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 .
This action is in response to the amendments filed on 10/22/2025, in which claims 1-20 are pending.
Response to Amendment
Applicant has amended the claims to overcome the claim objections. Accordingly, the previous claim objections have been withdrawn.
Applicant has amended the claims to overcome the 35 U.S.C. 112(b) rejections. Accordingly, the previous 35 U.S.C. 112(b) rejections have been withdrawn.
Applicant has amended the claims to overcome the 35 U.S.C. 101 rejections. Accordingly, the previous 35 U.S.C. 101 rejections have been withdrawn.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 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
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 6, 9-13, and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Schwie et al., U.S. Patent Application Publication No. 2020/0103239 A1 (hereinafter Schwie), in view of Brandon et al., U.S. Patent Application Publication No. 2022/0068139 A1 (hereinafter Brandon).
Regarding claim 1, Schwie discloses a computer-implemented method (see at least Schwie Fig. 2) comprising:
receiving, by a processing device, identification of an origin location and a destination location corresponding to a transportation trip request for an autonomous vehicle (AV) (see at least Schwie [0119]: “Even still, methods include analyzing location data 144 of the remote computing device 12 after a most recent drop-off 166 of the user, and then sending 160, by the vehicle management system 65, the self-driving vehicle 2 to the pick-up location 120 at a time 162 determined, by the vehicle management system 65, based on analyzing the location data 144.”; [0078]: “As illustrated in FIG. 5, in some embodiments, the system 4 may receive a pickup request 6d for the vehicle 2 to pick up the person 1.”);
determining, by the processing device, a set of pullover locations comprising pickup locations for the origin location and drop-off locations for the destination location (see at least Schwie [0114]: “In some embodiments, the parking location 134 is the same, or nearly the same, location as the pick-up location 120. In some embodiments, the pick-up location 120 is within fifty yards of the parking location 134 or a drop-off location 140 where the self-driving vehicle 2 last dropped off the user.”);
for each pullover location of the set of pullover locations: determining, by the processing device, an estimated time of arrival (ETA) time window for the AV at the pullover location (see at least Schwie [0081]: “With continued reference to FIG. 5, the system 4 may also perform additional steps to precisely coordinate the arrival time of the person 1 at the requested location 20 with the arrival time of the vehicle 2 at the requested location 20…It should be appreciated the term “approximately” may be defined as arriving within plus or minus 5 minutes.”);
determining a number of other AVs…expected to be at the pullover location during the ETA time window (see at least Schwie [0169]: “In some embodiments, methods of using the vehicle management system will include determining, by the vehicle management system 4, whether traffic adjacent 422 to the primary pick-up location 420 is greater than a predetermined traffic threshold (at step 1602a).”; [0167]: “Generally, data can be gathered from any variety of sources to determine actual and predicted roadway congestion and traffic levels.”);
and responsive to the number of other AVs…expected to be at the pullover location during the ETA time window exceeding an AV pullover crowding metric, removing the pullover location from the set of pullover locations to produce a revised set of pullover locations (see at least Schwie [0200]: “In response to determining, by the vehicle management system 4, that the at least one traffic indication regarding the primary pick-up location 420 is greater than the predetermined threshold, methods further include sending, by the vehicle management system 4, a wireless communication 460 to the remote computing device 12 (at step 1904). The wireless communication 460 may be configured to prompt the rider to select an alternate pick-up location 428.”; the pullover location is removed from the set of pullover locations because an alternate pick-up location is chosen when the traffic at the primary pick-up location exceeds the traffic threshold);
identifying a selected pickup location from the revised set of pullover locations (see at least Schwie [0179]-[0180]: “Likewise, methods may also include receiving, by the vehicle management system 4, a response wireless communication 470 from the remote computing device 12 of the rider (at step 1706). The response wireless communication 470 may comprise a confirmation to meet at the alternate pick-up location 428a…Sometimes, riders may wish to meet the self-driving vehicle 2 at another location, such as a secondary alternate location 428b, which is different from the alternate location 428a. In such embodiments, the response wireless communication 470 may comprise the secondary alternate pick-up location 428b.”);
and autonomously navigating the AV to the selected pickup location (see at least Schwie [0080]: “Once the system 4 receives the requested location 20, the vehicle 2 can thereby travel towards the requested location 20 of the person 1.”; [0164]: “The self-driving vehicle 2 and/or vehicle management system 4 may also perform the exact same functions, but in this case when the rider is currently aboard the vehicle 2 and be driven to a drop-off location.”).
