DETAILED ACTION
Response to Amendments
This office action regarding application number 18/357,084, filed July 21, 2023, is in response to the applicants arguments and amendments filed May 7, 2026. Claim 13, 16-18, 20, 23-25, 27, and 29-32 have been amended. Claims 14, 19, 21, 26, and 28 have been cancelled. New claims 33-37 have been added. Claims 13, 15-18, 20, 22-25, 27, and 29-37 are currently pending and are addressed below.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
The applicants arguments and amendments to the application have overcome some of the objections and rejections previously set forth in the Non-Final action mailed February 20, 2026. Claims 14, 19, 21, 26, and 28 have been cancelled, therefore all associated objections and rejections are withdrawn. Applicants amendments to claim 13, 20 and 27 have been deemed sufficient to overcome the previous 35 USC 103 rejection through the inclusion of “the passenger input comprising manually relocating a boundary of the goal region on a user interface”, therefore the rejections are withdrawn. However as this changes the scope of the claims, new art rejections have been made based on the changes in scope. Additionally applicants arguments have been fully considered but are not fully persuasive for the reasons below.
Applicant’s arguments with respect to claim(s) 13, 20 and 27, specifically with regards to “the passenger input comprising manually relocating a boundary of the goal region on a user interface”, 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.
On pages 8-9 the applicant argues “Applicant respectfully submits that the cited references at least fail to teach or suggest the features "obtaining, by the at least one processor, data representing perceptions of conditions at the goal region from at least one sensor of the vehicle while the vehicle is in motion toward the goal position," "identifying, by the at least one processor, that the one or more first acceptable stopping places are infeasible based on the perceptions of actual conditions,”, the examiner respectfully disagrees.
MPEP 2142-2144 discusses the requirements for a case of obviousness using 35 USC 103 and provides examples of such cases. MPEP 2111 discusses Broadest Reasonable Interpretation and the interpretation of claims.
As discussed in the rejections below Rander teaches obtaining, by the at least one processor, data representing perceptions of conditions at the goal region from at least one sensor of the vehicle while the vehicle is in motion toward the goal position (Paragraph [0040], "In certain implementations, the event logic 124 can dynamically compare the sensor data 111 with current sub-maps 171 as the SDV 100 travels throughout the given region") here the system obtains sensor/perception data of an area around the vehicle including a goal region while the vehicle travels throughout the given regions; Rander further teaches identifying by the at least one processor, that the one or more first acceptable stopping places are infeasible based on the perceptions of actual conditions (Paragraph [0047], “Thus, as the control system 120 operates the acceleration, braking, and steering systems of the SDV 100 along the current route to within a certain distance from the pick-up area, the control system can trigger the rendezvous logic 185 to begin analyzing the sensor data 111 based on the options set 177. In one example, the SDV 100 utilizes the determined ranking of the options set 177 and performs the selection operation by identifying an availability of each of the ranked options as the SDV 100 encounters and detects them. In such an example, the rendezvous logic 185 can, on a high level, check off each of the encountered pick-up location options in the options set 177 in a binary manner (e.g., either available or unavailable)”) here the system is using sensor data to identify an availability/feasibility of each of the encountered options as the vehicle traverses the area.
Therefore the combination of Rander, Cassandras, and Davidson teaches obtaining, by the at least one processor, data representing perceptions of conditions at the goal region from at least one sensor of the vehicle while the vehicle is in motion toward the goal position and identifying, by the at least one processor, that the one or more first acceptable stopping places are infeasible based on the perceptions of actual conditions.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 13, 14-17, 20, 22-24, 27-31, and 33-37 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rander (US-20170344010) in view of Cassandras (US-20140149153) and further in view of Davidson (US-20130304349).
Regarding claim 13, Rander teaches a computer implemented method comprising (Paragraph [0018], "One or more examples described herein provide that methods, techniques, and actions performed by a computing device are performed programmatically, or as a computer-implemented method.")
obtaining a goal position from a passenger to be dropped off at a stopping place by a vehicle (Paragraph [0063], "In some examples, the transport directive 113 can include a pick-up location specified by the requesting user (302). Additionally, the transport directive 113 can include an inputted destination by the requesting user (304)," here the system is receiving a destination and pickup location from a user)
by at least one processor (Paragraph [0021], "Furthermore, one or more examples described herein may be implemented through the use of instructions that are executable by one or more processors.")
