DETAILED ACTION
Status of Claims
This Office action is in response to the request for continued examination filed on 12/24/2025. Claims 19-20 were previously canceled. Claims 1-18 and 21-22 are currently pending and are presented for examination.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/24/2025 has been entered.
Response to Arguments
Applicant’s arguments filed 12/24/2025 have been fully considered.
Applicant has argued that the claims should not be rejected under 35 U.S.C. § 103. Specifically, applicant has argued that the prior art fails to teach that “a reservation plan is determined that includes indications of what is reserved in a reservation of the reservation plan, a location at which to access the reservation, and a timing of the reservation.” The examiner respectfully disagrees, because this limitation is taught by Wang ¶¶ 56-59 and FIGS. 6-7, which disclose that “for each potential trip, a vehicle of a fleet of autonomous vehicles of the autonomous vehicle service are assigned” and also that “once a vehicle is assigned, the server computing devices 410 may also determine other information about the trip, such as an ETA at the pickup location for the vehicle.” These sections also disclose a “pickup location (P)” for each trip that reads on the recited “location at which to access the reservation.”
Applicant has also argued that the prior art fails to teach “the use of context information such as parking availability for one or more modes of transportation at a destination, reservation availability, a time of day, or a current season to determine a trip plan based at least in part by which multi-feature criteria are satisfied by the trip intent and the context information.” The examiner respectfully disagrees, because Wang ¶¶ 49-55 disclose that potential pickup and destination locations can be set according to context information such as time of day related to the user’s typical work schedule, and then “a set of potential trips are determined using the potential pickup location and each destination location of the set of potential destination locations.” Further, Wang ¶ 56 discloses that “for each potential trip, a vehicle of a fleet of autonomous vehicles of the autonomous vehicle service are assigned” based on several factors including the determined pickup and destination locations.
Applicant has additionally argued that the cited prior art fails to teach that “the route from the current location of the user to the destination is determined based at least in part on a respective mode of transportation of the one or more modes of transportation determined for a respective portion of the route and the context information,” because “Wang is not concerned with determining a route where a portion of the route is determined based on a mode of transportation to be used for that portion of the route,” and because none of the other cited prior art cures the deficiencies of Wang. Applicant has also argued that “Modifying Wang to determine routes based on a respective mode of transportation of the one or more modes of transportation determined for a respective portion of the route would change the principle operation of Wang and the modified operation of Wang would be unsatisfactory for its intended purpose.” The examiner respectfully disagrees, because Balva ¶¶ 16 and 21 teach that a set of routes can be determined with vehicles that are assigned based on cargo information specifying that a certain mode of transportation is required, where an origination and a destination location can be selected based on the user’s current location. While Wang does not explicitly disclose the consideration of the one or more modes of transportation when determining the route, Wang ¶¶ 28 and 49-54 disclose that the route can be determined based on other information such as map information, typical user schedules, and point of interest information. Modifying Wang to also consider the mode of transportation when determining the route would simply include an additional similar factor and would not render the system and method of Wang unsatisfactory for its intended purpose as argued. A person having ordinary skill in the art could have been motivated to modify Wang to consider the mode of transportation when determining a route because Balva ¶ 26 teaches that by considering factors such as the cargo requirements, “A function of these factors can then be optimized in order to provide for an improved customer experience, or transport experience for transported objects, while also providing for improved profitability, or at least operational efficiency, with respect to other available routing option.”
For the reasons explained above, the claim rejections under 35 U.S.C. § 103 are maintained.
Claim Objections
Claims 10 and 22 are objected to because of the following informalities:
In claim 10, it appears that the phrase “identification of a destination” should be changed to “identification of [[a]]the destination.”
In claim 22, the phrase “a reservation id” should be changed to “a reservation [[id]]ID.”
Appropriate correction is required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-7, 9-16, 18, and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (US 2020/0326194 A1), hereinafter referred to as Wang, in view of Balva (US 2021/0142248 A1), and further in view of Wang et al. (CN 105868834 A), hereinafter referred to as Xiao.