Schwie fails to expressly disclose determining a number of other AVs requested to be at the pullover location. However, Brandon teaches
determining a number of other AVs requested to be at the pullover location and expected to be at the pullover location during the ETA time window (see at least Brandon [0072]: “The fleet management system determines 720 a number of AVs needed to carry the number of passengers in the ride request, e.g., by dividing the number of passengers by a passenger capacity of the AVs available to drive the passengers. The fleet management system selects 730 the determined number of AVs that are located near the requested pickup location. For example, the vehicle selector 650 selects a set of available AVs that are closest to the pickup location from a fleet of AVs managed by the fleet management system 120.”; [0038]: “Alternatively, if the fleet management system 120 determines that the number of passengers exceeds passenger capacity of an individual AV, the fleet management system 120 arranges a multi-AV coordinated ride for the group.”);
and responsive to the number of other AVs requested at the pullover location and expected to be at the pullover location during the ETA time window exceeding an AV pullover crowding metric, removing the pullover location from the set of pullover locations to produce a revised set of pullover locations (see at least Brandon [0067]: “In some embodiments, the coordinated ride manager 670 selects a drop-off location at or near the destination location…The coordinated ride manager 670 may select a drop-off location that is suitable for receiving the number of passengers in the group. For example, if a group of 40 people are meeting to go to a restaurant located on a narrow street, the coordinated ride manager 670 may select a drop-off location on a larger street around the corner from the restaurant where the group may more easily assemble. The coordinated ride manager 670 provides the drop-off location (e.g., as an address or set of latitude/longitude coordinates) to the vehicle dispatcher 660, which instructs the AVs accordingly.”; Brandon teaches removing the pullover location from the set of pullover locations because the coordinated ride manager selects a drop-off location around the corner from the restaurant instead of at the restaurant location since the restaurant is located on a narrow street that is not suitable for receiving the number of passengers)
It would have been obvious to one of ordinary skill in the art before the effective filing data of the instant application to modify the method disclosed by Schwie with the determination taught by Brandon with reasonable expectation of success. Brandon is directed towards the related field of coordinating dispatch of multiple autonomous vehicles for groups of passengers. Therefore, one of ordinary skill in the art would be motivated to modify Schwie with Brandon to improve group cohesion (see at least Brandon [0013]: “The coordinated dispatching allows large groups to obtain the right number of vehicles to transport them quickly and easily, without requiring multiple group members to volunteer to order rides separately. In addition, the fleet management system can help maintain group cohesion as the group travels from place to place…As another example, the fleet management system can instruct the AVs to drop their respective passengers at a same drop-off point, or at nearby drop-off points, so the group members can easily find each other at their destination.”).
Regarding claim 2, Schwie in view of Brandon teach all elements of the computer-implemented method according to claim 1 as explained above. Schwie further discloses
wherein the pickup locations and the drop-off locations are within a determined radius of the respective origin location and destination location (see at least Schwie [0114]: “In some embodiments, the parking location 134 is the same, or nearly the same, location as the pick-up location 120. In some embodiments, the pick-up location 120 is within fifty yards of the parking location 134 or a drop-off location 140 where the self-driving vehicle 2 last dropped off the user.”; [0072]: “However, in some embodiments, if the remote computing device 12 is located within the predetermined distance of the vehicle 2, the system 4 can send the second wireless communication 15b to the remote computing device 12. The second wireless communication 15b can prompt the remote computing device 12 to ask the person 1 whether the person 1 wants the vehicle 2 to move towards the person 1.”)
and are identified as locations that an autonomous vehicle (AV) is capable of pulling over at for the AV to service the transportation trip request (see at least Schwie [0114]: “In some embodiments, the parking location 134 is the same, or nearly the same, location as the pick-up location 120.”; [0121]: “As such, methods include determining that the user is not located at the pick-up location 120, and instructing, by the vehicle management system 65, the self-driving vehicle 2 to move to a predetermined parking location 192 that is located remotely relative to the pick-up location 120 and a most-recent drop-off location 166.”).
