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 .
Respond to Amendment
This action is in response to the amendment filed on ----6/17/2026 for application 19/029,232. Claim 1 – 20 are pending and have been examined.
Claim 1 and 14 are amended.
Respond to Argument
Applicant’s arguments with respect to claim(s) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1 – 7, 10, 12, 14 – 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saavedera, US20210012584 in view of Basir, US20140148972, with evidential reference of Bailey et al., (hereinafter Bailey), “Electric Vehicle Autonomy: Realtime Dynamic Route Planning and Range Estimation Software”.
Saavedera teaches a system for calculating available driving range based on the SOC and/or fuel of the vehicle and determines the route based on current location and the destination of the trip.
Basir teaches a system of monitoring the classified driving events and classified driving conditions (specifically road type such as dirt road, pavement and concrete) during a trip based on map data and sensor data which, when road condition deviates from map or historical record is detected, triggers to reconstruct the entire journey. Fuel efficiency is part of the gathered/captured conditional data for later use.
Claim 1. Saavedera discloses: A system, comprising a processor and a memory, the memory storing instructions executable by the processor (Fig. 1, ECU 102, Memory 104, 0018, “non-transitory memory … store instructions usable by the ECU 102 to drive autonomously, may store instructions usable by the ECU 102 to predict a driving range of the vehicle 100, or the like””), including instructions to:
generate a range prediction for the vehicle; actuate a vehicle component based on the range (Fig. 4A – 4B & 0042 – 0057, “ECU may predict a driving range of the vehicle based on the determined fuel economy, the profiles, the map data, the weather data, the traffic data, the route, and the remaining levels of fuel or electrical energy”, “ECU may be updating driving range and the planification of refueling/recharging based on real-time information about the route that would have impact in forecasted fuel economy”, “ECU may identify at least one recharging station or fuel station along the route”, “ECU may be updating driving range and the planification of refueling/recharging based on real-time information”, “The ECU may also control the output device to output the amount of fuel or electricity to be burned along the route along with the remaining amount of fuel or electricity”,” increase their specific fuel economy accelerate more slowly ... As another example, stop more gradually in order to increase energy generation during regenerative braking”, “the ECU may … control the vehicle to maneuver to the station”; i.e., the system calculates/predicts driving range and determines mitigation strategy such as accelerate/decelerate slower to increase fuel efficiency or navigate/steer the vehicle to charge/refuel station).
Saavedera does not explicitly teach:
detect a change of a classification of a ground surface from a prior ground surface on which a vehicle was traveling to a new ground surface on which the vehicle is traveling;
in response to detecting the change of classification of the ground surface, based on the classification of the new ground surface and an amount of stored energy in the vehicle, generate a new range prediction for the vehicle on the new ground surface wherein the new range prediction replaces a prior range prediction that was based on the prior ground surface
Basir, in the same field of endeavor, explicitly teach:
detect a change of a classification of a ground surface from a prior ground surface on which a vehicle was traveling to a new ground surface on which the vehicle is traveling; (Basir, 0019 – 0033, “Driving events … can be associated with classifications including: … road type (dirt road, pavement, concrete)”, “Although some of these driving events can be derived by cross-referencing basic in-vehicle information with external sources … For example, if external Sources are included, the road type can be quickly determined by cross referencing the location of the vehicle against a map dataset that has road types encoded. Unfortunately, even road types can change faster than the underlying map can be updated. Use of in-vehicle sources to infer road types ensures accurate and up-to-date information”, “The in-vehicle sensors typically employed are a 3-axis accelerometer paired with vehicle speed sensors. The time-series data describing high precision vehicle dynamics is then applied to classify specific driving events without requiring external inputs like map datasets”; i.e., system monitors/detects/classifies the ground surface type/class during traveling and the events can be associated with the detected ground surface change )
in response to detecting the change of classification of the ground surface, based on the classification of the new ground surface and an amount of stored energy in the vehicle, generate a new range prediction for the vehicle on the new ground surface wherein the new range prediction replaces a prior range prediction that was based on the prior ground surface (Basir, 0016, “The server 30 includes a plurality of profiles 32, each associated with a vehicle 10 (or alternatively, with a user). Among other things, the profiles 32 each contain information about the vehicle 10 (or user) including … fuel efficiency, environmental issues, location, maintenance, etc.”, “user can also customize some aspects of the profile 32.”; i.e., the fuel efficiency of different driving environment (road types) are stored as reference.
