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 is in response to Applicant’s case, no. 19/100,064, with an effective filing date of 1/30/2025. Claims 1-4 and 6-12 are currently pending. Claim 5 has been canceled.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 8/31/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner.
Response to Amendments
In response to Applicant’s amendments dated June 26, 2026, Examiner withdraws the previous specification objections; withdraws the previous claim objections; and maintains the previous indefiniteness, patentability, and prior art rejections.
Response to Arguments
Applicant's arguments, filed June 26, 2026, have been fully considered but they are not persuasive.
Regarding the rejection of claims 1-12 under 35 USC § 101 as being patent ineligible because the claimed invention is directed to an abstract idea without significantly more, the Applicant argues that the claim is directed to a specific process for generating and evaluating verification cycles for vehicle development through a sequence of technical operations. However, the verifying validity required for vehicle development is construed as a mental process as “verifying validity …,” which in the context of this claim, encompasses a person looking at data collected and forming a simple judgement. Further, the Applicant argues that generating verification cycle is not insignificant extra-solution activity and that it is effectively impossible to cover the boundary conditions for many vehicles using a single conventional verification cycle based on historical data and experience, the Examiner submits that this is construed as performing repetitive calculations using a known data source which is a form of insignificant activity. see MPEP 2106.05(d) in re: Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012). Further, the Applicant argues that claimed invention addresses this technological problem by generating realistic verification cycles that satisfy boundary conditions for a plurality of vehicles, however the claimed invention does not properly address how “to improve the functioning of the computer itself" as it is interpreted that the invention is a mere automation of manual processes, such as using a generic computer, by inputting a plurality of cycles in order to validate and select a representative cycle. See MPEP 2106.05(a). Lastly, the Applicant argues that claimed process is applied within a specific vehicle-development testing environment and is not merely a generic computer implementation because of the carrying out of each generated verification cycle on a simulation device, an actual machine, or a combination thereof. However, under broadest reasonable interpretation the carrying out can be done solely on a simulation device (i.e., without an actual machine) and the simulation device is described by the Applicant in their disclosure in [0018] as a computer (i.e., a generic computer).
Therefore, these arguments are not persuasive.
Regarding the prior art rejection of claims 1-2, 4-8, 11-12 under 35 USC § 102 as being anticipated by Krysmon et al. article titled, “Real Driving Emissions—Conception of a Data-Driven Calibration Methodology for Hybrid Powertrains Combining Statistical Analysis and Virtual Calibration Platforms” [hereinafter referred to as Krysmon], the Applicant argues regarding claim 1, see pp.8-10, that Krymson fails to disclose the limitations clustering each of the plurality of datasets based on parameters of vehicle speed and/or acceleration of a vehicle, and accelerator opening and/or engine torque, and classifying the plurality of datasets into a plurality of data groups according to classes of the parameters. However, as detailed below, Fig. 4 and pg.8 ¶1 disclose comparisons between the vehicle speed v and the derivation of it (acceleration), the relative air charge rl and its derivation, the engine speed n and change of engine speed as well as the change of selected gear, where engine speed is inversely related to engine torque and may be derived. Further, Fig 5. discloses an “event” comprising pedal position, acceleration, velocity, engine load, torque. Further, Fig 6. discloses data clustering and classification. Further, pg.2 ¶7 s.2, discloses that instead of static accelerations and speed levels, the WLTC describes a more dynamic test profile used for standard tests on the chassis dynamometer, which is interpreted as a device used to measure the power output, torque, and performance of a vehicle by simulating road conditions in a controlled environment Further, pg.4 ¶2 s.2 discloses profiles are usually based on speed sequences that are supposed to be highly critical for the Original Equipment Manufacturers (OEM’s) vehicles and especially to the performance of the exhaust aftertreatment system (EATS). These are construed as clustering and classifying vehicle events based at least upon parameters of vehicle speed, acceleration of a vehicle, accelerator opening, and engine torque.
