DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 is presented for examination.
The drawing is objected under 37 CFR 1.83(a).
The specification is objected because of informalities
Claims 1-20 are rejected under 35 U.S.C. 112(a) and (b).
Claims 1-20 are rejected under 35 U.S.C. 101.
Claims 1-2, 9, and 13-16 are rejected under 35 U.S.C. 103 as being unpatentable over Mike, MATLAB, "How to Model and Simulate Automotive Systems Using Powertrain Blockset", April 17, 2018, XP055865500, URL: https://www.youtube.com/watch?v=O3loJOOSRGI, in the view of van Berkel, Koos, et al. "Topology and flywheel size optimization for mechanical hybrid powertrains." IEEE Transactions on Vehicular Technology 63.9 (2014): further in the view of Zhou, Xingyu, et al. "Representation, generation, and optimization methodology of hybrid electric vehicle powertrain architectures." Journal of Cleaner Production 256 (2020): 120711.
Claims 3 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Mike, MATLAB, "How to Model and Simulate Automotive Systems Using Powertrain Blockset", April 17, 2018, XP055865500, URL: https://www.youtube.com/watch?v=O3loJOOSRGI, in the view of van Berkel, Koos, et al. "Topology and flywheel size optimization for mechanical hybrid powertrains." IEEE Transactions on Vehicular Technology 63.9 (2014): further in the view of Zhou, Xingyu, et al. "Representation, generation, and optimization methodology of hybrid electric vehicle powertrain architectures." Journal of Cleaner Production 256 (2020): further in the view of Kabalan, Bilal, et al. "Systematic methodology for architecture generation and design optimization of hybrid powertrains." IEEE Transactions on Vehicular Technology 69.12 (2020): 14846-14857.
Claims 4-6 are rejected under 35 U.S.C. 103 as being unpatentable over Mike, MATLAB, "How to Model and Simulate Automotive Systems Using Powertrain Blockset", April 17, 2018, XP055865500, URL: https://www.youtube.com/watch?v=O3loJOOSRGI, in the view of van Berkel, Koos, et al. "Topology and flywheel size optimization for mechanical hybrid powertrains." IEEE Transactions on Vehicular Technology 63.9 (2014): further in the view of Zhou, Xingyu, et al. "Representation, generation, and optimization methodology of hybrid electric vehicle powertrain architectures." Journal of Cleaner Production 256 (2020): further in the view of Kabalan, Bilal, et al. "Systematic methodology for architecture generation and design optimization of hybrid powertrains." IEEE Transactions on Vehicular Technology 69.12 (2020): 14846-14857, further in the view of Millo, Federico, Luciano Rolando, and Maurizio Andreata. "Numerical simulation for vehicle powertrain development." Numerical Analysis-Theory and Application. IntechOpen, 2011.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Mike, MATLAB, "How to Model and Simulate Automotive Systems Using Powertrain Blockset", April 17, 2018, XP055865500, URL: https://www.youtube.com/watch?v=O3loJOOSRGI, in the view of van Berkel, Koos, et al. "Topology and flywheel size optimization for mechanical hybrid powertrains." IEEE Transactions on Vehicular Technology 63.9 (2014): further in the view of Zhou, Xingyu, et al. "Representation, generation, and optimization methodology of hybrid electric vehicle powertrain architectures." Journal of Cleaner Production 256 (2020): further in the view of Shubhamhegade, "Bond graph", Wikipedia, September 7, 2020, XP055865508, URL: https://en.wikipedia.org/w/index.php?title=Bond_graph&oldid=977168675
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Mike, MATLAB, "How to Model and Simulate Automotive Systems Using Powertrain Blockset", April 17, 2018, XP055865500, URL: https://www.youtube.com/watch?v=O3loJOOSRGI, in the view of van Berkel, Koos, et al. "Topology and flywheel size optimization for mechanical hybrid powertrains." IEEE Transactions on Vehicular Technology 63.9 (2014): further in the view of Zhou, Xingyu, et al. "Representation, generation, and optimization methodology of hybrid electric vehicle powertrain architectures." Journal of Cleaner Production 256 (2020): further in the view of Shubhamhegade, "Bond graph", Wikipedia, September 7, 2020, XP055865508, URL: https://en.wikipedia.org/w/index.php?title=Bond_graph&oldid=977168675 further in the view of Kabalan, Bilal, et al. "Systematic methodology for architecture generation and design optimization of hybrid powertrains." IEEE Transactions on Vehicular Technology 69.12 (2020): 14846-14857
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mike, MATLAB, "How to Model and Simulate Automotive Systems Using Powertrain Blockset", April 17, 2018, XP055865500, URL: https://www.youtube.com/watch?v=O3loJOOSRGI, in the view of van Berkel, Koos, et al. "Topology and flywheel size optimization for mechanical hybrid powertrains." IEEE Transactions on Vehicular Technology 63.9 (2014): further in the view of Zhou, Xingyu, et al. "Representation, generation, and optimization methodology of hybrid electric vehicle powertrain architectures." Journal of Cleaner Production 256 (2020): further in the view of Millo, Federico, Luciano Rolando, and Maurizio Andreata. "Numerical simulation for vehicle powertrain development." Numerical Analysis-Theory and Application. IntechOpen, 2011.
