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 .
The Office Action is in response to the application filed on 08/24/2023. Claims 1-15 are pending in
the application. Claims 1 and 12-15 are independent claims.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the
following reference character(s) not mentioned in the description: FIG.1: 222b; FIG.4: 500, 160, 170 and 180. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference
character “22” in Figs.1, 2 and 4 has been used to designate both wheels and implementation model. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to under 37 CFR 1.83(a) because they fail to show paragraph
[0037] The TSM function 370 may also base the determination of vehicle maneuver on the model 380, as indicated in FIG. 2 as described in the specification. Any structural detail that is essential for a proper understanding of the disclosed invention should be shown in the drawing. MPEP § 608.02(d). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to because FIG.6: “input variabel” should be “input variable; FIG.3: 360
“VEHICLE MOTION AND POWER MANAGEMENT (VMM)” should be “VEHICLE MOTION MANAGEMENT (VMM).” Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The abstract of the disclosure is objected to because Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because “said generated reference trajectories” should be “the generated reference trajectories” and “each optimized vehicle dynamic model” should be “each optimized vehicle dynamic implementation model.” In additional, the extra spaces between ‘optimized’ and ‘simulating’ should be corrected. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
The disclosure is objected to because of the following informalities:
Paragraph [0032]: “ECU 130” should be “VCU 130”;
Paragraphs [0040], [0043], [0045] and [0086]: “24” should be “22”;
Paragraphs [0045] and [0057]: “24a-e” should be “22a-e”;
Paragraph [0045]: the recitation of optimizing the abstract model is inconsistent with the remaining disclosure and should be corrected accordingly;
Paragraph [0059]: “22-e” should be “22a-22e”;
Paragraph [0067]: “input variable Vis related … The input variable V depend on …” should be “input variable V is related … The input variable V depends on …”;
Paragraph [0070]: “implantation model” should be “implementation model”;
Paragraph [0073]: “111a,b” should be “111c”;
Paragraph [0076]: “jacobains and hessians” should be “Jacobians and Hessians”;
Paragraph [0093]: “vehicle 10” should be “vehicle 1.”
Appropriate correction is required.
Claim Objections
Claims 2-3, 5-6, and 13-15 objected to because of the following informalities:
Claim 2 recites “the vehicle” in line 2, should read “a vehicle.”
Claims 3, 5 and 6 also recite “the vehicle”, and are objected for the same reason.
Claim 5 recites “the tire” in line 3, should read “a tire.”
Claim 13 recites “the processor device” in line 2, should read “a processor device.”
Claim 14 recites “… to-claim 1” in line 2, should read “to claim 1.”
Claim 15 recites “comprising instructions, which when executed by the processor device, cause the processor device to perform the method of claim 1” should read “comprising instructions that, when executed by a processor device, cause the processor device to perform the method of claim 1.”
Appropriate correction is required.
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.
Claims 1-15 are 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.
Claim 1 recites “… each optimized vehicle dynamic model …,” which is indefinite because it is unclear if the later recited “each optimized vehicle dynamic model” refers to the “each optimized vehicle dynamic implementation model” or to different model. For the purposes of examination, it interpreted as “saving each optimized vehicle dynamic implementation model together with its validation level.”
The remaining claims are dependent upon, or incorporate, one of the claims listed above and are rejected for the same reason.
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.
The claims 1-15 are rejected under 35 USC § 101 because the claimed invention is directed to judicial exception, an abstract idea, it has not been integrated into practical application, and the claims further do not recite significantly more than the judicial exception. Examiner has evaluated the claims under the framework provided in the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register 01/07/2019, as well as subsequent USPTO eligibility guidance updates, and has provided such analysis below.
Step 1: Are the claims to a process, machine, article of manufacture or composition of matter?"
Yes, Claims 1-11 are directed to method and fall within the statutory category of process;
Yes, Claim 12 is directed to computer system and falls within the statutory category of machine;
Yes, Claim 13 is directed to computer program product and falls within the statutory category of article of manufacture;
Yes, Claim 14 is directed to control system and falls within the statutory category of machine;
Yes, Claim 15 is directed to non-transitory computer-readable storage medium and falls within the statutory category of articles of manufacture.
In order to evaluate the Step 2A inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?" we must determine, at Step 2A Prong 1, whether the claim recites a law of nature, a natural phenomenon or an abstract idea and further whether the claim recites additional elements that integrate the judicial exception into a practical application.
Step 2A Prong 1:
Mental Processes:
As explained in MPEP 2106.04(a)(2)(III): Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer").
Further, as explained in MPEP 2106.04(a)(2)(III)(A): In contrast, claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include:
• a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016);
• claims to "comparing BRCA sequences and determining the existence of alterations," where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind, University of Utah Research Foundation v. Ambry Genetics, 774 F.3d 755, 763, 113 USPQ2d 1241, 1246 (Fed. Cir. 2014);
• a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011); and
• a claim to identifying head shape and applying hair designs, which is a process that can be practically performed in the human mind, In re Brown, 645 Fed. App'x 1014, 1016-17 (Fed. Cir. 2016) (non-precedential).
Further, as explained in MPEP 2106.04(a)(2)(III)(C): 1. Performing a mental process on a generic computer. An example of a case identifying a mental process performed on a generic computer as an abstract idea is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018) … 2. Performing a mental process in a computer environment. An example of a case identifying a mental process performed in a computer environment as an abstract idea is Symantec Corp., 838 F.3d at 1316-18, 120 USPQ2d at 1360 … 3. Using a computer as a tool to perform a mental process. An example of a case in which a computer was used as a tool to perform a mental process is Mortgage Grader, 811 F.3d. at 1324, 117 USPQ2d at 1699.
