DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to application 18/653,935 filed 5/2/2024. Claims 1, 3-9, and 11-18 were amended in the reply filed 5/13/2026. Claims 1-20 are pending. This action is final.
Response to Arguments
Regarding Applicant’s argument starting on page 8 regarding claims 1-20: Applicant’s arguments filed with respect to the rejections made under 35 USC § 101 have been fully considered, but are not persuasive.
Applicant first argues that the claims are directed to a specific technological improvement in Mobility as a Service (MaaS) platforms and computerized tariff computation systems. Examiner respectfully disagrees. The alleged improvements that Applicant’s invention provides are business improvements to a business related process, and not improvements to a computer system technology itself (See MPEP § 2106.04(d)(1) and 2106.05(a) for examples and description of what is considered an improvement to a computer-functionality or an improvement to a technology). "Identifying, analyzing, and presenting certain data to a user is not an improvement specific to computing." International Business Machines Corp. v. Zillow Group, Inc., (Fed. Cir. No. 2021-2350, Oct. 17, 2022, pg. 8). The claimed computer components are generic and broadly recited, and the alleged improvements are not to the generic computer components themselves, but to the abstract process being performed by the computer components. Examiner respectfully argues that the claimed limitations not analogous to the MPEP descriptions and examples of improvements to computer-functionality or improvements to a technology, and that the claims are directed to an abstract idea.
Regarding Applicant’s argument starting on page 8 regarding claims 1-20: Applicant’s arguments filed with respect to the rejections made under 35 USC § 102 have been fully considered, but are not persuasive.
Applicant first argues that the cited portions of Mishra describe route optimization and booking functionality, but not centralized tariff computation using locally maintained comprehensive fare datasets. Examiner respectfully disagrees. As described in the rejection below, Mishra uses locally stored pricing information in order to make its determination on an optimized route suggestion.
Applicant further argues that Mishra does not disclose autonomous internal tariff computation without delegation to third-party mobility services. Examiner respectfully disagrees. As describe din the rejection below, Mishra collects and stores pricing data, service availability data, etc., and autonomously calculates an optimized route suggestion without depending on third-party services for this calculation.
Applicant further argues that Mishra does not disclose real-time validation of travel availability. Examiner respectfully disagrees. Mishra teaches validating the availability of services as part of its calculation in determining an optimized route to suggest to a user. Mishra further teaches that determining an optimized route to suggest to a user also includes collecting and analyzing real-time weather and traffic data.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1, 9, and 17 each recite a method, a system, and a platform, respectively, for assembling a plurality of factors for tariff determination, the plurality of factors comprising tariff data including validated tariff data, real-time travel-condition data, optimized tariff-pricing data, and processible tariff-computation data; invoking an algorithm of choice embedded within the MaaS platform to process the plurality of factors; and generating a tailored tariff for a user based on the processed plurality of factors by the algorithm of choice embedded within the MaaS platform, wherein the tailored tariff is customized to suit the individual needs and usage patterns of the user within the MaaS platform, wherein the MaaS platform comprises a modular architecture including a central mobility provider component and a tariff calculator configured to perform a centralized tariff computation using locally maintained tariff data, and wherein the tailored tariff is generated based on real- time travel conditions and travel availability validated by the MaaS platform without delegating tariff calculations to third-party mobility services. Therefore, claims 1, 9, and 17 are each directed to one of the four statutory categories of invention: a method, a machine, and a machine, respectively.
Step 2A – Prong One: The limitations assembling a plurality of factors for tariff determination, the plurality of factors comprising tariff data including validated tariff data, real-time travel-condition data, optimized tariff-pricing data, and processible tariff-computation data; invoking an algorithm of choice ... to process the plurality of factors; and generating a tailored tariff for a user based on the processed plurality of factors by the algorithm of choice ... wherein the tailored tariff is customized to suit the individual needs and usage patterns of the user ... perform a centralized tariff computation using locally maintained tariff data, and wherein the tailored tariff is generated based on real-time travel conditions and travel availability validated ... without delegating tariff calculations to third-party mobility services, as drafted, is a method that, under its broadest reasonable interpretation, only covers concepts of “Certain Methods of Organizing Human Activity” (e.g., commercial interactions – business relations). That is, nothing in the claim elements disclose anything outside the groupings of “Certain Methods of Organizing Human Activity” (e.g., commercial interactions – business relations). Accordingly, the claim recites an abstract idea.
Step 2A – Prong Two: The judicial exception is not integrated into a practical application. Claims 1, 9, and 17 merely describe how to generally “apply” the concept of the aforementioned abstract idea using generic computer components. The additional elements of claims 1, 9, and 17, a computer (claim 1), a Mobility as a Service platform (claims 1, 9, and 17), a system (claim 9), at least one processor (claim 9), a non-transitory computer-usable medium (claim 9), a plurality of interfaces for data declaration (claim 17), a modular architecture (claims 1, 9, and 17), a central mobility provider component (claims 1, 9, and 17), and a tariff calculator (claims 1, 9, and 17), are recited at a high level of generality and are merely invoked as generic computer tools to perform the aforementioned abstract idea. Simply implementing the abstract idea on a generic computerized system is not a practical application of the abstract idea. Accordingly, alone and in combination, the additional elements of claims 1, 9, and 17 do not integrate the abstract idea into a practical application. The claims are directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the claims as a whole merely describe the abstract idea generally “applied” to a generic computer environment. The additional elements of claims 1, 9, and 17, a computer (described in spec. para. [0100]), a Mobility as a Service platform (described in spec. para. [0026]), a system (described in spec. para. [0024]), at least one processor (described in spec. para. [0100]), a non-transitory computer-usable medium (described in spec. para. [0103]), a plurality of interfaces for data declaration (described in spec. para. [0026]), a modular architecture (described in spec. para. [0044]), a central mobility provider component (described in spec. para. [0044]), and a tariff calculator (described in spec. para. [0052]), are recited at a high level of generality and are merely invoked as generic computer components upon which the abstract idea is “applied.” The high level of generality in which this additional element is described indicates that the additional element is sufficiently known such that the specification does not need to describe the particulars of the additional element to satisfy the statutory disclosure requirements. Thus, even when viewed as a whole, nothing in the claims add significantly more to the abstract idea. Therefore, the claims are not patent eligible.