determining, by the at least one processor, one or more first acceptable stopping places in a goal region based on the goal position obtained from the passenger (Paragraph [0012], "When receiving “a pick-up location” from the requesting user, the transport facilitation system and/or the selected SDV itself can expand the inputted location to a “pick-up area” (e.g., a radius of twenty or forty meters for the inputted location pin) in order to generate a set of pick-up location options for an SDV selected to service the pick-up request," here after receiving a pick up location from a user the system will expand the location into a pick up area/goal region to generate a set of pick up locations/stopping places) (Paragraph [0057], "According to some examples, the drop-off of the requesting user can be performed in a similar manner," here the processing for pick up locations and drop off location can be performed in the same manner)
wherein the one or more first acceptable stopping places are identified from map data associated with the goal region (Paragraph [0035], "In various implementations, the control system 120 can include a database 170 that stores operational sub-maps 172 for the given region," here the system includes a map data database including sub maps for a given region) (Paragraph [0044], "Furthermore, the database 170 of the SDV 100 can store pick-up and drop-off location sets 174 (PDOLS 174) for specified location areas ... Thus, if a requesting user inputs a pick-up location into a pick-up request 178—which may be indicated in the transport directive 113—the rendezvous logic 185 can expand the pick-up location into a pick-up area, and perform a lookup in the PDOLS 174 to identify a set of pick-up location options, or an options set 177, to rendezvous with the requesting user," here the map database includes stopping locations for a specified area, and once a user inputs a pick up or drop off location that information is used with the map database to identify pick up location options/stopping places)
obtaining, by the at least one processor, data representing perceptions of conditions at the goal region from at least one sensor of the vehicle while the vehicle is in motion toward the goal position (Paragraph [0040], "In certain implementations, the event logic 124 can dynamically compare the sensor data 111 with current sub-maps 171 as the SDV 100 travels throughout the given region," here the system obtains sensor/perception data of an area around the vehicle including a goal region while the vehicle travels throughout the given regions)
identifying by the at least one processor, that the one or more first acceptable stopping places are infeasible based on the perceptions of actual conditions (Paragraph [0047], “Thus, as the control system 120 operates the acceleration, braking, and steering systems of the SDV 100 along the current route to within a certain distance from the pick-up area, the control system can trigger the rendezvous logic 185 to begin analyzing the sensor data 111 based on the options set 177. In one example, the SDV 100 utilizes the determined ranking of the options set 177 and performs the selection operation by identifying an availability of each of the ranked options as the SDV 100 encounters and detects them. In such an example, the rendezvous logic 185 can, on a high level, check off each of the encountered pick-up location options in the options set 177 in a binary manner (e.g., either available or unavailable),” here the system is using sensor data to identify an availability/feasibility of each of the encountered options)
expanding, by the at least one processor, the goal region (Paragraph [0044], “In various implementations, the control system 120 can expand the pick-up location into a pick-up area with a certain radius (e.g., forty meters) from the inputted pick-up location by the requesting user. Furthermore, the database 170 of the SDV 100 can store pick-up and drop-off location sets 174 (PDOLS 174) for specified location areas.,” here the system is receiving a passenger input indicating a pickup/dropoff location, the system is then updating the feasible stopping places based on this input using an expanded radius)
providing , by the at least one processor, the one or more second acceptable stopping places to a planning process configured to select a second acceptable stopping place for the vehicle from among the one or more second acceptable stopping places (Paragraph [0072], “Thus, if none of the location options in the options set 177 is available, and the traffic is below the threshold, the control system 120 can stop the SDV 100 at a current location, or at a location most proximate to the requesting user, to make the pick-up. However, if none of the location options are available and the traffic is above the threshold, then the control system 120 can perform a reserve operation to make the pick-up, as described herein. Such a reserve operation may include stopping in a red zone, double parking, or transmitting an update to the requesting user or backend transport facilitation system 190 indicating that the SDV 100 will loop around to make a second attempt.”)
and causing, by the at least one processor, the vehicle to drive to the selected second acceptable stopping place (Paragraph [0027], "For example, the control system 120 can operate the vehicle 100 by autonomously steering, accelerating, and braking the vehicle 100 as the vehicle progresses to a destination.").
However Rander does not explicitly teach expanding, by the at least one processor, the goal region based on a passenger input to include one or more second acceptable stopping places that are feasible.
Cassandras teaches a "smart parking" system and method for an urban environment based on a dynamic resource allocation including
expanding, by the at least one processor, the goal region based on a passenger input to include one or more second acceptable stopping places that are feasible (Paragraph [0080], “Users may change their preferences or requirements at any time, including at any time after submitting a request. For example, during periods of limited resources or because the user's preferences are overly restrictive or if he/she fails to accept an offered resource, he/she will have to wait until the next decision point. During intervals between allocation decisions, users with no parking assignment have the opportunity to change their cost preference or their walking-distance requirements, possibly to increase the chance to be allocated. Because each user establishes his/her own preferences, it is, of course, possible that no parking space is ever assigned to a particular user,” here the system is using a user input/preferences in order to adjust search parameters for parking/stopping places, in this case, after the initial user input the system gives the user the option to adjust preferences including walking-distance requirements which expand the goal region, this system of changing preferences based on user input could reasonably be combined with the pickup/dropoff searching of Rander to expand a search area based on user input).
Rander and Cassandras are analogous art as they are both generally related to systems and methods for determining parking locations for vehicles.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include expanding, by the at least one processor, the goal region based on a passenger input to include one or more second acceptable stopping places that are feasible of Cassandras in the systems and methods for selecting a stopping place for a vehicle of Rander with a reasonable expectation of success in order to improve the user experience by allowing them to change their preferences in order to improve the users chances of finding a stopping location (Paragraph [0016], “During intervals between allocation decisions made by the allocation center, users with no parking assignment may change their cost or walking-distance requirements sua sponte or may be prompted by the system to change their preference information, to improve the user's chances of an allocation if the system is highly utilized.”).
However the combination does not explicitly teach the passenger input comprising manually relocating a boundary of the goal region on a user interface.
