DETAILED ACTION
Receipt is acknowledged of applicant’s argument(s)/remark(s) filed on October 13, 2025, claims 1-20 are pending and an action on the merits is as follows. Applicant's arguments with respect to amended claims have been fully considered but are moot in view of the following new ground(s) of rejection. Applicant has amended claims 1, 8, 9, 10, 15, and 17.
Response to Argument
Regarding applicant’s arguments with respect to the amendment of the claims, applicant is kindly invited to consider the Office Action below to view the new ground of rejection, cited prior art(s)’ sections and motivation.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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 1-5, 7-12, and 14-19 are rejected under 35 U.S.C. 103 as being unpatentable over Cella (Pub No.: US 2021/0272394 A1) in view of Knudsen et al. (Pub. No.: US 20090123340 A1).
Regarding claim 1, Cella discloses an intelligent transportation method including digital twin interface for vehicle comprising:
receiving an input dataset (e.g., computing platform 60124 / processor 60132 receives input data from vehicle’s sensors 60108 to indicate / track one or more vehicle states – par. 497),
wherein the input dataset is associated with the recreational vehicle (e.g., parameter data from the vehicle (par. 46 and abstract), wherein the vehicle covers recreational vehicle), a plurality of vehicle components of the recreational vehicle (e.g., powertrain, suspension system, steering system, charging system, seats and other component of the vehicle – par. 497) and one or more performance factors (e.g., energy utilization state, component state, user experience state and others state / data from the vehicle ( par. 497) or vehicle’s real-time or near real time traffic activity, road condition and the like (par.511) ), and
wherein the plurality of vehicle components comprises materials and components used to construct one or more systems of the recreational vehicle (e.g., various components and materials of the vehicle including engine, wheels, interiors, exterior, color, accessories, etc. – par. 514),
Note, the specification discloses the term (i) “recreational vehicle” as a motor vehicle (par. 19) and (ii) “performance factor” as parameter data /signal related to the vehicle and its environment of operation; for instance, environment factor, material parameter, actual data observed, time, actual condition of vehicle component and other related data – see publication: par. 23, 38, 42, 43.
generating a digital twin of the recreational vehicle using the received input dataset (e.g., generating and operating “a digital twin 60136 of the vehicle 60104” based on input from vehicle sensors 60108 – par. 497); and
simulating, using the generated digital twin, one or more conditions on one or more of the plurality of vehicle components and the one or more performance factors (e.g., “run simulation to quality test the vehicle and its components in real world” (par. 31 and 504). For instance, “to run simulation to predict the behavior of the vehicle 60104 in a given scenario” (par. 513) – e.g., (i) behavior of the vehicle in case of a sudden tire blowout, (ii) vehicle trajectory in case of brake failure and impact on occupant and other vehicles, and (iii) structure impact of a head-on collision of the vehicle to determine safety of the occupant (par. 513));
determining one or more predicted conditions associated with an impact on the plurality of vehicle components and the one or more performance factors based on the simulating (e.g., generating “what-if scenarios” for quality testing a vehicle and predict how the vehicle will behave under such scenario (par. 513, 537) to model and visualize an effect of change of the vehicle and its components parameters on vehicle performance” (par. 31 and 504)); and
providing, to a user, a recommendation based on the one or more predicted conditions (e.g., the digital twin system configured to provide a service and maintenance view showing wear and failure of components of the vehicle and predict a need for service, repairs or replacement based on a condition of the vehicle – par. 49, 523))
Cella discloses vehicle state / performance control comprising vehicle configuration state, component states (par. 152), fuel efficiency, regenerative braking state and other vehicle states (par. 179); wherein evaluations uses feedback indicative of an effect of at least one of a state of performance of the vehicle (par. 179 and 180). Cella further discloses the digital twin system configured to provide a service and maintenance view showing wear and failure of components of the vehicle and predict a need for service, repairs or replacement based on a condition of the vehicle – par. 49, 523).
However, Cella failed to specifically disclose water quality and process performance information of a plumbing system as input dataset.
