 35 USC 103 as being unpatentable over the teachings of
Sweeney in view of Lynch in view of
Arden et al, US Patent No. 9910438 B1, hereinafter Arden. As per,
Claim 20
Sweeney / Lynch do not explicitly teach, Arden however in the analogous art of ride sharing teaches
further comprising: when the autonomous vehicle is expected to reach the pickup location within a predetermined amount of time and the passenger is not moving towards the pickup location, sending a notification to the client computing device asking if the passenger would like to request more time to reach the pickup location. (Arden col. 4, ln. 12-17 “the passenger may receive a third notification. The further notification may indicate that the vehicle is about to leave. The timing of the second notification may coincide with an amount of time left in the countdown. As an example, the notification may be provided half way through the countdown, after a fixed amount of time has passed or some predetermined amount of time before the end of the countdown”)
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify Sweeney’s ridesharing autonomous vehicle and Lynch’s context to include the ability to ask a passenger if more time is needed if they are not in route in view of Arden in an effort to account for delays and improve efficiency of other trips (see Arden col. 15, ln. 48-51 & MPEP 2143G).
Claims 28 is rejected under 35 USC 103 as being unpatentable over the teachings of
Sweeney in view of Lynch in view of
Peters et al., US Publication No. 20140108663 A1, hereinafter Peters. As per
Claim 28
Sweeney / Lynch do not explicitly teach, Peters however in the analogous art of ride sharing teaches
further comprising: when it is determined that the route would result in the autonomous vehicle reaching the pickup location earlier than the estimated time for the passenger to reach the pickup location, adding, by the one or more processors of the autonomous vehicle, a cost to the route. (Peters [0095] “The processing core 1 has access to information 12 such as pricing matrices, fleet and driver details for each supplier, data relating to additional costs set by the supplier (for example costs incurred when a cab arrives on time but then has to wait for the customer to be ready to depart)”)
Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to modify Sweeney’s ridesharing autonomous vehicle and Lynch’s context to include adding a cost to the route for the vehicle arriving early in view of Peters in an effort to account for the additional costs of the vehicle waiting (see Peters [0148] & MPEP 2143G).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 2019/0243368 A1: A control device is configured to cause a vehicle parked in a parking lot to move to a pick-up area in accordance with a call from a user who is a passenger of the vehicle. The control device includes a user position acquiring unit, a predicted time calculator, and a timing manager. The user position acquiring unit is configured to acquire, as a user position, a current position of the user who has made the call. The predicted time calculator is configured to calculate a predicted user arrival time on the basis of the user position, and the predicted user arrival time is a time in which the user is predicted to arrive at the pick-up area. The timing manager is configured to manage a movement start timing at which the vehicle to be called starts moving to the pick-up area on the basis of the predicted user arrival time.
WO 2020/055769 A1: A ride hailing provisioning system identifies and dispatches available vehicles to provide transportation of passengers while providing requested items or amenities. Vehicles may be dispatched to passenger pick-up locations, item pick-up locations, and, or rendezvous locations to make specifically requested items accessible to passengers without inordinately delaying or inconveniencing the passengers. Items may take a wide variety of forms, for example cooked food items or meals, prepare beverages, meal kits, groceries and sundries, various retail items, Various computational techniques may identify vehicles and efficient data structures employed for dispatching the vehicles.
Li et al., Benefits of Short-Distance Walking and Fast-Route Scheduling in Public Vehicle Service, 2019: Public vehicle service (PVS), as a paradigm to manage and share large-capacity vehicles for public passenger delivery, is promising to improve the quality of urban transportation. In the PVS system, a command center receives requests sent by passengers, periodically assigns them to public vehicles and schedules vehicle routes to serve the requests. However, in the PVS system, the passengers’ waiting time is not well utilized. Moreover, it is observed that the driving distance on low-speed roads accounts for a rather high percentage. These two factors impact on system efficiency. In order to utilize the waiting time, we propose to let passengers walk a short distance instead of standing at their origins. At the same time, the driving distance on low-speed roads will be reduced. In this paper, the closest meeting point algorithm is proposed to address the challenge of determining the best pick-up and drop-off locations. The large-scale simulations show that the passenger walking and the proposed fast-route scheduling strategy can shorten the total vehicle travel distance by 34%.
THIS ACTION IS MADE FINAL. 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 extension fee 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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/MOHAMED N EL-BATHY/Primary Examiner, Art Unit 3624
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