Regarding claim 1:
Wang discloses the following limitations:
“A method performed by an apparatus, the method comprising: obtaining, by a processor of the apparatus, at least in part by executing computer program instructions stored in a memory of the apparatus, a trip intent associated with a user.” (Wang ¶ 12: “a dispatching system may automatically identify potential trips that a user of the service might want to take now or at some time in the future.” Also, Wang ¶¶ 14 and 24: “In some examples, the dispatching system may include one or more server computing devices configured to identify potential rides,” where “Computing devices 110 may include all of the components normally used in connection with a computing device such as the processor and memory.”)
“obtaining, by the processor, context information corresponding to the trip intent, wherein the context information comprises one or more of parking availability for one or more modes of transportation at a destination, reservation availability, a time of day, or a current season.” (Wang ¶ 60: “trips may be ranked and displayed according to those having the shortest ETA, those that are easiest for the vehicles of the fleet to reach (e.g., no complicated maneuvers, avoids certain areas, good traffic conditions, reachable given current or expected weather conditions and vehicle capabilities, etc.), those that the user has designated as ‘favorites’, the most recent trips, the aforementioned scores, etc.” Additionally, Wang ¶ 12 discloses the use of “information about the availability of the fleet of autonomous vehicles,” and Wang ¶¶ 49-50 state that “a potential pickup location may be an expected future location of the user's client computing device. For example, if the user's historical trip data indicates that the user uses the service for trips to go home (e.g., from work) on a particular day of the week at a particular time (Mondays, Wednesdays, and Fridays at 5:30 pm), the work location may be a potential pickup location. … the set of potential destination locations may be identified based on both the potential pickup location for the user as well as a time of day or day of the week. For example, a user's historical trip information may indicate that the user typically takes trip from home to work Mondays, Wednesdays, and Fridays at 8:30 am.” This at least teaches the context information comprising a “reservation availability” and “a time of day” as claimed.)
“causing, via execution of the computer program instructions by the processor, execution of a trip intent engine to determine a trip plan based at least in part on the trip intent and the context information using a trip plan model, wherein the trip intent engine comprises the trip plan model, the trip plan model is a machine learning-trained model providing one or more multi-feature criteria.” (Wang ¶ 52: “potential pickup and/or destination locations may be identified using a machine learning model. The model may be trained on the historical trip information for a plurality of users as well as each additional trip taken by the user. … The model may be trained such that for any input of historical trip information for a user, the model may output a set of potential pickup and/or destination locations as well as a ranked order or other value representing a score or likelihood that a (e.g. any) user will take a trip using the set of potential pickup and/or destination locations.” Also, Wang ¶ 53 discloses that “the model may eventually recognize certain behaviors of individual users (e.g., User A always takes trips around 8 am on Sunday to a supermarket) as well as behaviors of groups of users (e.g., users typically move from suburban areas to dense urban areas around 8 am on weekdays and from dense urban areas to suburban areas around 5 pm on weekdays, etc.),” and Wang ¶ 56 discloses that vehicles can be assigned to each trip based on various factors including user preferences.)
“and the trip plan is determined based at least in part by which of the one or more multi-feature criteria are satisfied by at least one of the trip intent and the context information.” (Wang ¶¶ 49-55: “if the user's historical trip data indicates that the user uses the service for trips to go home (e.g., from work) on a particular day of the week at a particular time (Mondays, Wednesdays, and Fridays at 5:30 pm), the work location may be a potential pickup location. … if it is close to 8:30 am on a Monday, the work location may be identified as a potential destination location. … the potential pickup and/or destination locations can be identified or adjusted (i.e. moved) based on routes (e.g. what particular set of direction or turns the vehicle would follow to reach the location) that would be most efficient or most preferable for the user and/or vehicles of the fleet (for instance, in terms of optimizing fuel efficiency, avoiding certain maneuvers such as unprotected turns or other difficult autonomous driving tasks, etc.). … a set of potential trips are determined using the potential pickup location and each destination location of the set of potential destination locations.”)