Davidson teaches methods, systems, apparatus, and computing entities are provided for forecasting travel delays corresponding to streets, street segments, geographic areas, geofenced areas, and/or user-specified criteria including
the passenger input comprising manually relocating a boundary of the goal region on a user interface (Paragraph [0367], “According to various embodiments, the data analyzed by the various modules described herein may be more particularly selected by a user by defining a geographical area on a map. … The user may then draw a polygon on the map and request analysis of data associated with the stops falling within the polygon. In response, the module associated with the particular user interface view the user is current viewing will then refine its analysis and display information for only those stops or travel occurring within the user-defined geographic area,” here Davidson is employing a map tool for a user in order to manually define boundaries of a goal region for analysis on a user interface, while Davidson is not explicitly directed towards determining feasible stopping location, this same map input methodology could reasonably be applied to the combination of Rander and Cassandra which allow a user to expand a boundary to search for feasible stopping locations).
Rander, Cassandras, and Davidson are analogous art as they are both generally related to systems for analyzing map data relating to stopping locations.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include the passenger input comprising manually relocating a boundary of the goal region on a user interface of Davidson in the system for determining a stopping place in response to a request of Rander and Cassandras with a reasonable expectation of success in order to improve the experience of the user by allowing data analysis of user specified regions (Paragraph [0390], “In one embodiment, the user may select one or more geographical areas using the map drawing tool shown in FIG. 43 and described above (e.g., by drawing a polygon in the map display area of the user interface), or using other methods described herein (e.g., selecting a predefined work area, delivery route, or other geographical area).”) (Paragraph [0158], “As described above, the central server 120 is configured for evaluating operational data (e.g., telematics data and service data) for a fleet of vehicles in order to assess various fleet efficiencies and aid fleet management system 5 users in improving the operational efficiency of the fleet.”).
Regarding claim 15, the combination of Rander, Cassandras, and Davidson teaches the method as discussed above in claim 13, Rander further teaches wherein the goal position is obtained from the passenger via a touch based user interface, a mobile application, a kiosk, a notebook, a tablet, a workstation, or any combinations thereof (Paragraph [0020], “For example, one or more examples described herein may be implemented, in whole or in part, on computing devices such as servers, desktop computers, cellular or smartphones, personal digital assistants (e.g., PDAs), laptop computers, network equipment (e.g., routers), and tablet devices.”).
Regarding claim 16, the combination of Rander, Cassandras, and Davidson teaches the method as discussed above in claim 13, Rander further teaches comprising prompting the passenger to select the second acceptable stopping place via a touch based user interface (Paragraph [0054], “In one variation, the rendezvous logic 185 can present the user device 175 with a plurality of available options (e.g., on a mapping feature), and the requesting user can select a particular option from the plurality. The rendezvous logic 185 can then instruct the vehicle control 128 to drive the SDV 100 to the selected location”).
Regarding claim 17, the combination of Rander, Cassandras, and Davidson teaches the method as discussed above in claim 13, Rander further teaches comprising prompting the passenger to select the second acceptable stopping place via a voice command (Paragraph [0054], “In one variation, the rendezvous logic 185 can present the user device 175 with a plurality of available options (e.g., on a mapping feature), and the requesting user can select a particular option from the plurality. The rendezvous logic 185 can then instruct the vehicle control 128 to drive the SDV 100 to the selected location,” here the system can present a user with a plurality of options for a user to select) (Paragraph [0058], “In certain implementations, the control system 120 of the SDV 100 can enable the user to have at least partial control over the drop-off. For example, during the ride, the control system 120 can execute speech recognition to translate the user's spoken words into control commands. The translated commands can cause the control system 120 to operate various controllable parameters of the SDV 100 itself. For example, the spoken words of the user can be translated to control the climate control system, the audio and/or display system, certain network services (e.g., phoning, conferencing, content access, gaming, etc.), seat adjustment, and the like. According to examples described herein, the control system 120 can also operate the acceleration, braking, and steering system of the SDV 100 based on certain speech commands from the user. In one aspect, as the SDV 100 approaches the destination, the user can ask or otherwise command the SDV 100 to stop at its current location,” here the system can recognize a users voice command as an input such as the selection of the earlier presented options).
Regarding claim 20, Rander teaches a system comprising (Paragraph [0010], “A self-driving car (SDV) is disclosed that can optimize pick-ups with requesting users. The SDV can communicate with a backend transport facilitation system that manages a transportation arrangement service for users throughout a given region.”)
at least one processor (Paragraph [0021], "Furthermore, one or more examples described herein may be implemented through the use of instructions that are executable by one or more processors.")
and at least one non-transitory storage medium storing instructions that, when executed by the at least one processor cause the at least one processor to (Paragraph [0021], “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.”)