Claims 11-12 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Mike, MATLAB, "How to Model and Simulate Automotive Systems Using Powertrain Blockset", April 17, 2018, XP055865500, URL: https://www.youtube.com/watch?v=O3loJOOSRGI, in the view of van Berkel, Koos, et al. "Topology and flywheel size optimization for mechanical hybrid powertrains." IEEE Transactions on Vehicular Technology 63.9 (2014): further in the view of Zhou, Xingyu, et al. "Representation, generation, and optimization methodology of hybrid electric vehicle powertrain architectures." Journal of Cleaner Production 256 (2020): further in the view of Tran, Dai-Duong, et al. "Thorough state-of-the-art analysis of electric and hybrid vehicle powertrains: Topologies and integrated energy management strategies." Renewable and sustainable energy reviews 119 (2020):
This action is non final rejection.
Priority
Acknowledgment is made of applicant's claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No.2014950.6, filed on 09/22/2020.
Information Disclosure Statement
The IDS filed on 04/03/2023 and 03/20/2023 is reviewed and considered. See the attached document.
Drawings
The drawings are objected to under 37 CFR 1.83(a). The drawings must show every feature of the invention specified in the claims. Therefore, “the flow scaling module in the fourth component model” in claim 8 must be shown or the feature(s) canceled from the claim(s). No new matter should be entered.
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 disclosure is objected to because of the following informalities:
On [0126] , the motor generator 130, should be the motor generator 120.
On [0174], a synchronous motor 210, should be a synchronous Ac motor 210 , or use distinct label if they are different.
On [0185], gear box 340 , should be gear box 330.
On [0188], final drive inertia 340 and final drive 340, should be labeled the same or should be the same structure on the drawing.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) are:
First component model in claim 14 .
Second component model in claim 14.
Third component model in claim 14
Fourth component model in claim14
First flow sum junction in claim 7.
Design tool in claim 14.
The architecture generation module in claim 14.
The model evaluate module in claim 16.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Examiner Note: The specification doesn’t provide a specific structure, so it also invokes 35 USC 112 (a) and (b), see below for further explanation in 35 USC 112(b) claim rejection.
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 1-20 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 14 disclose “First , second , third and fourth component model”, “design tool” , claim 14, “ the architecture generation module”, claim 7, “First flow sum junction”, and claim 16, “the model evaluate module”, doesn’t have any structure which is capable to perform the claim limitations other saying they are implemented on the computer [0008]. The specification does not appear to disclose any structure for the model, modules on system claims. Therefore what structure is included in these modules is indefinite.
Claim 1 and 14 recites the limitation "the connections module" in step II line 6, There is insufficient antecedent basis for this limitation in the claim.
Claims 1 -20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 1- 20 are directed to a system, however the modules disclosed therein do not have structural support in the specification. The specification is therefore lacking written description in view of citation for each module below.
First component model in [0087], [0107], [0108]
Second component model in [0108], [0117], [0146]
Third component model in [0118], [0119], [0120], [0158], [0187], [0189],
Fourth component model in [0086], [0118], [0133],- [0136], [0139] –[0146].
First flow sum junction in [0117]
Design tool in [0072] –[0084], [0109], [0140] –[0143], [0163] -0166]- [0236]
The architecture generation module in [0072], [0075], [0123], [0143] – [0152]. [0210], [0233].
evaluate module in [0213]- [0217], [0231].
The dependent claims are also rejected under the same rational as the independent claims, since they are dependent on either claim 1 or 14.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claim invention recites a
judicial exception, which is directed to judicial exception of an abstract idea, as it has
not been integrated into practical application and the claim further do not recite
significantly more that the judicial exception.
Step 1: Yes , the claims 1-20 are directed to a method and system claims, so they fails within the statutory category of a process.
Step 2A: prong 1: Yes, the claim recites abstract ideas. Abstract ideas in the claims are bolded as shown below
Claim 1:
providing an input file to the design tool, the input file comprising architecture selection constraints and load requirements for the powertrain to be designed; (insignificant extra solution activity - data gathering such as such as 'obtaining information'. See MPEP 2106.05(g).))
providing a generic powertrain component library comprising (insignificant extra solution activity - data gathering such as such as 'obtaining information'. See MPEP 2106.05(g).)
a plurality of configurable first component models from which N power source models are configurable based on first component parameters,
a plurality of configurable second component models from which M power sink models are configurable based on second component parameters,
a plurality of configurable third component models from which at least one inertance coupling model is configurable based on third component parameters,
a plurality of configurable fourth component models from which a compliance-based coupling model is configurable based on fourth component parameters, (under its broadest reasonable interpretation the above claim limitations recites a mental process. Since it recites a blueprint/ drawing with adjustable parameter. A human can create a design/ drawing a first , second, third and fourth model by varying each specific parameters for the power source, power sink and the rest of the components. For example since there is no specify way of performing this limitation is recited , a human can draw a first component model with engine of 50 KW, Battery of 60KW and the same for other components, so using human mind’s ability of observation and evaluation, this limitations can be performed by a human mind by using pen and paper).
