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 .
Status of Claims
This action is reply to the Application Number 18/147,184 filed on 06/26/2025
Claims 1, 3, 5 – 10 and 12 – 20 are currently pending and have been examined. Claims 1, 3, 5, 10, 12, 13, 14 and 18 have been amended. Claims 2, 4 and 11 are cancelled.
This action is made NON-FINAL
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/26/2025 has been entered.
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.
Claims 1, 5, 6, 8, 9, 10, 13 – 15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Dyer et al. (US 20210053567 A1) in view of Rasmusson et al. (US 20210056320 A1).
Regarding claim 1, Dyer teaches a method of determining a stopping location for a ride service request, the method comprising, (Dyer: Paragraph 0019: “The features described herein may enable a vehicle having an autonomous driving mode to more easily locate pullover locations as in the examples described above. Regions where parking is likely to currently be available can be identified without the vehicle actually having to observe those regions. This may reduce the amount of time that the vehicle may spend looking for a pullover location and also reduce inconvenience convenient to passenger and other road users.”; Paragraph 0075: “In some instances, real time information about the availability of pullover locations observed by a vehicle, such as any of vehicles 100, 100A, 100B, 100C, may be shared with the dispatching server computing devices and/or other vehicles of the fleet.”,
Supplemental Note: a fleet of vehicle is used by a ride service as they also dispatch vehicles to user pick up locations)
… in response to the processor determining that the DSL is not a reachable stopping location, using map data received from a map service and sensor data captured by one or more sensors onboard the autonomous vehicle to select an intermediate stopping location (ISL); (Dyer: Paragraph 0035: “The various systems of the vehicle may function using autonomous vehicle control software in order to determine how to and to control the vehicle. As an example, a perception system software module of the perception system 172 may use sensor data generated by one or more sensors of an autonomous vehicle, such as cameras, LIDAR sensors, radar units, sonar units, etc., to detect and identify objects and their characteristics.”; Paragraph 0061: “For the purposes of demonstration, FIG. 6 is an example representation of a section of roadway 600 corresponding to the map information 200. In this example, the section of roadway 600 includes various features such as lanes 610-616, intersections 620-626, buildings 630-636, parking spaces 640-646, a driveway entrance (for example to a parking garage or other location) 650, shoulder areas 652-654, and no parking zone 656 that correspond to each of lanes 210-216, intersections 220-226, buildings 230-236, parking spaces 240-246, a driveway entrance (for example to a parking garage or other location) 250, shoulder areas 252-254, and no parking zone 256 of the map information 200. Vehicle 100 is also depicted driving in lane 616 and approaching intersection 626. In this example, vehicle 100 may be attempting a pickup or drop off at the location of marker 680.”; Paragraph 0062: “For instance, a plurality of regions nearby a location may be identified by the computing devices 110. For instance, this given location may be a current location of the vehicle (when the vehicle is unable to find a place to pullover or when the vehicle needs to move to a new pullover location because an emergency vehicle nearby, etc.). Alternatively, the given location may be a pickup or drop off location for a passenger. In either example, the vehicle is very likely to be outside of the plurality of regions or rather, unable to use the sensors of the perception system to determine whether there are available pullover locations in the regions of the plurality of regions.”,
Supplemental Note: Since the pickup location to pick up the passenger is a geographic constraint, the vehicle is able to use its sensors for additional locations to select. This can also be performed for selecting drop off locations)
confirming that the ISL satisfies a stopping location rule set that is associated with the dispatching service, (Dyer: Paragraph 0046: “Other signals stored in the storage system 450 may only be available or determined by the server computing devices 410 and/or human operators after a pullover is actually attempted by one of the vehicles of the fleet. Such signals may include how long passengers take to arrive, board and depart at a pullover location, the passenger inconvenience value of a pullover location, the vehicle inconvenience at pullover location, how long the vehicle is able to stay in a pullover location, etc. The values for passenger inconvenience and vehicle inconvenience may be determined, for instance, on a scale of 0 to 1 using a model which generates such values given map data and sensor data collected during such attempted pullovers. Passenger inconvenience values may represent how convenient a particular pickup up or drop off was for a passenger by measuring how much extra distance is imposed on the passenger by the selection of a particular pullover location.”; Paragraph 0048: “In order to build a model, the server computing devices 410 may access the signals of the storage system 450 described above. Using these signals or values determined from these signals, a model that identifies expected characteristics of regions may be built by the server computing devices 410. The model may be trained such that for a given geographic constraint (such as a specific location or a region) and time constraint (such as day of the year, calendar month, day of week, and/or time of day), the model may provide a list of expected qualities for a region. In this regard, if a specific location is provided, the region may correspond to a region that includes the specific location. In this regard, the signals or values determined from these signals for a given region where the sensor data corresponding to the signals was collected may be used as training outputs for the model, and the specific location or given region and a time when the sensor data was collected may be used as training inputs to the model. In the case of a machine-learned model, the training may essentially tune parameter values for the model.”; Paragraph 0075: “In some instances, real time information about the availability of pullover locations observed by a vehicle, such as any of vehicles 100, 100A, 100B, 100C, may be shared with the dispatching server computing devices and/or other vehicles of the fleet. This information may be used to update the model or in conjunction with the lists of qualities to rank the regions. This information could also be used to directly update the values for the lists of qualities or as some form of additional cost in the ranking.”,
Supplemental Note: passenger inconvenience values measure extra distance imposed on the passenger to reach a pickup/drop off location, this interpreted as a stopping location rule.)