Claim 1: The limitations of “defining at least two operational design domains; choosing one vehicle dynamic abstract model; choosing one vehicle dynamic implementation model to be optimized; parameterizing the implementation model by using at least one geometric parameter from the vehicle dynamic abstract model,” as drafted, is process that, but for the recitation of generic computing components, under the broadest reasonable interpretation (BRI) in light of the specification, covers performance of the limitation in the human mind.
For example, a person is capable of identifying at least two operating conditions, selecting one representation of vehicle dynamics as an abstract representation and another representation as an implementation representation, and identifying a geometric characteristic from the abstract representation for a corresponding parameter in the implementation representation. The steps include observation, evaluation, judgment, and reasoning processes that can be performed mentally or with the aid of pen and paper (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).).
Examiner note: The claimed limitation is broadly recited and does not require any particular implementations or degree of processing complexity beyond human evaluation and judgement that would prevent the limitation from being performed in the human mind. See MPEP 2106.04(a)(2)(III).
Claim 1: The limitations of “simulating the vehicle dynamic abstract model for each operational design domain, wherein the simulation generates reference trajectories; optimizing the vehicle dynamic implementation model for each operational design domain so as to create at least two optimized vehicle dynamic implementation models, wherein each optimization is based on at least one of said generated reference trajectories; validating each optimized vehicle dynamic implementation model to achieve a validation level for each optimized vehicle dynamic implementation model,” as drafted, is process that, but for the recitation of generic computing components, under the broadest reasonable interpretation (BRI) in light of the specification, covers performance of the limitation in the human mind.
For example, a person is capable of determining, for each of at least two operating conditions, an expected response of the selected abstract representation and representing the response as a corresponding behavior trace, using at least one of the behavior traces to compare with and adjust the selected implementation representation for each operating condition so as to obtain at least two adjusted implementation representation, and comparing each adjusted implementation representation with the corresponding reference behavior to determine a degree of fit or accuracy. The steps include observation, evaluation, judgment, and reasoning processes that can be performed mentally or with the aid of pen and paper (The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011).).
Examiner note: The claimed limitations are broadly recited and does not require any particular simulation technique, optimization algorithm, validation mechanism, or degree of processing complexity that would prevent the limitations from being performed in the human mind. See MPEP 2106.04(a)(2)(III).
In addition, as explained in MPEP 2106.04(II)(B): A claim may recite multiple judicial exceptions. For example, claim 4 at issue in Bilski v. Kappos, 561 U.S. 593, 95 USPQ2d 1001 (2010) recited two abstract ideas, and the claims at issue in Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 101 USPQ2d 1961 (2012) recited two laws of nature. However, these claims were analyzed by the Supreme Court in the same manner as claims reciting a single judicial exception, such as those in Alice Corp., 573 U.S. 208, 110 USPQ2d 1976.
Mathematical Concepts:
As explained in MPEP 2106.4(a)(2)(I): “The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. It is important to note that a mathematical concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). See, e.g., SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018) (holding that claims to a “series of mathematical calculations based on selected information” are directed to abstract ideas); Digitech Image Techs., LLC v. Elecs. for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014) (holding that claims to a “process of organizing information through mathematical correlations” are directed to an abstract idea); and Bancorp Servs., LLC v. Sun Life Assurance Co. of Can. (U.S.), 687 F.3d 1266, 1280, 103 USPQ2d 1425, 1434 (Fed. Cir. 2012) (identifying the concept of “managing a stable value protected life insurance policy by performing calculations and manipulating the results” as an abstract idea).
Further, as explained in MPEP 2106.04(a)(2)(I)(A): “A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols.”
Further, as explained in MPEP 2106.04(a)(2)(I)(C)recites: “For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.
Claim 1: The limitations of “simulating the vehicle dynamic abstract model for each operational design domain, wherein the simulation generates reference trajectories; optimizing the vehicle dynamic implementation model for each operational design domain so as to create at least two optimized vehicle dynamic implementation models, wherein each optimization is based on at least one of said generated reference trajectories,” under its broadest reasonable interpretation (BRI) in light of specification, recites a mathematical concept, including a mathematical relationship, formula or equation, calculation. In particular, the specification explains in paragraph [0068] that the simulation may be based on frequency analysis, including a sweep of different frequencies , and generates reference trajectories. Paragraph [0072] explains that the optimization may use automatic differentiation to calculate Jacobians and Hessians, and that the model may be defined by Differential Algebraic Equations (DAEs). Paragraph [0077] further explains that the objective function used in the optimization may be the integral of the squared difference between the reference trajectories and corresponding implementation model trajectories. Accordingly, under the BRI in light of the specification, the claimed simulation encompasses applying a mathematical analysis to determine reference trajectories, and the claimed optimization encompasses performing mathematical calculations based on the reference trajectories to optimize the implementation model. Thus, the limitation recites a mathematical concept. See MPEP 2106.04(a)(2)(I).
Claims 12-15 recite the similar elements as claim 1, and are rejected for the same reasons under 35 U.S.C. 101.
Therefore, claims 1 and 12-15 recite judicial exceptions. The claims have been identified to recite judicial exceptions, Step 2A Prong 2 will evaluate whether the claims are directed to the judicial exception.
Step 2A Prong 2: Claims 1 and 12-15: The judicial exception is not integrated into a practical application.
