DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 09/01/2026 has been entered.
Information Disclosure Statement
The information Disclosure Statements filed on 06/02/2026, 08/26/2026, 09/01/2026 have been considered. An initialed copy of form 1449 for each is enclosed herewith.
Status of Claims
This action is in response to the amendments filed on 09/01/2026, wherein claims 1, 16, and 20 are amended. Claims 1-20 are rejected.
Response to Arguments
Applicant’s arguments, see REMARKS, filed 09/01/2026, with respect to the rejection(s) of claim(s) 1-20 under 35 USC §103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claim(s) 1, 2, 4, 16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bavar et al. (US 2018/0061242 A1, “Bavar”) in view of Goldman et al. (US 2020/0134525 A1, “Goldman”).
Regarding claims 1, 16, and 20, Bavar discloses hybrid trip planning for autonomous vehicles and teaches:
A system for providing transportation service using an autonomous vehicle (AV) of a [] system, the system comprising: (For each received transport request, the hybrid trip planning system can determine a set of candidate vehicles that are within a predetermined proximity or time from a pickup location identified in the transport request. Additionally or alternatively, the hybrid trip planning system can determine whether the transport request satisfies the set of criteria for the on-demand AV service. For transport requests in which the pickup location and drop off location, i.e., multiple stopping locations, are within the autonomy grid map, the hybrid trip planning system can instruct and AV to operate in an autonomous mode to rendezvous with the requesting user at the pickup location and transport the requesting user to the drop off location without manual control by the human safety driver – See at least ¶ [0015])
one or more processors; and (Furthermore, one or more examples described herein may be implemented through the use of instructions that are executable by one or more processors - See at least ¶ [0029])
a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: (Furthermore, one or more examples described herein may be implemented through the use of instructions that are executable by one or more processors These instructions may be carried on a computer-readable medium. Machines shown or described with figures below provide examples of processing resources and computer-readable mediums on which instructions for implementing examples disclosed herein can be carried and/or executed. In particular, the numerous machines shown with examples of the invention include processors and various forms of memory for holding data and instructions – See at least ¶ [0029])
receiving, by a service assignment system from a computing device of a user, a service request from the user for a transportation service; (Hybrid trip planning system 100 can communicate, over one or more networks 160, with requesting users or riders 174 throughout a given region where on-demand transportation services are provided. Specifically, each requesting user 174 can execute a rider application 175 on the user's 174 computing device 170 – See at least ¶ [0032])
providing, by the service assignment system to the [] system, a pickup location associated with the user; (According to examples, the hybrid trip planning system 100 can include a provider interface 115 that connects, via the one or more networks 160, with a fleet of provider vehicles 180 available to provide on-demand transportation services to the requesting users 174. In various examples, the provider vehicles 180 can comprise a fleet of AVs, any number of drivers, and/or a blend of human-driven vehicles and AVs servicing a given region – See at least ¶ [0034])
in response to providing the pickup location associated with the user, receiving multiple AV stopping locations from the [] system; (On a higher level, 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, a drop off location, or an exit point within an autonomy grid map. In various aspects, the route planning engine 260 can generate the route plan 267 based on transport instructions 291 received from the hybrid trip planning system 290 over one or more networks 285. According to examples described herein, the AV 200 can include location-based resource, such as a GPS module 222, that that provide location data 221 (e.g., periodic location pings) to the hybrid trip planning system 290. Based on the AV ' s 200 location data 221, the hybrid trip planning system 290 may select the AV 200 to service a particular transport request, as described above with respect to FIG.1 – See at least ¶ [0059] Here, the system is sending the pick-up and drop-off locations, i.e., stopping locations, to the vehicle and the vehicle is using its route planning engine to determine the route from the various locations to the destination.)
selecting an AV stopping location from the multiple AV stopping locations; and (Accordingly, transport instructions 291 can provide the route planning engine 260 with an overall route at least from given pickup location to a drop off location for requesting user. In some aspects, the transport instructions 291 can also provide route data from the AV’s 200 current location to the pickup location – See at least ¶ [0059]; Here, the system is selecting a pick-up location from the multiple stopping locations, e.g., pick-up and drop-off locations.)