… using the ISL to identify a final stopping location (FSL), determining a trajectory to the FSL, and causing a motion control system of the vehicle to use the trajectory to cause one or more subsystems of the vehicle to move the vehicle along a route to the FSL. (Dyer: Paragraph 0074: “In some instances, the computing devices 100 may possibly identify a region to travel to which may be less convenient to the passenger, but will now have a higher ranking than previously because of the inconvenience to the another vehicle. In such situations, if the vehicle is going to pick up a passenger, a notification may be sent to the passenger's client computing device identifying the highest ranked region and indicating that the vehicle is currently going to that region to drop off the passenger or that the vehicle is able to go to that region to pick up the passenger (and requesting confirmation).”; Paragraph 0077: “The model may enable various improvements to current transportation services that utilize autonomous vehicles. For instance, when a passenger is requesting or setting up a trip, the dispatching server computing devices may use the model to make recommendations for where a vehicle can pick up or drop off a passenger. For example, the dispatching server computing devices can recommend locations in nearby regions to a requested pickup or drop off location where a vehicle can more easily find a place to pullover. Depending upon the expected and desired qualities of the nearby regions, in turn, may reduce inconvenience to the passenger and/or other vehicles. As noted above, the model may be used to identify regions that are suitable for finding long term parking for a vehicle for a specific point in time, day of the week, etc. In addition, in the event of a problem with a vehicle's systems that requires the vehicle to pullover within a certain period of time, the model may be used to find a nearby location within the period of time where there is likely a pullover location available. Further, in situations in which a vehicle is unable to find a place to pullover, rather than simply looping around to return to the same region without availability, a new region may be identified and the vehicle routed to that new region to find a pullover location. This may be significantly faster than looping and/or waiting for a pullover location to become available. In situations in which regions are specific to different sides of a street, the model may be used to determine which side of the street to approach in order to be more likely to find a pullover location.”,
Supplemental Note: once an alternate location has been found, a message is sent to the passenger to confirm the alternate location. This approval allows the alternate location to be the final pick up location. The recommendations of the pickup/drop off spots are interpreted as part of the stopping rule set of the dispatching service. The trajectory relates to the autonomous vehicle navigating to those pickup spots)
In sum, Dyer teaches A method of determining a stopping location for a ride service request, the method comprising, in response to the processor determining that the DSL is not a reachable stopping location, using map data received from a map service and sensor data captured by one or more sensors onboard the autonomous vehicle to select an intermediate stopping location (ISL); confirming that the ISL satisfies a stopping location rule set that is associated with the dispatching service, using the ISL to identify a final stopping location (FSL), determining a trajectory to the FSL, and causing a motion control system of the vehicle to use the trajectory to cause one or more subsystems of the vehicle to move the vehicle along a route to the FSL. Dyer however does not teach a processor onboard an autonomous vehicle: receiving a ride service request, wherein the ride service request includes a desired stopping location (DSL) for a passenger; wherein the stopping location rule set includes a maximum threshold walking distance from the DSL, and the confirming comprises confirming that the ISL is within the maximum threshold walking distance from the DSL; transmitting, to a dispatching service, a message that includes the ISL; and in response to the dispatching service determining that the passenger has approved the ISL whereas Rasmusson does.