Saavedera and Basir both teach system and method for detecting and analyzing driving environment for vehicle and are analogous. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable likelihood of success to further include the real-time road surface type detection and classification taught by Basir’s in the system of Saavedera to achieve the claimed teaching. One of the ordinary skills in art would have motivated to make this modification to achieve “more accurate range prediction and efficient route planning” (Bailey, Abs.).
Claim 2. Saavedera and Basir combination renders obviousness of all the limitation of Claim 1. The combination further teach: predicting the vehicle range for the new ground surface ignores a prior predicted range from when the vehicle was traveling on the prior ground surface (Basir, 0055, “only the exceptions or deviations from a historically derived pattern need to be described and shared reconstruct the entire journey”; i.e., the system detect the deviation of the road surface/type and reconstruct/recalculate/replace the range using the detected power consumption profile to reconstruct a journey/plan).
The reason for combination is same as Claim 1.
Claim 3. Saavedera and Basir combination renders obviousness of all the limitation of Claim 2. The combination further teach: after predicting the vehicle range based on a stored energy consumption rate for the vehicle based on the classification of the new ground surface, thereby obtaining a predicted range for the new ground surface, then updating the predicted range for the new ground surface based on measuring energy consumption while the vehicle travels on the new ground surface (Basir, 0013 – 0017, “other data monitoring systems could be utilized within the contemplation of this invention. Data may also be collected from an onboard diagnostic port (OBD) 22 that provides data indicative of vehicle engine operating parameters such as … fuel consumption (or electricity consumption)”, “The in vehicle appliance 12 gathers data from the various sensors mounted within the vehicle 10 and stores that data.”, “The server 30 includes a plurality of profiles 32, each associated with a vehicle 10 (or alternatively, with a user). Among other things, the profiles 32 each contain information about the vehicle 10 (or user) including some or all of the gathered data (or summaries thereof) … Such as fuel efficiency (energy consumption rate), environmental issues, location, maintenance, etc.”, “the profiles 32 each contain information about the vehicle 10 (or user) including some or all of the gathered data (or summaries thereof)”; i.e., the energy consumption data used by the calculation is based on the data gathering during the operation of the vehicle in an environment).
The reason for combination is same as Claim 1.
Claim 4. Saavedera and Basir combination renders obviousness of all the limitation of Claim 1. The combination further teaches: predicting the vehicle range includes obtaining a stored energy consumption rate for the vehicle based on the classification of the new ground surface from an on-board memory or from a remote server (Basir, 0016 – 0018, “server 30”, “The server 30 may not only reside in traditional physical or virtual servers, but may also coexist with the on-board appliance, or may reside within a mobile device.”).
The reason for combination is same as Claim 1.
Claim 5. Saavedera and Basir combination renders obviousness of all the limitation of Claim 1. The combination further teaches: the vehicle component is a propulsion or a brake (Saavedera, 0055, ““The ECU may also control the output device to output the amount of fuel or electricity to be burned along the route along with the remaining amount of fuel or electricity”,” increase their specific fuel economy accelerate more slowly ... As another example, stop more gradually in order to increase energy generation during regenerative braking”).
Claim 6. Saavedera and Basir combination renders obviousness of all the limitation of Claim 1. The combination further teach: actuating the vehicle component includes actuating the computer or a second computer in the vehicle to determine a route for the vehicle based on the range (Saavedera, 0054 – 0057, “ECU may identify at least one recharging station or fuel station along the route”, “ECU may be updating driving range and the planification of refueling/recharging based on real-time information”, “ECU may control the output device to output navigation instructions to the recharging station or the fuel station, or may control the vehicle to maneuver to the station”).