in response to the selecting, triggering the AV of the [] system (The selection engine 130 can receive the transport requests 171 from the rider interface 125. The transport requests 171 can include respective pickup locations of the requesting users 174. In some aspects, the selection engine can also receive rider locations 173 (e.g., from location based resources, such as GPS data, from the rider devices 170. Utilizing the pickup location and/or the rider location 173 for a given transport request 171, the selection engine 130 can identify a set of candidate vehicles to service the transport request 171. In doing so, the selection engine 130 can identify vehicles proximate to the pickup location indicated in the transport request 171 or the rider location 173, and determine the set of candidate vehicles based on the vehicles being a predetermined distance or time from the pickup location or rider location 173 – See at least ¶ [0037]) to travel to the selected AV stopping location. (The hybrid trip planning system 100 may then select an AV 189 from the candidate set to service the transport request 171 (510). Based on the pickup location and drop off location, the hybrid trip planning system 100 can determine optimal entry and exit points to and from the autonomy grid map 142 (515). Hybrid trip planning system 100 may then transmit transport data 122 to the selected AV 189 to enable a combination of the AV safety driver in the autonomous control system 220 of the AV 189 to execute an overall route plan in order to service the transport request 171 (520) – See at least ¶ [0077])
Bavar does not explicitly teach that the system provides data to a third-party system. However, Goldman discloses on-demand transport selection process facilitating third-party autonomous vehicles and teaches:
providing, by the service assignment system to the third-party system that is separate from the service assignment system and manages the AV as part of a third-party fleet independent of the service assignment system, a pickup location associated with the user; (According to certain implementations, the network computing system 100 can include a candidate filter 110 that can process the transport requests received from the requesting users 180 and determine a set of candidate transport providers that can potentially service the transport request – See at least ¶ [0032]; In variations, the candidate filter 110 can transmit capability inquiries to third-party systems 175 corresponding to fleet management or ownership entities that provide the respective third - party fleets for transport services. In further variations, the candidate filter 110 can transmit capability queries to the third-party systems 175 to generally determine whether a particular third-party AV 196 can service a given transport request, and if so, the candidate filter can transmit a confirmation query to the third-party AV 196 to specifically determine whether that particular AV can service the request (e.g., given local conditions on the AV, such as fuel or power, a current number of passengers, and the like) – See at least ¶ [0034]; Examiner notes that this includes a pickup location – See at least ¶ [0018])
in response to providing the pickup location associated with the user, receiving multiple AV stopping locations generated by the third-party system for the AV that is part of the third-party fleet based on the pickup location; (In further implementations, the trip negotiator 245 can communicate with a management system 295, corresponding to a third-party system 175 that manages or owns the AV 200, to determine whether the management system 295 identifies the transport request as desirable for the AV 200 (e.g., based on cost, location, routing information, etc.) – See at least ¶ [0058]; Examiner notes that the routing information includes multiple pick-ups, i.e., stopping locations – See at least ¶ [0055])
in response to the selecting, triggering the AV of the third-party system to travel to the selected AV stopping location. (For each transport provider in the subset, the computing system 100 can determine an optimization score for the transport provider, if the transport provider were selected to service the transport request (315). Based on the optimization score of each transport provider in the subset, the computing system 100 can select a transport provider to service the transport request (320). The computing system 100 may then transmit a transport invitation or assignment to the selected transport provider to service the transport request (325) – See at least ¶ [0064])
In summary, Bavar discloses a system which receives requests from users, including multiple stopping locations, e.g., pick-up and drop-off locations. Bavar further teaches the system selects an autonomous vehicle to fulfill the user request by transporting the user from the pick-up location along a route to a destination. Bavar does not explicitly teach that the system provides the information to a third-party. However, Goldman discloses on-demand transport selection process facilitating third-party autonomous vehicles and teaches that the system may communicate data to an external fleet manager of AVs that are owned by third-parties.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the hybrid trip planning for autonomous vehicles of Bavar to provide for the on-demand transport selection process facilitating third-party autonomous vehicles, as taught in Goldman, to incorporate third-party AVs that may include differing capabilities into a single vehicle ride-sharing platform and through the inclusiveness of a diverse field of AVs, a network computing system may support convergence towards safer, more efficient, and more cost-effective solutions to the current challenges in autonomous driving technology. (At Goldman ¶ [0021])