Further regarding claim1, Applicant argues regarding the limitation generating a plurality of verification cycles based on the plurality of clustered data groups using a Markov chain. However, the new driving profiles are construed as being based on the events described above and Markov chains are used to develop most probable sequences. Further, on pg. 15 ¶3 s.2 a cycle is performed multiple times with a virtual vehicle using the Matlab Simulink toolbox Powertrain Blockset. Lastly, on pg.17 ¶6, discloses dynamic generation of statistically relevant scenarios (i.e., selecting a cycle from a plurality of candidate cycles) being performed in many environments. System evaluations can be carried out directly in the Model-in-the-Loop (MiL) environment (i.e., a simulation), while the cycle is being created. Hardware-in-the-Loop test bench (HiL) and Engine-in-the-Loop testing (EiL) applications (i.e., an actual machine) can be used by either first generating the cycle in the MiL environment and then feeding the static profile into the corresponding HiL and EiL control or by generating the profile dynamically during the HiL or EiL operation. This is interpreted as generating a plurality of verification cycles based on a plurality of clustered groups and carrying out verification of those cycles on a simulation device, an actual machine, and/or a combination thereof.
Therefore, these arguments are not persuasive.
Regarding independent claims 11-12, Applicant argues, while differing in scope, these claims recite similar features to claim 1 and their rejections should likewise be withdrawn.
However, this argument is unpersuasive for the same reasons as given above.
Applicant argues that the dependent claims are patentable by virtue of their dependency.
This argument is unpersuasive as each independent claim has been fully rejected for the reasons as given above.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 4 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 4, the claim recites the limitation one of the verification cycles, and it is unclear if this is the same verification cycle as the plurality of verification cycles stated in claim 1 line 6 and claim 4 line 2 or the representative cycle as recited in claim 1 lines 9-10. For purposes of prosecution, the limitation is interpreted to mean the plurality of cycles as they are the ones generated in claim 1 as opposed to the representative cycle which is selected.
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 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The determination of whether a claim recites patent ineligible subject matter is a 2 step inquiry.
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), see MPEP 2106.03, or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: see MPEP 2106.04
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? see MPEP 2106.04(II)(A)(1)
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? see MPEP 2106.04(II)(A)(2)
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? see MPEP 2106.05
101 Analysis – Step 1
Claim 1 is directed to a method of generating a verification cycle (i.e., a process). Therefore, claim 1 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. see MPEP 2106(A)(II)(1) and MPEP 2106.04(a)-(c)
Independent claim 1 includes limitations that recite an abstract idea (emphasized below [with the category of abstract idea in brackets]) and will be used as a representative claim for the remainder of the 101 rejection.
Claim 1 recites:
A verification-cycle generation method that generates a verification cycle for vehicle development, comprising:
inputting a plurality of datasets concerning a drive cycle;
clustering each of the plurality of datasets based on parameters of vehicle speed and/or acceleration of a vehicle, and accelerator opening and/or engine torque, and classifying the plurality of datasets into a plurality of data groups according to classes of the parameters;
generating a plurality of verification cycles for verifying validity required for vehicle development based on the plurality of clustered data groups using a Markov chain; and
carrying out each of the plurality of verification cycles on a simulation device, on an actual machine, or on a combination thereof, and selecting a representative cycle that best satisfies user- defined criteria from among the plurality of verification cycles [mental process/step].
The Examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “verifying…,” and “selecting…” in the context of this claim encompasses a person looking at data collected and forming a simple judgement. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. see MPEP 2106.04(II)(A)(2) and MPEP 2106.04(d)(2). It must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” [with a description of the additional limitations in brackets], while the bolded portions continue to represent the “abstract idea”.):
A verification-cycle generation method that generates a verification cycle for vehicle development, comprising:
inputting a plurality of datasets concerning a drive cycle [insignificant extra-solution activity (data gathering)];
clustering each of the plurality of datasets based on parameters of vehicle speed and/or acceleration of a vehicle, and accelerator opening and/or engine torque, and classifying the plurality of datasets into a plurality of data groups according to classes of the parameters [insignificant extra-solution activity (performing repetitive calculations)];
generating a plurality of verification cycles [insignificant extra-solution activity (performing repetitive calculations)] for verifying validity required for vehicle development based on the plurality of clustered data groups using a Markov chain [applying the abstract idea using generic computing module]; and
carrying out each of the plurality of verification cycles on a simulation device , on an actual machine, or on a combination thereof [applying the abstract idea using generic computing module] and selecting a representative cycle that best satisfies user- defined criteria from among the plurality of verification cycles.