The additional limitation of claims 23 and 24, "A computer-implemented method of deriving a vehicle dynamic library comprising at least two optimized vehicle dynamic implementation models, wherein each optimized vehicle dynamic implementation model is correlated to different operational design domains, the method comprising:” and “A computer system comprising a processor device configured to perform …” and “A computer program product comprising program code for performing, when executed by the processor device, …” and “A control system comprising one or more control units configured to perform …” and “A non-transitory computer-readable storage medium comprising instructions, which when executed by the processor device, cause the processor device to perform …” which are merely recitations of instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to implement the judicial exception, which does not integrate judicial exception into a practical application (see MPEP §2106.05(f)).
Examiner note: the preamble “deriving a vehicle dynamic library comprising …” merely states the intended purpose and result of the claimed method and does not impose a separate technological operation beyond the steps recited in the body of the claim.
Further, the additional limitation of clam 26, “saving each optimized vehicle dynamic model together with its validation level,” which is merely an recitation of insignificant extra-solution activity, such as post-solution activity, i.e., outputting/saving results produced by the preceding simulation, optimization, and validation processes after processes have been completed, which does not integrate a judicial exception into practical application (see MPEP § 2106.05(g)). Examiner note: The additional limitation recites the saving process only at a high level of generality, and does not require any particular storage mechanism, data structure, storage format, or technological manner of performing the saving.
Alternatively, even if the limitation of claim 1, “simulating the vehicle dynamic abstract model for each operational design domain, wherein the simulation generates reference trajectories,” is considered an additional limitation. The limitation is merely adding the words "apply it" (or an equivalent) with the judicial exception, or instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See MPEP 2106.05(f). The limitation merely recites, at a high level of generality, simulating the vehicle dynamic abstract model for each operational design domain and generating reference trajectories, without specifying any particular simulation technique, computing architecture, or particular manner in which the simulation is performed. Accordingly, the limitation merely applies generic computer implementation to the modeling process and does not impose a meaningful limit on the judicial exception.
Therefore, "Do the claims recite additional elements that integrate the judicial exception into a practical application? No, these additional elements do not integrate the abstract idea into a practical application, and they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
After having evaluated the inquiries set forth in Steps 2A Prong 1 and 2, it has been concluded that claims 1 and 12-15 are not only recite a judicial exception but that the claims are directed to the judicial exception as the judicial exception has not been integrated into practical application.
Step 2B: Claims 1 and 12-15: The claim does not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than generic computing components to the judicial exception and an recitation of insignificant extra-solution activity, which do not amount to significantly more than the abstract idea.
As explained in MPEP 2106.05(A), “Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include:
i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g));
iv. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)).”
As explained in MPEP 2106.05(d)(II), “The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. i. Receiving or transmitting data over a network, …; ii. Performing repetitive calculations, …; iii. Electronic recordkeeping, … (updating an activity log); iv. Storing and retrieving information in memory, …”
In particular, the additional limitations of claims 1 and 12-15, when considered individually and in combination, do not amount to significantly more than the judicial exception. The recitation that the method is computer-implemented, and the recited computer system, processor device, control system, control units, computer program product, and non-transitory computer-readable storage medium merely implement the judicial exception using generic computer components performing their ordinary functions. The saving limitation merely stores the result of the judicial exception and constitutes insignificant post-solution activity. The simulating limitation merely uses generic computing functionality to simulate the vehicle dynamic abstract model and generate reference trajectories. The claims do not recite a particular unconventional arrangement or additional functionality that amounts to significantly more than the judicial expectation.
Therefore, "Do the claims recite additional elements that amount to significantly more than the judicial exception? No, these additional elements, alone or in combination, do not amount to significantly more than the judicial exception. Having concluded analysis within the provided framework, claims 1 and 12-15 do not recite patent eligible subject matter under 35 U.S.C. § 101.
Dependent claims 2-11 are also similar rejected under same rationale as cited above wherein these claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. These claims are merely further elaborate the mental process itself (or mathematical operations) or providing additional definition of process which does not impose any meaningful limits on practicing the abstract idea. Claims 2-11 are also rejected for incorporating the deficiency of their independent claim 1.
Claim 2 recites “The method according to claim 1, wherein the at least two operational design domains are related to different use cases of the vehicle.”
This limitation merely further specifies the operational design domains recited in claim 1 by relating the operational design domains to different vehicle use cases. Thus, it is merely an extension of the identified mental process. See MPEP 2106.04(a)(2)(III). Therefore, the claim 2 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim 3 recites “The method according to claim 1, wherein the at least two operational design domains are related to any one of: vehicle speed levels, steering angles, environmental parameters, weight of the vehicle and/or a combination of one or more.”
This limitation merely further specifies the operational design domains recited in claim 1 by identifying vehicle speed level, steering angles, environmental parameters, and vehicle weigh as conditions associated with the operational design domains. Thus, it is merely an extension of the identified mental process. See MPEP 2106.04(a)(2)(III). Therefore, the claim 3 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim 4 recites “The method according to claim 1, wherein the simulation is performed with different values of at least one input variable.”
This limitation merely further specifies the simulation recited in claim 1, by requiring the simulation to be performed with different values of at least one input variable. Thus, it is merely an extension of the identified mental process and mathematical concepts. See MPEP 2106.04(a)(2)(I) and MPEP 2106.04(a)(2)(III). Alternatively, The limitation, “input variable for simulation” merely a recitation of insignificant extra-solution activity such as data gathering (i.e., input data for simulation), which does not integrate a judicial exception into practical application. See MPEP § 2106.05(g). Therefore, the claim 4 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim 5 recites “The method according claim 4, wherein the at least one input variable is related to a steering angle of the vehicle, the speed of the vehicle, vertical road displacement and/or vertical forces on the tire of the vehicle.”