wherein each first component model is configured to receive at least one of a plurality of first component specific inputs and to calculate an effort output or flow output based on the at least one of the plurality of first component specific inputs;
wherein each second component model is configured to receive at
least one of a plurality of second component specific inputs and to calculate an effort output or flow output based on at least one of the plurality of second component specific inputs; and
wherein each third component model is configured to receive a plurality of effort inputs and to calculate a flow output based on the effort inputs, and
wherein each fourth component model is configured to receive a plurality of flow inputs and to calculate an effort output;
All the above claim limitations also recites abstract idea of mathematical concepts which can be performed by a human mind. A human mind can calculate the effort output for each of the above model based on the specific input (data gathered). While according to [0089] and [0111] the input files are received from the input file, and [0179] the effort output for the fourth component model is calculated by using a mathematical concept of integrating the net flow input and by the inverse of the compliance parameter. Therefore a human mind can substitute the input parameters in order to compute the effort output for each of the above component model based on their specific input by a pencil and paper using humans’ skill of observation, analysis , evaluation and judgment. Therefore those limitations recites an abstract idea of a mental process.
wherein X coupling models may be configured from the third and fourth component models, this recites an abstract idea of mental process. A human can assemble to models together to create a coupling model since there is no specific step or reequipment is recited, so this claim is a mental process.
wherein the design tool generates a plurality of candidate powertrain architectures based on the generic powertrain component library, and the load requirements and architecture selection constraints of the input file, each candidate powertrain architecture comprising N power source models with first component specific inputs, M power sink models with second component specific inputs, and X coupling models (additional element - Mere Instructions To Apply An Exception – ideal solution )
i) the design tool generates a connections model of the N power source models, M power sink models and X coupling models which is representative of the candidate powertrain architecture comprising flow weight parameters and effort weight parameters, wherein the flow weight parameters define any flow connections from the flow outputs of the N power source models, the flow outputs of the M power sink models, and the flow outputs of the inertance coupling models of the X couplings to the flow inputs of the compliance based coupling models of the X couplings of the model architecture; and the effort weight parameters define any effort connections from the effort outputs of the N power source models, the effort outputs of the M power sink models, and the effort outputs of the compliance based coupling models of the X couplings to the effort inputs of the inertance coupling models of the X coupling models of the model architecture; (additional element - Mere Instructions To Apply An Exception – ideal solution )
ii) the design tool selects candidate first, second, third and fourth component parameters for the N power source models, M power sink models, and X coupling models of the candidate powertrain architecture, and generates a model of the candidate powertrain architecture based on the candidate first, second, third and fourth component parameters, the N power source models, M power sink models and X coupling models, and the connections module; (this claim limitation recites an abstract idea which fall under a mental process. A human mind can select a candidate parameters form each of the components and based on those parameters, a human can also create a powertrain design using a pencil and paper by putting those components together. In this claim there is no any additional element or specific step is recited besides selecting parameters and based on those parameters generating a powertrain architecture and generating a powertrain architecture was made by hand previously according to [0017]. Therefore this claim limitation is a mental process. )
iii) the design tool evaluates the model of the candidate powertrain architecture based on the load requirements of the input file and generates a cost associated with the candidate powertrain architecture and the candidate first, second, third, and fourth component parameters; (this claim limitation recites a mental process. By using humans’ ability of observation, evaluation and judgment, a human can evaluate the architecture based on the requirements and can analyses the cost of each architecture based on the components used, for example on [0092] –[0102] , it shows the architecture selection constraints for an internal combustion engine including its cost, so based on the components we used to create the architecture, we can generate the cost).
iv) the design tool calculates optimized first, second, third and fourth component parameters for the candidate powertrain architecture by optimizing the candidate first, second, third, and fourth component parameters based on the cost associated with the candidate powertrain architecture and the candidate first, second, third, and fourth component parameters; and ( additional element – insignificant extra solution activity).
wherein the design tool outputs an optimized powertrain architecture having optimized first, second, third and fourth component parameters based on the optimized first, second, third and fourth component (insignificant extra-solution activity – data gathering, such as 'outputting data'. See MPEP 2106.05(g).)
Step 2A: prong 2: No
The above judicially exceptions do not recite additional elements that integrate
the exceptions into a practical application of the exception because the claims do not
have additional elements of a combination of additional elements that apply, rely or use
the judicial exception in a manner that impose a meaningful limit on the judicial
exception.
Claims recites gathering data which is insignificant extra solution activity. 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)).