For the following reason(s), the Examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “inputting…,” “clustering…,” “classifying…,” and “generating…,” the Examiner submits that these limitations are insignificant extra-solution activities that merely use a computer to perform the process. In particular, the inputting step is recited at a high level of generality (i.e. as a general means of gathering vehicle speed and load data for use in the selecting step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The clustering, classifying, and generating steps are also recited at a high level of generality (i.e. as a general means of manipulating the data from the inputting step), and amounts to mere data source manipulation, which is a form of insignificant extra-solution activity. Lastly, the using and carrying out steps is recited at a high-level of generality (i.e., interpreted as being an equivalent to “apply it” language that utilizes a generic processor to apply the generic computer function to execute a known algorithm of iteratively running a simulation based on input data) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception. see MPEP § 2106.05. Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the Revised Guidance, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a vehicle controller to perform the selecting … amounts to nothing more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of “inputting…,” “clustering…,” “classifying…,” and “generating…,” the Examiner submits that these limitations are insignificant extra-solution activities.
A conclusion that an additional element is insignificant extra solution activity in Step 2A must be re-evaluated in Step 2B to determine if the element is more than what is well-understood, routine, and conventional in the field. In this case, the additional limitations of acquiring a set of points at a current time and acquiring an attitude of the vehicle at the current time are well-understood, routine, and conventional activities, because they have all been deemed insignificant extra solution activity by one or more Courts; see at least MPEP 2106.05(d) and MPEP 2106.05(g):
“inputting…,” amount to no more than what is well-understood, routine and conventional activity under buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
“clustering…,” “classifying…,” amount to no more than what is well-understood, routine and conventional activity under Performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting values).
“generating…,” amount to no more than what is well-understood, routine and conventional activity under Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("computer… is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.").
In addition, these additional limitations (and the combination, thereof) amount to no more than what is well-understood, routine and conventional activity.
Hence, claim 1 is not patent eligible.
Independent claims 11-12 recite a system and a non-transitory computer readable medium, respectively, having substantially the same features of claim 1 above, therefore claims 11-12 is rejected for the same reasons as claim 1.
Dependent claim(s) 2-10 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application as they provide for further examples of mental processes such as selecting and additional limitations that also do not integrate the judicial exception into a practical application such as adjust, input and carry out. Therefore, dependent claims 2-10 are not patent eligible under the same rationale as provided for in the rejection of independent claims 1 and 11-12.
Therefore, claims 1-12 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 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.
Claims 1-2, 4-8, 11-12 are rejected under 102(a)(1) as being anticipated by Krysmon et al. article titled, “Real Driving Emissions—Conception of a Data-Driven Calibration Methodology for Hybrid Powertrains Combining Statistical Analysis and Virtual Calibration Platforms”, hereinafter referred to as Krysmon.