This limitation merely further specifies the input variable used in the simulation recited in clam 4 by identifying steering angle, vehicle speed, vertical road displacement, and vertical tire force as possible input variables. Thus, it is merely an extension of the identified mental process and mathematical concepts. See MPEP 2106.04(a)(2)(I) and MPEP 2106.04(a)(2)(III). Alternatively, The limitation, “at least one of different input variable for simulation” merely a recitation of insignificant extra-solution activity such as data gathering (i.e., input data for simulation), which does not integrate a judicial exception into practical application. See MPEP § 2106.05(g).Therefore, the claim 5 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim 6 recites “The method according to claim 1, wherein the at least one geometric parameter relates to one or more of; wheel-bases, track widths, body mass of one or more parts of the vehicle, inertia of one or more parts of the vehicle, center of gravity of one or more parts of the vehicle and coupling positions of the vehicle.”
This limitation merely further specifies the geometric parameter used in parameterizing the implementation model recited in claim 1 by identifying wheelbase, track width, body mass, inertia, center of gravity, and coupling position as possible geometric parameters. Thus, it is merely an extension of the identified mental process. See MPEP 2106.04(a)(2)(III). Therefore, the claim 6 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim 7 recites “The method according to claim 1, wherein the validation is a dynamic model validation process.”
This limitation merely further specifies the validation recited in claim 1 as validation of the dynamic model. The validation may include comparing the model response with reference trajectories and evaluating the degree of correspondence. Thus, it is merely an extension of the identified mental process and mathematical concepts. See MPEP 2106.04(a)(2)(I) and MPEP 2106.04(a)(2)(III). Therefore, the claim 7 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim 8 recites “The method according to claim 1, wherein the validation is performed by using at least one of said generated reference trajectories.”
This limitation merely further specifies the validation recited in claim 1 by requiring the validation to use at least one generated reference trajectory. Thus, it is merely an extension of the identified mental process and mathematical concepts. See MPEP 2106.04(a)(2)(I) and MPEP 2106.04(a)(2)(III). Therefore, the claim 8 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim 9 recites “The method according to claim 1, wherein the optimization further is based on defining an objective function that is at least based on the reference trajectories.”
This limitation merely further specifies the optimization recited in claim 1 by defining an objective function based at least on the reference trajectories. Thus, it is merely an extension of mathematical concepts. See MPEP 2106.04(a)(2)(I). Therefore, the claim 9 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim 10 recites “The method according to claim 9, wherein the objective function further is based on implementation model trajectories originating from the implementation model.”
This limitation merely further specifies the objective function recited in claim 9 by further basing the objective function on implementation model trajectories originating from the implementation model. Thus, it is merely an extension of the identified mathematical concepts. See MPEP 2106.04(a)(2)(I). Therefore, the claim 10 does not recite patent eligible subject matter under 35 U.S.C. § 101.
Claim 11 recites “The method according to claim 1, wherein the optimization is gradient based.”
This limitation merely further specifies the optimization recited in claim 1 by requiring the optimization to be gradient based. Thus, it is merely an extension of the identified mathematical concepts. See MPEP 2106.04(a)(2)(I). Therefore, the claim 11 does not recite patent eligible subject matter under 35 U.S.C. § 101.
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.
Claim(s) 1-10 and 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over Hunt
(“CONSTRUCTIVE EMPIRICAL MODELLING OF LONGITUDINAL VEHICLE DYNAMICS USING LOCAL MODEL NETWORKS,” published in 1996) in view of Gubitosa (“DEVELOPMENT OF A PARAMETER ESTIMATION SCHEME FOR CONCEPT AND REAL-TIME SIMULATIONS OF VEHICLE SYSTEM DYNAMICS,” published in 2010) and Ray (“Recommended Procedures for Verification and Validation of Computer Simulations used for Roadside Safety Applications,” published in 2008).