However, Knudsen et al. teach a water monitoring on a pipe / plumbing system for recreational vehicle (par. 38) configured to obtain water quality reading and transmitting it (par. 38 and 50) to a central facility for further analysis (par. 47). Figure 3 show a water line / plumbing system for a recreational vehicle (par. 36 and 38 and Figure 3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify the vehicle and the digital twin system taught by Cella, such that the vehicle comprises a water monitoring on a pipe / plumbing system configured to obtain water quality reading and transmitting it to a central facility for further analysis (par. 47), in view of Knudsen et al., with reasonable expectation of success, since doing so would have achieved the benefit of detecting changes in water quality and provide a response – e.g., outputting an audible or visible alarm – when a comparison of water quality measurement exceed a value / threshold (Knudsen et al., par. 10 and 32) while providing performance state of the vehicle component(s) (Cella par. 152, 179 and 180) (e.g., vehicle plumbing system) and providing service and maintenance view showing wear and failure the vehicle components for service, repairs or replacement based on a condition of the vehicle using the digital twin system (Cella’s par. 49 and 523).
Regarding claim 2, Cella discloses an intelligent transportation method wherein the input dataset is received from a real-time data feed connected to one or more of the plurality of vehicle components (e.g., energy utilization state, component state, user experience state and other state / data from the vehicle ( par. 497) or vehicle’s real-time or near real time traffic activity, road condition and the like (par.511)), and
wherein the method further comprises: identifying the one or more performance factors using data and information received from the real-time data feed (e.g., vehicle’s sensor(s) input related to energy utilization state, component state, user experience state and other state / data from the vehicle, wherein each state is identified based on sensor(s)’ input (par. 497), which covers real time data from the vehicle’s sensor(s)).
Regarding claim 3, Cella discloses an intelligent transportation method further comprising generating an augmented reality (AR) environment and/or a virtual reality (VR) environment, wherein the AR environment and/or VR environment comprises a digital model of the recreational vehicle in one or more environments (e.g., “an augmented reality (AR) view” is presented to a user and “enhances the screen with one or more elements from the digital twin 60136 of the vehicle 60104” – par. 508); and providing, on a graphical user interface, the predicted conditions associated with the impact on the one or more of the plurality of vehicle components and the one or more performance factors (e.g., displaying condition of the vehicle (par. 25 and 31) based on predicted behavior of the vehicle under a particular scenario (par. 513, 537).
Regarding claim 4, Cella discloses an intelligent transportation method wherein the plurality of vehicle components further comprises
how each of the materials and components is configured (e.g., the configurator view 60724 enables a user with configuring the various components and materials of the vehicle including engine, wheels, interiors, exterior, color, accessories, etc. – par. 514),
data information associated with each of the plurality of vehicle components, and one or more performance parameters (e.g., the service view 60332 presents information and view related to wear and failure of components of the vehicle 60104 – par. 509).
Regarding claim 5, Cella discloses an intelligent transportation method further comprising providing one or more users with remote maintenance access, wherein the remote maintenance access enables each of the one or more users to view, via an AR or VR headset, the digital model of the recreational vehicle (e.g., the digital twin system provide a service and maintenance view to a user for showing wear and failure of components of the vehicle and predict a need for service, repairs or replacement based on a condition of the vehicle condition – par. 49, 523); and
providing visual and/or auditory instructions to each of the one or more users, via the AR or VR headset, regarding how each of the one or more users should perform one or more maintenance plans (e.g., “an augmented reality (AR) view” is presented to a user and “enhances the screen with one or more elements from the digital twin 60136 of the vehicle 60104” (par. 508). “The digital twin 60136 assists the driver 60244 in resolving any issues related to the vehicle 60104 by diagnosing such issues and then indicating options for fixing them and/or adjusting an operating parameter or mode of the vehicle 60104 to mitigate a potential for such issues to continue or worsen” – par. 511).
Regarding claim 7, Cella discloses an intelligent transportation method further comprising providing, on the graphical user interface, how the recreational vehicle operates and performs in each of the one or more environments (e.g., “digital twin 60136” combines vehicle states – for instance, velocity, acceleration, climate, road grade and other data – “to run simulation to predict the behavior of the vehicle 60104 in a given scenario” (par. 513). For example, displaying (i) behavior of the vehicle in case of a sudden tire blowout, (ii) vehicle trajectory in case of brake failure and impact on occupant and other vehicles, and (iii) structure impact of a head-on collision of the vehicle to determine safety of the occupant (par. 513)).