Claim 7. Saavedera and Basir combination renders obviousness of all the limitation of Claim 1. The combination further teach: the vehicle component is a human-machine interface (HMI) that displays the range for the new ground surface (Saavedera, 0012 “present disclosure describes systems and methods for providing accurate fuel economy and driving range data to a user.” 0030, “The output device 140 may include any output device such as … a display, a touchscreen, or the like. The output device 140 may output data to a user of the vehicle.”).
Claim 10. Saavedera and Basir combination renders obviousness of all the limitation of Claim 1. The combination further teaches: classification of the ground surface is based on a surface roughness score determined from a plurality of types of vehicle sensors (Basir, 0033, “The in-vehicle sensors typically employed are a 3-axis accelerometer paired with vehicle speed sensors. The time-series data describing high precision vehicle dynamics”, “Use of commodity sensors (3-axis accelerometer) also ensures this approach can be applied to any moving vehicle, such as a trailer, construction vehicle, off-road vehicle, or passenger vehicle without requiring changes to the vehicle itself”; i.e., the system uses the 3 dimensional accelerations of the vehicle to detect the road type. One of ordinary skilled in the art would appreciate that the vertical and lateral vibration/acceleration/displacement represents the surface roughness. Such understanding can be easily found online, for example Giovanardi, US20240317008, 0057 – 0059).
The reason for combination is same as Claim 1.
Claim 12. Saavedera and Basir combination renders obviousness of all the limitation of Claim 1. The combination further teaches: the classification of the ground surface is based on map data (Basir, 0033, “road type can be quickly determined by cross referencing the location of the vehicle against a map dataset that has road types encoded.”).
The reason for combination is same as Claim 1.
Claim 14 – 19, these are the corresponding method claims of Claim 1, 2, 3 and 5 – 7 and thus are rejected with same reason.
Claim 20. Claim 20 recites optional limitations corresponding to Claim 9 – 13. Within BRI, only one option (surface roughness score of Claim 10) is required. Thus Claim 20 is rejected for the same reason as Claim 10.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saavedera, US20210012584 in view of Basir, US20140148972, with evidential reference of Bailey et al., (hereinafter Bailey), “Electric Vehicle Autonomy: Realtime Dynamic Route Planning and Range Estimation Software” as applied to Claim 7, and further in view of Friden et al., (hereinafter Friden), US20250118111.
Claim 8. Saavedera and Basir combination renders obviousness of all the limitation of Claim 7. The combination does not explicitly teach: the HMI displays a second range determined based on the prior ground surface in addition to the range for the new ground surface.
Friden, in the same field of endeavor, explicitly teach: the HMI displays a second range determined based on the prior ground surface in addition to the range for the new ground surface (Friden, fig. 5D & 0246 - 0248, “Once the data … is received or … is detected, the system may consider this new configuration of the vehicle and estimate the range. FIG. 5D shows an example message displayed on the infotainment system of the vehicle according to an embodiment” i.e., when the expected fuel consumption rate changes, the system recalculate/estimate the remaining range and prompt/alert both new and original range to user).
Saavedera and Basir combination and Friden both teach system and method for vehicle trip management based on SoC and driving range and are analogous. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable likelihood of success to further include the message/alert of Friden in the system of Saavedera and Basir combination to achieve the claimed teaching. One of the ordinary skills in the art would have motivated to make this modification to give user better information for the planning of the trip (Friden, 0002 – 0003).
Claim(s) 9, 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saavedera, US20210012584 in view of Basir, US20140148972, with evidential reference of Bailey et al., (hereinafter Bailey), “Electric Vehicle Autonomy: Realtime Dynamic Route Planning and Range Estimation Software” as applied to Claim 1, and further in view of Giovanardi et al., (hereinafter Giovanardi), US20240317008.
Claim 9. Saavedera and Basir combination renders obviousness of all the limitation of Claim 1. The combination does not explicitly teach: the classification of the ground surface is based on image data from a vehicle image sensor.