Regarding claim 2, Bavar further teaches:
wherein the providing the pickup location associated with the user comprises:
determining whether to share a user location of the user with the [] system; and (The hybrid trip planning system 100 may then select an AV 189 from the candidate set to service the transport request 171 (510). Based on the pickup location and drop off location, the hybrid trip planning system 100 can determine optimal entry and exit points to and from the autonomy grid map 142 (515). Hybrid trip planning system 100 may then transmit transport data 122, i.e., user location, to the selected AV 189 to enable a combination of the AV safety driver in the autonomous control system 220 of the AV 189 to execute an overall route plan in order to service the transport request 171 (520) – See at least ¶ [0077]; Here, the system is only sharing user location with the selected AV, therefore a determination was made to only share this data with the selected AV instead of the entire AV fleet.)
based on the determining, providing the user location of the user and/or a selected pickup location to the [] system, the selected pickup location comprising a pickup location selected by the service assignment system or the user. (In various aspects, the executing rider application 175 can cause a user interface 172 to be generated on a display screen of the rider device 170. Using the user interface 172, the requesting user 174 can generate and transmit a transport request 171 to the rider interface 125 – See at least ¶ [0033]; The transport requests 171 can include respective pickup locations of the requesting users 174 – See at least ¶ [0037])
Bavar does not explicitly teach, but Goldman further teaches:
[] third-party system (n certain examples, the third-party AV 196 can also transmit contextual information regarding a route plan, estimated time of completion, estimate cost of completion, and the like. In variations, the candidate filter 110 can transmit capability inquiries to third-party systems 175 corresponding to fleet management or ownership entities that provide the respective third-party fleets for transport services – See at least ¶ [0034])
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the hybrid trip planning for autonomous vehicles of Bavar to provide for the on-demand transport selection process facilitating third-party autonomous vehicles, as taught in Goldman, to incorporate third-party AVs that may include differing capabilities into a single vehicle ride-sharing platform and through the inclusiveness of a diverse field of AVs, a network computing system may support convergence towards safer, more efficient, and more cost-effective solutions to the current challenges in autonomous driving technology. (At Goldman ¶ [0021])
Regarding claim 4, Bavar further teaches:
wherein: the determining whether to share the user location comprises determining whether a manual selection of a pickup location was made by the user; and (In various aspects, the executing rider application 175 can cause a user interface 172 to be generated on a display screen of the rider device 170. Using the user interface 172, the requesting user 174 can generate and transmit a transport request 171 to the rider interface 125 – See at least ¶ [0033]; The transport requests 171 can include respective pickup locations of the requesting users 174 – See at least ¶ [0037])
the providing comprises providing the manually selected pickup location. (The hybrid trip planning system 100 may then select an AV 189 from the candidate set to service the transport request 171 (510). Based on the pickup location and drop off location, the hybrid trip planning system 100 can determine optimal entry and exit points to and from the autonomy grid map 142 (515). Hybrid trip planning system 100 may then transmit transport data 122, i.e., user location, to the selected AV 189 to enable a combination of the AV safety driver in the autonomous control system 220 of the AV 189 to execute an overall route plan in order to service the transport request 171 (520) – See at least ¶ [0077])
Claim(s) 3 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Bavar in view of Goldman as applied to claim 1, and in further view of Uber (Uber.com Documents, “Uber”)
Regarding claim 3, the combination of Bavar and Goldman does not explicitly teach comprises determining whether the service request is associated with a required location based on restrictions that restrict where AVs can stop; and the providing comprises providing the required location based on the service request being associated with the require location. However, Uber discloses a ridesharing platform and teaches:
wherein: the determining whether to share the user location (Before accepting a trip, the driver only sees your approximate pickup location*…*More on approximate pickup and dropoff locations: The type of approximate pickup and dropoff location shown to drivers varies based on address structures in your market, local regulations and driver loyalty programs. Some examples of approximate location formats include: Cross-Street, Street Name, Point of Interest and Special Formats such as the terminal and door number you should meet at for an airport pickup – See at least pg. 1) comprises determining whether the service request is associated with a required location based on restrictions that restrict where AVs can stop; and (Avoid restricted areas safety first. The app flags restricted and illegal pickup locations, so you can make an informed and responsible decision when setting your pickup location – See at least pg. 6)
the providing comprises providing the required location based on the service request being associated with the require location. (Frequently asked questions: Can I choose a different location from the one suggested?