“identification of the destination based at least in part on point of interest data accessed from a point of interest database and the trip intent.” (Wang ¶ 12: “a dispatching system may automatically identify potential trips that a user of the service might want to take now or at some time in the future. Each of these potential trips may include both a pickup location and a destination location.” Further, Wang ¶ 54: “different potential destination locations may be identified, for instance, based on points of interest that are nearby the user's current location, rather than simply using the user's work location as a potential destination location.” Identifying a potential destination based on nearby points of interest implies the use of a point of interest database.)
“determination of the route from a current location of the user to the destination based at least in part on data accessed from a geographic database… and the context information.” (Wang ¶ 28: “the routing system 170 may use map information to determine a route from a current location of the vehicle to a destination location.” Also, Wang ¶ 52: “a set of potential pickup locations (e.g., at least the last received or current location of the user's client computing device) may also be used as inputs in order to have the model identify a set of potential destination locations.”)
“determination of a time for beginning a trip to the destination based at least in part on data accessed from the geographic database and the context information.” (Wang ¶¶ 49-55: “a set of potential trips are determined using the potential pickup location and each destination location of the set of potential destination locations,” where the pickup and destination locations can be determined based on context information such a user’s typical work schedule and the time of day. Additionally, Wang ¶ 59: “the set of potential trips may be sent by the server computing devices 410 to the user's client computing device for display to the user. The sent information may also include sending the pickup and destination locations, ETAs, costs, as well as corresponding map information.”)
“and determination of a reservation plan based on at least one of the route, the destination, the one or more modes of transportation, the time for beginning the trip to the destination, or the context information.” (Wang ¶¶ 55-56: “a set of potential trips are determined using the potential pickup location and each destination location of the set of potential destination locations. … for each potential trip, a vehicle of a fleet of autonomous vehicles of the autonomous vehicle service are assigned. For instance, the server computing devices 410 may assign a vehicle based on currently available vehicles or vehicles that are expected to be available (for instance, if the trip is expected to occur sometime in the future, the trip may be entered into a future trip queue in order to identify certain assigned vehicles as unavailable at different times in the future).” This at least teaches the determination of a reservation plan based on the destination, the time for beginning the trip to the destination, and the context information as claimed.)
“wherein the reservation plan comprises indications of what is reserved in a reservation of the reservation plan, a location at which to access the reservation, and a timing of the reservation.” (Wang ¶ 56: “for each potential trip, a vehicle of a fleet of autonomous vehicles of the autonomous vehicle service are assigned.” Further, Wang ¶ 57: “once a vehicle is assigned, the server computing devices 410 may also determine other information about the trip, such as an ETA at the pickup location for the vehicle.” Also, Wang ¶ 59 and FIGS. 6-7 disclose a “pickup location (P)” for each trip.)
“causing, via execution of the computer program instructions by the processor, at least a portion of the trip plan to be provided to the user via a user interface of a user apparatus.” (Wang ¶ 59: “the trip information for each potential trip to is provided to a client computing device for display to the user.”)
The following limitations are not specifically disclosed by Wang, but are taught by Balva:
“wherein executing the trip intent engine causes: determination of a cargo requirement corresponding to the trip intent.” (Balva ¶ 62: “in order to determine the set of routes, one or more passenger conditions associated with a passenger request of the predicted demand is determined, in which the one or more conditions includes an amount of passenger capacity (e.g., how many passengers) and cargo capacity needed, such as for luggage, groceries, etc. Similarly, one or more cargo conditions associated with a cargo request of the predicted demand may be determined, in which the one or more conditions includes an amount of cargo capacity required.”)