Giovanardi, in the same field of endeavor, explicitly teach:
The combination further teach: the classification of the ground surface is based on image data from a vehicle image sensor (Giovanardi, 0043, “Due to the changing nature, and previously unmapped layout, of a road surface, typically vehicles have sensed the interactions of a vehicle with the road surface and then operated the various autonomous and/or semi-autonomous systems of the vehicle in reaction to the detected characteristics and road surface features the vehicle encounters”; 0074, “any appropriate type of sensor capable of measuring height variations in the road surface, or other parameters related to height variations of the road surface (e.g., accelerations of one or more portions of a vehicle as it traverses a road surface) may be used as the disclosure is not so limited. For example, inertial measurement units (IMU s ), accelerometers, optical sensors ( e.g., cameras, LIDAR), radar, suspension position sensors, gyroscopes, and/or any other appropriate type of sensor may be used in the various embodiments disclosed herein to measure a road surface profile of a road segment a vehicle is traversing as the disclosure is not limited in this fashion.”).
Saavedera and Basir combination and Giovanardi both teach system and method for vehicle operation adjustment based on the road surface/type and are analogous. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable likelihood of success to further include the image sensor of Giovanardi in the system of Saavedera and Basir combination to achieve the claimed teaching since the optical sensor/camera is “capable of measuring height variations in the road surface, or other parameters related to height variations of the road surface” (Giovanardi, 0074); “This, in our view, presents strong evidence of obviousness in substituting one for the other” 209 USPQ at 759.(MPEP 2144.06).
Claim 11. Saavedera and Basir combination renders obviousness of all the limitation of Claim 1. The combination does not explicitly teach: the classification of the ground surface is based on a ground friction coefficient determined from vehicle sensor data.
Giovanardi, in the same field of endeavor, explicitly teach:
the classification of the ground surface is based on a ground friction coefficient determined from vehicle sensor data (Giovanardi, 0059, “a road profile may incorporate information about distributed road surface characteristics such as road roughness and/or road surface friction”).
Saavedera and Basir combination and Giovanardi both teach system and method for vehicle operation adjustment based on the road surface/type and are analogous. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable likelihood of success to further include the ground friction of Giovanardi in the system of Saavedera and Basir combination to achieve the claimed teaching. One of the ordinary skills in the art would have motivated to make this modification to better “incorporate information about distributed road surface characteristics” (Giovanardi, 0059).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Saavedera, US20210012584 in view of Basir, US20140148972, with evidential reference of Bailey et al., (hereinafter Bailey), “Electric Vehicle Autonomy: Realtime Dynamic Route Planning and Range Estimation Software” as applied to Claim 1, and further in view of TelleTire, “How Do New Tires Save Gas Millage”
Claim 13. Saavedera and Basir combination renders obviousness of all the limitation of Claim 1. The combination does not explicitly teach predict the vehicle range based on a tire type deployed on the vehicle in addition to the classification of the ground surface and the amount of stored energy in the vehicle.
TelleTire, in the same field of endeavor, explicitly teach:
predict the vehicle range based on a tire type deployed on the vehicle in addition to the classification of the ground surface and the amount of stored energy in the vehicle (TelleTire, “20% - 30% of fuel consumption and 24% of CO2 emissions from vehicles are actually tire-related”. Giovanardi teaches that “Vehicle information 110 may include … an estimated tire type, an estimated tire wear condition”, TelleTire explicitly point out that the tire information greatly affect the fuel consumption. The combination renders obviousness that the range calculation/prediction also based on the tire type of the vehicle information).
Saavedera and Basir combination and TelleTire both teach the consideration of driving range consideration and are analogous. It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention with a reasonable likelihood of success to further include the consideration of tire type taught by TelleTire in the system of Saavedera and Basir combination to achieve the claimed teaching. One of the ordinary skills in the art would have motivated to make this modification to better estimate/predict the remaining range of the vehicle.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure: Macneille et al., US20160076899, which teaches a method and device that calculates and displays remaining range as a boundary on human machine interface.
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 SHIEN MING CHOU whose telephone number is (571)272-9354. The examiner can normally be reached Monday- Friday 9 am - 5 pm.
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/SHIEN MING CHOU/Examiner, Art Unit 3667
/ANSHUL SOOD/Primary Examiner, Art Unit 3667
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