Regarding claim 3, Schwie in view of Brandon teach all elements of the computer-implemented method according to claim 1 as explained above. Schwie discloses the method further comprising:
presenting the revised set of pullover locations to a user requesting the transportation trip request (see at least Schwie [0178]: “Furthermore, some methods include the step of sending, by the vehicle management system 4, a wireless communication 460 to the remote computing device 12 to request to meet at the alternate pick-up location 428a (at step 1704). The wireless communication 460 may be configured to prompt the rider to select the alternate pick-up location 428a.”);
receiving identification of the selected pickup location and a selected drop-off location selected by the user from the revised set of pullover locations (see at least Schwie [0179]-[0180]: “Likewise, methods may also include receiving, by the vehicle management system 4, a response wireless communication 470 from the remote computing device 12 of the rider (at step 1706). The response wireless communication 470 may comprise a confirmation to meet at the alternate pick-up location 428a…Sometimes, riders may wish to meet the self-driving vehicle 2 at another location, such as a secondary alternate location 428b, which is different from the alternate location 428a. In such embodiments, the response wireless communication 470 may comprise the secondary alternate pick-up location 428b.”; [0164]: “The self-driving vehicle 2 and/or vehicle management system 4 may also perform the exact same functions, but in this case when the rider is currently aboard the vehicle 2 and be driven to a drop-off location.”);
and generating a route for the AV between the selected pickup location and the selected drop-off location (see at least Schwie [0080]: “Once the system 4 receives the requested location 20, the vehicle 2 can thereby travel towards the requested location 20 of the person 1.”; [0164]: “The self-driving vehicle 2 and/or vehicle management system 4 may also perform the exact same functions, but in this case when the rider is currently aboard the vehicle 2 and be driven to a drop-off location.”).
Regarding claim 4, Schwie in view of Brandon teach all elements of the computer-implemented method according to claim 1 as explained above. Schwie further discloses
wherein the AV pullover crowding metric defines an AV cluster based on a number of AVs within a determined distance and within a determined time frame (see at least Schwie [0185]-[0187]: “In order to determine, or predict, whether traffic adjacent 422 to the primary pick-up location 420 will be greater than a predetermined traffic threshold at the predetermined future time, the vehicle management system 4 may determine that any of the previously disclosed conditions exists. For example, the system 4 may determine that at least one of the following conditions presently exists or will exist within a predetermined amount of time (e.g. 30 minutes) within the predetermined future time and/or the predetermined pick-up time:…traffic data 442 collected via wireless communications indicates that traffic adjacent 422 the primary pick-up location 420 exceeds or will exceed a predetermined traffic data threshold”; Schwie [0061] discloses the system comprises self-driving vehicles).
Regarding claim 6, Schwie in view of Brandon teach all elements of the computer-implemented method according to claim 1 as explained above. Schwie discloses the method further comprising
estimating a presence of other actors at the pullover location during the ETA time window, wherein removing the pullover location from the set of pullover locations is further based on data corresponding to the presence of the other actors (see at least Schwie [0185]-[0187]: “In order to determine, or predict, whether traffic adjacent 422 to the primary pick-up location 420 will be greater than a predetermined traffic threshold at the predetermined future time, the vehicle management system 4 may determine that any of the previously disclosed conditions exists. For example, the system 4 may determine that at least one of the following conditions presently exists or will exist within a predetermined amount of time (e.g. 30 minutes) within the predetermined future time and/or the predetermined pick-up time: a number of remote computing device hot spots 440 adjacent 422 the primary pick-up location 420 exceeds or will exceed a predetermined hot spot threshold; traffic data 442 collected via wireless communications indicates that traffic adjacent 422 the primary pick-up location 420 exceeds or will exceed a predetermined traffic data threshold”; Schwie [0200] discloses the pullover location is removed from the set of pullover locations because an alternate pick-up location is chosen when the traffic at the primary pick-up location exceeds the traffic threshold),
and wherein the estimating the presence of the other actors is based at least on information provided by the other Avs (see at least Schwie [0201]: “In some embodiments, the determining step 1902 comprises detecting, by an antenna of the self-driving vehicle 2, a number of remote computing device hot spots 440 (at step 1906), and then analyzing, by the vehicle management system 4, the number to determine that the number is greater than a predetermined hot spot threshold (at step 1908). While, in some embodiments the determining step 1902 includes receiving, by the vehicle management system 4, traffic data collected via wireless communications, (at step 1910) and then determining, by the vehicle management system 4, that the traffic data indicates that traffic at the primary pick-up location 420 exceeds a predetermined traffic threshold (at step 1912). The traffic data can be collected by a third party such as Google, Apple, and the like. These third parties can track remote computing devices located in millions of cars to provide traffic data.”; Schwie [0061] discloses self-driving vehicles are communicatively coupled to the system).