Regarding claim 8, Cella discloses an intelligent transportation system comprising a memory 60126 for storing data and process by processor 60132 (par. 497) comprising
receiving an input dataset (e.g., computing platform 60124 / processor 60132 receives input data from vehicle’s sensors 60108 to indicate / track one or more vehicle states – par. 497),
wherein the input dataset is associated with the recreational vehicle (e.g., parameter data from the vehicle (par. 46 and abstract), wherein the vehicle covers recreational vehicle), a plurality of vehicle components of the recreational vehicle (e.g., powertrain, suspension system, steering system, charging system, seats and other component of the vehicle – par. 497) and one or more performance factors (e.g., energy utilization state, component state, user experience state and others state / data from the vehicle ( par. 497) or vehicle’s real-time or near real time traffic activity, road condition and the like (par.511) ), and
wherein the plurality of vehicle components comprises materials and components used to construct one or more systems of the recreational vehicle (e.g., various components and materials of the vehicle including engine, wheels, interiors, exterior, color, accessories, etc. – par. 514),
Note, the specification discloses the term (i) “recreational vehicle” as a motor vehicle (par. 19) and (ii) “performance factor” as parameter data /signal related to the vehicle and its environment of operation; for instance, environment factor, material parameter, actual data observed, time, actual condition of vehicle component and other related data – see publication: par. 23, 38, 42, 43.
generating a digital twin of the recreational vehicle using the received input dataset (e.g., generating and operating “a digital twin 60136 of the vehicle 60104” based on input from vehicle sensors 60108 – par. 497); and
simulating, using the generated digital twin, one or more conditions on one or more of the plurality of vehicle components and the one or more performance factors (e.g., “run simulation to quality test the vehicle and its components in real world” (par. 31 and 504). For instance, “to run simulation to predict the behavior of the vehicle 60104 in a given scenario” (par. 513) – e.g., (i) behavior of the vehicle in case of a sudden tire blowout, (ii) vehicle trajectory in case of brake failure and impact on occupant and other vehicles, and (iii) structure impact of a head-on collision of the vehicle to determine safety of the occupant (par. 513));
determining one or more predicted conditions associated with an impact on the plurality of vehicle components and the one or more performance factors based on the simulating (e.g., generating “what-if scenarios” for quality testing a vehicle and predict how the vehicle will behave under such scenario (par. 513, 537) to model and visualize an effect of change of the vehicle and its components parameters on vehicle performance” (par. 31 and 504)); and
providing, to a user, a recommendation based on the one or more predicted conditions (e.g., the digital twin system configured to provide a service and maintenance view showing wear and failure of components of the vehicle and predict a need for service, repairs or replacement based on a condition of the vehicle – par. 49, 523))
Cella discloses vehicle state / performance control comprising vehicle configuration state, component states (par. 152), fuel efficiency, regenerative braking state and other vehicle states (par. 179); wherein evaluations uses feedback indicative of an effect of at least one of a state of performance of the vehicle (par. 179 and 180). Cella further discloses the digital twin system configured to provide a service and maintenance view showing wear and failure of components of the vehicle and predict a need for service, repairs or replacement based on a condition of the vehicle – par. 49, 523).
However, Cella failed to specifically disclose water quality and process performance information of a plumbing system as input dataset.
However, Knudsen et al. teach a water monitoring on a pipe / plumbing system for recreational vehicle (par. 38) configured to obtain water quality reading and transmitting it (par. 38 and 50) to a central facility for further analysis (par. 47). Figure 3 show a water line / plumbing system for a recreational vehicle (par. 36 and 38 and Figure 3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify the vehicle and the digital twin system taught by Cella, such that the vehicle comprises a water monitoring on a pipe / plumbing system configured to obtain water quality reading and transmitting it to a central facility for further analysis (par. 47), in view of Knudsen et al., with reasonable expectation of success, since doing so would have achieved the benefit of detecting changes in water quality and provide a response – e.g., outputting an audible or visible alarm – when a comparison of water quality measurement exceed a value / threshold (Knudsen et al., par. 10 and 32) while providing performance state of the vehicle component(s) (Cella par. 152, 179 and 180) (e.g., vehicle plumbing system) and providing service and maintenance view showing wear and failure the vehicle components for service, repairs or replacement based on a condition of the vehicle using the digital twin system (Cella’s par. 49 and 523).