obtain a goal position from a passenger to be dropped off at a stopping place by a vehicle (Paragraph [0063], "In some examples, the transport directive 113 can include a pick-up location specified by the requesting user (302). Additionally, the transport directive 113 can include an inputted destination by the requesting user (304)," here the system is receiving a destination and pickup location from a user)
determine, one or more first acceptable stopping places in a goal region based on the goal position obtained from the passenger (Paragraph [0012], "When receiving “a pick-up location” from the requesting user, the transport facilitation system and/or the selected SDV itself can expand the inputted location to a “pick-up area” (e.g., a radius of twenty or forty meters for the inputted location pin) in order to generate a set of pick-up location options for an SDV selected to service the pick-up request," here after receiving a pick up location from a user the system will expand the location into a pick up area/goal region to generate a set of pick up locations/stopping places) (Paragraph [0057], "According to some examples, the drop-off of the requesting user can be performed in a similar manner," here the processing for pick up locations and drop off location can be performed in the same manner)
wherein the one or more first acceptable stopping places are identified from map data associated with the goal region (Paragraph [0035], "In various implementations, the control system 120 can include a database 170 that stores operational sub-maps 172 for the given region," here the system includes a map data database including sub maps for a given region) (Paragraph [0044], "Furthermore, the database 170 of the SDV 100 can store pick-up and drop-off location sets 174 (PDOLS 174) for specified location areas ... Thus, if a requesting user inputs a pick-up location into a pick-up request 178—which may be indicated in the transport directive 113—the rendezvous logic 185 can expand the pick-up location into a pick-up area, and perform a lookup in the PDOLS 174 to identify a set of pick-up location options, or an options set 177, to rendezvous with the requesting user," here the map database includes stopping locations for a specified area, and once a user inputs a pick up or drop off location that information is used with the map database to identify pick up location options/stopping places)
obtain data representing perceptions of conditions at the goal region from at least one sensor of the vehicle, while the vehicle is in motion toward the goal position (Paragraph [0040], "In certain implementations, the event logic 124 can dynamically compare the sensor data 111 with current sub-maps 171 as the SDV 100 travels throughout the given region," here the system obtains sensor/perception data of an area around the vehicle including a goal region while the vehicle travels throughout the given regions)
identify that the one or more first acceptable stopping places are infeasible based on the perceptions of actual conditions (Paragraph [0047], “Thus, as the control system 120 operates the acceleration, braking, and steering systems of the SDV 100 along the current route to within a certain distance from the pick-up area, the control system can trigger the rendezvous logic 185 to begin analyzing the sensor data 111 based on the options set 177. In one example, the SDV 100 utilizes the determined ranking of the options set 177 and performs the selection operation by identifying an availability of each of the ranked options as the SDV 100 encounters and detects them. In such an example, the rendezvous logic 185 can, on a high level, check off each of the encountered pick-up location options in the options set 177 in a binary manner (e.g., either available or unavailable),” here the system is using sensor data to identify an availability/feasibility of each of the encountered options)
expand the goal region to include one or more second acceptable stopping places that are feasible (Paragraph [0044], “In various implementations, the control system 120 can expand the pick-up location into a pick-up area with a certain radius (e.g., forty meters) from the inputted pick-up location by the requesting user. Furthermore, the database 170 of the SDV 100 can store pick-up and drop-off location sets 174 (PDOLS 174) for specified location areas.,” here the system is receiving a passenger input indicating a pickup/dropoff location, the system is then updating the feasible stopping places based on this input using an expanded radius)
provide the one or more second acceptable stopping places to a planning process configured to select a second acceptable stopping place for the vehicle from among the one or more second acceptable stopping places (Paragraph [0072], “Thus, if none of the location options in the options set 177 is available, and the traffic is below the threshold, the control system 120 can stop the SDV 100 at a current location, or at a location most proximate to the requesting user, to make the pick-up. However, if none of the location options are available and the traffic is above the threshold, then the control system 120 can perform a reserve operation to make the pick-up, as described herein. Such a reserve operation may include stopping in a red zone, double parking, or transmitting an update to the requesting user or backend transport facilitation system 190 indicating that the SDV 100 will loop around to make a second attempt.”)
and cause the vehicle to navigate to the selected second acceptable stopping place (Paragraph [0027], "For example, the control system 120 can operate the vehicle 100 by autonomously steering, accelerating, and braking the vehicle 100 as the vehicle progresses to a destination.").
However Rander does not explicitly teach expand the goal region based on a passenger input to include one or more second acceptable stopping places that are feasible.
Cassandras teaches a "smart parking" system and method for an urban environment based on a dynamic resource allocation including
expand the goal region based on a passenger input to include one or more second acceptable stopping places that are feasible (Paragraph [0080], “Users may change their preferences or requirements at any time, including at any time after submitting a request. For example, during periods of limited resources or because the user's preferences are overly restrictive or if he/she fails to accept an offered resource, he/she will have to wait until the next decision point. During intervals between allocation decisions, users with no parking assignment have the opportunity to change their cost preference or their walking-distance requirements, possibly to increase the chance to be allocated. Because each user establishes his/her own preferences, it is, of course, possible that no parking space is ever assigned to a particular user,” here the system is using a user input/preferences in order to adjust search parameters for parking/stopping places, in this case, after the initial user input the system gives the user the option to adjust preferences including walking-distance requirements which expand the goal region, this system of changing preferences based on user input could reasonably be combined with the pickup/dropoff searching of Rander to expand a search area based on user input).