Rasmusson teaches a processor onboard an autonomous vehicle: receiving a ride service request, wherein the ride service request includes a desired stopping location (DSL) for a passenger; (Rasmusson: Paragraph 0035: “During the ride and as autonomous vehicle 140 approaches a destination, graphical interface 400 may include graphical representations of potential drop-off locations as selectable icons. In particular embodiments, graphical user interface 400 may be displayed on autonomous-vehicle UI device 148, on user device 130, or on both autonomous-vehicle UI device 148 and user device 130 simultaneously. The user may select a drop-off location from among the graphical representations of the potential drop-off locations.”; Paragraph 0004: “FIG. 1 is a functional diagram of an example vehicle in accordance with an exemplary embodiment.”,
Supplemental Note: the passenger is able to interact with the UI onboard of the autonomous vehicle)
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wherein the stopping location rule set includes a maximum threshold walking distance from the DSL, and the confirming comprises confirming that the ISL is within the maximum threshold walking distance from the DSL; transmitting, to a dispatching service, a message that includes the ISL; and (Rasmusson: Paragraph 0007: “FIG. 5 illustrates an example method for determining a pick-up or drop-off location for a user and presenting potential pick-up or drop-off locations in a real-time situational-awareness view.”; Paragraph 0038: “At step 520, the computing device determines whether the autonomous vehicle is within a threshold proximity of a current path of a current ride of the autonomous vehicle. The current path may include the ride origin or the ride destination. The ride origin may be the location where the autonomous vehicle picks up the requestor. In particular embodiments, the ride origin is an address associated with the requestor. In particular embodiments, the ride origin includes GPS coordinates of the client device 130 associated with the requestor, a request location entered into a requestor device, and/or may be identified based on historical ride information associated with the requestor (e.g., a recurring location known to the requestor). The ride destination may be the destination (e.g., address) specified by the requestor. To be within a threshold proximity, the autonomous vehicle may need to be within a predetermined number of feet, yards, miles, or any other relevant distance measurement from the origin or destination location. If this is the case, then the method may proceed to step 530. If not, the method may repeat step 510 until the condition of step 520 is satisfied.”; Paragraph 0043: “As an example of historical data and not by way of limitation, dynamic transportation matching system 160 may have facilitated 1,000 rides for users to travel from the San Francisco Airport to a hotel located in downtown San Francisco. For each of these 1,000 rides, dynamic transportation matching system 160 may record the pick-up location and the drop-off location, along with other relevant information (e.g. the route used, traffic data, road blocks, the ride rating, information related to the user). This information may be referred to as historical data. From the historical data the computing device may determine one or more historical pick-up or drop-off locations. A historical pick-up or drop-off location may be a particular location that has been used as a pick-up or drop-off location for a threshold number of rides or a threshold number of users. As another example and not by way of limitation, dynamic transportation matching system 160 may have facilitated 25 rides for a single user. That user may request another ride from dynamic transportation matching system 160. Dynamic transportation matching system 160 (e.g. via the computing device) may access historical data for that particular user, which may include information associated with the 25 rides that the user has previously taken with dynamic transportation matching system 160. The historical data may indicate that when the user requests a ride from a particular location (e.g., an office building where he works) the user has most often been picked-up at a particular street corner near the office building. The computing device may take this information into account when determining a suitable pick-up location for the user, as discussed below. As another example and not by way of limitation, a threshold number or proportion of users (e.g. 100 users, 40% of users with a common destination) may have been dropped off 35 feet from the northeast corner of the block on which 185 Berry Street is located. The computing device may take this information into account when calculating a viability score for an available location located 35 feet from the northeast corner of that block.”,
Supplemental Note: as seen in Fig. 5, the system is able to determine if an alternate pick up location is within the threshold proximity of the destination. The alternate pick up location is stored by the system as historical data so it can be referenced again when dropping off at a particular location)
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in response to the dispatching service determining that the passenger has approved the ISL, (Rasmusson: Paragraph 0011: “the autonomous vehicle may use sensor data and take many factors into consideration when determining an appropriate pick-up or drop-off location, as well as allow passengers to communicate with the autonomous vehicle through an autonomous-vehicle user interface (UI) device. This may be done by (1) identifying, based on map data, an area for pick-up or drop-off of the passenger (e.g., based on the origin or destination coordinates or address); (2) determining, based on autonomous-vehicle sensor data, one or more potential pick-up or drop-off locations within the area; (3) calculating, based at least in part on the autonomous-vehicle sensor data and historical data, a viability score for each of the potential pick-up or drop-off locations; and (4) providing for display in, e.g., a situational awareness view, a visual representation of at least a portion of the area for pick-up or drop-off that indicates at least one of the potential pick-up or drop-off locations. Thus, instead of talking to a human driver, the passenger may use the autonomous-vehicle UI device to communicate with the autonomous vehicle. The autonomous-vehicle UI device may display the situational-awareness view, which includes a representation of the environment surrounding the autonomous vehicle. A situational-awareness view is a graphical representation of an external environment of the autonomous vehicle that is updated in real time. The situational-awareness view may also be displayed on the passenger's own computing device in addition to or instead of the autonomous-vehicle UI device.”)