Yes. Simply move the pin to the area where you want to be picked up.
Why is the red area on the map not allowing me to set my pin?
Certain zones have areas where pickups are not allowed. These could be cases where the app will block and highlight the zone to indicate that the pin needs to be set in a different location – See at least pg. 7)
In summary, Bavar discloses sharing user locations with third parties. The combination of Bavar and Goldman does not explicitly teach comprises determining whether the service request is associated with a required location based on restrictions that restrict where AVs can stop; and the providing comprises providing the required location based on the service request being associated with the require location. However, Uber discloses a ridesharing platform and teaches only sharing user locations after the ride has been accepted by the driver. Uber further teaches that you cannot request a ride to areas that are located in certain zones. Thus, the location data will only be sent when the user location is set to a non-restricted area and after the driver accepts the ride.
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the hybrid trip planning for autonomous vehicles of Bavar and Goldman to provide for the rideshare platform, as taught in Uber, to help protect user privacy. (At Uber pg. 3)
Regarding claim 5, the combination of Bavar and Goldman does not explicitly teach, but Uber further teaches:
wherein the determining whether to share the user location comprises: determining whether the user allows sharing of the user location; and (You don't need to turn on your device's location services to use the Uber app. However, certain features, like sharing your trip with trusted contacts, require location information to work. If you don't use location services, you can still use Uber by manually entering your pickup and dropoff locations in the app. You can also use cross streets or landmarks instead of an address. You can choose whether to use location services in the privacy settings of your Uber app – See at least pg. 3)
based on the user allowing sharing of the user location, determining whether the user location of the user is accurate, wherein the providing comprises providing the user location is based on the location being accurate. (Preferred access (pick-up) point accuracy: Pick-up points are an extremely important metric to the rider experience, especially at large venues such as airports and stadiums. For this metric, we compute the distance of an address or place’s location, as shown by the map pin in Figure 4, below, from all actual pick-up and drop-off points used by drivers. We then set the closest actual location to be the preferred access point for the said location pin. When a rider requests the location indicated by the map pin, the map guides the driver to the preferred access point. We continually compute this metric with the latest actual pick-up and drop-off locations to ensure freshness and accuracy of the suggested preferred access points – See at least pg. 6)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the hybrid trip planning for autonomous vehicles of Bavar and Goldman to provide for the rideshare platform, as taught in Uber, to help protect user privacy. (At Uber pg. 3)
Claim(s) 7 is rejected under 35 U.S.C. 103 as being unpatentable over Bavar in view of Goldman, as applied to claim 1, in view of Uber and in further view of Li et al. (US 2022/0222587 A1, “Li”).