Claim 1, Hunt teaches A computer-implemented method of deriving a vehicle dynamic library comprising at least two optimized vehicle dynamic implementation models, wherein each optimized vehicle dynamic implementation model is correlated to different operational design domains (Page.178, Conclusion, “The models in Sections 3-5 were obtained with the MATLAB-based tool ORBIT …” Page.167, Abstract, “In this paper a number of empirically derived parametric models of longitudinal vehicle dynamics are compared. The vehicle concerned is a fully instrumented Mercedes-Benz commercial lorry. The main focus is on a class of models known as local model networks. In this structure, a number of simple local linear models are combined ('interpolated') by 'scheduling' on a number of physical variables which are known to, or can be found to, capture the system non-linearities.” Page.171, left column, “The objective of parameter estimation is to determine the best estimate of the parameter vector … A measure of model fidelity is provided by the least-squares criterion … This criterion is minimised with respect to θ …” Page.173, 4.2 Parameter estimation, “ One way to do the local model parameter estimation is to perform identification experiments in restricted regions around the equilibria. The local parameters can then be estimated using the procedures given in Section 3.2. Alternatively, a set of local parameter estimators can be employed. In this case, the local criteria are defined by … The local parameter estimates are …”. Page.175, 5.2 Experimental results, “In this section a heuristic search algorithm … is applied to search for the best decomposition of the operating range into operating regimes, according to α and v.… Interactive use of the constructive algorithm resulted in an LMN with 7 local models as shown in Table 6 (global estimation criterion) and Table 7 (local estimation criteria) … For the first to third gears, the LMN has different local models for low and high throttle, while for the fourth gear such a refinement did not give a significant improvement.” Examiner note: the reference teaches using a computer implemented vehicle dynamics modeling method in which a local model network contains multiple local vehicle dynamics models. The local model parameters are estimated using a model fidelity criterion that is minimized, thereby optimizing the local models, and different local models are provided to different low and high throttle operating regimes), the method comprising:
defining at least two operational design domains (Page.175, 5.2 Experimental results, “In this section a heuristic search algorithm (Johansen and Foss, 1995a; Johansen and Foss, 1995b) is applied to search for the best decomposition of the operating range into operating regimes, according to α and v … … A finer partitioning into operating regimes using three local linear models corresponding to low, medium and high throttle for each gear was also tried.” Examiner note: the reference teaches decomposing the vehicle operating range into different operating regimes, including low throttle, medium throttle and high throttle operating regimes. Under BRI, these distinct operating regimes correspond to at least two operational design domains);
choosing one vehicle dynamic abstract model (Page.168, 2. NON-LINEAR VEHICLE DYNAMICS, “A simplified realistic dynamic longitudinal model of the vehicle is developed which contains the essential effects and non-linearities (Fritz, 1995). The model mainly describes the powertrain system. It includes the combustion engine, automatic transmission and rear axle differential.” Examiner note:. Under BRI, the model is an abstract representation of the vehicle dynamics and corresponds to the claimed vehicle dynamic abstract model);
choosing one vehicle dynamic implementation model to be optimized (Page.167, Abstract, “The main focus is on a class of models known as local model networks. In this structure, a number of simple local linear models are combined ('interpolated') by 'scheduling' on a number of physical variables which are known to, or can be found to, capture the system non-linearities … Local 1st-order linear ARX models are found to be sufficient.” Page.171, left column, “The objective of parameter estimation is to determine the best estimate of the parameter vector … This criterion is minimised with respect to θ …” Page.173, 4.2 Parameter estimation, “ One way to do the local model parameter estimation is to perform identification experiments in restricted regions around the equilibria. The local parameters can then be estimated using the procedures given in Section 3.2. Alternatively, a set of local parameter estimators can be employed.” Examiner note: the reference teaches simple local linear models for modeling longitudinal vehicle dynamics and identifies first-order linear ARX models as sufficient, and further teaches estimating the parameters of those local models using the parameter estimation procedure of Section 3.2, in which a model fidelity is minimized. Therefore, under BRI, an individual first order local linear model corresponds to the vehicle dynamic implementation model, and estimation of its parameters by minimizing the model fidelity criterion corresponds to the model being optimized);
(See Page.175, 5.2 Experimental results),
optimizing the vehicle dynamic implementation model for each operational design domain so as to create at least two optimized vehicle dynamic implementation models, wherein each optimization(Page.171, left column, “The objective of parameter estimation is to determine the best estimate of the parameter vector … This criterion is minimised with respect to θ …” Page.173, 4.2 Parameter estimation, “One way to do the local model parameter estimation is to perform identification experiments in restricted regions around the equilibria. The local parameters can then be estimated using the procedures given in Section 3.2. Alternatively, a set of local parameter estimators can be employed. In this case, the local criteria are defined by … The local parameter estimates are …”. Page.175, 5.2 Experimental results, “Interactive use of the constructive algorithm resulted in an LMN with 7 local models as shown in Table 6 (global estimation criterion) and Table 7 (local estimation criteria) … For the first to third gears, the LMN has different local models for low and high throttle, while for the fourth gear such a refinement did not give a significant improvement.” Examiner note: the reference teaches estimating parameters of the local models using local estimation criteria that are minimized, thereby optimizing the local models. The reference further teaches an LMN having multiple local models, including different local models for low and high throttle operating regimes. Under BRI, the low and high throttle operating regimes correspond to different operational design domains, and the respective optimized local models correspond to at least two optimized vehicle dynamic implementation models);
(See page.173, 4.2 Parameter estimation and page.175, 5.2 Experimental results); and
(See page.173, 4.2 Parameter estimation and page.175, 5.2 Experimental results).
However, Hunt fails to teach parameterizing the implementation model by using at least one geometric parameter from the vehicle dynamic abstract model; simulating the vehicle dynamic abstract model, wherein the simulation generates reference trajectories; optimization is based on at least one of said generated reference trajectories.