Claim 1:
providing an input file to the design tool, the input file comprising architecture selection constraints and load requirements for the powertrain to be designed; (insignificant extra solution activity - data gathering such as such as 'obtaining information'. See MPEP 2106.05(g).))
providing a generic powertrain component library comprising (insignificant extra solution activity - data gathering such as such as 'obtaining information'. See MPEP 2106.05(g).)
iv) the design tool calculates optimized first, second, third and fourth component parameters for the candidate powertrain architecture by optimizing the candidate first, second, third, and fourth component parameters based on the cost associated with the candidate powertrain architecture and the candidate first, second, third, and fourth component parameters; and wherein the design tool outputs an optimized powertrain architecture having optimized first, second, third and fourth component parameters based on the optimized first, second, third and fourth component (insignificant extra-solution activity – data gathering, such as 'outputting data'. See MPEP 2106.05(g).)
Step 2B: No, The claims do not cite additional elements which are significantly more than the abstract idea. As outlined above the claims merely use a computer to perform abstract ideas. Merly using of a computer and applying abstract ideas into a system without making improvement to the functionality of a computer is not a significantly more.
Claim 1: recites additional elements which is listed below in extra solution activity of field of use and technological environment/ ideal solution. This claim limitations are mere instruction , without reciting how this limitation can be done.
wherein the design tool generates a plurality of candidate powertrain architectures based on the generic powertrain component library, and the load requirements and architecture selection constraints of the input file, each candidate powertrain architecture comprising N power source models with first component specific inputs, M power sink models with second component specific inputs, and X coupling models (additional element - 2106.05(h) Field of Use and Technological Environment)
i), the design tool generates a connections model of the N power source models, M power sink models and X coupling models which is representative of the candidate powertrain architecture comprising flow weight parameters and effort weight parameters, wherein the flow weight parameters define any flow connections from the flow outputs of the N power source models, the flow outputs of the M power sink models, and the flow outputs of the inertance coupling models of the X couplings to the flow inputs of the compliance based coupling models of the X couplings of the model architecture; and the effort weight parameters define any effort connections from the effort outputs of the N power source models, the effort outputs of the M power sink models, and the effort outputs of the compliance based coupling models of the X couplings to the effort inputs of the inertance coupling models of the X coupling models of the model architecture; (additional element - 2106.05(h) Field of Use and Technological Environment).
Generally claim 1, recites abstract idea of designing a powertrain architecture using a computer as a tool without claiming any improvement on the computer itself or the practical application.
Regarding dependent claims :
Claims 2 and 15:
Wherein the load requirements of the input file comprise a reference load for the powertrain (it further define the type of data used - (insignificant extra solution activity - data gathering such as such as 'obtaining information'. See MPEP 2106.05(g).))
Claims 3 and 19:
Wherein the architecture selection constraints of the input file comprise one or more of: a minimum number of power sources constraint, a maximum number of power sources constraint, a minimum number of power sinks constraint, a maximum number of power sinks constrain, a minimum number of couplings constraint, and a maximum number of couplings constraint (it further define the type of data used - (insignificant extra solution activity - data gathering such as such as 'obtaining information'. See MPEP 2106.05(g).)).
Claim 4:
Wherein each configurable third component model comprises a first effort sum junction configurable to calculate a net effort input for the third component model based on at least one of: the effort output from one or more power source models, the effort output from one or more power sink models, and the effort output from one or more compliance-based coupling models, wherein the flow output is calculated based on the net effort input ( it further add details to the abstract idea. A human can perform computing of net effort input as it is cited on claim 1, based on the effort output of one of the component model).
Claim 5:
Wherein each configurable third component model comprises an effort scaling module configurable to scale at least one of: the effort output from one or more power source models, the effort output from one or more power sink models, and the effort output from one or more compliance-based coupling models based on a first scaling parameter and to scale the flow output calculated by the configurable third component model by a first complementary scaling parameter, (it further add details to the abstract idea. A human can perform computing scaling of one of the effort output by computing the mathematical equation as it is cited on [0191]).
wherein the first scaling parameter is provided by the design tool when generating the model of each candidate powertrain architecture; and the design tool calculates an optimized first scaling parameter based on the cost associated with the candidate powertrain architecture and the candidate first, second, third, and fourth component parameters( this limitation further narrow the abstract idea, and a human mind can perform calculation using a pencil and paper)
Claim 6:
wherein the net effort input calculated by the first effort sum junction is based on efforts in the same energy domain ((it further narrow the abstract idea of computing effort by limiting to the same energy domain)
Claim 7:
Wherein each configurable fourth component model comprises a first flow sum junction configured to calculate the net flow input for the fourth component model based at least one of: the flow output from one or more power source models, the flow output from one or more power sink models, and the flow output from one or more inertance coupling models (it further narrow the abstract idea of computing net flow input for the fourth component model base on at least one of those component flow outputs, so a human mind can calculate the net flow input based on the parameter( at least one of those component flow outputs) using a pencil and paper).