“determination of one or more modes of transportation for use in traversing one or more portions of a route corresponding to the trip intent based at least in part on respective cargo capabilities of the one or more modes of transportation and the cargo requirement.” (Balva ¶ 16: “a set of proactive passenger requests and cargo requests corresponding to the predicted demand may be generated and submitted to a simulation module of a vehicle selection route determination system, which can determine a set of routes for a future period of time and assign the routes to vehicles of the transportation service. The vehicles may include different types of vehicles, including passenger-only vehicles which are only used to serve passenger requests, cargo-only vehicles which are only used to serve cargo delivery requests, and mixed passenger and cargo vehicles which can be used to serve both passenger requests and cargo requests.”)
“determination of the route from a current location of the user to the destination based at least in part on … a respective mode of transportation of the one or more modes of transportation determined for a respective portion of the route.” (Balva ¶ 16: “a set of proactive passenger requests and cargo requests corresponding to the predicted demand may be generated and submitted to a simulation module of a vehicle selection route determination system, which can determine a set of routes for a future period of time and assign the routes to vehicles of the transportation service. The vehicles may include different types of vehicles, including passenger-only vehicles which are only used to serve passenger requests, cargo-only vehicles which are only used to serve cargo delivery requests, and mixed passenger and cargo vehicles which can be used to serve both passenger requests and cargo requests.” Also, Balva ¶ 21: “a given user can enter an origination location 112 and a destination location 114, either manually or from a set of suggested locations 116, among other such options, such as by selecting from a map 118 or other interface element. In other embodiments, source such as a machine learning algorithm or artificial intelligence system can select the appropriate locations based on relevant information, such as historical user activity, current location, and the like.”)
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to modify the method of Wang by selecting an appropriate mode of transportation for the route based on cargo requirements and the capabilities of the available modes of transportation as taught by Balva with a reasonable expectation of success. A person having ordinary skill in the art could have been motivated to do this because Balva ¶ 26 teaches that by considering factors such as the cargo requirements, “A function of these factors can then be optimized in order to provide for an improved customer experience, or transport experience for transported objects, while also providing for improved profitability, or at least operational efficiency, with respect to other available routing option.”
The combination of Wang and Balva does not specifically teach “causing, via execution of the computer program instructions by the processor, the user apparatus to communicate with a reservation apparatus corresponding to the reservation of the reservation plan of the trip plan responsive to a location sensor of the user apparatus determining that the user apparatus is at a location corresponding to the reservation, wherein the user apparatus communicates credentials configured for receipt by the reservation apparatus to enable the user to access the reservation.” However, Xiao does teach this limitation. (Xiao ¶ 49: “When the system determines that the distance between the car rental personnel and the vehicle is less than 200 meters, the system sends a horn and flashing light command to the on-board terminal. After receiving the command, the onboard terminal activates the horn and lights of the vehicle reserved by the car rental personnel, so that the car rental personnel can quickly find the vehicle. After the car rental staff finds the vehicle, they send a self-service car pickup instruction to the time-sharing control center module by operating the mobile phone APP. The control center module sends a text message of the car pickup password to the car rental staff's mobile phone. After the car rental staff enters the car pickup password in the APP, the control center module verifies the validity of the car pickup password. When the password is valid, it sends a door unlocking instruction to the terminal.”)
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to modify the method disclosed by the combination of Wang and Balva by communicating credentials for allowing access to a reserved rental vehicle when the user location approaches the rental vehicle as is taught by Xiao with a reasonable expectation of success. A person having ordinary skill in the art could have been motivated to do this because Xiao ¶ 23 teaches that “Through the control center module of the present invention, car rental companies do not need to open offline stores to carry out rental business, thus greatly saving the operating costs of rental companies.” Also, a person having ordinary skill in the art would have recognized that communicating the credentials when the user is close to the reservation location would help to enhance security by preventing unauthorized users from accessing the reserved vehicle.