Claims 2-8, 10-16, and 18-20 have been given the full two-part analysis including analyzing the limitations both individually and in combination. Claims 2-8, 10-16, and 18-20 when analyzed individually, and in combination, are also held to be patent ineligible under 35 U.S.C. 101. The recited limitations of the dependent claims fail to establish that the claims do not recite an abstract idea because the recited limitations of the dependent claims merely further narrow the abstract idea.
Step 2A – Prong Two: The limitations of the dependent claims fail to integrate an abstract idea into a practical application because the claims as a whole merely describe how to generally “apply” a method of the aforementioned abstract idea. Although claims 11 and 12 recite the additional elements an autonomous platform, claims 11, 12, and 14 recite the additional elements a product referential module, a topological referential module, a validation server, claims 11, 13, and 14 recite external mobility-provider computational systems, the claims as a whole merely describe how to generally “apply” the aforementioned abstract idea in a generic computer environment. Thus, even when viewed as a whole, nothing in the claims integrates the abstract idea into a practical application.
Step 2B: Performing the further narrowed abstract ideas of the dependent claims on the additional elements of the independent claim, individually or in combination, does not impose any meaningful limits on practicing the abstract ideas and amount to merely using a computer, in its ordinary capacity, as a tool to perform the abstract idea. Similarly, the recited limitations of the dependent claims fail to establish that the claims provide an inventive concept because claims that merely use a computer, in its ordinary capacity, as a tool to perform the abstract idea cannot provide an inventive concept. Although claims 11 and 12 recite the additional elements an autonomous platform (described in spec. para. [0082]), claims 11, 12, and 14 recite the additional elements a product referential module (described in spec. para. [0044]), a topological referential module (described in spec. para. [0052]), a validation server (described in spec. para. [0052]), claims 11, 13, and 14 recite external mobility-provider computational systems (described in spec. para. [0075]), they are recited at a high level of generality and are merely invoked as generic computer components upon which the abstract idea is “applied.” The high level of generality in which the additional elements are described indicates that the additional elements are sufficiently known such that the specification does not need to describe the particulars of the additional elements to satisfy the statutory disclosure requirements. Thus, even when viewed as a whole, nothing in the claims add significantly more to the abstract idea. Therefore, the claims are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Mishra (U.S. Pub. No. 2024/0027202).
Regarding claims 1 and 9, Mishra discloses the following limitations:
A computer-implemented method of implementing a tariff within a Mobility as a Service (MaaS) platform, comprising: [See [0021] Mishra teaches a system and method for multimodal trip planning, allowing a user to book one or more transport services for transit along a determined optimal route (i.e., A computer-implemented method ... within a Mobility as a Service (MaaS) platform). Mishra [0027] further teaches that a route with a cheapest combined cost (i.e., implementing a tariff within a Mobility as a Service (MaaS) platform) for one or more modes of travel may be selected and booked.]
assembling a plurality of factors for tariff determination, the plurality of factors comprising tariff data including validated tariff data, real-time travel-condition data, optimized tariff-pricing data, and processible tariff-computation data; [See [0021] Mishra teaches a system and method for multimodal trip planning, allowing a user to book one or more transport services for transit along a determined optimal route. Mishra [0024-0027] further teaches that a route with a cheapest combined cost for one or more modes of travel may be selected and booked, a route with the fastest travel time may be selected and booked, or a route that best fits a user’s preferences based on collected user data may be selected and booked. Specifically, Mishra teaches that booking the transportation service (i.e., tariff data) includes validating acceptance of the booking by a driver (i.e., validated tariff data), fastest route data based on weather and traffic conditions (i.e., real-time travel-condition data), optimal route cost data (i.e., optimized tariff-pricing data) based on ride cost data (i.e., processible tariff-computation data), and user preference data.]
invoking an algorithm of choice embedded within the MaaS platform to process the plurality of factors; [See [0021] Mishra teaches a system and method for multimodal trip planning, allowing a user to book one or more transport services for transit along a determined optimal route. Mishra [0026-0027] further teaches that a route with a cheapest combined cost for one or more modes of travel may be selected and booked, a route with the fastest travel time may be selected and booked, or a route that best fits a user’s preferences based on collected user data may be selected and booked. This shows that data related to the booking of the transportation service includes route cost data, route time data, and user preference data (i.e., the plurality of factors). Mishra [0026-0028] further teaches the system suggesting at least one optimal route option based on this data (i.e., invoking an algorithm of choice embedded within the MaaS platform to process the plurality of factors).]