Regarding claim 9, Schwie in view of Brandon teach all elements of the computer-implemented method according to claim 1 as explained above. Schwie discloses the method further comprising
responsive to a real-time traffic metric exceeding a traffic threshold value during the ETA time window, removing the pullover location from the set of pullover locations to produce a revised set of pullover locations (see at least Schwie [0177]-[0178]: “As shown in FIGS. 15b-17, in response to determining that traffic adjacent 422 to the primary pick-up location 420 is greater than the predetermined traffic threshold (at step 1700), methods may include identifying, by the vehicle management system 4, an alternate pick-up location 428a (at step 1702). It should be appreciated that the alternate pick-up location 428a may be a pick-up location that has lower traffic levels (i.e. congestion) than the primary pick-up location 420…The wireless communication 460 may be configured to prompt the rider to select the alternate pick-up location 428a. Furthermore, it should be appreciated that “identifying an alternate pick-up location 428a” can comprise analyzing data (e.g. real-time traffic data) received via the cloud and/or any type of wireless communication.”; the pullover location is removed from the set of pullover locations because an alternate pick-up location is chosen when the traffic at the primary pick-up location exceeds the traffic threshold).
Regarding claim 10, this claim recites an apparatus that performs the method of claim 1. Schwie in view of Brandon also discloses a memory and hardware processors (Schwie [0226]) for performing the method of claim 1 as outlined in the rejection to claim 1 above. Therefore, claim 10 is rejected for the same rationale as claim 1.
Regarding claim 11, this claim recites an apparatus that performs the method of claim 2 as explained above. Therefore, claim 11 is rejected for the same rationale as claim 2.
Regarding claim 12, this claim recites an apparatus that performs the method of claim 3 as explained above. Therefore, claim 12 is rejected for the same rationale as claim 3.
Regarding claim 13, this claim recites an apparatus that performs the method of claim 4 as explained above. Therefore, claim 13 is rejected for the same rationale as claim 4.
Regarding claim 16, this claim recites a medium that performs the method of claim 1. Schwie in view of Brandon also discloses a medium for performing the method of claim 1 as outlined in the rejection to claim 1 above. Specifically, Schwie discloses a non-transitory computer-readable medium (Schwie [0237]) and processors (Schwie [0226]). Therefore, claim 16 is rejected for the same rationale as claim 1.
Regarding claim 17, this claim recites a medium that performs the method of claim 2 as explained above. Therefore, claim 17 is rejected for the same rationale as claim 2.
Regarding claim 18, this claim recites a medium that performs the method of claim 3 as explained above. Therefore, claim 18 is rejected for the same rationale as claim 3.
Regarding claim 19, this claim recites a medium that performs the method of claim 4 as explained above. Therefore, claim 19 is rejected for the same rationale as claim 4.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Schwie in view of Brandon, further in view of Urano et al., U.S. Patent Application Publication No. 2022/0084412 A1 (hereinafter Urano), and further in view of Li et al., “A data-driven two-level clustering model for driving pattern analysis of electric vehicles and a case study” (hereinafter Li).