Rander and Cassandras are analogous art as they are both generally related to systems and methods for determining parking locations for vehicles.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include expand the goal region based on a passenger input to include one or more second acceptable stopping places that are feasible of Cassandras in the systems and methods for selecting a stopping place for a vehicle of Rander with a reasonable expectation of success in order to improve the user experience by allowing them to change their preferences in order to improve the users chances of finding a stopping location (Paragraph [0016], “During intervals between allocation decisions made by the allocation center, users with no parking assignment may change their cost or walking-distance requirements sua sponte or may be prompted by the system to change their preference information, to improve the user's chances of an allocation if the system is highly utilized.”).
However the combination does not explicitly teach the passenger input comprising manually relocating a boundary of the goal region on a user interface.
Davidson teaches methods, systems, apparatus, and computing entities are provided for forecasting travel delays corresponding to streets, street segments, geographic areas, geofenced areas, and/or user-specified criteria including
the passenger input comprising manually relocating a boundary of the goal region on a user interface (Paragraph [0367], “According to various embodiments, the data analyzed by the various modules described herein may be more particularly selected by a user by defining a geographical area on a map. … The user may then draw a polygon on the map and request analysis of data associated with the stops falling within the polygon. In response, the module associated with the particular user interface view the user is current viewing will then refine its analysis and display information for only those stops or travel occurring within the user-defined geographic area,” here Davidson is employing a map tool for a user in order to manually define boundaries of a goal region for analysis on a user interface, while Davidson is not explicitly directed towards determining feasible stopping location, this same map input methodology could reasonably be applied to the combination of Rander and Cassandra which allow a user to expand a boundary to search for feasible stopping locations).
Rander, Cassandras, and Davidson are analogous art as they are both generally related to systems for analyzing map data relating to stopping locations.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include the passenger input comprising manually relocating a boundary of the goal region on a user interface of Davidson in the system for determining a stopping place in response to a request of Rander and Cassandras with a reasonable expectation of success in order to improve the experience of the user by allowing data analysis of user specified regions (Paragraph [0390], “In one embodiment, the user may select one or more geographical areas using the map drawing tool shown in FIG. 43 and described above (e.g., by drawing a polygon in the map display area of the user interface), or using other methods described herein (e.g., selecting a predefined work area, delivery route, or other geographical area).”) (Paragraph [0158], “As described above, the central server 120 is configured for evaluating operational data (e.g., telematics data and service data) for a fleet of vehicles in order to assess various fleet efficiencies and aid fleet management system 5 users in improving the operational efficiency of the fleet.”).
Regarding claim 22, claim 22 is similar in scope to claim 15 and therefore is rejected under similar rationale.
Regarding claim 23, claim 23 is similar in scope to claim 16 and therefore is rejected under similar rationale.
Regarding claim 24, claim 24 is similar in scope to claim 17 and therefore is rejected under similar rationale.
Regarding claim 27, Rander teaches a system, comprising: at least one processor, and at least one non-transitory storage medium storing instructions that, when executed by the at least one processor, cause the at least one processor to: (Paragraph [0021], "Furthermore, one or more examples described herein may be implemented through the use of instructions that are executable by one or more processors.") (Paragraph [0021], “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.”)
obtain a goal position from a passenger to be dropped off at a stopping place by a vehicle (Paragraph [0063], "In some examples, the transport directive 113 can include a pick-up location specified by the requesting user (302). Additionally, the transport directive 113 can include an inputted destination by the requesting user (304)," here the system is receiving a destination and pickup location from a user)
determine, one or more first acceptable stopping places in a goal region based on the goal position obtained from the passenger (Paragraph [0012], "When receiving “a pick-up location” from the requesting user, the transport facilitation system and/or the selected SDV itself can expand the inputted location to a “pick-up area” (e.g., a radius of twenty or forty meters for the inputted location pin) in order to generate a set of pick-up location options for an SDV selected to service the pick-up request," here after receiving a pick up location from a user the system will expand the location into a pick up area/goal region to generate a set of pick up locations/stopping places) (Paragraph [0057], "According to some examples, the drop-off of the requesting user can be performed in a similar manner," here the processing for pick up locations and drop off location can be performed in the same manner)
wherein the one or more first acceptable stopping places are identified from map data associated with the goal region (Paragraph [0035], "In various implementations, the control system 120 can include a database 170 that stores operational sub-maps 172 for the given region," here the system includes a map data database including sub maps for a given region) (Paragraph [0044], "Furthermore, the database 170 of the SDV 100 can store pick-up and drop-off location sets 174 (PDOLS 174) for specified location areas ... Thus, if a requesting user inputs a pick-up location into a pick-up request 178—which may be indicated in the transport directive 113—the rendezvous logic 185 can expand the pick-up location into a pick-up area, and perform a lookup in the PDOLS 174 to identify a set of pick-up location options, or an options set 177, to rendezvous with the requesting user," here the map database includes stopping locations for a specified area, and once a user inputs a pick up or drop off location that information is used with the map database to identify pick up location options/stopping places)