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Dyer with the teachings of Rasmusson with a reasonable expectation of success. Both Dyer and Rasmusson teach autonomous vehicles which are able to pick and drop off passengers while also having the ability to select a better drop location if necessary. Rasmusson differs from Dyer as it teaches the ability of a maximum threshold an alternate drop off location can be from the desired location while also providing a UI for the passenger in the vehicle to make their selection. One with knowledge in the art would find the ability of Rasmusson to have a maximum threshold to be a use of a known technique ready for improvement to yield predictable results. Dyer already teaches the use of a passenger inconvenience values that measure extra distance from the desired location, the longer the distance the higher the inconvenience (Dyer: Paragraph 0046). Combining Rasmusson’s maximum threshold with the passenger inconvenience values as taught by Dyer, limits the values to a certain amount of distance, thus both yield the predictable results of not choosing an alternate location too far from the desired location and the improvement being that this threshold has a maximum so the passengers only have to walk a maximum of said distance. Furthermore, the ability to include a UI within the autonomous vehicle as taught by Rasmusson is a simple substitution by one with knowledge in the art with the autonomous vehicles of Dyer. For example, the users of Dyer can use a mobile device to make changes with the drop off locations while Rasmusson teaches the UI directly incorporated within the vehicle. Both are used to communicate with the vehicle about the user preferences about the drop off location and the vehicle communicating its list of alternate locations back to the user, thus merely a simple substitution.
Regarding claim 5, Dyer, as modified, teaches wherein selecting the ISL also comprises selecting a location that satisfies at least one of the following rules: the passenger must not need to cross a street when walking from the ISL to the DSL; the ISL must be located more than a minimum distance from a nearest intersection; (Dyer: Paragraph 0063: “In some instances, one or a plurality of regions may be identified by the computing devices 110 based on a time and/or distance that a vehicle would need to travel in order to reach each of the regions from the given location. The time constraint may correspond to a current date/time or an expected date/time when a vehicle is expected to reach the region. In this regard, the time it would take for a particular vehicle to reach each region may also be determined. In some instances, if the time it would take a vehicle to reach a region would be too great, such regions may be excluded or filtered from the plurality of regions. As an example, one region might be right across the street, but if it takes too long (e.g. longer than a predetermined period of time such as 5 minutes or more or less) to get there because the vehicle is unable to perform a u-turn for several blocks then that region may not be included in the plurality of regions.”,
Supplemental Note: to perform a u-turn at an intersection is too far to reach a pickup stop across the street, that is a constraint to set that location as an ISL from the DSL) or
the ISL must be of at least a minimum size. (Dyer: Paragraph 0017: “In use, for a given location, a plurality of regions nearby that location may be identified. The regions may be input into the model as geographic constraints or alternatively, some location within that region may be input as the geographic constraint. A time constraint may also be input into the model. Alternatively, other inputs to the model may include additional features of roads within the region such as proximity to places of interest, size, road speed, typical traffic, etc.”,
Supplemental Note: the size of a location to be used as an ISL is an additional feature when evaluating the plurality of locations to select)
Regarding claim 6, Dyer, as modified, teaches determining that the passenger has approved the ISL comprises receiving a message from the dispatching service indicating that the passenger has accepted the ISL; and the FSL is the ISL. (Dyer: Paragraph 0074: “In some instances, the computing devices 100 may possibly identify a region to travel to which may be less convenient to the passenger, but will now have a higher ranking than previously because of the inconvenience to the another vehicle. In such situations, if the vehicle is going to pick up a passenger, a notification may be sent to the passenger's client computing device identifying the highest ranked region and indicating that the vehicle is currently going to that region to drop off the passenger or that the vehicle is able to go to that region to pick up the passenger (and requesting confirmation).”,
Supplemental Note: once an alternate location has been found, a message is sent to the passenger to confirm the alternate location. This approval allows the alternate location to be the final pick up location)
Regarding claim 8, Dyer, as modified, teaches wherein at least some criteria that the processor uses to select the ISL are not communicated to the dispatching service. (Dyer: Paragraph 0002: “The method includes identifying, by the one or more processors, a time constraint for a pullover maneuver; identifying, by the one or more processors, a geographic constraint for the pullover maneuver; inputting, by the one or more processors, the time constraint and the geographic constraint into a model in order to receive a list of qualities for a region, the region including a plurality of pullover locations; determine, by the one or more processors, whether to attempt to find a pullover location within the region to perform the pullover maneuver based on the list of qualities for the region; and maneuvering, by the one or more processors, the vehicle in the autonomous driving mode based on the determination whether to attempt to find a pullover location within the region.”; Paragraph 0020: “The vehicle may have one or more computing devices, such as computing device 110 containing one or more processors 120, memory 130 and other components typically present in general purpose computing devices.”,