Regarding claim 1, Krysmon discloses:
A verification-cycle generation method that generates a verification cycle for vehicle development, comprising:
inputting a plurality of datasets concerning a drive cycle (pg. 5 ¶2 sentence (s.) 6, the generated cycle mainly relies on sufficient variance of the input data and is—similarly as for all the current existing approaches—fixed before the test starts and pg. 7 ¶4 s. 1-2, the systems can be used to support the calibration process by being operated based on a data-driven methodology to validate and optimize existing datasets);
clustering each of the plurality of datasets based on parameters of vehicle speed and/or acceleration of a vehicle, and accelerator opening and/or engine torque, and classifying the plurality of datasets into a plurality of data groups according to classes of the parameters (Fig. 4, below, where comparisons between the vehicle speed v and the derivation of it (acceleration), the relative air charge rl and its derivation, the engine speed n and change of engine speed as well as the change of selected gear, where engine speed is inversely related to engine torque and may be derived at constant power; Fig 5., below, where an “event” comprises pedal position, acceleration, velocity, engine load, torque; Fig 6., below, which describes data clustering and classification; pg.2 ¶7 s.2, Instead of static accelerations and speed levels, the WLTC describes a more dynamic test profile used for standard tests on the chassis dynamometer, which is interpreted as a device used to measure the power output, torque, and performance of a vehicle by simulating road conditions in a controlled environment; pg.4 ¶2 s.2 profiles are usually based on speed sequences that are supposed to be highly critical for the Original Equipment Manufacturers (OEM’s) vehicles and especially to the performance of the exhaust aftertreatment system (EATS); and pg. 7 ¶5 s. 4 Similarities in signals must be clustered to get an idea of patterns that typically represent critical sequences which is construed as clustering and classifying datasets into data groups);
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generating a plurality of verification cycles for verifying validity required for vehicle development based on the plurality of clustered data groups using a Markov chain (pg.12 ¶4 s. 2, different calibration datasets of a vehicle can be compared with each other throughout the development process in order to quantify the success achieved in terms of system efficiency enhancement due to the hybrid operating strategy or the reduction of emissions, which is construed as verifying the validity required for vehicle development; pg.17 ¶6 s. 1, dynamic generation of such statistically relevant scenarios can be performed for multiple environments; and pg.4 ¶4 s.9, discloses use of Markov chains to develop new driving profiles that are based on events); and
carrying out each of the plurality of verification cycles on a simulation device, on an actual machine, or on a combination thereof (pg.4 ¶3 s.2,generation of such test scenarios is often associated with either simulation or engine test bench measurements), and selecting a representative cycle that best satisfies user- defined criteria from among the plurality of verification cycles (pg.3 ¶4 s.4, representative Real Driving Emissions (RDE) cycle driven with the same vehicle as for the shown NEDC and WLTC extends the used range even further, in theory only restricted by the engine limits itself; pg. 15 ¶3 s.2 of a cycle is performed multiple times with a virtual vehicle using the Matlab Simulink toolbox Powertrain Blockset; and pg.17 ¶6, discloses dynamic generation of statistically relevant scenarios (i.e., a plurality of candidate cycles) being performed in many environments where system evaluations can be carried out directly in the Model-in-the-Loop (MiL) environment (i.e., a simulation device), while the cycle is being created, then Hardware-in-the-Loop test bench (HiL) and Engine-in-the-Loop testing (EiL) applications (i.e., an actual machine) can be used by either first generating the cycle in the MiL environment and then feeding the static profile (i.e., selecting a representative cycle that best satisfies defined criteria from among the plurality of verification cycles) into the corresponding HiL and EiL control or by generating the profile dynamically during the HiL or EiL operation).
Claims 11-12 recite a system and a non-transitory computer readable medium, respectively, having substantially the same features of claim 1 above, therefore claims 11-12 is rejected for the same reasons as claim 1.
Regarding claim 2, Krysmon discloses:
The verification-cycle generation method according to claim 1, wherein the
dataset includes vehicle speed information regarding vehicle speed and load information regarding load (pg.2 ¶7 s.2, Instead of static accelerations and speed levels, the WLTC describes a more dynamic test profile used for standard tests on the chassis dynamometer, which is interpreted as a device used to measure the power output, torque, and performance of a vehicle by simulating road conditions in a controlled environment which is also construed as load information based on the Applicant’s description in the disclosure ([0026]) and pg.4 ¶2 s1-2, Fleet-generic test cycles are frequently used by the Original Equipment Manufacturers (OEM) to validate many different vehicles with the same test profile and these profiles are usually based on speed sequences that are supposed to be highly critical for the OEM’s vehicles and especially to the performance of the EATS which is construed as including vehicle speed information as an input).