Regarding claim 7, The combination of Bavar and Goldman does not explicitly, but Uber further teaches:
wherein the determining whether to share the user location comprises: determining whether the user allows sharing of the user location; and (You don't need to turn on your device's location services to use the Uber app. However, certain features, like sharing your trip with trusted contacts, require location information to work. If you don't use location services, you can still use Uber by manually entering your pickup and dropoff locations in the app. You can also use cross streets or landmarks instead of an address. You can choose whether to use location services in the privacy settings of your Uber app – See at least pg. 1)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the hybrid trip planning for autonomous vehicles of Bavar and Goldman to provide for the rideshare platform, as taught in Uber, to help protect user privacy. (At Uber pg. 1)
The combination of Bavar, Goldman, and Uber does not explicitly teach based on the user allowing sharing of the user location, determining whether the request is for a transportation service for a guest user, the guest user being an individual different from the user; and based on the transportation service being for a guest user, determining a pickup location associated with a location of the guest user. However, Li discloses machine learning based geolocation trajectory threshold determination and teaches:
based on the user allowing sharing of the user location, determining whether the request is for a transportation service for a guest user, the guest user being an individual different from the user; and (At block 706, the distance prediction system 222 determines one or more guest rider thresholds based at least in part on the ride characteristics. Determining the one or more guest rider thresholds may include applying the guest machine learning model determined using the process 600. In some cases, the one or more guest rider thresholds determined at the block 706 may be guest rider thresholds that are used to determine whether a particular distance between the account holder and a location (e.g., pickup location, driver location, drop off location, etc.) is indicative of whether the account holder is taking the requested ride or whether the ride was requested for another user, a guest rider – See at least ¶ [0132]; At block 710, the guest rider detector 220 determines, based at least in part on the location of the account holder and the one or guest rider thresholds, whether the a rider is a guest rider – See at least ¶ [0134] Examiner notes that the use of the account holders location is the user allowing sharing of the user location.)
based on the transportation service being for a guest user, determining a pickup location associated with a location of the guest user. (At stage 404, the driver and the user, who may or may not be the actual rider, are paired or matched together. The pairing may be performed by the ride scheduler 218. Pairing or matching the driver and the user may include sharing a pickup and/or drop off location of the user to the driver – See at least ¶ [0091])
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the hybrid trip planning for autonomous vehicles of Bavar, Goldman, and Uber to provide for the machine learning based geolocation trajectory threshold determination, as taught in Li, so an account holder user associated with a business (e.g. , a car repair shop) can schedule rides for one or more guest riders. (At Li ¶ [0004])
Claim(s) 8, 9, and 17 rejected under 35 U.S.C. 103 as being unpatentable over Bavar in view of Goldman, as applied to claims 1 and 16, and in further view of Gao et al. (US 2021/0035450 A1, “Gao”).
Regarding claims 8 and 17, the combination of Bavar and goldman does not explicitly teach wherein the selecting the AV stopping location from the multiple AV stopping locations comprises: applying, by the service assignment system, an objective function to the multiple AV stopping locations to generate weighted scores for each of the multiple AV stopping locations, the object function considering one or more of walking distance, walking difficulty, walking time to each of the multiple AV stopping locations, or decreased time to travel to a drop-off location. However, Gao discloses passenger walking points in pick-up/drop-off zones and teaches:
wherein the selecting the AV stopping location from the multiple AV stopping locations comprises:
applying, by the service assignment system, (The environment 100 also includes a PDZ/Walking Point Calculation system 106 that implements PDZ/Walking Point Algorithms 107 to calculate to which PDZ 114A, 114B, 114C, 114D, etc. to guide the vehicle 102 and the passenger 108 for pickup – See at least ¶ [0020]) an objective function to the multiple AV stopping locations to generate weighted scores for each of the multiple AV stopping locations, the object function considering one or more of walking distance, walking difficulty, walking time to each of the multiple AV stopping locations, or decreased time to travel to a drop-off location. (PDZs 114 that are along the route 204 of the vehicle 102 on the way to the pickup location 202 requested by the user (e.g., PDZs 114A and 114B) may be given priority (i.e., weighted more heavily) so long as the walking route 206 is predicted to enable the customer to get to the PDZ 114 without rushing, i.e., walking difficulty – See at least ¶ [0032])