Supplemental Note: the processors inside the autonomous vehicle are able to identify an ISL that does not need to be communicated with the dispatching service based on the list of qualities of the region)
Regarding claim 9, Dyer, as modified, teaches wherein the processor onboard the vehicle and the passenger do not communicate with each other before the processor identifies the FSL and causes the vehicle to move along the route. (Dyer: Paragraph 0074: “In some instances, the computing devices 100 may possibly identify a region to travel to which may be less convenient to the passenger, but will now have a higher ranking than previously because of the inconvenience to the another vehicle. In such situations, if the vehicle is going to pick up a passenger, a notification may be sent to the passenger's client computing device identifying the highest ranked region and indicating that the vehicle is currently going to that region to drop off the passenger or that the vehicle is able to go to that region to pick up the passenger (and requesting confirmation).”; Paragraph 0075: “In some instances, real time information about the availability of pullover locations observed by a vehicle, such as any of vehicles 100, 100A, 100B, 100C, may be shared with the dispatching server computing devices and/or other vehicles of the fleet.”,
Supplemental Note: once an alternate location has been found, a message is sent to the passenger to confirm the alternate location. This approval allows the alternate location to be the final pick up location and all of this can be done with a dispatching server computing devices, not communicating with the processor)
Regarding claim 10, Dyer teaches a computer program product for determining a stopping location for a ride service request, (Dyer: Paragraph 0019: “The features described herein may enable a vehicle having an autonomous driving mode to more easily locate pullover locations as in the examples described above. Regions where parking is likely to currently be available can be identified without the vehicle actually having to observe those regions. This may reduce the amount of time that the vehicle may spend looking for a pullover location and also reduce inconvenience convenient to passenger and other road users.”; Paragraph 0022: “The instructions 132 may be any set of instructions to be executed directly (such as machine code) or indirectly (such as scripts) by the processor. For example, the instructions may be stored as computing device code on the computing device-readable medium. In that regard, the terms “instructions” and “programs” may be used interchangeably herein. The instructions may be stored in object code format for direct processing by the processor, or in any other computing device language including scripts or collections of independent source code modules that are interpreted on demand or compiled in advance. Functions, methods and routines of the instructions are explained in more detail below.”; Paragraph 0075: “In some instances, real time information about the availability of pullover locations observed by a vehicle, such as any of vehicles 100, 100A, 100B, 100C, may be shared with the dispatching server computing devices and/or other vehicles of the fleet.”,
Supplemental Note: a fleet of vehicle is used by a ride service as they also dispatch vehicles to user pick up locations)
the computer program product comprising a memory device and programming instructions that are (Dyer: Paragraphs 0020 – 0021: “As shown in FIG. 1, a vehicle 100 in accordance with one aspect of the disclosure includes various components. While certain aspects of the disclosure are particularly useful in connection with specific types of vehicles, the vehicle may be any type of vehicle including, but not limited to, cars, trucks, motorcycles, buses, recreational vehicles, etc. The vehicle may have one or more computing devices, such as computing device 110 containing one or more processors 120, memory 130 and other components typically present in general purpose computing devices. The memory 130 stores information accessible by the one or more processors 120, including instructions 132 and data 134 that may be executed or otherwise used by the processor 120. The memory 130 may be of any type capable of storing information accessible by the processor, including a computing device-readable medium, or other medium that stores data that may be read with the aid of an electronic device, such as a hard-drive, memory card, ROM, RAM, DVD or other optical disks, as well as other write-capable and read-only memories. Systems and methods may include different combinations of the foregoing, whereby different portions of the instructions and data are stored on different types of media”)