Regarding claim 2:
The combination of Wang, Balva, and Xiao teaches “The method of claim 1,” and Wang further teaches the method “further comprising obtaining the current location of the user, the current location determined based at least in part on a location sensor of the user apparatus.” (Wang ¶ 49 discloses the use of “a current or last received location (for instance, GPS location) of the user's client computing device (mobile phone).”)
Regarding claim 3:
The combination of Wang, Balva, and Xiao teaches “The method of claim 1,” and Wang also teaches “wherein the context information further comprises one or more of traffic data, weather data, driving conditions data, or vehicle availability for one or more modes of transportation.” (Wang ¶ 60: “trips may be ranked and displayed according to those having the shortest ETA, those that are easiest for the vehicles of the fleet to reach (e.g., no complicated maneuvers, avoids certain areas, good traffic conditions, reachable given current or expected weather conditions and vehicle capabilities, etc.), those that the user has designated as ‘favorites’, the most recent trips, the aforementioned scores, etc.” This at least teaches that the context information can further comprise traffic data as claimed.)
Note that under the BRI of claim 3, consistent with the instant specification, the context information further comprising “one or more of traffic data, weather data, driving conditions data, or vehicle availability for one or more modes of transportation” is treated as an alternative limitation. Applicant has elected to use the phrase “one or more” in the claim language, and therefore, the BRI covers the scenario in which only one of the limitations applies. Accordingly, while only the “traffic data” has been addressed here, the claim is still rejected in its entirety.
Regarding claim 4:
The combination of Wang, Balva, and Xiao teaches “The method of claim 3,” and Wang further teaches “wherein the trip intent engine is configured to determine the trip plan based at least in part on the context information satisfying at least one of the one or more multi-feature criteria.” (Wang ¶ 60: “trips may be ranked and displayed according to … those that are easiest for the vehicles of the fleet to reach (e.g., no complicated maneuvers, avoids certain areas, good traffic conditions, reachable given current or expected weather conditions and vehicle capabilities, etc.)… The top 2 or 3 trips may then be provided by the server computing devices 410 to the user's client computing device and displayed in the ranked order. Alternatively, the order of these potential trips, TRIP 1, TRIP 2, TRIP 3 may correspond to the ranked order provided by the model or by ordering according to an overall user score for each of the potential trips.” Further, Wang ¶ 68: “rather than waiting for the user to confirm the trip, the server computing devices 410 may automatically arrange one of the potential trips (i.e. a highest ranked potential trip) by assigning and dispatching a vehicle to the potential pickup location for that one.”)
Regarding claim 5:
The combination of Wang, Balva, and Xiao teaches “The method of claim 1,” and Wang further teaches “wherein the trip intent comprises one or more items, services, or experiences the user would like to obtain.” (Wang ¶ 72 discloses that when reserving a car, “the user may receive a notification indicating that a vehicle is available for a trip via the user's client computing device from the server computing devices and may open the application via notification. In response, the client computing device by way of the application may send a second notification to the server computing devices. In such cases, the server computing devices may extend the time for which the server computing devices will wait for the user to confirm the trip or may hold an assigned vehicle exclusively for the user.” The reserved use of a vehicle reads on the service(s) the user would like to obtain as claimed.)
Note that under the BRI of claim 5, consistent with the instant specification, the trip intent comprising “one or more items, services, or experiences the user would like to obtain” is treated as an alternative limitation. Applicant has elected to use the word “or” in the claim language, and therefore, the BRI covers the scenario in which only one of the limitations applies. Accordingly, while only the service(s) has been addressed here, the claim is still rejected in its entirety.
Regarding claim 6:
The combination of Wang, Balva, and Xiao teaches “The method of claim 1,” and Wang further teaches “wherein the trip plan model is at least one of a user-specific model, a location-specific model, or a user demographic-specific model.” (Wang ¶ 53: “the model may eventually recognize certain behaviors of individual users (e.g., User A always takes trips around 8 am on Sunday to a supermarket) as well as behaviors of groups of users (e.g., users typically move from suburban areas to dense urban areas around 8 am on weekdays and from dense urban areas to suburban areas around 5 pm on weekdays, etc.).” This at least teaches the trip plan model being “a user-specific model” as claimed.)