Regarding claim 9, Cella discloses an intelligent transportation system, wherein the input dataset is received from a real-time data feed connected to one or more of the plurality of vehicle components (e.g., energy utilization state, component state, user experience state and other state / data from the vehicle ( par. 497) or vehicle’s real-time or near real time traffic activity, road condition and the like (par.511)), and
wherein the method further comprises: identifying the one or more performance factors using data and information received from the real-time data feed (e.g., vehicle’s sensor(s) input related to energy utilization state, component state, user experience state and other state / data from the vehicle, wherein each state is identified based on sensor(s)’ input (par. 497), which covers real time data from the vehicle’s sensor(s)).
Regarding claim 10, Cella discloses an intelligent transportation system, further comprising generating an augmented reality (AR) environment and/or a virtual reality (VR) environment, wherein the AR environment and/or VR environment comprises a digital model of the recreational vehicle in one or more environments (e.g., “an augmented reality (AR) view” is presented to a user and “enhances the screen with one or more elements from the digital twin 60136 of the vehicle 60104” – par. 508); and providing, on a graphical user interface, the predicted conditions associated with the impact on the one or more of the plurality of vehicle components and the one or more performance factors (e.g., displaying condition of the vehicle (par. 25 and 31) based on predicted behavior of the vehicle under a particular scenario (par. 513, 537).
Regarding claim 11, Cella discloses an intelligent transportation system, wherein the plurality of vehicle components further comprises
how each of the materials and components is configured (e.g., the configurator view 60724 enables a user with configuring the various components and materials of the vehicle including engine, wheels, interiors, exterior, color, accessories, etc. – par. 514),
data information associated with each of the plurality of vehicle components, and one or more performance parameters (e.g., the service view 60332 presents information and view related to wear and failure of components of the vehicle 60104 – par. 509).
Regarding claim 12, Cella discloses an intelligent transportation system, further comprising providing one or more users with remote maintenance access, wherein the remote maintenance access enables each of the one or more users to view, via an AR or VR headset, the digital model of the recreational vehicle (e.g., the digital twin system provide a service and maintenance view to a user for showing wear and failure of components of the vehicle and predict a need for service, repairs or replacement based on a condition of the vehicle condition – par. 49, 523); and
providing visual and/or auditory instructions to each of the one or more users, via the AR or VR headset, regarding how each of the one or more users should perform one or more maintenance plans (e.g., “an augmented reality (AR) view” is presented to a user and “enhances the screen with one or more elements from the digital twin 60136 of the vehicle 60104” (par. 508). “The digital twin 60136 assists the driver 60244 in resolving any issues related to the vehicle 60104 by diagnosing such issues and then indicating options for fixing them and/or adjusting an operating parameter or mode of the vehicle 60104 to mitigate a potential for such issues to continue or worsen” – par. 511).
Regarding claim 14, Cella discloses an intelligent transportation system, further comprising providing, on the graphical user interface, how the recreational vehicle operates and performs in each of the one or more environments (e.g., “digital twin 60136” combines vehicle states – for instance, velocity, acceleration, climate, road grade and other data – “to run simulation to predict the behavior of the vehicle 60104 in a given scenario” (par. 513). For example, displaying (i) behavior of the vehicle in case of a sudden tire blowout, (ii) vehicle trajectory in case of brake failure and impact on occupant and other vehicles, and (iii) structure impact of a head-on collision of the vehicle to determine safety of the occupant (par. 513)).