and generating a tailored tariff for a user based on the processed plurality of factors by the algorithm of choice embedded within the MaaS platform [See [0021] Mishra teaches a system and method for multimodal trip planning, allowing a user to book one or more transport services for transit along a determined optimal route. Mishra [0026-0027] further teaches that a route with a cheapest combined cost for one or more modes of travel may be selected and booked, a route with the fastest travel time may be selected and booked, or a route that best fits a user’s preferences based on collected user data may be selected and booked. This shows that data related to the booking of the transportation service includes route cost data, route time data, and user preference data (i.e., the plurality of factors). Mishra [0026-0028] further teaches the system suggesting at least one optimal route option based on this data. Mishra [0028]; [0030] further teaches a ticket booking subsystem 140 and a ticket payment subsystem 160 which allow the user to book and pay for a ticket for a multimodal trip they selected form the suggested plurality of optimal routes (i.e., generating a tailored tariff for a user based on the processed plurality of factors by the algorithm of choice embedded within the MaaS platform).]
wherein the tailored tariff is customized to suit the individual needs and usage patterns of the user within the MaaS platform. [See [0021] Mishra teaches a system and method for multimodal trip planning, allowing a user to book one or more transport services for transit along a determined optimal route. Mishra [0026-0027] further teaches that a route with a cheapest combined cost for one or more modes of travel may be selected and booked, a route with the fastest travel time may be selected and booked, or a route that best fits a user’s preferences based on collected user data may be selected and booked. This shows that data related to the booking of the transportation service includes route cost data, route time data, and user preference data. Mishra [0026-0028] further teaches the system suggesting at least one optimal route option based on this data. Mishra [0028]; [0030] further teaches a ticket booking subsystem 140 and a ticket payment subsystem 160 which allow the user to book and pay for a ticket for a multimodal trip they selected form the suggested plurality of optimal routes. Mishra [0026] further teaches that the selected and booked optimal route may be based on the user’s mode of transportation preferences, the user’s commute preferences, or the user’s previous ride scores (i.e., wherein the tailored tariff is customized to suit the individual needs and usage patterns of the user within the MaaS platform).]
wherein the MaaS platform comprises a modular architecture including a central mobility provider component and a tariff calculator configured to perform a centralized tariff computation using locally maintained tariff data, and wherein the tailored tariff is generated based on real- time travel conditions and travel availability validated by the MaaS platform without delegating tariff calculations to third-party mobility services. [See [0021] Mishra teaches a system and method for multimodal trip planning, allowing a user to book one or more transport services for transit along a determined optimal route (i.e., perform a centralized tariff computation using locally maintained tariff data). Mishra [0028]; [0032]; further teaches that booking information associated with a ticket booked for the one or more transport services may be stored in a service provider’s database hosted on a centralized server (i.e., a central mobility provider component). Mishra [0028] further teaches that one or more transport service providers may participate in the system (i.e., wherein the MaaS platform comprises a modular architecture including a central mobility provider component and a tariff calculator). Mishra [0024-0027] further teaches that a route with a cheapest combined cost for one or more modes of travel may be selected and booked, a route with the fastest travel time may be selected and booked, or a route that best fits a user’s preferences based on collected user data may be selected and booked. Specifically, Mishra teaches that booking the transportation service (i.e., tariff data) includes validating acceptance of the booking by a driver (i.e., wherein the tailored tariff is generated based on ... travel availability validated by the MaaS platform without delegating tariff calculations to third-party mobility services), fastest route data based on weather and traffic conditions (i.e., wherein the tailored tariff is generated based on real-time travel conditions ... by the MaaS platform without delegating tariff calculations to third-party mobility services), optimal route cost data based on ride cost data, and user preference data.]
Regarding claim 9, Mishra further discloses the following limitations:
A system for implementing a tariff within a Mobility as a Service (MaaS) platform, comprising: at least one processor; and a non-transitory computer-usable medium embodying computer program code, the computer-usable medium operable to communicate with the at least one processor, the computer program code comprising instructions executable by the at least one processor and operable for: [See [0021] Mishra teaches a system and method for multimodal trip planning, allowing a user to book one or more transport services for transit along a determined optimal route (i.e., A system ... within a Mobility as a Service (MaaS) platform). Mishra [0027] further teaches that a route with a cheapest combined cost (i.e., implementing a tariff within a Mobility as a Service (MaaS) platform) for one or more modes of travel may be selected and booked. Mishra [0042-0043]; (Fig. 5, elements 210, 230); further teaches the system being a server comprising at least one processor 230 (i.e., at least one processor) and a memory 210 (i.e., a non-transitory computer-usable medium). Mishra [0043] further teaches that the processor may perform he steps of the system by executing a program stored in the memory (i.e., the computer-usable medium operable to communicate with the at least one processor, the computer program code comprising instructions executable by the at least one processor and operable for).]