obtain data representing perceptions of conditions at the goal region from at least one sensor of the vehicle while the vehicle is in motion toward the goal position (Paragraph [0040], "In certain implementations, the event logic 124 can dynamically compare the sensor data 111 with current sub-maps 171 as the SDV 100 travels throughout the given region," here the system obtains sensor/perception data of an area around the vehicle including a goal region while the vehicle travels throughout the given regions)
identify that the one or more first acceptable stopping places are infeasible based on the perceptions of actual conditions (Paragraph [0047], “Thus, as the control system 120 operates the acceleration, braking, and steering systems of the SDV 100 along the current route to within a certain distance from the pick-up area, the control system can trigger the rendezvous logic 185 to begin analyzing the sensor data 111 based on the options set 177. In one example, the SDV 100 utilizes the determined ranking of the options set 177 and performs the selection operation by identifying an availability of each of the ranked options as the SDV 100 encounters and detects them. In such an example, the rendezvous logic 185 can, on a high level, check off each of the encountered pick-up location options in the options set 177 in a binary manner (e.g., either available or unavailable),” here the system is using sensor data to identify an availability/feasibility of each of the encountered options)
expand the goal region to include one or more second acceptable stopping places that are feasible (Paragraph [0044], “In various implementations, the control system 120 can expand the pick-up location into a pick-up area with a certain radius (e.g., forty meters) from the inputted pick-up location by the requesting user. Furthermore, the database 170 of the SDV 100 can store pick-up and drop-off location sets 174 (PDOLS 174) for specified location areas.,” here the system is receiving a passenger input indicating a pickup/dropoff location, the system is then updating the feasible stopping places based on this input using an expanded radius)
provide the one or more second acceptable stopping places to a planning process configured to select a second acceptable stopping place for the vehicle from among the one or more second acceptable stopping places (Paragraph [0072], “Thus, if none of the location options in the options set 177 is available, and the traffic is below the threshold, the control system 120 can stop the SDV 100 at a current location, or at a location most proximate to the requesting user, to make the pick-up. However, if none of the location options are available and the traffic is above the threshold, then the control system 120 can perform a reserve operation to make the pick-up, as described herein. Such a reserve operation may include stopping in a red zone, double parking, or transmitting an update to the requesting user or backend transport facilitation system 190 indicating that the SDV 100 will loop around to make a second attempt.”)
and cause the vehicle to navigate to the selected second acceptable stopping place (Paragraph [0027], "For example, the control system 120 can operate the vehicle 100 by autonomously steering, accelerating, and braking the vehicle 100 as the vehicle progresses to a destination.").
However Rander does not explicitly teach expand the goal region based on a passenger input to include one or more second acceptable stopping places that are feasible.
Cassandras teaches a "smart parking" system and method for an urban environment based on a dynamic resource allocation including
expand the goal region based on a passenger input to include one or more second acceptable stopping places that are feasible (Paragraph [0080], “Users may change their preferences or requirements at any time, including at any time after submitting a request. For example, during periods of limited resources or because the user's preferences are overly restrictive or if he/she fails to accept an offered resource, he/she will have to wait until the next decision point. During intervals between allocation decisions, users with no parking assignment have the opportunity to change their cost preference or their walking-distance requirements, possibly to increase the chance to be allocated. Because each user establishes his/her own preferences, it is, of course, possible that no parking space is ever assigned to a particular user,” here the system is using a user input/preferences in order to adjust search parameters for parking/stopping places, in this case, after the initial user input the system gives the user the option to adjust preferences including walking-distance requirements which expand the goal region, this system of changing preferences based on user input could reasonably be combined with the pickup/dropoff searching of Rander to expand a search area based on user input).
Rander and Cassandras are analogous art as they are both generally related to systems and methods for determining parking locations for vehicles.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include expand the goal region based on a passenger input to include one or more second acceptable stopping places that are feasible of Cassandras in the systems and methods for selecting a stopping place for a vehicle of Rander with a reasonable expectation of success in order to improve the user experience by allowing them to change their preferences in order to improve the users chances of finding a stopping location (Paragraph [0016], “During intervals between allocation decisions made by the allocation center, users with no parking assignment may change their cost or walking-distance requirements sua sponte or may be prompted by the system to change their preference information, to improve the user's chances of an allocation if the system is highly utilized.”).
However the combination does not explicitly teach the passenger input comprising manually relocating a boundary of the goal region on a user interface.
Davidson teaches methods, systems, apparatus, and computing entities are provided for forecasting travel delays corresponding to streets, street segments, geographic areas, geofenced areas, and/or user-specified criteria including
the passenger input comprising manually relocating a boundary of the goal region on a user interface (Paragraph [0367], “According to various embodiments, the data analyzed by the various modules described herein may be more particularly selected by a user by defining a geographical area on a map. … The user may then draw a polygon on the map and request analysis of data associated with the stops falling within the polygon. In response, the module associated with the particular user interface view the user is current viewing will then refine its analysis and display information for only those stops or travel occurring within the user-defined geographic area,” here Davidson is employing a map tool for a user in order to manually define boundaries of a goal region for analysis on a user interface, while Davidson is not explicitly directed towards determining feasible stopping location, this same map input methodology could reasonably be applied to the combination of Rander and Cassandra which allow a user to expand a boundary to search for feasible stopping locations).