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the hybrid trip planning for autonomous vehicles of Bavar and Goldman to provide for the passenger walking points in pick-up/drop-off zones, as taught in Gao, to improve optimization techniques to maximize passenger convenience and safety in navigating the passenger convenience and safety in navigating the passenger and the AV to the most convenient PDZ. (At Gao ¶ [0004])
Regarding claim 9, the combination of Bavar and Goldman does not explicitly teach, but Gao further teaches:
wherein the weighted scores are further based on user preferences of the user. (PDZs 114 that are along the route 204 of the vehicle 102 on the way to the pickup location 202 requested by the user (e.g., PDZs 114A and 114B) may be given priority (i.e., weighted more heavily) so long as the walking route 206 is predicted to enable the customer to get to the PDZ 114 without rushing, i.e., walking difficulty – See at least ¶ [0032])
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the hybrid trip planning for autonomous vehicles of Bavar and Goldman to provide for the passenger walking points in pick-up/drop-off zones, as taught in Gao, to improve optimization techniques to maximize passenger convenience and safety in navigating the passenger convenience and safety in navigating the passenger and the AV to the most convenient PDZ. (At Gao ¶ [0004])
Claim(s) 10 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Bavar in view of Goldman, as applied to claims 1 and 16, in view of Gao, as applied in claims 8 and 17, and in further view of Farmer et al. (US 2018/0328747 A1, “Farmer”).
Regarding claims 10 and 18, the combination of Bavar, Goldman, and Gao does not explicitly teach wherein the weighted scores are further based on avoiding walking routes where the user needs to cross multiple intersections or cross a busy road. However, Farmer discloses dynamic geolocation optimization of pickup paths using curb segment data and teaches:
wherein the weighted scores are further based on avoiding walking routes where the user needs to cross multiple intersections or cross a busy road. (According to an embodiment, instead of modifying the threshold distance 304, a subset of the alternate request location 306 within the threshold distance 304 may be selected, such as based on data as described above. For example, a requestor may indicate that she does not want to cross any roads in order to reach an alternate request location 306; therefore, a subset of alternate request locations 306 within the threshold distance 304 (e.g., that do not require road crossing) is selected – See at least ¶ [0050])
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the hybrid trip planning for autonomous vehicles of Bavar, Goldman, and Gao to provide for the dynamic geolocation optimization of pickup paths using curb segment data, as taught in Farmer, to improve the identification of interaction locations and improve interactions between providers and requestors for overall travel time determinations, efficient use of system and processor resources, and overall improved experiences between providers and requestors. (At Farmer ¶ [0003])
Claim(s) 11, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Bavar in view of Goldman, as applied to claims 1 and 16, and in further view of Farmer.
Regarding claims 11 and 19, the combination of Bavar and Goldman does not explicitly teach, but Farmer further teaches:
wherein the selecting the AV stopping location from the multiple AV stopping locations comprises: (As shown in Fig. 3, multiple stopping locations 306 are selected – See at least ¶ [0050]) causing presentation, on a user interface displayed on the computing device, of at least some of the multiple AV stopping locations; and (Fig. 4A shows a user interface displaying one of the stopping locations, e.g., 402 – See at least ¶ [0053])
receiving, via the user interface from the user, a selection of the AV stopping location from the multiple AV stopping locations. (As shown in Fig. 4B, the user is able to select or deny a stopping location – See at least ¶ [0055])
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the hybrid trip planning for autonomous vehicles of Bavar and Goldman to provide for the dynamic geolocation optimization of pickup paths using curb segment data, as taught in Farmer, to improve the identification of interaction locations and improve interactions between providers and requestors for overall travel time determinations, efficient use of system and processor resources, and overall improved experiences between providers and requestors. (At Farmer ¶ [0003])