Regarding claims 2 and 10, Mishra discloses all claim 1 and 9 limitations. Mishra further discloses the following limitations:
offering the tailored tariff to the user through the MaaS platform after the tailored tariff is created based on the plurality of factors processed by the algorithm of choice. [See [0021] Mishra teaches a system and method for multimodal trip planning, allowing a user to book one or more transport services for transit along a determined optimal route. Mishra [0026-0027] further teaches that a route with a cheapest combined cost for one or more modes of travel may be selected and booked, a route with the fastest travel time may be selected and booked, or a route that best fits a user’s preferences based on collected user data may be selected and booked. This shows that data related to the booking of the transportation service includes route cost data, route time data, and user preference data. Mishra [0026-0028] further teaches the system suggesting at least one optimal route option based on this data. Mishra [0028]; [0030] further teaches a ticket booking subsystem 140 and a ticket payment subsystem 160 which allow the user to book and pay for a ticket for a multimodal trip they selected form the suggested plurality of optimal routes. Mishra [0026] further teaches that the selected and booked optimal route may be based on the user’s mode of transportation preferences, the user’s commute preferences, or the user’s previous ride scores (i.e., offering the tailored tariff to the user through the MaaS platform after the tailored tariff is created based on the plurality of factors processed by the algorithm of choice).]
Regarding claim 3, Mishra discloses all claim 1 limitations. Mishra further discloses the following limitations:
wherein the validated tariff data includes data related to the user including a user profile associated with the user. [See [0021] Mishra teaches a system and method for multimodal trip planning, allowing a user to book one or more transport services for transit along a determined optimal route. Mishra [0026-0027] further teaches that a route with a cheapest combined cost for one or more modes of travel may be selected and booked, a route with the fastest travel time may be selected and booked, or a route that best fits a user’s preferences based on collected user data may be selected and booked. This shows that data related to the booking of the transportation service includes route cost data, route time data, and user preference data (i.e., wherein the validated tariff data includes data related to the user including a user profile associated with the user). Mishra [0026-0028] further teaches the system suggesting at least one optimal route option based on this data. Mishra [0028]; [0030] further teaches a ticket booking subsystem 140 and a ticket payment subsystem 160 which allow the user to book and pay for a ticket for a multimodal trip they selected form the suggested plurality of optimal routes. Mishra [0026] further teaches that the selected and booked optimal route may be based on the user’s mode of transportation preferences, the user’s commute preferences, or the user’s previous ride scores (i.e., wherein the validated tariff data includes data related to the user including a user profile associated with the user).]
Regarding claim 4, Mishra discloses all claim 1 limitations. Mishra further discloses the following limitations:
wherein the real-time travel-condition data includes data comprising at least one of: travel availability, real-time travel conditions, and available seating. [See [0021] Mishra teaches a system and method for multimodal trip planning, allowing a user to book one or more transport services for transit along a determined optimal route. Mishra [0026-0027] further teaches that a route with a cheapest combined cost for one or more modes of travel may be selected and booked, a route with the fastest travel time may be selected and booked, or a route that best fits a user’s preferences based on collected user data may be selected and booked. This shows that data related to the booking of the transportation service includes route cost data, route time data, and user preference data. Mishra [0026-0028] further teaches the system suggesting at least one optimal route option based on this data. Mishra [0028]; [0030] further teaches a ticket booking subsystem 140 and a ticket payment subsystem 160 which allow the user to book and pay for a ticket for a multimodal trip they selected form the suggested plurality of optimal routes. Mishra [0026] further teaches that the selected and booked optimal route may be based on the user’s mode of transportation preferences, the user’s commute preferences, or the user’s previous ride scores. Mishra [0025] further teaches its system determining one or more available transport services by identifying one or more drivers available within a predefined distance based on traffic and weather conditions (i.e., wherein the real-time travel-condition data includes data comprising at least one of: travel availability, real-time travel conditions, and available seating).]
Regarding claim 5, Mishra discloses all claim 1 limitations. Mishra further discloses the following limitations:
wherein: the centralized tariff computation includes selecting products according to at least one of topology criteria, schedule criteria, user criteria, or context criteria prior to generating the tailored tariff [See [0021] Mishra teaches a system and method for multimodal trip planning, allowing a user to book one or more transport services for transit along a determined optimal route. Mishra teaches its system suggesting at least one optimal route option to a user. Mishra [0026-0027] further teaches that the at least one optimal route may include a route with a cheapest combined cost for one or more modes of travel may be selected and booked, a route with the fastest travel time may be selected and booked, or a route that best fits a user’s preferences based on collected user data (i.e., the centralized tariff computation includes selecting products according to ... user criteria ...) may be selected and booked.]
wherein: ... the optimized tariff-pricing comprises data based on a best tariff price determined by a central tariff computation of at least one of: local fares, combined fares, marketing offers, regional discount offers, national discount offers. [See [0021] Mishra teaches a system and method for multimodal trip planning, allowing a user to book one or more transport services for transit along a determined optimal route. Mishra teaches its system suggesting at least one optimal route option to a user. Mishra [0026-0027] further teaches that the at least one optimal route may include a route with a cheapest combined cost (i.e., wherein the tariff data that is optimized comprises data based on a best tariff price determined by a central tariff computation of at least one of: local fares, combined fares) for one or more modes of travel may be selected and booked, a route with the fastest travel time may be selected and booked, or a route that best fits a user’s preferences based on collected user data may be selected and booked.]