Claim 8:
Wherein each configurable fourth component model comprises a flow scaling module configurable to scale at least one of: the flow output from one or more power source models, the flow output from one or more power sink models, and the flow output from one or more effort based coupling models based on a second scaling parameter and to scale the effort output calculated by the configurable fourth component model by a second complementary scaling parameter, (it further add details to the abstract idea. A human can perform computing scaling of one of the effort output by computing the mathematical equation as it is cited on [0191]).
wherein the second scaling parameter is provided by the design tool when generating the model of each candidate powertrain architecture; and the design tool calculates an optimized second scaling parameter based on the cost associated with the candidate powertrain architecture and the candidate first, second, third, and fourth component parameters.( this limitation further narrow the abstract idea, and a human mind can perform calculation using a pencil and paper)
Claim 9:
Wherein generating a candidate powertrain architecture comprises: selecting a set of first, second, third and/or fourth component models from the generic component library based on the architecture selection constraints; (this further define abstract idea and a human can select component models from a library using humans’ ability of observation, evaluation ,and judgment)
Claims 10 and 20:
Wherein generating a connections model of the N power source models, M power sink models and X coupling models for each candidate powertrain architecture comprises generating a causal relationship between the N power source models, M power sink models and X coupling models. (it further defines the additional element which is not significantly more besides of Field of Use and Technological Environment 2106.05(h)).
Claims 11 and 17:
Wherein the design tool optimizes the candidate first, second, third, and fourth component parameters using a stratified sampling strategy ( this can be performed by a human using a pen and paper, by grouping the parameters and select samples from the group, and by using observation/ evaluation , make a judgment until optimize parameter is found).
Claims 12 and 18:
Wherein the design tool further optimizes the candidate first, second, third, and fourth component parameters following the stratified sampling strategy using a line search strategy.(it further the abstract idea of finding optimal parameter as is cited above).
Claims 13 and 16
Wherein the design tool calculates a response of the model of the candidate powertrain architecture to the reference load and evaluates the response of the model of the candidate powertrain architecture based on the load requirements ( this also recite a mental process, of performing observation and evaluation on the candidate architecture based on a constraint).
Claim 14: is in the same scope as that of claim 1, and it recites abstract ideas as of claim 1 and it also invokes software per se. Claim 14 recites “A computer-implemented design tool” and this doesn’t fall within at least one of the four categories of patent eligible subject matter because only if at least one of the claimed elements of the system is a physical part of a device can the system as claimed constitute part of a device or combination of devices to be a machine within the meaning of 101. Since a “A computer-implemented design tool” consists merely instruction and the system is not the part of claim so the claim can be reasonably implemented as software routines, the claim is a system of software, failing to fall within a statutory category of invention.
Therefore based on the above analysis claims 1- 20 is not eligible under 35 USC 101.
Claim Rejections - 35 USC § 103
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.
Claims 1-2, 9, and 13-16 are rejected under 35 U.S.C. 103 as being unpatentable over Mike, MATLAB, "How to Model and Simulate Automotive Systems Using Powertrain Blockset", April 17, 2018, XP055865500, URL: https://www.youtube.com/watch?v=O3loJOOSRGI, in the view of van Berkel, Koos, et al. "Topology and flywheel size optimization for mechanical hybrid powertrains." IEEE Transactions on Vehicular Technology 63.9 (2014): further in the view of Zhou, Xingyu, et al. "Representation, generation, and optimization methodology of hybrid electric vehicle powertrain architectures." Journal of Cleaner Production 256 (2020): 120711.
As of claim 1, Mike teaches A method for designing a powertrain using a design tool implemented on a computer, the method comprising: ([2:11 – 32:19] designing a powertrain using a powertrain block set and perform design optimization studies).
providing a generic powertrain component library comprising ([3:47], library blocks
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i, the design tool generates a connections model of the N power source models, M power sink models and X coupling models which is representative of the candidate powertrain architecture comprising flow weight parameters and effort weight parameters, (a connection model is created with a powertrain blockset library components ([4:16 ], pull together a punch of components, [10:04] left bottom),
it comprises flow weight and effort weight parameters ([15:18], optimize parameters , 5 control parameters ( speed, power, force ,as it shown on the graph ), and 1 hardware parameters, final differential ratio).
wherein the flow weight parameters define any flow connections from the flow outputs of the N power source models, the flow outputs of the M power sink models, and the flow outputs of the inertance coupling models of the X couplings to the flow inputs of the compliance-based coupling models of the X couplings of the model architecture; and ([14:49], when it in to high-speed load
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As shown on the figure it shows a flow connection of speed and power between the components for example the power flows from batter to motor and engine to front tire, the output of engine is the input for front tires as it shown on the flow connection.
the effort weight parameters define any effort connections from the effort outputs of the N power source models, the effort outputs of the M power sink models, and the effort outputs of the compliance-based coupling models of the X couplings to the effort inputs of the inertance coupling models of the X coupling models of the model architecture;( [11:40], “designed oriented CAE model”, it would visit each of the operating point is the speed in the torque command points kind of one by one and find the combination of input that would get you the commanded torque and [14:36], when the power demand on the vehicle increase: the effort connection flow is
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iii, the design tool evaluates the model of the candidate powertrain architecture based on the load requirements of the input file and generates a cost associated with the candidate powertrain architecture and the candidate first, second, third, and fourth component parameters; and ( [17;00] sensitive analysis , determine sensitivity of the fuel economy to change in design parameters. It create a monte carlo analysis where I scatter shot a bunch of random points throughout design space to see what kind of impact it has. It use parameter sets including pressure, vehicle mass, wheel radius).