Rander, Cassandras, and Davidson are analogous art as they are both generally related to systems for analyzing map data relating to stopping locations.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include the passenger input comprising manually relocating a boundary of the goal region on a user interface of Davidson in the system for determining a stopping place in response to a request of Rander and Cassandras with a reasonable expectation of success in order to improve the experience of the user by allowing data analysis of user specified regions (Paragraph [0390], “In one embodiment, the user may select one or more geographical areas using the map drawing tool shown in FIG. 43 and described above (e.g., by drawing a polygon in the map display area of the user interface), or using other methods described herein (e.g., selecting a predefined work area, delivery route, or other geographical area).”) (Paragraph [0158], “As described above, the central server 120 is configured for evaluating operational data (e.g., telematics data and service data) for a fleet of vehicles in order to assess various fleet efficiencies and aid fleet management system 5 users in improving the operational efficiency of the fleet.”).
Regarding claim 29, claim 29 is similar in scope to claim 15 and therefore is rejected under similar rationale.
Regarding claim 30, claim 30 is similar in scope to claim 16 and therefore is rejected under similar rationale.
Regarding claim 31, claim 31 is similar in scope to claim 17 and therefore is rejected under similar rationale.
Regarding claim 33, the combination of Rander, Cassandras, and Davidson teaches the method as discussed above in claim 13, Davidson further teaches wherein the passenger input is performed via a boundary expansion tool (Paragraph [0367], “According to various embodiments, the data analyzed by the various modules described herein may be more particularly selected by a user by defining a geographical area on a map. … The user may then draw a polygon on the map and request analysis of data associated with the stops falling within the polygon. In response, the module associated with the particular user interface view the user is current viewing will then refine its analysis and display information for only those stops or travel occurring within the user-defined geographic area,” here Davidson is employing a map tool for a user in order to manually define boundaries of a goal region for analysis on a user interface, while Davidson is not explicitly directed towards determining feasible stopping location, this same map input methodology could reasonably be applied to the combination of Rander and Cassandra which allow a user to expand a boundary to search for feasible stopping locations).
Rander, Cassandras, and Davidson are analogous art as they are both generally related to systems for analyzing map data relating to stopping locations.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include wherein the passenger input is performed via a boundary expansion tool of Davidson in the system for determining a stopping place in response to a request of Rander and Cassandras with a reasonable expectation of success in order to improve the experience of the user by allowing data analysis of user specified regions (Paragraph [0390], “In one embodiment, the user may select one or more geographical areas using the map drawing tool shown in FIG. 43 and described above (e.g., by drawing a polygon in the map display area of the user interface), or using other methods described herein (e.g., selecting a predefined work area, delivery route, or other geographical area).”) (Paragraph [0158], “As described above, the central server 120 is configured for evaluating operational data (e.g., telematics data and service data) for a fleet of vehicles in order to assess various fleet efficiencies and aid fleet management system 5 users in improving the operational efficiency of the fleet.”).
Regarding claim 34, the combination of Rander, Cassandras, and Davidson teaches the method as discussed above in claim 13, Davidson further teaches wherein the boundary expansion tool comprises a touch-based drag-and-drop tool configured to allow the passenger to manually relocate the boundary of the goal region on a displayed map (Paragraph [0286], “FIG. 26 shows a user-selected geographic area 1756, which the user may generate by clicking on a particular point with a mouse-operated pointer and dragging the pointer to form the illustrated area 1756.”).
Rander, Cassandras, and Davidson are analogous art as they are both generally related to systems for analyzing map data relating to stopping locations.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include wherein the boundary expansion tool comprises a touch-based drag-and-drop tool configured to allow the passenger to manually relocate the boundary of the goal region on a displayed map of Davidson in the system for determining a stopping place in response to a request of Rander and Cassandras with a reasonable expectation of success in order to improve the experience of the user by allowing data analysis of user specified regions (Paragraph [0390], “In one embodiment, the user may select one or more geographical areas using the map drawing tool shown in FIG. 43 and described above (e.g., by drawing a polygon in the map display area of the user interface), or using other methods described herein (e.g., selecting a predefined work area, delivery route, or other geographical area).”) (Paragraph [0158], “As described above, the central server 120 is configured for evaluating operational data (e.g., telematics data and service data) for a fleet of vehicles in order to assess various fleet efficiencies and aid fleet management system 5 users in improving the operational efficiency of the fleet.”).
Regarding claim 35, the combination of Rander, Cassandras, and Davidson teaches the method as discussed above in claim 13, Davidson further teaches wherein expanding the goal region comprises providing a visual indicator on the user interface representing an expanding proximity region and a corresponding expanded goal region generated based on the manual relocation of the boundary (See Figures 26 and 43 showing the visual indicator of the expanded goal region on a map).
Rander, Cassandras, and Davidson are analogous art as they are both generally related to systems for analyzing map data relating to stopping locations.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include wherein expanding the goal region comprises providing a visual indicator on the user interface representing an expanding proximity region and a corresponding expanded goal region generated based on the manual relocation of the boundary of Davidson in the system for determining a stopping place in response to a request of Rander and Cassandras with a reasonable expectation of success in order to improve the experience of the user by allowing data analysis of user specified regions (Paragraph [0390], “In one embodiment, the user may select one or more geographical areas using the map drawing tool shown in FIG. 43 and described above (e.g., by drawing a polygon in the map display area of the user interface), or using other methods described herein (e.g., selecting a predefined work area, delivery route, or other geographical area).”) (Paragraph [0158], “As described above, the central server 120 is configured for evaluating operational data (e.g., telematics data and service data) for a fleet of vehicles in order to assess various fleet efficiencies and aid fleet management system 5 users in improving the operational efficiency of the fleet.”).