… in response to the processor determining that the DSL is not a reachable stopping location, using map data received from a map service and sensor data captured by one or more sensors onboard the vehicle to select an intermediate stopping location (ISL); (Dyer: Paragraph 0035: “The various systems of the vehicle may function using autonomous vehicle control software in order to determine how to and to control the vehicle. As an example, a perception system software module of the perception system 172 may use sensor data generated by one or more sensors of an autonomous vehicle, such as cameras, LIDAR sensors, radar units, sonar units, etc., to detect and identify objects and their characteristics.”; Paragraph 0061: “For the purposes of demonstration, FIG. 6 is an example representation of a section of roadway 600 corresponding to the map information 200. In this example, the section of roadway 600 includes various features such as lanes 610-616, intersections 620-626, buildings 630-636, parking spaces 640-646, a driveway entrance (for example to a parking garage or other location) 650, shoulder areas 652-654, and no parking zone 656 that correspond to each of lanes 210-216, intersections 220-226, buildings 230-236, parking spaces 240-246, a driveway entrance (for example to a parking garage or other location) 250, shoulder areas 252-254, and no parking zone 256 of the map information 200. Vehicle 100 is also depicted driving in lane 616 and approaching intersection 626. In this example, vehicle 100 may be attempting a pickup or drop off at the location of marker 680.”; Paragraph 0062: “For instance, a plurality of regions nearby a location may be identified by the computing devices 110. For instance, this given location may be a current location of the vehicle (when the vehicle is unable to find a place to pullover or when the vehicle needs to move to a new pullover location because an emergency vehicle nearby, etc.). Alternatively, the given location may be a pickup or drop off location for a passenger. In either example, the vehicle is very likely to be outside of the plurality of regions or rather, unable to use the sensors of the perception system to determine whether there are available pullover locations in the regions of the plurality of regions.”,
Supplemental Note: Since the pickup location to pick up the passenger is a geographic constraint, the vehicle is able to use its sensors for additional locations to select. This can also be performed for selecting drop off locations)
confirming that the ISL satisfies a stopping location rule set that is associated with the dispatching service, (Dyer: Paragraph 0046: “Other signals stored in the storage system 450 may only be available or determined by the server computing devices 410 and/or human operators after a pullover is actually attempted by one of the vehicles of the fleet. Such signals may include how long passengers take to arrive, board and depart at a pullover location, the passenger inconvenience value of a pullover location, the vehicle inconvenience at pullover location, how long the vehicle is able to stay in a pullover location, etc. The values for passenger inconvenience and vehicle inconvenience may be determined, for instance, on a scale of 0 to 1 using a model which generates such values given map data and sensor data collected during such attempted pullovers. Passenger inconvenience values may represent how convenient a particular pickup up or drop off was for a passenger by measuring how much extra distance is imposed on the passenger by the selection of a particular pullover location.”; Paragraph 0048: “In order to build a model, the server computing devices 410 may access the signals of the storage system 450 described above. Using these signals or values determined from these signals, a model that identifies expected characteristics of regions may be built by the server computing devices 410. The model may be trained such that for a given geographic constraint (such as a specific location or a region) and time constraint (such as day of the year, calendar month, day of week, and/or time of day), the model may provide a list of expected qualities for a region. In this regard, if a specific location is provided, the region may correspond to a region that includes the specific location. In this regard, the signals or values determined from these signals for a given region where the sensor data corresponding to the signals was collected may be used as training outputs for the model, and the specific location or given region and a time when the sensor data was collected may be used as training inputs to the model. In the case of a machine-learned model, the training may essentially tune parameter values for the model.”; Paragraph 0075: “In some instances, real time information about the availability of pullover locations observed by a vehicle, such as any of vehicles 100, 100A, 100B, 100C, may be shared with the dispatching server computing devices and/or other vehicles of the fleet. This information may be used to update the model or in conjunction with the lists of qualities to rank the regions. This information could also be used to directly update the values for the lists of qualities or as some form of additional cost in the ranking.”,
Supplemental Note: passenger inconvenience values measure extra distance imposed on the passenger to reach a pickup/drop off location, this interpreted as a stopping location rule)
… using the ISL to identify a final stopping location (FSL), generating a trajectory to the FSL, and causing a motion control system of the vehicle to use the trajectory to cause one or more subsystems of the vehicle to move the vehicle along a route to the FSL (Dyer: Paragraph 0074: “In some instances, the computing devices 100 may possibly identify a region to travel to which may be less convenient to the passenger, but will now have a higher ranking than previously because of the inconvenience to the another vehicle. In such situations, if the vehicle is going to pick up a passenger, a notification may be sent to the passenger's client computing device identifying the highest ranked region and indicating that the vehicle is currently going to that region to drop off the passenger or that the vehicle is able to go to that region to pick up the passenger (and requesting confirmation).”; Paragraph 0077: “The model may enable various improvements to current transportation services that utilize autonomous vehicles. For instance, when a passenger