Regarding claim 12, the combination of Bavar and Goldman does not explicitly teach, but Farmer further teaches:
further comprising updating a user interface displayed on the computing device to show the selected AV stopping location and a time of arrival of the AV to the AV stopping location. (As shown in Fig. 4A, the arrival time is shown at the pickup location, e.g., 2 minutes.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the hybrid trip planning for autonomous vehicles of Bavar and Goldman to provide for the dynamic geolocation optimization of pickup paths using curb segment data, as taught in Farmer, to improve the identification of interaction locations and improve interactions between providers and requestors for overall travel time determinations, efficient use of system and processor resources, and overall improved experiences between providers and requestors. (At Farmer ¶ [0003])
Claim(s) 6 is rejected under 35 U.S.C. 103 as being unpatentable over Bavar in view of Goldman, as applied to claim 1, and in further view of Pubnub (How to Build a Rideshare App for Android (Uber/Lyft Clone), “Pubnub”) and in further view of Liang et al. (Analyzing the Gap Between Ride-Hailing Location and Pick-up Location with Geographical Contexts, “Liang”)
Regarding claim 6, the combination of Bavar and Goldman does not explicitly teach wherein the determining whether to share the user location comprises: determining whether the user allows sharing of the user location. However, Pubnub discloses how to build a rideshare app for android and teaches:
wherein the determining whether to share the user location comprises: determining whether the user allows sharing of the user location; (When the app is instantiated, we can check whether permission is granted. If it isn’t, we can prompt the user to share location with the following lines in your main activity – See at least pg. 2)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the hybrid trip planning for autonomous vehicles of Bavar and Goldman to provide for the location permission request, as taught in Pubnub, applications require location permissions for geo-tracking work. (At Pubnub pg. 2)
The combination of Bavar, Goldman, and Pubnub does not explicitly teach based on the user allowing sharing of the user location, determining whether the user location of the user is accurate; and based on the user location not being accurate, determining, by the service assignment system, a pickup location closest to the user that satisfies one or more conditions or user preferences. However, Liang discloses analyzing the gap between ride-hailing location and pick-up location with geographical contexts and teaches:
based on the user allowing sharing of the user location, determining whether the user location of the user is accurate; and (The locations of ride-hailing users are important but often inaccurate especially when users are inside buildings, underground parking lots, elevators or even on the street that is surrounded by skyscrapers. As a result, the ride-hailing applications sometimes cannot identify the correct location of the user or the location is not accessible by vehicles due to transportation regulation (e.g., inside an enterprise park or a university campus). In such cases, most of the users would type in a pick-up location around their actual locations in order to get the ride or the ride-hailing application may recommend a more accessible pick-up location instead. The gap between the users’ actual location and the location identified by the ride-hailing applications might contains useful information reflecting the reasons why such inaccuracy appears. Therefore, by computing the distance gap between the location identified by the rider-hailing application and the user’s actual pick-up location and analyzing potential geographical factors that are related to this gap, this research provides insights into further improvement of the location accuracy in the ride-hailing applications – See at least pg. 1; Here, by comparing the system determines it cannot identify the correct location of the user.)
based on the user location not being accurate, determining, by the service assignment system, a pickup location closest to the user that satisfies one or more conditions or user preferences. (In such cases, most of the users would type in a pick-up location around their actual locations in order to get the ride or the ride-hailing application may recommend a more accessible pick-up location instead – See at least pg. 1)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the hybrid trip planning for autonomous vehicles of Bavar, Goldman, and Pubnub to provide for analyzing the gap between ride-hailing locations and pick-up locations with geographical contexts, as taught in Liang, to identify some most influential factors to the location inaccuracy such as existence of enterprises, dense road network, dense population, education area, etc.. (At Liang pg. 2)
Claim(s) 13 is rejected under 35 U.S.C. 103 as being unpatentable over Bavar in view of Goldman, as applied to claim 1, and in further view of Pubnub.