Regarding claim 15, Cella discloses a memory for storing one or more programs / instructions to be executed by processor(s) (par. 589, 598) for an intelligent transportation system having digital twin interface for vehicle, the instruction comprising:
receiving an input dataset (e.g., computing platform 60124 / processor 60132 receives input data from vehicle’s sensors 60108 to indicate / track one or more vehicle states – par. 497),
wherein the input dataset is associated with the recreational vehicle (e.g., parameter data from the vehicle (par. 46 and abstract), wherein the vehicle covers recreational vehicle), a plurality of vehicle components of the recreational vehicle (e.g., powertrain, suspension system, steering system, charging system, seats and other component of the vehicle – par. 497) and one or more performance factors (e.g., energy utilization state, component state, user experience state and others state / data from the vehicle ( par. 497) or vehicle’s real-time or near real time traffic activity, road condition and the like (par.511) ), and
wherein the plurality of vehicle components comprises materials and components used to construct one or more systems of the recreational vehicle (e.g., various components and materials of the vehicle including engine, wheels, interiors, exterior, color, accessories, etc. – par. 514),
Note, the specification discloses the term (i) “recreational vehicle” as a motor vehicle (par. 19) and (ii) “performance factor” as parameter data /signal related to the vehicle and its environment of operation; for instance, environment factor, material parameter, actual data observed, time, actual condition of vehicle component and other related data – see publication: par. 23, 38, 42, 43.
generating a digital twin of the recreational vehicle using the received input dataset (e.g., generating and operating “a digital twin 60136 of the vehicle 60104” based on input from vehicle sensors 60108 – par. 497); and
simulating, using the generated digital twin, one or more conditions on one or more of the plurality of vehicle components and the one or more performance factors (e.g., “run simulation to quality test the vehicle and its components in real world” (par. 31 and 504). For instance, “to run simulation to predict the behavior of the vehicle 60104 in a given scenario” (par. 513) – e.g., (i) behavior of the vehicle in case of a sudden tire blowout, (ii) vehicle trajectory in case of brake failure and impact on occupant and other vehicles, and (iii) structure impact of a head-on collision of the vehicle to determine safety of the occupant (par. 513));
determining one or more predicted conditions associated with an impact on the plurality of vehicle components and the one or more performance factors based on the simulating (e.g., generating “what-if scenarios” for quality testing a vehicle and predict how the vehicle will behave under such scenario (par. 513, 537) to model and visualize an effect of change of the vehicle and its components parameters on vehicle performance” (par. 31 and 504)); and
providing, to a user, a recommendation based on the one or more predicted conditions (e.g., the digital twin system configured to provide a service and maintenance view showing wear and failure of components of the vehicle and predict a need for service, repairs or replacement based on a condition of the vehicle – par. 49, 523))
Cella discloses vehicle state / performance control comprising vehicle configuration state, component states (par. 152), fuel efficiency, regenerative braking state and other vehicle states (par. 179); wherein evaluations uses feedback indicative of an effect of at least one of a state of performance of the vehicle (par. 179 and 180). Cella further discloses the digital twin system configured to provide a service and maintenance view showing wear and failure of components of the vehicle and predict a need for service, repairs or replacement based on a condition of the vehicle – par. 49, 523).
However, Cella failed to specifically disclose water quality and process performance information of a plumbing system as input dataset.
However, Knudsen et al. teach a water monitoring on a pipe / plumbing system for recreational vehicle (par. 38) configured to obtain water quality reading and transmitting it (par. 38 and 50) to a central facility for further analysis (par. 47). Figure 3 show a water line / plumbing system for a recreational vehicle (par. 36 and 38 and Figure 3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify the vehicle and the digital twin system taught by Cella, such that the vehicle comprises a water monitoring on a pipe / plumbing system configured to obtain water quality reading and transmitting it to a central facility for further analysis (par. 47), in view of Knudsen et al., with reasonable expectation of success, since doing so would have achieved the benefit of detecting changes in water quality and provide a response – e.g., outputting an audible or visible alarm – when a comparison of water quality measurement exceed a value / threshold (Knudsen et al., par. 10 and 32) while providing performance state of the vehicle component(s) (Cella par. 152, 179 and 180) (e.g., vehicle plumbing system) and providing service and maintenance view showing wear and failure the vehicle components for service, repairs or replacement based on a condition of the vehicle using the digital twin system (Cella’s par. 49 and 523).