is requesting or setting up a trip, the dispatching server computing devices may use the model to make recommendations for where a vehicle can pick up or drop off a passenger. For example, the dispatching server computing devices can recommend locations in nearby regions to a requested pickup or drop off location where a vehicle can more easily find a place to pullover. Depending upon the expected and desired qualities of the nearby regions, in turn, may reduce inconvenience to the passenger and/or other vehicles. As noted above, the model may be used to identify regions that are suitable for finding long term parking for a vehicle for a specific point in time, day of the week, etc. In addition, in the event of a problem with a vehicle's systems that requires the vehicle to pullover within a certain period of time, the model may be used to find a nearby location within the period of time where there is likely a pullover location available. Further, in situations in which a vehicle is unable to find a place to pullover, rather than simply looping around to return to the same region without availability, a new region may be identified and the vehicle routed to that new region to find a pullover location. This may be significantly faster than looping and/or waiting for a pullover location to become available. In situations in which regions are specific to different sides of a street, the model may be used to determine which side of the street to approach in order to be more likely to find a pullover location.”,
Supplemental Note: once an alternate location has been found, a message is sent to the passenger to confirm the alternate location. This approval allows the alternate location to be the final pick up location. The recommendations of the pickup/drop off spots are interpreted as part of the stopping rule set of the dispatching service. The trajectory relates to the autonomous vehicle navigating to those pickup spots)
In sum, Dyer teaches a computer program product for determining a stopping location for a ride service request, the computer program product comprising a memory device and programming instructions that are in response to the processor determining that the DSL is not a reachable stopping location, using map data received from a map service and sensor data captured by one or more sensors onboard the vehicle to select an intermediate stopping location (ISL); confirming that the ISL satisfies a stopping location rule set that is associated with the dispatching service, using the ISL to identify a final stopping location (FSL), generating a trajectory to the FSL, and causing a motion control system of the vehicle to use the trajectory to cause one or more subsystems of the vehicle to move the vehicle along a route to the FSL. Dyer however does not teach a processor onboard an autonomous vehicle to perform a method comprising: upon receiving a ride service request, wherein the ride service request includes a desired stopping location (DSL) for a passenger: wherein the stopping location rule set includes a maximum threshold walking distance from the DSL, and the confirming comprises confirming that the ISL is within the maximum threshold walking distance from the DSL; transmitting, to a dispatching service, a message that includes the ISL; and in response to the dispatching service determining that the passenger has approved the ISL whereas Rasmusson does.
Rasmusson teaches configured to cause a processor onboard an autonomous vehicle to perform a method comprising: upon receiving a ride service request, wherein the ride service request includes a desired stopping location (DSL) for a passenger: (Rasmusson: Paragraph 0035: “During the ride and as autonomous vehicle 140 approaches a destination, graphical interface 400 may include graphical representations of potential drop-off locations as selectable icons. In particular embodiments, graphical user interface 400 may be displayed on autonomous-vehicle UI device 148, on user device 130, or on both autonomous-vehicle UI device 148 and user device 130 simultaneously. The user may select a drop-off location from among the graphical representations of the potential drop-off locations.”; Paragraph 0004: “FIG. 1 is a functional diagram of an example vehicle in accordance with an exemplary embodiment.”,
Supplemental Note: the passenger is able to interact with the UI onboard of the autonomous vehicle)
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wherein the stopping location rule set includes a maximum threshold walking distance from the DSL, and the confirming comprises confirming that the ISL is within the maximum threshold walking distance from the DSL; transmitting, to a dispatching service, a message that includes the ISL; and (Rasmusson: Paragraph 0007: “FIG. 5 illustrates an example method for determining a pick-up or drop-off location for a user and presenting potential pick-up or drop-off locations in a real-time situational-awareness view.”; Paragraph 0038: “At step 520, the computing device determines whether the autonomous vehicle is within a threshold proximity of a current path of a current ride of the autonomous vehicle. The current path may include the ride origin or the ride destination. The ride origin may be the location where the autonomous vehicle picks up the requestor. In particular embodiments, the ride origin is an address associated with the requestor. In particular embodiments, the ride origin includes GPS coordinates of the client device 130 associated with the requestor, a request location entered into a requestor device, and/or may be identified based on historical ride information associated with the requestor (e.g., a recurring location known to the requestor). The ride destination may be the destination (e.g., address) specified by the requestor. To be within a threshold proximity, the autonomous