Regarding claim 6, Mishra discloses all claim 1 limitations. Mishra further discloses the following limitations:
wherein the processible tariff-computation data comprises data processible through the MaaS platform, wherein the MaaS platform comprises an autonomous platform. [See [0021] Mishra teaches a system and method for multimodal trip planning, allowing a user to book one or more transport services for transit along a determined optimal route. Mishra [0026]; (Fig. 6) teaches its system suggesting at least one optimal route option to a user based on collected user data (i.e., wherein the processible tariff-computation data comprises data processible through the MaaS platform, wherein the MaaS platform comprises an autonomous platform). Mishra [0026-0027] further teaches that a route with a cheapest combined cost for one or more modes of travel may be selected and booked, a route with the fastest travel time may be selected and booked, or a route that best fits a user’s preferences based on collected user data may be selected and booked. This shows that data related to the booking of the transportation service includes route cost data, route time data, and user preference data. Mishra [0026-0028] further teaches the system suggesting at least one optimal route option based on this data. Mishra [0028]; [0030] further teaches a ticket booking subsystem 140 and a ticket payment subsystem 160 which allow the user to book and pay for a ticket for a multimodal trip they selected form the suggested plurality of optimal routes. Mishra [0026] further teaches that the selected and booked optimal route may be based on the user’s mode of transportation preferences, the user’s commute preferences, or the user’s previous ride scores. Mishra [0025] further teaches its system determining one or more available transport services by identifying one or more drivers available within a predefined distance based on traffic and weather conditions.]
Regarding claims 7 and 15, Mishra discloses all claim 1 and 9 limitations. Mishra further discloses the following limitations:
wherein the MaaS platform comprises a modular architecture including the central component comprising the mobility provider, the MaaS platform configured to adapt to various strategies employed by different providers. [See [0028]; [0032]; Mishra teaches that booking information associated with a ticket booked for the one or more transport services may be stored in a service provider’s database hosted on a centralized server (i.e., the central component comprising the mobility provider). Mishra [0028] further teaches that one or more transport service providers may participate in the system (i.e., wherein the MaaS platform comprises a modular architecture including a central component comprising a mobility provider, the MaaS platform configured to adapt to various strategies employed by different providers).]
Regarding claims 8 and 16, Mishra discloses all claim 1, 7, 9, and 15 limitations. Mishra further discloses the following limitations:
wherein the MaaS platform includes a plurality of interfaces for data declaration. [See [0035]; (Fig. 6, element 310); Mishra teaches a user providing a source address and a destination address via their electronic handheld device (i.e., an interface for data declaration). Mishra [0028]; (Fig. 6, element 350); further teaches the user may be presented a plurality of optimal route suggestions and select one of them (i.e., an interface for data declaration).]
Regarding claim 11, Mishra discloses all claim 9 limitations. Mishra further discloses the following limitations:
wherein the MaaS platform comprises an autonomous platform including a product referential module, a topological referential module configured to communicate with the product referential module, and a validation server configured to communicate with the product referential module and the topological referential module, wherein coordinated communication between the product referential module, the topological referential module, and the validation server enables internally validated real-time tariff computation while reducing dependency on external mobility-provider computational systems. (Examiner’s Note: The claimed topological referential module is interpreted as referring to network topology and not geographic topology based on the context provided in spec. paras. [0051-0052].) [See (Fig. 1); [0023] Mishra teaches its system autonomously booking a ride for a user based on a plurality of data. Mishra further teaches that its system comprises a trip route planning subsystem 120 (i.e., a product referential module) which comprises data on available rides. Mishra (Fig. 1); [0028] further teaches its system comprises a route suggestion subsystem 130 (i.e., a topological referential module) capable of making suggestions (prioritizations) of certain routes based on the route data and user data. Mishra [0024-0027] further teaches that booking the transportation service includes validating acceptance of the booking by a driver (i.e., a validation server), fastest route data based on weather and traffic conditions, optimal route cost data based on ride cost data, and user preference data. Mishra (Fig. 1); (Fig. 3); [0032] further teaches that all of the components of the system are communicatively connected to each other (i.e., wherein coordinated communication between the product referential module, the topological referential module, and the validation server enables internally validated real-time tariff computation while reducing dependency on external mobility-provider computational systems).]
Regarding claim 12, Mishra discloses all claim 9 limitations. Mishra further discloses the following limitations:
wherein the MaaS platform comprises an autonomous platform including a product referential module, a topological referential module configured to communicate with the product referential module, and a validation server configured to communicate with the product referential module and the topological referential module. (Examiner’s Note: The claimed topological referential module is interpreted as referring to network topology and not geographic topology based on the context provided in spec. paras. [0051-0052].) [See (Fig. 1); [0023] Mishra teaches its system autonomously booking a ride for a user based on a plurality of data. Mishra further teaches that its system comprises a trip route planning subsystem 120 (i.e., a product referential module) which comprises data on available rides. Mishra (Fig. 1); [0028] further teaches its system comprises a route suggestion subsystem 130 (i.e., a topological referential module) capable of making suggestions (prioritizations) of certain routes based on the route data and user data. Mishra [0024-0027] further teaches that booking the transportation service includes validating acceptance of the booking by a driver (i.e., a validation server), fastest route data based on weather and traffic conditions, optimal route cost data based on ride cost data, and user preference data. Mishra (Fig. 1); (Fig. 3); [0032] further teaches that all of the components of the system are communicatively connected to each other (i.e., wherein the MaaS platform comprises an autonomous platform including a product referential module, a topological referential module configured to communicate with the product referential module, and a validation server configured to communicate with the product referential module and the topological referential module).]