Regarding claim 13, the combination of Bavar and Goldman does not explicitly teach wherein the determining whether to share occurs at a booking time prior to selection of the AV. However, Pubnub discloses how to build a rideshare app for android and teaches:
wherein the determining whether to share occurs at a booking time prior to selection of the AV. (When the app is instantiated, we can check whether permission is granted. If it isn’t, we can prompt the user to share location with the following lines in your main activity – See at least pg. 2)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the hybrid trip planning for autonomous vehicles of Bavar and Goldman to provide for the location permission request, as taught in Pubnub, applications require location permissions for geo-tracking work. (At Pubnub pg. 2)
Claim(s) 14 is rejected under 35 U.S.C. 103 as being unpatentable over Bavar in view of Goldman, as applied to claim 1, and in further view of Brinig et al. (US 2021/0256794 A1, “Brinig”).
Regarding claim 14, the combination of Bavar and Goldman does not explicitly teach detecting that the user has reached the selected AV stopping location and is waiting for arrival of the AV, wherein the determining whether to share is triggered based on the detecting that the user has reached the selected AV stopping location and is waiting. However, Brinig discloses facilitating direct rendezvous for a network service and teaches:
detecting that the user has reached the selected AV stopping location and is waiting for arrival of the AV, wherein the determining whether to share is triggered based on the detecting that the user has reached the selected AV stopping location and is waiting. (As used herein, a “geo-fence area 137” corresponds to an area encompassing a mass egress location or venue. Thus, requesting users 174 and available drivers 184 within a geo-fence area 137, as described herein, are provided with a respective app state update 152, 133 triggered by the transport system 100 identifying their respective locations within the geo-fence area 137. As provided herein, the app state update 152, 133 can cause the rider application 175 and driver application 185 to enable a direct pairing experience, and bypass the normal selection-pairing process performed by the selection engine 110 of the transport system 100 – See at least ¶ [0036]; Examiner notes that the selection-pairing process includes sharing the location of the rider at the pickup spot – See at least ¶ [0035])
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the hybrid trip planning for autonomous vehicles of Bavar and Goldman to provide for the facilitating direct rendezvous for a network service, as taught in Brinig, so as to not leave patrons and users of mass gathering locations stranded and waiting for aggravating periods of time. (At Brinig ¶ [0002])
Claim(s) 15 is rejected under 35 U.S.C. 103 as being unpatentable over Bavar in view of Goldman, as applied to claim 1, and in further view of Kim et al. (US 2021/0407032 A1, “Kim”).
Regarding claim 15, the combination of Bavar and Goldman does not explicitly teach detecting that the user will not reach the selected AV stopping location prior to arrival of the AV at the selected AV stopping location, wherein the determining whether to share is triggered based on the detecting that the user will not reach the selected AV stopping location prior to arrive of the AV. However, Kim discloses computer system arranging transport services for users based on the estimated time of arrival information and teaches:
detecting that the user will not reach the selected AV stopping location prior to arrival of the AV at the selected AV stopping location, wherein the determining whether to share is triggered based on the detecting that the user will not reach the selected AV stopping location prior to arrive of the AV. (The request proxy 150 can periodically receive the user ETA 143 from the client ETA determine 140 and can communicate with the third party application 182 to periodically receive the vehicle ETA 151. Because the third party application 182 can be in communication with both the third party system 100a and the system 100b, the request proxy 150 can periodically check the ETAs 143, 151, as described in FIG. 1A. When the ETA match determines that the ETAs 143, 151 are substantially equal or are within a predetermined amount of time of each other (e.g., within 30 seconds), the request proxy 150 can transmit a request trigger 153 to the third party application 182. The third party application 182 can then automatically transmit the transport request 155 to the dispatch 110 on behalf of the user – See at least ¶ [0054]; Here, if the ETA for the rider and the ETA for the vehicle are not the same, i.e., the rider does not arrive before the vehicle, then the request, i.e., sharing of user location, will not be sent.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the instant application to have modified the hybrid trip planning for autonomous vehicles of Bavar and Goldman to provide for the computer system arranging transport services for users based on the estimated time of arrival information, as taught in Kim, to reduce the overall amount of wait times for users. (At Kim ¶ [0013])
Conclusion
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/CHASE L COOLEY/Examiner, Art Unit 3662