vehicle may need to be within a predetermined number of feet, yards, miles, or any other relevant distance measurement from the origin or destination location. If this is the case, then the method may proceed to step 530. If not, the method may repeat step 510 until the condition of step 520 is satisfied.”; Paragraph 0043: “As an example of historical data and not by way of limitation, dynamic transportation matching system 160 may have facilitated 1,000 rides for users to travel from the San Francisco Airport to a hotel located in downtown San Francisco. For each of these 1,000 rides, dynamic transportation matching system 160 may record the pick-up location and the drop-off location, along with other relevant information (e.g. the route used, traffic data, road blocks, the ride rating, information related to the user). This information may be referred to as historical data. From the historical data the computing device may determine one or more historical pick-up or drop-off locations. A historical pick-up or drop-off location may be a particular location that has been used as a pick-up or drop-off location for a threshold number of rides or a threshold number of users. As another example and not by way of limitation, dynamic transportation matching system 160 may have facilitated 25 rides for a single user. That user may request another ride from dynamic transportation matching system 160. Dynamic transportation matching system 160 (e.g. via the computing device) may access historical data for that particular user, which may include information associated with the 25 rides that the user has previously taken with dynamic transportation matching system 160. The historical data may indicate that when the user requests a ride from a particular location (e.g., an office building where he works) the user has most often been picked-up at a particular street corner near the office building. The computing device may take this information into account when determining a suitable pick-up location for the user, as discussed below. As another example and not by way of limitation, a threshold number or proportion of users (e.g. 100 users, 40% of users with a common destination) may have been dropped off 35 feet from the northeast corner of the block on which 185 Berry Street is located. The computing device may take this information into account when calculating a viability score for an available location located 35 feet from the northeast corner of that block.”,
Supplemental Note: as seen in Fig. 5, the system is able to determine if an alternate pick up location is within the threshold proximity of the destination. The alternate pick up location is stored by the system as historical data so it can be referenced again when dropping off at a particular location)
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in response to the dispatching service determining that the passenger has approved the ISL, (Rasmusson: Paragraph 0011: “the autonomous vehicle may use sensor data and take many factors into consideration when determining an appropriate pick-up or drop-off location, as well as allow passengers to communicate with the autonomous vehicle through an autonomous-vehicle user interface (UI) device. This may be done by (1) identifying, based on map data, an area for pick-up or drop-off of the passenger (e.g., based on the origin or destination coordinates or address); (2) determining, based on autonomous-vehicle sensor data, one or more potential pick-up or drop-off locations within the area; (3) calculating, based at least in part on the autonomous-vehicle sensor data and historical data, a viability score for each of the potential pick-up or drop-off locations; and (4) providing for display in, e.g., a situational awareness view, a visual representation of at least a portion of the area for pick-up or drop-off that indicates at least one of the potential pick-up or drop-off locations. Thus, instead of talking to a human driver, the passenger may use the autonomous-vehicle UI device to communicate with the autonomous vehicle. The autonomous-vehicle UI device may display the situational-awareness view, which includes a representation of the environment surrounding the autonomous vehicle. A situational-awareness view is a graphical representation of an external environment of the autonomous vehicle that is updated in real time. The situational-awareness view may also be displayed on the passenger's own computing device in addition to or instead of the autonomous-vehicle UI device.”)
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Dyer with the teachings of Rasmusson with a reasonable expectation of success. Please refer to the rejection of claim 1 as both state the same functional language and therefore rejected under the same pretenses.
Regarding claim 13, Dyer, as modified, does not teach wherein the instructions to select the ISL also comprise instructions to: retrieve the maximum threshold walking distance from the stopping location rule set whereas Rasmusson does.
Rasmusson teaches wherein the instructions to select the ISL also comprise instructions to: retrieve the maximum threshold walking distance from the stopping location rule set. (Rasmusson: Paragraph 0007: “FIG. 5 illustrates an example method for determining a pick-up or drop-off location for a user and presenting potential pick-up or drop-off locations in a real-time situational-awareness view.”; Paragraph 0038: “At step 520, the computing device determines whether the autonomous vehicle is within a threshold proximity of a current path of a current ride of the autonomous vehicle. The current path may include the ride origin or the ride destination. The ride origin may be the location where the autonomous vehicle picks up the requestor. In particular embodiments, the ride origin is an address associated with the reque