Regarding claim 5, Schwie in view of Brandon teach all elements of the computer-implemented method according to claim 4 as explained above. Schwie in view of Brandon fails to expressly disclose defining the crowding metric based on historical data that increases remote assistance sessions. However, Urano teaches
wherein the AV pullover crowding metric is defined based on analyzing historical data to identify values of the number of AVs, distance, and time frame that result in an increase in a rate of remote assistance sessions above a determined threshold increase (see at least Urano [0068]-[0069]: “The remote assistance history database 5 may record the request frequency of the autonomous driving vehicle 2 when turning right at each intersection. When the autonomous driving vehicle 2 has a function of automatically turning right at an intersection without requesting remote assistance under certain conditions (for example, when an oncoming vehicle is not detected) (self-determination right turn function), a remote assistance request is not always made at the time of turning right at the intersection. The higher the traffic volume of the vehicle and the higher the probability of waiting for an oncoming vehicle when turning right at the intersection, the higher the frequency of requests when turning right at the intersection… The request frequency described above may be recorded in association with time. The request frequency is recorded in association with time zones such as early morning, daytime, evening, and night. The request frequency may be recorded in association with traffic information such as vehicle density, or may be recorded in association with weather.”)
It would have been obvious to one of ordinary skill in the art before the effective filing data of the instant application to modify the method disclosed by Schwie in view of Brandon with the historical data of remote assistance sessions taught by Urano with reasonable expectation of success. Urano is directed towards the related field of a vehicle dispatch system. Therefore, one of ordinary skill in the art would be motivated to modify Schwie in view of Brandon with Urano to reduce the load of a remote operator (see at least Urano [0013]: “According to the vehicle dispatch method according to another aspect of the present disclosure, it is calculated the remote assistance request number which is the number of the remote assistance requests by the autonomous driving vehicle to the remote operator for each route to the point of dispatch, and the vehicle dispatch route to the point of dispatch where the autonomous driving vehicle travels is determined on the basis of the required time for each route and the remote assistance request number for each route. Therefore, the load of the remote operator can be reduced compared with when the remote assistance request number is not considered.”).
Schwie in view of Brandon and Urano fails to expressly disclose defining the crowding metric using two-stage clustering. However, Li teaches
and wherein the AV pullover crowding metric is defined using two-stage clustering to vary the values of the number of AVs, distance, and time frame with reference to the remote assistance sessions (This limitation is taught through the combination of Li and Urano. Li teaches two-stage clustering used to define driving patterns of vehicles (see at least Li page 3: “The two-level clustering model is decomposed into two segmental phases: level-1 clustering and level-2 clustering. The first phase is used to identify the daily driving patterns of a vehicle. In order to provide different and deeper view points on clustering results, the daily driving patterns grouped in level-1 clustering are further clustered in level-2 clustering according to their subsequences to achieve the multifaceted driving patterns of a vehicle.”). Li also teaches varying values during the two-stage clustering (see at least Li page 6). Li fails to expressly disclose the values of the two-stage clustering as including number of vehicles, distance, and time frame with reference to the remote assistance sessions. However, Urano teaches storing data regarding number of vehicles, distance, and time frame with reference to the remote assistance sessions (see at least Urano [0068]-[0069]). Urano further teaches a calculation unit to calculate the remote assistance requests (see at least Urano [0098]-[0100]). Therefore, the combination of Li and Urano teach the entirety of this limitation.).
It would have been obvious to one of ordinary skill in the art before the effective filing data of the instant application to modify the method disclosed by Schwie in view of Brandon and Urano with the two-stage clustering taught by Li with reasonable expectation of success. Li is directed towards the related field of using a two-level clustering model for driving pattern analysis. Li demonstrates that two-stage clustering is a known technique for analyzing data. Therefore, it would be obvious to one of ordinary skill in the art to analyze the historical data of the frequency of remote assistance taught by Urano with any known data analysis technique, such as the two-stage clustering taught by Li. Furthermore, one of ordinary skill in the art would be motivated to modify Schwie in view of Brandon and Urano with the two-stage clustering taught by Li to provide deeper insight into behavior at different time granules (see at least Li page 2: “We develop a two-level clustering model, which provides deeper insights into driving patterns with consideration of different time granules. In the proposed model, the EVs driving data are first processed to identify the daily driving patterns by the level-1 clustering. Then, based on the result of the level-1 clustering, the multifaceted driving patterns of the EVs are obtained by the level-2 clustering.”).