Mike does not explicitly teach providing an input file to the design tool, the input file comprising architecture selection constraints and load requirements for the powertrain to be designed; a plurality of configurable first component models from which N power source models are configurable based on first component parameters, wherein each first component model is configured to receive at least one of a plurality of first component specific inputs and to calculate an effort output or flow output based on the at least one of the plurality of first component specific inputs; a plurality of configurable second component models from which M power sink models are configurable based on second component parameters, wherein each second component model is configured to receive at least one of a plurality of second component specific inputs and to calculate an effort output or flow output based on at least one of the plurality of second component specific inputs; and a plurality of configurable third component models from which at least one inertance coupling model is configurable based on third component parameters, wherein each third component model is configured to receive a plurality of effort inputs and to calculate a flow output based on the effort inputs, and a plurality of configurable fourth component models from which a compliance-based coupling model is configurable based on fourth component parameters, wherein each fourth component model is configured to receive a plurality of flow inputs and to calculate an effort output; wherein X coupling models may be configured from the third and fourth component models, wherein the design tool generates a plurality of candidate powertrain architectures based on the generic powertrain component library, and the load requirements and architecture selection constraints of the input file, each candidate powertrain architecture comprising N power source models with first component specific inputs, M power sink models with second component specific inputs, and X coupling models; ii, the design tool selects candidate first, second, third and fourth component parameters for the N power source models, M power sink models, and X coupling models of the candidate powertrain architecture, and generates a model of the candidate powertrain architecture based on the candidate first, second, third and fourth component parameters, the N power source models, M power sink models and X coupling models, and the connections module; iv, the design tool calculates optimized first, second, third and fourth component parameters for the candidate powertrain architecture by optimizing the candidate first, second, third, and fourth component parameters based on the cost associated with the candidate powertrain architecture and the candidate first, second, third, and fourth component parameters; and wherein the design tool outputs an optimized powertrain architecture having optimized first, second, third and fourth component parameters based on the optimized first, second, third and fourth component parameters of each candidate powertrain architecture.
While Van teaches providing an input file to the design tool, the input file comprising architecture selection constraints and load requirements for the powertrain to be designed; (page 4193,
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a plurality of configurable first component models from which N power source models are configurable based on first component parameters, wherein each first component model is configured to receive at least one of a plurality of first component specific inputs and to calculate an effort output or flow output based on the at least one of the plurality of first component specific inputs; (Page 4196, section A components. 1,Engine: The internal combustion engine converts fuel into mechanical power through chemical and mechanical processes. Mechanical power Pe is given by
Pe =ωeτe
where ωe denotes the rotational speed, and τe denotes the generated torque. For power generation (i.e., Pe > 0), the engine speed needs to exceed a minimum of ωe ≥ ωe (105 rad/s). During engine idling (i.e., Pe = 0) with vehicle standstill (i.e., vv = 0), the engine speed is reduced to ωe = ω idle (84 rad/s) for lower fuel consumption [49]. Input parameter module parameters table II including engine torque, engine efficiency and others).
a plurality of configurable second component models from which M power sink models are configurable based on second component parameters, wherein each second component model is configured to receive at least one of a plurality of second component specific inputs and to calculate an effort output or flow output based on at least one of the plurality of second component specific inputs; and (page 4197, 3)Continuously Variable Transmission: The main components of the pushbelt CVT are the drive clutch, the pump, the variator, and the final drive, where the variator enables a continuously variable speed ratior cvt. The power dissipation in the CVT includes the power consumption of the pump and the power dissipation in the clutch, the variator, and the final drive.
The power dissipation of the down sized CVT P small cvt is then given by
Psmall cvt =μ small cvt P cvt
(specific inputs (Input parameter module parameters table II, page 4196))
a plurality of configurable third component models from which at least one inertance coupling model is configurable based on third component parameters, wherein each third component model is configured to receive a plurality of effort inputs and to calculate a flow output based on the effort inputs, ( page 4196 -4197, Flywheel System: The flywheel system contains a vacuum-placed steel rotor and a two-stage gear set to operate the rotor at high rotational speeds (up to 3140 rad/s). The kinetic energy content of the rotor Er is determined by the rotor inertia and the rotational speed as follows:
Er=1/2Jrω2f
Where ωf denotes the rotational speed at the output shaft, Jr denotes the equivalent rotor inertia at this shaft, and the energy storage is limited Er∈[0,Er].