Note that under the BRI of claim 6, consistent with the specification, the trip plan model being “at least one of a user-specific model, a location-specific model, or a user demographic-specific model” is treated as an alternative limitation. Applicant has elected to use the phrase “at least one” in the claim language, and therefore, the BRI covers the scenario in which only one of the limitations applies. Accordingly, while only the “user-specific model” has been addressed here, the claim is still rejected in its entirety.
Regarding claim 7:
The combination of Wang, Balva, and Xiao teaches “The method of claim 1,” and Balva further teaches “wherein the trip intent is determined based at least on user input received by the user apparatus.” (Balva ¶ 18: “a user can request transportation from an origination to a destination location using, for example, an application executing on a client computing device 110.”)
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to modify the method disclosed by the combination of Wang and Xiao by allowing the user to input information relating to a transportation request as taught by Balva, because this is amounts to a combination of prior art elements according to known methods to yield predictable results (see MPEP 2143(I)(A)). Allowing a user to input information indicative of a trip request would have predictably functioned similarly whether done within the vehicle selection method of Balva or whether integrated into the trip planning method that is disclosed by the combination of Wang and Xiao. A person having ordinary skill in the art would have recognized that incorporating user inputs in this way would contribute to satisfactory trip planning without any need to guess what the user wants.
Regarding claim 9:
The combination of Wang, Balva, and Xiao teaches “The method of claim 1,” and Wang also teaches the method “further comprising reserving at least one of a vehicle, a parking spot, or service based on the reservation plan.” (Wang ¶ 72: “the server computing devices may extend the time for which the server computing devices will wait for the user to confirm the trip or may hold an assigned vehicle exclusively for the user. Alternatively, even if the user does not interact with the notification to open the application, the server computing devices may still hold the assigned vehicle but do so for less time based on the user's historical and/or current user score (e.g. how ‘good’ of a user he or she appears to be) and the current level of demand for the vehicles (e.g. if demand is low, the server computing devices may hold the assigned vehicle for longer). These factors could be blended together as one score or used to a duration of time for which to reserve the car.” This at least teaches to reserve a vehicle as claimed.)
Note that under the BRI of claim 9, consistent with the instant specification, “reserving at least one of a vehicle, a parking spot, or service based on the reservation plan” is being treated as an alternative limitation. Applicant has elected to use the phrase “at least one” in the claim language, and therefore, the BRI covers the scenario in which only one of the limitations applies. Accordingly, while only reserving a vehicle has been addressed here, the claim is still rejected in its entirety.
Regarding claim 10:
Wang discloses “An apparatus comprising at least one processor and at least one memory storing computer program code, the at least one memory and the computer program code configured to, with the processor, cause the apparatus to” perform operations. (Wang ¶¶ 20-24: “The memory 130 stores information accessible by the one or more processors 120, including instructions 132 and data 134 that may be executed or otherwise used by the processor 120. … Computing devices 110 may include all of the components normally used in connection with a computing device such as the processor and memory described above.”)
The remaining limitations of claim 10 are taught by the combination of Wang, Balva, and Xiao using the same rationale applied to claim 1 above, mutatis mutandis.
Regarding claims 11-16 and 18:
Claims 11-16 and 18 are rejected using the same rationale, mutatis mutandis, applied to claims 2-7 and 9 above, respectively.