Regarding claim 36, the combination of Rander, Cassandras, and Davidson teaches the method as discussed above in claim 13, Rander further teaches wherein identifying that one or more first acceptable stopping places are infeasible comprises detecting at least one or a temporarily parked vehicle, construction works, or road debris using data from the at least one sensor on the vehicle (Paragraph [0003], “when a primary pick-up location is unavailable, such as in crowded areas or when a specified location is occupied by another vehicle,” here the system can identifying that parking space is infeasible/unavailable because of another temporarily parked vehicle).
Regarding claim 37, the combination of Rander, Cassandras, and Davidson teaches the method as discussed above in claim 13, Rander further teaches wherein selecting the second acceptable stopping place comprises ranking the one or more second acceptable stopping places in the expanded goal region based on at least one of walking distance to the goal position, covered walking distance, or a clear sightline between each second acceptable stopping place and the goal position (Paragraph [0014], “For example, as the SDV approaches the pick-up area, the SDV can make the availability determinations based on encounter time (e.g., first encountered options first) and calculate a probability of whether a higher ranked available option will be encountered in relation to a current option. Thus, if an available low ranked option is encountered first, in certain conditions the SDV may disregard the option and hold out for a higher ranked available option. Such a hierarchical selection process can be performed by the SDV as a number of cost probability calculations with primary concerns corresponding to a successful pick-up on the first attempt, time delta between the SDV stopping and the user entering the SDV (e.g., walking time), distance between the location option and the inputted pick-up location or actual location of the requesting user, and potential hindrance on traffic.”).
Claim 18, 25, and 32 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rander (US-20170344010) in view of Cassandras (US-20140149153) further in view of Davidson (US-20130304349) and further in view of Benenson (US-20160061618).
Regarding claim 18, the combination of Rander, Cassandras, and Davidson teaches the method as discussed above in claim 13, Rander further teaches prompting the passenger to select the second acceptable stopping place (Paragraph [0054], “In one variation, the rendezvous logic 185 can present the user device 175 with a plurality of available options (e.g., on a mapping feature), and the requesting user can select a particular option from the plurality. The rendezvous logic 185 can then instruct the vehicle control 128 to drive the SDV 100 to the selected location”).
However Rander does not explicitly teach automatically selects the updated acceptable stopping place in response to failing to make a selection within a specified time.
Benenson teaches systems and methods for determining a parking place for a vehicle including
OR automatically selecting the second updated acceptable stopping place in response to failing to make a selection within a specified time (Paragraph [0084-0085], “As discussed, a specific optimized parking route may be terminated when the driver finds a parking place (PP), or when the driver's decides to change the set of preferences or just to cancel the search and park on the parking lot. The latter may happen, for example, when the time budget goes to expire, so the driver (or the system) may cancel the best parking route and select a spare (additional) route parking which will navigate the user's vehicle to a paid parking lot close to the destination. Other examples may be found,” here the system in response to not receiving a driver input within a time budget, such as selecting a stopping place as taught by Rander, the system will take a default action and select an updated parking spot).
Rander, Cassandras, Davidson and Benenson are analogous art as they are both generally related to systems for determining a stopping place for a vehicle.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to include automatically selects the updated acceptable stopping place in response to failing to make a selection within a specified time of Benenson in the system for selecting a stopping place of Rander, Cassandras, and Davidson with a reasonable expectation of success in order to improve the efficiency of the parking search by using preferences such as search time to limit a search (Paragraph [0200-0201], “The facility thus will be capable either to provide the user with a group of the most efficient parking routes, each satisfying a given set of criteria (parking preferences), or will be capable to report to the user that his/her preferences cannot be satisfied. Taken together, the group of alternative parking routes covers all best parking options in the area and makes the driver's parking search maximally efficient.”).
Regarding claim 25, claim 25 is similar in scope to claim 18 and therefore is rejected under similar rationale.
Regarding claim 32, claim 32 is similar in scope to claim 18 and therefore is rejected under similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Arden (US-20190179319) teaches a request for a vehicle to stop at a particular location; in response to the request, receiving, by the client computing device, information identifying a current location of the vehicle; generating, by the client computing device, a map for display, the map including a first marker identifying the location of the vehicle, a second marker identifying the particular location, and a shape defining an area around the second marker at which the vehicle may stop. Rosen (US-20160180712) teaches a system including: (a) a database of individual on-street parking spots, and individual off-street parking spots, each of the individual parking spots associated with a unique identifier (UID) and location coordinates; and (b) an off street spot management module in communication one or more external client devices, adapted to receive parking rules from an external client device operated by an owner of each of the individual off-street parking spots. Kojo (US-20160209845) teaches A method and apparatus for autonomous vehicle routing and navigation using passenger docking locations.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/CHRISTOPHER GEORGE FEES/Primary Examiner, Art Unit 3662
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