For its purpose of urban driving, the speed range of the flywheel system is selected to match the engine speed range (between105and265 rad/s) under normal urban driving conditions, there by facilitating a combined operation).
a plurality of configurable fourth component models from which a compliance-based coupling model is configurable based on fourth component parameters, wherein each fourth component model is configured to receive a plurality of flow inputs and to calculate an effort output; (page 4194 , section A, In this abstraction, the transmission represents either an automated gearbox or a CVT, whereas smaller components such as shafts, fixed gears, clutches, planetary brakes,1 torque converters, and auxiliaries are not explicitly shown yet implied in the branches between the main components… page 4198, 7, vehicle, The wheel shaft speed is related to the vehicle velocity by the effective wheel radius Rw, thereby neglecting any wheel slip. The vehicle propulsion or braking power Pv is given by
Pv =(1−β)(mv av vv +Fv(vv) vv)
where mv is the equivalent mass of the vehicle with two passengers and the inertia of the wheels, where other (minor) powertrain inertias are neglected. The term Fv describes the combined rolling and aerodynamic force as a function of vehicle velocity vv). Wheel shift is considered as a fourth component models and effort output of power and force can be calculated by using the above equation.
wherein X coupling models may be configured from the third and fourth component models, (page 4198 4) Automated Manual Transmission: The main components of the AMT are the drive clutch, the pump, the gearbox, and the final drive, where the gearbox enables six different gear ratios r amt. The upcoming six-speed AMT is selected rather than the currently more common five-speed AMT [2], which matches better with the wide ratio coverage of the CVT. The power dissipation in the AMT includes the power consumption of the pump and the power dissipation in the clutch, the gearbox, and the final drive).
wherein the design tool generates a plurality of candidate powertrain architectures based on the generic powertrain component library, and the load requirements and architecture selection constraints of the input file, (figure 3, page 4195
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each candidate powertrain architecture comprising N power source models with first component specific inputs, M power sink models with second component specific inputs, and X coupling models; ( page 4195, The four most competitive topology classes in Table I (P2−P5), with the main focus on fuel saving and hybridization cost, are selected for further investigation. Of the P2 class, the Flybrid topology [8] is selected, using an AMT with a torque converter for the engine and a small toroidal CVT with two clutches for the flywheel. Of the P3 class, the Brake-Hybrid topology [36] is selected, using an AMT with a clutch for the engine and two planetary brakes for the flywheel. Of the P4 class, the mecHybrid topology [7] is selected, using a CVT with three clutches for the engine and the flywheel. Of the P5 class, the Flywheel Hybrid Drive II topology [44] is selected, using a CVT with five clutches for the engine and the flywheel. (Different architecture is generated including p1 -p2 and use specific input including number of clutches as it is listed above).
Mike and Van are considered to analogous to the claimed invention since they focus on powertrain architecture design. Therefore it would be obvious for a person of ordinary skill in the art before the effective filing date to integrate Van teaching of creating different model components in to Mike teaching in order to create different model components using the provided load requirements of Van and component library of Mike to create different powertrain architectures.
The motivation would have been to create optimized powertrain architecture using component library through simulation, optimization and by performing evaluation, testing on different models (Mike [0;00- 32;20).
The modified model of Mike- Van does not explicitly teach ii, the design tool selects candidate first, second, third and fourth component parameters for the N power source models, M power sink models, and X coupling models of the candidate powertrain architecture, and, generates a model of the candidate powertrain architecture based on the candidate first, second, third and fourth component parameters, the N power source models, M power sink models and X coupling models, and the connections module; iv, the design tool calculates optimized first, second, third and fourth component parameters for the candidate powertrain architecture by optimizing the candidate first, second, third, and fourth component parameters based on the cost associated with the candidate powertrain architecture and the candidate first, second, third, and fourth component parameters; and wherein the design tool outputs an optimized powertrain architecture having optimized first, second, third and fourth component parameters based on the optimized first, second, third and fourth component parameters of each candidate powertrain architecture.
While Zhou teaches ii, the design tool selects candidate first, second, third and fourth component parameters for the N power source models, M power sink models, and X coupling models of the candidate powertrain architecture, and (Table 2,3, and 10, page 12, 15
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generates a model of the candidate powertrain architecture based on the candidate first, second, third and fourth component parameters, the N power source models, M power sink models and X coupling models, and the connections module; (page 15, In the optimization for each category of HEV powertrain, all design schemes comprise three parts, namely, the elements of the kinematic matrix (Ci,i¼ 1, 2, 3 …), power plant parameters(Spi, Sni, bi, where i ¼ 1, 2), and mechanical transmission parameters. The optimizations on HEV powertrain architectures are conducted by modifications in the element of kinematic matrix.
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) based on the parameter this is a scheme for 0-0 type HEV powertrain architecture design.
Iv, the design tool calculates optimized first, second, third and fourth component parameters for the candidate powertrain architecture by optimizing the candidate first, second, third, and fourth component parameters based on the cost associated with the candidate powertrain architecture and the candidate first, second, third, and fourth component parameters; and (page 17- 18
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) optimized parameters are presented on table 10 and its design is shown on figure 20 above.
wherein the design tool outputs an optimized powertrain architecture having optimized first, second, third and fourth component parameters based on the optimized first, second, third and fourth component parameters of each candidate powertrain architecture (Page 16 , Finally, all feasible results obtained in optimizations in individual HEV powertrain categories are combined together, and the final Pareto optimal design schemes corresponding to different situation of trade-off between design objectives are produced by applying the non-dominated sorting).