Regarding claim 21:
The combination of Wang, Balva, and Xiao teaches “The method of claim 1,” and Wang also teaches the method “further comprising causing the user interface of the user apparatus to provide guidance for completing at least a portion of a trip in accordance with the trip plan based at least in part on a location of the user apparatus as determined by a location sensor of the user apparatus.” (Wang ¶ 59: “the trip information for each potential trip to is provided to a client computing device for display to the user. … For instance, FIG. 6 is an example of client computing device 420 including display 610 which includes a map 620 as well as a set of potential trips 630, 632, 634. The map may correspond to an area that is broad enough to identify the pickup location (P) and destination locations (1, 2, and 3 corresponding to TRIPS 1, 2, and 3, respectively) for each potential pickup location.” Also, Wang ¶ 49: “a pickup location may correspond to a current or last received location (for instance, GPS location) of the user's client computing device (mobile phone).”)
Regarding claim 22:
The combination of Wang, Balva, and Xiao teaches “The method of claim 1,” and Xiao also teaches “wherein the credentials comprise one or more of a password, a code, or a reservation id configured to enable the user to access the reservation.” (Xiao ¶ 49: “After the car rental staff finds the vehicle, they send a self-service car pickup instruction to the time-sharing control center module by operating the mobile phone APP. The control center module sends a text message of the car pickup password to the car rental staff's mobile phone. After the car rental staff enters the car pickup password in the APP, the control center module verifies the validity of the car pickup password. When the password is valid, it sends a door unlocking instruction to the terminal.” This at least teaches the credentials comprising a password configured to enable the user to access the reservation as claimed.)
Note that under the BRI of claim 22, consistent with the specification, the credentials comprising “one or more of a password, a code, or a reservation id” is treated as an alternative limitation. Applicant has elected to use the phrase “one or more” in the claim language, and therefore, the BRI covers the scenario in which only one of the limitations applies. Accordingly, while only the password has been addressed here, the claim is still rejected in its entirety.
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to modify the method disclosed by the combination of Wang and Balva by communicating password credentials for allowing access to a reserved rental vehicle as taught by Xiao with a reasonable expectation of success. A person having ordinary skill in the art could have been motivated to do this because Xiao ¶ 23 teaches that “Through the control center module of the present invention, car rental companies do not need to open offline stores to carry out rental business, thus greatly saving the operating costs of rental companies.” Also, a person having ordinary skill in the art would have recognized that the use of password credentials would help to enhance security by preventing unauthorized users from accessing the reserved vehicle.
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Balva and Xiao as applied to claims 1 and 10 above, and further in view of Papp et al. (US 2023/0050118 A1), hereinafter referred to as Papp.
Regarding claim 8:
The combination of Wang, Balva, and Xiao teaches “The method of claim 1,” but does not specifically teach “wherein the point of interest database comprises point of interest data associated with intent data.” However, Papp does teach this limitation. However, _ (Papp ¶ 37 discloses “receiving a desired activity of interest from a user, finding a point of interest related to the activity of interest, wherein the point of interest has a charging station in near proximity to that point of interest so that the end user can conveniently visit that point of interest while the vehicle is charging at the nearby charging station.” The use of a relationship between the activity of interest and the point of interest implies the use of some sort of database that defines this relationship.)
Before the effective filing date of the claimed invention, it would have been obvious to a person having ordinary skill in the art to modify the method that is disclosed by the combination of Wang, Balva, and Xiao by using a defined relationship between activities of interest and points of interest as taught by Papp with a reasonable expectation of success. A person having ordinary skill in the art could have been motivated to do this since Papp ¶ 2 teaches that this modification can provide a convenient way for a user to visit a point of interest to obtain the items during their free time, like when their vehicle is charging. A person having ordinary skill in the art would have recognized that referencing a database relating trip intent with points of interest could provide more accurate and satisfactory trip plan recommendations------- for the user.
Regarding claim 17:
Claim 17 is rejected using the same rationale applied to claim 8 above, mutatis mutandis.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Madison R Inserra whose telephone number is (571)272-7205. The examiner can normally be reached Monday - Friday: 9:30 AM - 6:30 PM EST.
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/Madison R. Inserra/Primary Examiner, Art Unit 3662