Regarding claim 16, Cella discloses a memory for intelligent transportation, wherein the input dataset is received from a real-time data feed connected to one or more of the plurality of vehicle components (e.g., energy utilization state, component state, user experience state and other state / data from the vehicle ( par. 497) or vehicle’s real-time or near real time traffic activity, road condition and the like (par.511)), and
wherein the method further comprises: identifying the one or more performance factors using data and information received from the real-time data feed (e.g., vehicle’s sensor(s) input related to energy utilization state, component state, user experience state and other state / data from the vehicle, wherein each state is identified based on sensor(s)’ input (par. 497), which covers real time data from the vehicle’s sensor(s)).
Regarding claim 17, Cella discloses a memory for intelligent transportation, further comprising generating an augmented reality (AR) environment and/or a virtual reality (VR) environment, wherein the AR environment and/or VR environment comprises a digital model of the recreational vehicle in one or more environments (e.g., “an augmented reality (AR) view” is presented to a user and “enhances the screen with one or more elements from the digital twin 60136 of the vehicle 60104” – par. 508); and providing, on a graphical user interface, the predicted conditions associated with the impact on the one or more of the plurality of vehicle components and the one or more performance factors (e.g., displaying condition of the vehicle (par. 25 and 31) based on predicted behavior of the vehicle under a particular scenario (par. 513, 537).
Regarding claim 18, Cella discloses a memory for intelligent transportation, wherein the plurality of vehicle components further comprises
how each of the materials and components is configured (e.g., the configurator view 60724 enables a user with configuring the various components and materials of the vehicle including engine, wheels, interiors, exterior, color, accessories, etc. – par. 514),
data information associated with each of the plurality of vehicle components, and one or more performance parameters (e.g., the service view 60332 presents information and view related to wear and failure of components of the vehicle 60104 – par. 509).
Regarding claim 19, Cella discloses a memory for intelligent transportation, further comprising providing one or more users with remote maintenance access, wherein the remote maintenance access enables each of the one or more users to view, via an AR or VR headset, the digital model of the recreational vehicle (e.g., the digital twin system provide a service and maintenance view to a user for showing wear and failure of components of the vehicle and predict a need for service, repairs or replacement based on a condition of the vehicle condition – par. 49, 523); and
providing visual and/or auditory instructions to each of the one or more users, via the AR or VR headset, regarding how each of the one or more users should perform one or more maintenance plans (e.g., “an augmented reality (AR) view” is presented to a user and “enhances the screen with one or more elements from the digital twin 60136 of the vehicle 60104” (par. 508). “The digital twin 60136 assists the driver 60244 in resolving any issues related to the vehicle 60104 by diagnosing such issues and then indicating options for fixing them and/or adjusting an operating parameter or mode of the vehicle 60104 to mitigate a potential for such issues to continue or worsen” – par. 511).
Claims 6, 13, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Cella (Pub No.: US 2021/0272394 A1) in view of Knudsen et al. (Pub. No.: US 20090123340 A1) and Shiraishi et al. (Pub. No.: US 2019/0384870 A1).
Regarding claim 6, 13 and 20, Cella, as modified by Knudsen et al., failed to specifically disclose updating, continuously, the AR and/or VR environment and the generated digital twin after corresponding to one or more actions performed on the digital model of the recreational vehicle by the one or more users.
However, Shiraishi et al. teach a mechanism / process for continuously updating a digital twin of a vehicle based on maintenance performed on the vehicle by dealerships, repair shop and others (par. 196, 30).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to further modify the intelligent transportation system and method for a digital twin of a vehicle as taught by the combination of Cella in view of Knudsen et al., such that the digital twin is updated based maintenance performed on the vehicle, with reasonable expectation of success since doing so would have achieved the benefit of having digital twin consistent with the real world condition and behavior of the vehicle (par. 15, 17).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 6058718 A - Portable, Potable Water Recovery And Dispensing Apparatus for Recreational Vehicle.
US 20030189002 A1 - Composite Water Filter for recreational vehicle.
US 20100282654 A1 – Integrated Water Processing Technology for water quality and recreational vehicle.
THIS ACTION IS MADE FINAL. 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 Jorge O. Peche whose telephone number is (571)270-1339. The examiner can normally be reached Monday-Friday 8:30 AM - 5:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Khoi H. Tran can be reached on 571 272 6919. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Jorge O Peche/ Examiner, Art Unit 3656
/SPENCER D PATTON/Primary Examiner, Art Unit 3656
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