Claims 7, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Schwie in view of Brandon, and further in view of Eisinga et al., U.S. Patent Application Publication No. 2024/0328803 A1 (hereinafter Eisinga).
Regarding claim 7, Schwie in view of Brandon teach all elements of the computer-implemented method according to claim 1 as explained above. Schwie discloses the method further comprising:
computing a probability that the AV pullover crowding metric is to be exceeded during the ETA time window (see at least Schwie [0184]: “Now, with reference to FIGS. 16 and 18, the disclosure includes predictive embodiments for determining and reacting to future, or predicted, traffic patterns, which indicate that an area will constitute a high traffic area at any point in the future.”; Schwie [0067] and [0163]-[0166] disclose example embodiments where statistical analysis is used to determine high traffic areas based on nearby events);
and filtering the pullover locations from the set of pullover locations responsive to the probability that the AV pullover crowding metric is to be exceeded during the ETA time window exceeding a threshold probability (see at least Schwie [0193]-[0194]: “As illustrated in FIG. 18, in response to determining that the traffic adjacent 422 to the primary pick-up location 420 will be greater than the predetermined traffic threshold (at step 1800), methods may include identifying, by the vehicle management system 4, an alternate pick-up location 428a (at step 1802). It should be appreciated that the system 4 may determine that the alternate pick-up location 428a,b may be less than the predetermined traffic threshold. Methods may thereby include sending, by the vehicle management system 4, a wireless communication 460 to the remote computing device 12 (at step 1804). The wireless communication 460 may include a request for the rider to meet the self-driving vehicle 2 at the alternate pick-up location 428a.”; Schwie discloses filtering the pullover locations because alternate locations in areas less than the predetermined traffic threshold are requested instead of locations within the area exceeding the traffic threshold).
Schwie in view of Brandon fails to expressly disclose determining a probability distribution of error of the ETA time window based on historical data. However, Eisinga teaches
determining a probability distribution of error of the ETA time window based on historical data (see at least Eisinga [0295]: “FIG. 11 thus shows the probability of observing a travel time for a specific route. The probability of the (recorded) travel time (RTT) has a distribution around a mean travel time. The travel time RTT typically has a normal distribution. The figure also shows an estimated travel time (ETT) that is different from the mean travel time. The difference is in effect representative a ‘bias’ (or error) for the estimated travel time of the ETT module.”; [0156]: “In that case, as explained above, the output value produced by the model is a bias value associated with the input set of characteristic data values, and the model is a model that has been trained using a set of biases, or error, values representing the differences between the recorded travel times and estimated travel times for the historic positional traces.”)
It would have been obvious to one of ordinary skill in the art before the effective filing data of the instant application to modify the method disclosed by Schwie in view of Brandon with the probability of error of the ETA taught by Eisinga with reasonable expectation of success. Eisinga is directed towards the related field of a navigation system for determining an estimated travel time. Therefore, one of ordinary skill in the art would be motivated to modify Schwie in view of Brandon with the probability of error of the ETA taught by Eisinga to improve the accuracy of estimated travel times (see at least Eisinga [0040]: “Compared to more traditional approaches for determining estimated travel times, the use of such a trained model thus provides the benefit that it can be more easily, e.g., and preferably automatically, updated over time to improve the accuracy of the estimated travel times determined using the model.”).
Regarding claim 14, this claim recites an apparatus that performs the method of claim 7 as explained above. Therefore, claim 14 is rejected for the same rationale as claim 7.
Regarding claim 20, this claim recites a medium that performs the method of claim 7 as explained above. Therefore, claim 20 is rejected for the same rationale as claim 7.
Claims 8 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Schwie in view of Brandon, and further in view of Agarwal et al., U.S. Patent Application Publication No. 2022/0415173 A1 (hereinafter Agarwal).