Regarding claim 4, Krysmon discloses:
The verification-cycle generation method according to claim 1, wherein a plurality of verification cycles are generated by carrying out a series of processes a plurality of times (pg.7 ¶6 s.3, means of moving average windows of different durations, the short- and long-term distance specific emission intensity is validated for each point of time of all available measurement data.), in which each of the plurality of datasets is clustered and classified into a plurality of data groups, and one of the verification cycles is generated based on the plurality of clustered data groups (Fig. 4, above, where comparisons between the vehicle speed v and the derivation of it (acceleration), the relative air charge rl and its derivation, the engine speed n and change of engine speed as well as the change of selected gear, where engine speed is inversely related to engine torque and may be derived at constant power; Fig 5., above, where an “event” comprises pedal position, acceleration, velocity, engine load, torque; Fig 6., above, which describes data clustering and classification; pg.2 ¶7 s.2, Instead of static accelerations and speed levels, the WLTC describes a more dynamic test profile used for standard tests on the chassis dynamometer, which is interpreted as a device used to measure the power output, torque, and performance of a vehicle by simulating road conditions in a controlled environment; pg.4 ¶2 s.2 profiles are usually based on speed sequences that are supposed to be highly critical for the Original Equipment Manufacturers (OEM’s) vehicles and especially to the performance of the exhaust aftertreatment system (EATS); and pg. 7 ¶5 s. 4 Similarities in signals must be clustered to get an idea of patterns that typically represent critical sequences which is construed as clustering and classifying datasets into data groups).
Regarding claim 5, the Applicant has elected to cancel the claim and therefore the claim is no longer under consideration.
Regarding claim 6, Krysmon discloses:
The verification-cycle generation method according to claim 1, wherein the dataset is adjusted by assigning a predetermined adjustment factor to the dataset to be input (pg. 4 ¶4 s.3-4 discloses computational adjustment factors of the load points and specific adjustments performed on the chassis dynamometer).
Regarding claim 7, Krysmon discloses:
The verification-cycle generation method according to claim 1, wherein the user-defined criteria is a criterion for selecting a worst verification cycle (pg. 4 1 2nd bullet, Worst-case cycles obtained by simulation construed as a criterion to select the worst verification cycle), a criterion for selecting a best verification cycle, or a criterion for selecting a most frequently used verification cycle among the plurality of verification cycles.
Regarding claim 8, Krysmon discloses:
The verification-cycle generation method according to claim 1, wherein the verification cycle is one related to an on-road emissions test (pg. 4 1 s.2, mandatory road tests, and ¶4 s.7 geodetic road profiles, ¶4 s.8 road altitude and gradient information), and
the plurality of input datasets include vehicle speed information and load information in the on-road emissions test (pg.2 ¶7 s.2, Instead of static accelerations and speed levels, the WLTC describes a more dynamic test profile used for standard tests on the chassis dynamometer, which is interpreted as a device used to measure the power output, torque, and performance of a vehicle by simulating road conditions in a controlled environment which is also construed as load information based on the Applicant’s description in the disclosure ([0026]) and pg.4 ¶2 s1-2, Fleet-generic test cycles are frequently used by the Original Equipment Manufacturers (OEM) to validate many different vehicles with the same test profile and these profiles are usually based on speed sequences that are supposed to be highly critical for the OEM’s vehicles and especially to the performance of the EATS which is construed as including vehicle speed information as an input).
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 (i.e., changing from AIA to pre-AIA ) 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:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 3 and 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Krysmon et al. article titled, “Real Driving Emissions—Conception of a Data-Driven Calibration Methodology for Hybrid Powertrains Combining Statistical Analysis and Virtual Calibration Platforms”, hereinafter referred to as Krysmon, in
view of List et al. (JP Pat. Pub. No. 6898101 B2), hereinafter referred to as List.