Regarding claim 13, Mishra discloses all claim 9 limitations. Mishra further discloses the following limitations:
wherein the MaaS platform operates autonomously with access to all necessary data required for computations without relying on external sources including the third-party mobility services. [See (Fig. 1); [0023] Mishra teaches its system autonomously booking a ride for a user based on a plurality of data. Mishra further teaches that its system comprises a trip route planning subsystem 120 (i.e., a product referential module) which comprises data on available rides (i.e., wherein the MaaS platform operates autonomously with access to all necessary data required for computations without relying on external sources including the third-party mobility services).]
Regarding claim 14, Mishra discloses all claim 9 limitations. Mishra further discloses the following limitations:
wherein the processible tariff-computation data comprises data processible through the MaaS platform, wherein the MaaS platform comprises an autonomous platform including a product referential module, a topological referential module configured to communicate with the product referential module, and a validation server configured to communicate with the product referential module and the topological referential module, wherein coordinated communication between the product referential module, the topological referential module, and the validation server enables internally validated real-time tariff computation while reducing dependency on external mobility-provider computational systems. (Examiner’s Note: The claimed topological referential module is interpreted as referring to network topology and not geographic topology based on the context provided in spec. paras. [0051-0052].) [See (Fig. 1); [0023] Mishra teaches its system autonomously booking a ride for a user based on a plurality of data (i.e., wherein the processible tariff-computation data comprises data processible through the MaaS platform, wherein the MaaS platform comprises an autonomous platform). Mishra further teaches that its system comprises a trip route planning subsystem 120 (i.e., a product referential module) which comprises data on available rides. Mishra (Fig. 1); [0028] further teaches its system comprises a route suggestion subsystem 130 (i.e., a topological referential module) capable of making suggestions (prioritizations) of certain routes based on the route data and user data. Mishra [0024-0027] further teaches that booking the transportation service includes validating acceptance of the booking by a driver (i.e., a validation server), fastest route data based on weather and traffic conditions, optimal route cost data based on ride cost data, and user preference data. Mishra (Fig. 1); (Fig. 3); [0032] further teaches that all of the components of the system are communicatively connected to each other (i.e., including a product referential module, a topological referential module configured to communicate with the product referential module, and a validation server configured to communicate with the product referential module and the topological referential module, wherein coordinated communication between the product referential module, the topological referential module, and the validation server enables internally validated real-time tariff computation while reducing dependency on external mobility-provider computational systems).]
Regarding claim 17, Mishra discloses the following limitations:
A Mobility as a Service (MaaS) platform, comprising: [See [0021] Mishra teaches a system and method for multimodal trip planning, allowing a user to book one or more transport services for transit along a determined optimal route (i.e., A Mobility as a Service (MaaS) platform).]
a modular architecture including a central component comprising a mobility provider [See [0028]; [0032]; Mishra teaches that booking information associated with a ticket booked for the one or more transport services may be stored in a service provider’s database hosted on a centralized server (i.e., a central component comprising a mobility provider). Mishra [0028] further teaches that one or more transport service providers may participate in the system (i.e., a modular architecture including a central component comprising a mobility provider).]
wherein the MaaS platform includes a plurality of interfaces for data declaration; [See [0035]; (Fig. 6, element 310); Mishra teaches a user providing a source address and a destination address via their electronic handheld device (i.e., an interface for data declaration). Mishra [0028]; (Fig. 6, element 350); further teaches the user may be presented a plurality of optimal route suggestions and select one of them (i.e., an interface for data declaration).]
the MaaS platform is configured to assemble a plurality of factors for tariff determination, the plurality of factors comprising validated tariff data, real-time travel-condition data, optimized tariff-pricing data, and processible tariff-computation data; [See [0021] Mishra teaches a system and method for multimodal trip planning, allowing a user to book one or more transport services for transit along a determined optimal route. Mishra [0026-0027] further teaches that a route with a cheapest combined cost for one or more modes of travel may be selected and booked, a route with the fastest travel time may be selected and booked, or a route that best fits a user’s preferences based on collected user data may be selected and booked. This shows that data related to the booking of the transportation service (i.e., tariff data) includes route cost data, route time data, and user preference data (i.e., the MaaS platform is configured to assemble a plurality of factors for tariff determination, the plurality of factors comprising validated tariff data, real-time travel-condition data, optimized tariff-pricing data, and processible tariff-computation data).]
wherein an algorithm of choice embedded within the MaaS platform is invokable to process the plurality of factors; [See [0021] Mishra teaches a system and method for multimodal trip planning, allowing a user to book one or more transport services for transit along a determined optimal route. Mishra [0026-0027] further teaches that a route with a cheapest combined cost for one or more modes of travel may be selected and booked, a route with the fastest travel time may be selected and booked, or a route that best fits a user’s preferences based on collected user data may be selected and booked. This shows that data related to the booking of the transportation service includes route cost data, route time data, and user preference data (i.e., the plurality of factors). Mishra [0026-0028] further teaches the system suggesting at least one optimal route option based on this data (i.e., wherein an algorithm of choice embedded within the MaaS platform is invokable to process the plurality of factors).]