Gubitosa teaches parameterizing the implementation model by using at least one geometric parameter from the vehicle dynamic abstract model (Page.4, Table 1, Assured: Obtained from the high fidelity model (the real vehicle) without experiments. Page.4, bottom, “… 1. in the first step, the Assured and Calculated parameters are provided as input to the simplified models and considered as fixed.” Page.9, Table 2, “front wheelbase Assured” “rear wheelbase Assured” “ front half track Assured” … Examiner note: the reference teaches that assured parameters are obtained from the high fidelity model and are provided as inputs to the simplified model. The assured parameters include front and rear wheelbase and front and rear half track. Under BRI, the high fidelity model corresponds to the vehicle dynamic abstract model, the simplified model corresponds to the vehicle dynamic implementation model, and the wheelbase or half track are geometric parameters of the vehicle);
simulating the vehicle dynamic abstract model, wherein the simulation generates reference trajectories (Page.12, CONCLUSIONS AND REMARKS, “An example is indeed in the notable gap between the prediction of the simplified model and the high fidelity multibody simulation when large lateral acceleration is required.” Page.6, REFERENCE VEHICLE MODEL, “A high fidelity multibody 3D vehicle dynamics benchmark case has been defined, which has been modeled in LMS Virtual.Lab Motion.” Pages. 10-11, “The results of the final comparison between the high fidelity model and simplified lateral dynamic model are shown in Figure 7 a, b, c, …” Figure 7: Comparison of the behavior of simplified and reference model for a) slalom maneuver, b) double lane change, c) step steer, d) polynomial steering angle. Examiner note: the reference teaches a high fidelity multibody 3D vehicle dynamics model and performs a high fidelity multibody simulation. The reference further shows time dependent yaw rate and lateral acceleration outputs from the reference model. Under BRI, the high fidelity reference model corresponds to the vehicle dynamic abstract model, and the time dependent reference model outputs correspond to the generated reference trajectories);
optimization is based on at least one of said generated reference trajectories (Page.4, top, “In the approach presented in this paper a piecewise distinction of the range of interest is obtained with a time rectangular windowing of the output signal from the high fidelity model on which a selection of optimization algorithms is tuning different set of parameters defined based on a priori sensitivity studies.” Pages 8-9, CASE STUDY: LATERAL DYNAMICS, “… different maneuvers have been selected as comparison between the high fidelity model and the simplified model: slalom, step steer and a form of pen loop double lane change. Since it is commonly accepted that yaw rate relates mainly to what a driver sees and lateral acceleration relates to the human feelings, both aspects will be considered as output parameters. The identification method illustrated in this paper is then based on the error minimization calculated in three different time windows …” Page.10, “The objective functions of each stage are hence defined by results of the sensitivity analysis … The subsequent optimization is divided into three stages … the differential evolution algorithm provides the Pareto set (collection of optimal solutions) that minimizes the concurrent objective functions.” Table 3: Combination of design variables and object functions for each stage: “part3_error_yaw_rate” “part3_error_lateral_acceleration” “part1_error_yaw_rate” … Examiner note: the reference teaches turning model parameters using optimization algorithms based on output signals from the high fidelity model, and further teaches comparing outputs of the high fidelity and simplified model outputs, including yaw rate and lateral acceleration, and performing error minimization using those outputs as objective functions. Under BRI, the time dependent high fidelity model outputs correspond to the generated reference trajectories.).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hunt to incorporate the teaching of Gubitosa, and apply a high fidelity reference model based parameter estimation technique to Hunt’s local linear models for the respective operating regimes, providing geometric parameters from the high fidelity model as inputs to the respective local linear models, simulating the high fidelity model to obtain reference dynamic responses, and using the reference dynamic responses in optimizing the local model parameters, in order to improve the parameter consistency and correlation of Hunt’s local linear models with the higher fidelity vehicle dynamics, while retaining Hunt’s operating regimes based local model network structure.
However, Hunt in view of Gubitosa fails to teach validating each model to achieve a validation level for each model; saving each model together with its validation level.
Ray teaches validating each model to achieve a validation level for each model (Page.116, Bottom, “The result of the phenomena verification or validation should be entered in the appropriate column with the metric score obtained in the verification or validation … Once all the scores have been obtained and inserted into the PIRT, the validation level of the model can be assessed by summing all the individual scores and dividing by the number of phenomena included. The validation level for the vehicle represented by Table X would be (0+3+14+0+21+15)/6 = 8.8.” Page.117, top, “Another model with the same phenomena and a lower score, would, in general, be a better model.” Examiner note: determining a validation level for a vehicle model based on obtained validation scores, and further teaches that another models may have a different validation score. Therefore, the validation level is associated with the particular model being evaluated, such that respective models may have respective validation levels);
saving each model together with its validation level (Pages.117-118, “… Rather, the PIRT is a document that represents the level of validation of major components of the model. For example, it is typical in roadside safety research to develop new roadside appurtenance models but to re-use vehicle models. A developer working on a new type of crash cushion, for example, would have to develop a PIRT for the crash cushion but would likely just use an existing vehicle model with its existing PIRT. The PIRT becomes a standard part of the documentation of model that should be stored with and retained with the model.” Examiner note: the reference teaches that a vehicle model is associated with its existing PIRT, the PIRT represents the model’s validation level, and the PIRT is stored with and retained with the model).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hunt in view of Gubitosa to incorporate the teaching of Ray, and apply model validation and documentation procedure to each Hunt local linear model after the parameters of that local linear model have been optimized using Gubitosa’s optimization procedure based on the high fidelity model outputs, including determining for each the optimized local linear model a validation level from obtained validation metric scores and retaining with that model the PIRT representing its validation information, in order to provide a quantitative assessment of the validity of each optimized local linear model and preserve the validation information for subsequent evaluation and use.
Claim 2, Hunt teaches The method according to claim 1, wherein the at least two operational design domains are related to different use cases of the vehicle (Page.175, right column, “finer partitioning into operating regimes using three local linear models corresponding to low, medium and high throttle for each gear was also tried.”).
Claim 3, Hunt teaches The method according to claim 1, wherein the at least two operational design domains are related to any one of: vehicle speed levels, steering angles, environmental parameters, weight of the vehicle and/or a combination of one or more (Page.174, 4.3 Experimental results, “For each gear two operating points were chosen: one for lower speeds for that gear, and one for higher speeds. The operating-point speeds chosen correspond to the centres of the validity functions used in the interpolation of the local models … Variation of the local model poles over the operating regimes is depicted in Figure 7. This figure also gives a good impression of the operating regime decomposition, with hard switching according to gear and smooth interpolation with speed.” Table 5. M6: linear models scheduled on gear and speed).