Regarding claim 3, Krysmon discloses:
The verification-cycle generation method according to claim 2, wherein the dataset further includes environmental factor information regarding environmental factors (pg.4 ¶3 s.3, when using a modeled environment many different scenarios can be evaluated in an efficient way and pg.15 1 s.1, controlled environmental conditions, and pg. 4 ¶4 ln 8 the reproduction of altitude and road gradient influences can have a major effect on the reproduction of engine operating points and must be reproduced with a high level of accuracy, which are interpreted as environmental factor information).
brake information regarding brakes
However, List, which is directed toward a system for analyzing an energy efficiency of a vehicle, teaches in pg.22 ¶4 a third dataset of at least one third parameter suitable for characterizing at least one running condition of a vehicle comprising at least two of constant speed, acceleration, regenerative braking, and mechanical braking.
Therefore it would have been obvious to one of ordinary skill in the art of vehicle emissions testing, simulations, and data engineering before the effective filing date of the current invention to modify the verification cycle method of Krysmon, by incorporating the regenerative braking and mechanical braking teachings of List, such that the combination would provide for the predictable result of better analysis of the energy efficiency of a vehicle having at least one drive device which is set up to generate mechanical drive power by energy conversion.
Regarding claim 9, Krysmon discloses:
The verification-cycle generation method according to claim 1, wherein the verification cycle is one related to an on-road battery evaluation test (pg.15 ¶2 s.1 The approach of the current RDE cycle generator is extended by a model predictive approach to increase the maturity of reproducing events, especially when working with hybrid propulsion systems, pg.15 ¶4 s.1 investigate the system’s sensitivity towards state of charge (SOC) deviations, and pg.17 ¶2 s.4 open-loop battery model consists of charge–discharge/delta-SOC map based on the delivered absolute power), and
the plurality of input datasets include vehicle speed information, load information (pg.2 ¶7 s.2, Instead of static accelerations and speed levels, the WLTC describes a more dynamic test profile used for standard tests on the chassis dynamometer, which is interpreted as a device used to measure the power output, torque, and performance of a vehicle by simulating road conditions in a controlled environment which is also construed as load information based on the Applicant’s description in the disclosure ([0026]) and pg.4 ¶2 s1-2, Fleet-generic test cycles are frequently used by the Original Equipment Manufacturers (OEM) to validate many different vehicles with the same test profile and these profiles are usually based on speed sequences that are supposed to be highly critical for the OEM’s vehicles and especially to the performance of the EATS which is construed as including vehicle speed information as an input), and deceleration information related to deceleration in the on-road battery evaluation test (List, as discussed in claim 3, teaches in pg.22 ¶4 a third dataset of at least one third parameter suitable for characterizing at least one running condition of a vehicle comprising at least two of constant speed, acceleration, regenerative braking, and mechanical braking).
Regarding claim 10, Krysmon, as modified by List, discloses:
The verification-cycle generation method according to claim 9, wherein the deceleration information includes an amount of regeneration (List, as discussed in claim 3, teaches in pg.22 ¶4 a third dataset of at least one third parameter suitable for characterizing at least one running condition of a vehicle comprising at least two of constant speed, acceleration, regenerative braking, and mechanical braking).
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see:
Yamashita et al. (US Pat. Pub. No. 2023/0092026 A1) is directed towards vehicle-related data related to vehicle running testing include information indicating a vehicle running test scenario;
Förster et al. article titled, “Data-driven identification of characteristic real-driving cycles based on k-means clustering and mixed-integer optimization” is directed towards a virtual powertrain analysis is widely applied in the automotive industry to cope with the increasing complexity and variance of future vehicle propulsion technologies and to identify multiple characteristic driving cycles (CDCs) from extensive vehicle measurement data which represent the full variety of possible real-driving scenarios;
Dahl et al. article titled, “Understanding association between logged vehicle data and vehicle marketing parameters: Using clustering and rule-based machine learning” is directed towards a proposed framework that aims to extract costumers’ vehicle behaviors from Logged Vehicle Data (LVD) in order to evaluate whether they align with vehicle configurations, so-called Global Transport Application (GTA) parameters.
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.
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/Keith A von Volkenburg/ Examiner, Art Unit 3665
/TIFFANY P YOUNG/Primary Examiner, Art Unit 3665
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