wherein a tailored tariff for a user is generated based on the processed plurality of factors by the algorithm of choice embedded within the MaaS platform [See [0021] Mishra teaches a system and method for multimodal trip planning, allowing a user to book one or more transport services for transit along a determined optimal route. Mishra [0026-0027] further teaches that a route with a cheapest combined cost for one or more modes of travel may be selected and booked, a route with the fastest travel time may be selected and booked, or a route that best fits a user’s preferences based on collected user data may be selected and booked. This shows that data related to the booking of the transportation service includes route cost data, route time data, and user preference data (i.e., the plurality of factors). Mishra [0026-0028] further teaches the system suggesting at least one optimal route option based on this data. Mishra [0028]; [0030] further teaches a ticket booking subsystem 140 and a ticket payment subsystem 160 which allow the user to book and pay for a ticket for a multimodal trip they selected form the suggested plurality of optimal routes (i.e., wherein a tailored tariff for a user is generated based on the processed plurality of factors by the algorithm of choice embedded within the MaaS platform).]
wherein the tailored tariff is customized to suit the individual needs and usage patterns of the user within the MaaS platform [See [0021] Mishra teaches a system and method for multimodal trip planning, allowing a user to book one or more transport services for transit along a determined optimal route. Mishra [0026-0027] further teaches that a route with a cheapest combined cost for one or more modes of travel may be selected and booked, a route with the fastest travel time may be selected and booked, or a route that best fits a user’s preferences based on collected user data may be selected and booked. This shows that data related to the booking of the transportation service includes route cost data, route time data, and user preference data. Mishra [0026-0028] further teaches the system suggesting at least one optimal route option based on this data. Mishra [0028]; [0030] further teaches a ticket booking subsystem 140 and a ticket payment subsystem 160 which allow the user to book and pay for a ticket for a multimodal trip they selected form the suggested plurality of optimal routes. Mishra [0026] further teaches that the selected and booked optimal route may be based on the user’s mode of transportation preferences, the user’s commute preferences, or the user’s previous ride scores (i.e., wherein the tailored tariff is customized to suit the individual needs and usage patterns of the user within the MaaS platform).]
wherein the MaaS platform further comprises a tariff calculator configured to perform centralized tariff computation using locally maintained tariff data, and wherein tariff calculations are autonomously performed by the MaaS platform without delegation to third-party mobility services. [See [0021] Mishra teaches a system and method for multimodal trip planning, allowing a user to book one or more transport services for transit along a determined optimal route (i.e., perform a centralized tariff computation using locally maintained tariff data). Mishra [0028]; [0032]; further teaches that booking information associated with a ticket booked for the one or more transport services may be stored in a service provider’s database hosted on a centralized server (i.e., a central mobility provider component). Mishra [0028] further teaches that one or more transport service providers may participate in the system (i.e., wherein the MaaS platform comprises a modular architecture including a central mobility provider component and a tariff calculator). Mishra [0024-0027] further teaches that a route with a cheapest combined cost for one or more modes of travel may be selected and booked, a route with the fastest travel time may be selected and booked, or a route that best fits a user’s preferences based on collected user data may be selected and booked. Specifically, Mishra teaches that booking the transportation service (i.e., tariff data) includes validating acceptance of the booking by a driver (i.e., wherein the MaaS platform further comprises a tariff calculator configured to perform centralized tariff computation using locally maintained tariff data, and wherein tariff calculations are autonomously performed by the MaaS platform without delegation to third-party mobility services), fastest route data based on weather and traffic conditions, optimal route cost data based on ride cost data, and user preference data.]
Regarding claim 18, Mishra discloses all claim 17 limitations. Mishra further discloses the following limitations:
the tariff data that is true includes data related to the user including a user profile associated with the user; [See [0021] Mishra teaches a system and method for multimodal trip planning, allowing a user to book one or more transport services for transit along a determined optimal route. Mishra [0026-0027] further teaches that a route with a cheapest combined cost for one or more modes of travel may be selected and booked, a route with the fastest travel time may be selected and booked, or a route that best fits a user’s preferences based on collected user data may be selected and booked. This shows that data related to the booking of the transportation service includes route cost data, route time data, and user preference data (i.e., the tariff data that is true includes data related to the user including a user profile associated with the user). Mishra [0026-0028] further teaches the system suggesting at least one optimal route option based on this data. Mishra [0028]; [0030] further teaches a ticket booking subsystem 140 and a ticket payment subsystem 160 which allow the user to book and pay for a ticket for a multimodal trip they selected form the suggested plurality of optimal routes. Mishra [0026] further teaches that the selected and booked optimal route may be based on the user’s mode of transportation preferences, the user’s commute preferences, or the user’s previous ride scores (i.e., the tariff data that is true includes data related to the user including a user profile associated with the user).]
the tariff data that is reliable includes data comprising at least one of: a travel availability of the tariff, real-time travel conditions, and available seating; [See [0021] Mishra teaches a system and method for multimodal trip planning, allowing a user to book one or more transport services for transit along a determined optimal route. Mishra [0026-0027] further teaches that a route with a cheapest combined cost for one or more modes of travel may be selected and booked, a route with the fastest travel time may be selected and booked, or a route that best fits a user’s preferences based on collected user data may be selected and booked. This shows that data related to the booking of the transportation service includes route cost data, route time data, and user preference data. Mishra [0026-0028] further teaches the system suggesting at least one optimal route option based on this data. Mishra [0028]; [0030] further teaches a ticket booking subsystem 140 and a ticket payment subsystem 160 which allow the user to book and pay for a ticket for a multimodal trip they selected form the suggested plurality of optimal routes. Mishra [0026] further teaches that the selected and booked optimal route may be based on the user’s mode of transportation preferences, the user’s commute preferences, or the user’s previous ride scores. Mishra [0025] further teaches its system determining one or more available transport services by identifying one or more drivers available within a predefined distance based on traffic and weather conditions (i.e., the tariff data that is reliable includes data comprising at least one of: a travel availability of the tariff, real-time travel conditions, and available seating).]