Claim 4, Hunt fails to teach, but Gubitosa teaches The method according to claim 1, wherein the simulation is performed with different values of at least one input variable (Pages. 8-9, CASE STUDY: LATERAL DYNAMICS, “ … different maneuvers have been selected as comparison between the high fidelity model and the simplified model: slalom, step steer and a form of pen loop double lane change. Since it is commonly accepted that yaw rate relates mainly to what a driver sees and lateral acceleration relates to the human feelings, both aspects will be considered as output parameters. The identification method illustrated in this paper is then based on the error minimization calculated in three different time windows, as shown here below, considering basically the equilibrium starting condition in error 1, transient behavior in error 2 and steady state after distortion (steering input) in error 3.” Page.12, CONCLUSIONS AND REMARKS, “An example is indeed in the notable gap between the prediction of the simplified model and the high fidelity multibody simulation when large lateral acceleration is required.” Figure 8: Step steer manoeuvre with large steering input (i.e. high lateral acceleration). Examiner note: the reference teaches a high fidelity multibody simulation and applies a step steer maneuver using a steering input. Under BRI, a step steer maneuver changes the steering input from an initial value to a stepped value over time. Therefore, the high fidelity multibody simulation is performed with different values of a t least one input variable).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hunt to incorporate the teaching of Gubitosa, and apply the a fidelity vehicle model simulation used in the parameter estimation process with a step steering input that changes in value over time, in order to provide informative simulation output for estimating the parameters of Hunt’s local linear models over the applicable operating regimes.
Claim 5, Hunt fails to teach, but Gubitosa teaches The method according claim 4, wherein the at least one input variable is related to a steering angle of the vehicle, the speed of the vehicle, vertical road displacement and/or vertical forces on the tire of the vehicle (See Page.8, CASE STUDY: LATERAL DYNAMICS; Page.13, LIST OF SYMBOLS: δ1 Steering angle of the front wheels and δ2 Steering angle of the rear wheels).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hunt to incorporate the teaching of Gubitosa, and apply step steer maneuver using a steering angle as the input during the high fidelity vehicle model simulation, in order to provide sufficiently exciting steering input for vehicle parameter identification and investigate transient vehicle behavior, thereby improving the characterization and parameter estimation of Hunt’s local linear models within their respective operating regimes.
Claim 6, Hunt fails to teach, but Gubitosa teaches The method according to claim 1, wherein the at least one geometric parameter relates to one or more of; wheel-bases, track widths, body mass of one or more parts of the vehicle, inertia of one or more parts of the vehicle, center of gravity of one or more parts of the vehicle and coupling positions of the vehicle (Page.4, bottom, “… 1. in the first step, the Assured and Calculated parameters are provided as input to the simplified models and considered as fixed.” Table 1, Assured: Obtained from the high fidelity model (the real vehicle) without experiments. Page.9, Table 2, “front wheelbase Assured” “rear wheelbase Assured ”).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hunt to incorporate the teaching of Gubitosa, and apply wheelbase parameters obtained from the high fidelity vehicle model as fixed inputs to Hunt’s local linear models, in order to maintain parameter consistency between the high fidelity vehicle model and the local linear models, thereby improving the physical consistency and accuracy of Hunt’s local linear models within their respective operating regimes.
Claim 7, Hunt fails to teach, but Gubitosa teaches The method according to claim 1, wherein the validation is a dynamic model validation process (Page.4, top, “Since we’re dealing with a dynamic model (represented by differential equations) and transient behavior is of interest, a sensitivity study in the time domain is the most appropriate choice [10].” Page.11, “In particular Figure 7 d proposes a cross validation of the model by applying to it a manoeuvre for which parameters estimation has not been performed (i.e. random steering action in time).” ).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hunt to incorporate the teaching of Gubitosa, and apply a cross validation of a dynamic model using a time varying maneuver to Hunt’s local linear models, in order to evaluate the local linear models under transient vehicle behavior, thereby improving the assessment of Hunt’s local linear models within their respective operating regimes.
Claim 8, Hunt fails to teach, but Gubitosa teaches The method according to claim 1, wherein the validation is performed by using at least one of said generated reference trajectories (Pages. 10-11, “The results of the final comparison between the high fidelity model and simplified lateral dynamic model are shown in Figure 7 a, b, c, d. In particular Figure 7 d proposes a cross validation of the model by applying to it a manoeuvre for which parameters estimation has not been performed (i.e. random steering action in time).” Figure 7: Comparison of the behavior of simplified and reference model for a) slalom maneuver, b) double lane change, c) step steer, d) polynomial steering angle).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hunt to incorporate the teaching of Gubitosa, and apply a cross validation procedure using outputs of the reference model to evaluate the corresponding outputs of the simplified lateral dynamic model, in order to evaluate the simplified model against the reference model under a maneuver not used for parameter estimation, thereby improving confidence in the validity of Hunt’s local linear models within their respective operating regimes.