Regarding claim 8, Schwie in view of Brandon teach all elements of the computer-implemented method according to claim 1 as explained above. Schwie in view of Brandon fail to expressly disclose removing a pullover location in response to exceeding the crowding metric for a threshold number of time increments of the ETA window. However, Agarwal teaches the method further comprising:
dividing the ETA time window into time increments (see at least Agarwal [0090]: “In some examples, future status is predicted (e.g., probabilistically predicted using a machine learning system) for several future times. For examples, in some scenarios, the future status represents a predicted status 10 seconds, 20 seconds, 30 seconds, 60 seconds, and 1 minute into the future. In some examples, some or all of these predicted future statuses are stored in the database of the server 520 for querying by vehicles 504 of the occupancy forecasting system 550.”);
for each pullover location of the set of pullover locations, determining a number of the time increments where the number of other AVs expected to be at the pullover location during each time increment of the ETA time window exceeding an AV pullover crowding metric (see at least Agarwal [0101]: “In an embodiment, the occupancy forecasting system 550 predicts (e.g., probabilistically predicts using a machine learning system) the future status of the parking location 610 based on the status history of the parking location. In some examples, the status history includes information that on a particular day of the year (e.g., July 4.sup.th) the parking locations 610 are occupied 98% of the time between 9 AM and 9 PM. In this scenario, the occupancy forecasting system 550 predicts that availability of the parking locations 610 on this particular day next year will be low (e.g., not likely to be available). In other examples, the status history includes information that at 5 PM on weekdays during a particular month, the parking locations 610 are occupied 98% of the time. In this scenario, the occupancy forecasting system 550 predicts that availability of the parking locations 610 on this particular time during the week of this particular month will also be low (e.g., not likely to be available).”; Agarwal [0045] teaches vehicles can be autonomous);
and in response to the number of time increments exceeding a threshold time increment value, removing the pullover location from the set of pullover locations to produce a revised set of pullover locations (see at least Agarwal [0155]: “In this scenario, when the probability of the availability of the parking location decreases below 50%, the server 906 determines that the destination of the vehicle should be changed. In an embodiment, the occupancy forecasting system 550 changes the destination of the vehicle based on the traffic congestion information. For example, if traffic congestion is present along a certain route and the occupancy forecasting system 550 determines that an alternate route can be taken but requires the parking location to be changed to an adjacent parking location, then, the occupancy forecasting system 550 changes the parking location.”; [0084]: “In some examples, the vehicle 504A increases a tolerance for wait time or congestion based on the parking location passenger preference in order to accommodate the parking preference.”).
It would have been obvious to one of ordinary skill in the art before the effective filing data of the instant application to modify the method disclosed by Schwie in view of Brandon with Agarwal with reasonable expectation of success. Agarwal is directed towards the related field of forecasting the occupancy of vehicle parking locations. Therefore, one of ordinary skill in the art would be motivated to modify Schwie in view of Brandon with Agarwal to reduce wait times of passengers (see at least Agarwal [0025]: “Among other things, an occupancy forecasting system improves situational awareness of a vehicle as it approaches a destination. For example, by knowing where the congestion is, the vehicle may redirect around the congestion to a different parking location within walking distance to the original parking location. This, in turn, reduces the resources consumed by the vehicle that would otherwise be expended waiting for a particular parking location. Additionally, the vehicle can discharge passengers and pick up different passengers more quickly, thereby reducing the wait time of passengers. The technology accommodates user preferences by requesting permission from passengers before changing the parking location. Furthermore, congestion within a radius of busy parking locations is reduced and wait times are reduced by not needing to wait for an available parking location to become available.”).
Regarding claim 15, this claim recites an apparatus that performs the method of claim 8 as explained above. Therefore, claim 15 is rejected for the same rationale as claim 8.
Conclusion
Applicant's amendment necessitated the new grounds of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIZABETH J SLOWIK whose telephone number is (571)270-5608. The examiner can normally be reached MON - FRI: 0900-1700.
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/ELIZABETH J SLOWIK/ Examiner, Art Unit 3662
/ANISS CHAD/ Supervisory Patent Examiner, Art Unit 3662