the tariff data that is optimized comprises data based on a best tariff price determined by a central tariff computation; [See [0021] Mishra teaches a system and method for multimodal trip planning, allowing a user to book one or more transport services for transit along a determined optimal route. Mishra teaches its system suggesting at least one optimal route option to a user. Mishra [0026-0027] further teaches that the at least one optimal route may include a route with a cheapest combined cost (i.e., the tariff data that is optimized comprises data based on a best tariff price determined by a central tariff computation) for one or more modes of travel may be selected and booked, a route with the fastest travel time may be selected and booked, or a route that best fits a user’s preferences based on collected user data may be selected and booked.]
wherein the tariff data that is performative comprises data processible through the MaaS platform, wherein the MaaS platform comprises an autonomous platform. [See [0021] Mishra teaches a system and method for multimodal trip planning, allowing a user to book one or more transport services for transit along a determined optimal route. Mishra [0026]; (Fig. 6) teaches its system suggesting at least one optimal route option to a user based on collected user data (i.e., wherein the tariff data that is performative comprises data processible through the MaaS platform, wherein the MaaS platform comprises an autonomous platform). Mishra [0026-0027] further teaches that a route with a cheapest combined cost for one or more modes of travel may be selected and booked, a route with the fastest travel time may be selected and booked, or a route that best fits a user’s preferences based on collected user data may be selected and booked. This shows that data related to the booking of the transportation service includes route cost data, route time data, and user preference data. Mishra [0026-0028] further teaches the system suggesting at least one optimal route option based on this data. Mishra [0028]; [0030] further teaches a ticket booking subsystem 140 and a ticket payment subsystem 160 which allow the user to book and pay for a ticket for a multimodal trip they selected form the suggested plurality of optimal routes. Mishra [0026] further teaches that the selected and booked optimal route may be based on the user’s mode of transportation preferences, the user’s commute preferences, or the user’s previous ride scores. Mishra [0025] further teaches its system determining one or more available transport services by identifying one or more drivers available within a predefined distance based on traffic and weather conditions.]
Regarding claim 19, Mishra discloses all claim 17 limitations. Mishra further discloses the following limitations:
wherein the tailored tariff is offered to the user through the MaaS platform. [See [0021] Mishra teaches a system and method for multimodal trip planning, allowing a user to book one or more transport services for transit along a determined optimal route. Mishra [0026-0027] further teaches that a route with a cheapest combined cost for one or more modes of travel may be selected and booked, a route with the fastest travel time may be selected and booked, or a route that best fits a user’s preferences based on collected user data may be selected and booked. This shows that data related to the booking of the transportation service includes route cost data, route time data, and user preference data. Mishra [0026-0028] further teaches the system suggesting at least one optimal route option based on this data. Mishra [0028]; [0030] further teaches a ticket booking subsystem 140 and a ticket payment subsystem 160 which allow the user to book and pay for a ticket for a multimodal trip they selected form the suggested plurality of optimal routes. Mishra [0026] further teaches that the selected and booked optimal route may be based on the user’s mode of transportation preferences, the user’s commute preferences, or the user’s previous ride scores (i.e., wherein the tailored tariff is offered to the user through the MaaS platform).]
Regarding claim 20, Mishra discloses all claim 17 limitations. Mishra further discloses the following limitations:
wherein the tailored tariff is offered to the user through the MaaS platform after the tailored tariff is created based on the plurality of factors processed by the algorithm of choice. [See [0021] Mishra teaches a system and method for multimodal trip planning, allowing a user to book one or more transport services for transit along a determined optimal route. Mishra [0026-0027] further teaches that a route with a cheapest combined cost for one or more modes of travel may be selected and booked, a route with the fastest travel time may be selected and booked, or a route that best fits a user’s preferences based on collected user data may be selected and booked. This shows that data related to the booking of the transportation service includes route cost data, route time data, and user preference data. Mishra [0026-0028] further teaches the system suggesting at least one optimal route option based on this data. Mishra [0028]; [0030] further teaches a ticket booking subsystem 140 and a ticket payment subsystem 160 which allow the user to book and pay for a ticket for a multimodal trip they selected form the suggested plurality of optimal routes. Mishra [0026] further teaches that the selected and booked optimal route may be based on the user’s mode of transportation preferences, the user’s commute preferences, or the user’s previous ride scores (i.e., wherein the tailored tariff is offered to the user through the MaaS platform after the tailored tariff is created based on the plurality of factors processed by the algorithm of choice).]
Prior Art
The following prior art is relevant to the invention but was not used in prior art rejections:
Singh (U.S. Pub. No. 2023/0259833) – Multimodal mobility facilitating seamless ridership with single ticket
Demarchi (U.S. Pub. No. 2016/0203422) – Method and electronic travel route building system, based on an intermodal electronic platform
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRIS GOMEZ whose telephone number is (571) 272-0926. The examiner can normally be reached Mon-Fri 7-4 CDT.
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/CHRISTOPHER GOMEZ/
Examiner, Art Unit 3628
Prosecution Insights