Claim 9, Hunt fails to teach, but Gubitosa teaches The method according to claim 1, wherein the optimization further is based on defining an objective function that is at least based on the reference trajectories (Page.9, top, “as comparison between the high fidelity model and the simplified model: slalom, step steer and a form of pen loop double lane change. Since it is commonly accepted that yaw rate relates mainly to what a driver sees and lateral acceleration relates to the human feelings, both aspects will be considered as output parameters. The identification method illustrated in this paper is then based on the error minimization calculated in three different time windows, as shown here below, considering basically the equilibrium starting condition in error 1, transient behavior in error 2 and steady state after distortion (steering input) in error 3.” Page.10, “The objective functions of each stage are hence defined by results of the sensitivity analysis … The subsequent optimization is divided into three stages, cascading from the highest (most contributing) to lower sensitive parameters with respect to the selected cost functions. The combinations of Design Variables and Object functions are summarized in the below Table 3, where part 1, 2 and 3 refer to the error classification scheme previously defined. As the multi-objective optimization of the third stage reaches the stopping criteria, the differential evolution algorithm provides the Pareto set (collection of optimal solutions) that minimizes the concurrent objective functions.” the differential evolution algorithm provides the Pareto set (collection of optimal solutions) that minimizes the concurrent objective functions.” Table 3: Combination of design variables and object functions for each stage: “part3_error_yaw_rate” “part3_error_lateral_acceleration”).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hunt to incorporate the teaching of Gubitosa, and apply an objective function based on errors between the outputs of the high fidelity model and the simplified model during optimization to Hunt’s local linear models, in order to quantify the difference between the outputs of Hunt’s local linear models and the corresponding high fidelity model outputs during parameter estimation, thereby improving the accuracy of Hunt’s local linear models within their respective operating regimes.
Claim 10, Hunt fails to teach, but Gubitosa teaches The method according to claim 9, wherein the objective function further is based on implementation model trajectories originating from the implementation model (Pages. 8-9, “… different maneuvers have been selected as comparison between the high fidelity model and the simplified model: slalom, step steer and a form of pen loop double lane change. Since it is commonly accepted that yaw rate relates mainly to what a driver sees and lateral acceleration relates to the human feelings, both aspects will be considered as output parameters. The identification method illustrated in this paper is then based on the error minimization calculated in three different time windows …” Table 3: Combination of design variables and object functions for each stage: “part3_error_yaw_rate” “part3_error_lateral_acceleration”. Figure 7: Comparison of the behavior of simplified and reference model for a) slalom maneuver, b) double lane change, c) step steer, d) polynomial steering angle).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hunt to incorporate the teaching of Gubitosa, and apply error based objective functions using outputs of the simplified model together with corresponding outputs of the high fidelity model to Hunt’s local linear models, in order to minimize difference between the outputs of Hunt’s local linear models and the corresponding high fidelity model outputs, thereby improving the accuracy of Hunt’s local linear models within their respective operating regimes.
The elements of claims 12-15 substantially the same as those of claim 1. Therefore, the elements of claims 12-15 are rejected due to the same reasons as outlined above for claim 1. Further. The additional limitation of claims 12-15, “A computer system comprising a processor device configured to perform the method …” and “A computer program product comprising program code for performing, when executed by the processor device …” and “A control system comprising one or more control units configured to perform the method …” and “A non-transitory computer-readable storage medium comprising instructions, which when executed by the processor device, cause the processor device to perform the method …” (See Hunt, 9. CONCLUSIONS; See also Ray, page.127, “The project team has already obtained the source code from TTI. At least initially, the code will be developed using MATLAB since that is a quick and easy development environment. When the code is completed it can be automatically converted to FORTRAN90 in MATLAB and then distributed in a binary or source form.” Examiner note: the references teach computer implemented software, including a MATLAB based modeling tool, and further teach source code developed using MATLAB and distributable in binary or source form. A POSITA would understand that the computer implemented software to be executed by processing hardware and stored a program instruction in computer readable memory or storage).
Claim(s) 11 is rejected under 35 U.S.C. 103 as being unpatentable over Hunt in view of Gubitosa
and Ray as applied to claim 1 above, and further in view of Thoresson (“Efficient optimisation of a vehicle suspension system, using a gradient-based approximation method, Part 1: Mathematical modelling,” published in 2009).
Claim 11, Hunt in view of Gubitosa and Ray fails to teach, but Thoresson teaches The method according to claim 1, wherein the optimization is gradient based (Page.1423, 2. Optimisation procedure, “The gradient-based optimisation algorithm, Dynamic-Q [26], using central finite difference approximations for the gradients, is used for the current research.” Page.1424, top, “ In this research simplified numerical models of the full vehicle model, are used for the determination of gradient information. Although the Dynamic-Q optimisation method is used, the principle can be applied to any gradient-based optimisation method.”).
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hunt in view of Gubitosa and Ray to further incorporate the teaching of Thoresson, and apply parameter optimization of Hunt’s local linear models using a gradient based optimization algorithm during the optimization, in order to reducing the number of numerically expensive simulations, thereby reducing the total optimization time.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chai (WO2021120575) teaches providing a vehicle dynamics model (S100). Carrying out a simulation test and a field test (S200). Providing an input scenario library (S300). Providing a vehicle dynamics model library (S400). Designing a control algorithm and verifying a stability of the control algorithm (S500). Using a Monte Carlo method to carry out a traversal test and determining whether analysis simulation results of the automated traversal test meet robustness and stability requirements of the control system (S600).
Rhode (“Online validity monitor for vehicle dynamics models,” published in 2019) teaches a method to measure the degree of validity of vehicle dynamics models during run time in a computational efficient way. The method is called online validity monitor and is constructed as follows. First, the characteristic validation error distribution of a vehicle dynamics model is stored. Second, during run time, an error distribution is recorded via a circular buffer. Third, the distance between stored and run time error distribution is taken as a measure to detect if the model operates in its validated domain.
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/YI . HAO/
Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187