Zhou is considered to be analogous to the claimed invention since it focus on optimization of powertrain architecture. Therefore it would be obvious for a person of ordinary skill in the art before the effective filing date to integrate Zhou teaching of optimizing of powertrain parameters and create optimized powertrain architecture using optimized parameters in to the modified model to create optimized powertrain architecture design.
The motivation would have been by performing numerical optimization methodology which can be used to perform the complete HEV powertrain optimization design including the architecture, powertrain component parameters, and control strategy, including parameter optimization and a bond graph, helps to create optimized result with improved fuel economy compared to other designing result (Zhou, conclusion).
Claim 14 is in the same scope as that of claim 1, and claim 14 is rejected under the same rational as of claim 1.
As of claim 2, the modified model teaches all the limitations of claim 1, and Mike also teaches wherein the load requirements of the input file comprise a reference load for the powertrain ( A speed target and distance, 11:40 “designed oriented CAE model”, it would visit each of the operating points the speed in the torque command points kind of one by one and find the combination of input that would get you the commanded torque.) according to [0079], commanded torque is interpreted as reference load.
Claim 15 is in the same scope as that of claim 2, and claim 15 is rejected under the same rational as of claim 2.
As of claim 9, the modified model teaches all the limitation of claim 1, and Mike also teaches wherein generating a candidate powertrain architecture comprises: selecting a set of first, second, third and/or fourth component models from the generic component library based on the architecture selection constraints;([3;52- 4 ;30], for the blocks you can see that we have a few different sub libraries here we have pieces for the drivetrain we have blocks for the energy storage we have propulsion both for combustion energy … so a typical user my start with the proposition there are goanna may be pull together a bunch of components like turbocharge).
As of claim 13, the modified model teaches all the limitation of claim 2, and Mike also teaches wherein the design tool calculates a response of the model of the candidate powertrain architecture to the reference load and evaluates the response of the model of the candidate powertrain architecture based on the load requirements ([0:50] – FTP-75 speed cycle, [14:30] low power low speed and high speed or high power as a reference load , response is also calculated as instantaneous fuel economy [1:38], [16: 55], and evaluation is performed based on sensitivity analysis and fuel economy [17:01])
Claim 16 is in the same scope as that of claim 13, and claim 16 is rejected under the same rational as of claim 13.
Claims 3 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Mike, MATLAB, "How to Model and Simulate Automotive Systems Using Powertrain Blockset", April 17, 2018, XP055865500, URL:, https://www.youtube.com/watch?v=O3loJOOSRGI, in the view of van Berkel, Koos, et al. "Topology and flywheel size optimization for mechanical hybrid powertrains." IEEE Transactions on Vehicular Technology 63.9 (2014): further in the view of Zhou, Xingyu, et al. "Representation, generation, and optimization methodology of hybrid electric vehicle powertrain architectures." Journal of Cleaner Production 256 (2020): further in the view of Kabalan, Bilal, et al. "Systematic methodology for architecture generation and design optimization of hybrid powertrains." IEEE Transactions on Vehicular Technology 69.12 (2020): 14846-14857.
As of claim 3, the modified model teaches all the limitation of claim 1, but it does not explicitly teach , wherein the architecture selection constraints of the input file comprise one or more of: a minimum number of power sources constraint, a maximum number of power sources constraint, a minimum number of power sinks constraint, a maximum number of power sinks constrain, a minimum number of couplings constraint, and a maximum number of couplings constraint.
While Kabalan teaches , wherein the architecture selection constraints of the input file comprise one or more of: a minimum number of power sources constraint, a maximum number of power sources constraint, a minimum number of power sinks constraint, a maximum number of power sinks constrain, a minimum number of couplings constraint, and a maximum number of couplings constraint (page 14851, In addition, constraints can be added on the minimum and maximum number of modes required. An example is shown in Figure 11. The Modes Table of the considered architecture of the example has 0 “ICE only” mode. This could be used for example as a constraint and will dismiss this architecture as the minimum number of “ICEonly” modes required in this example is 1).
Kabalan is considered to be analogous to the claimed invention since it focus on architecture generation and design optimization of hybrid powertrains. Therefore it would be obvious for a person of ordinary skill in the art before the effective filing date to integrate Kabalan teaching of having a minimum number of power source as a constraint into the modified model in order to design optimized powertrain architecture.
The motivation would have been by starting from a set of chosen components, the methodology automatically generates all the possible graphs of architectures using constraint-programming techniques and by automatically filtered the architectures the most promising ones are selected ( Kabalan, abstract).
Claim 19 is in the same scope as that of claim 3, and claim 19 is rejected under the same rational as of claim 3.
Claims 4-6 are rejected under 35 U.S.C. 103 as being unpatentable over Mike, MATLAB, "How to Model and Simulate Automotive Systems Using Powertrain Blockset", April 17, 2018, XP055865500, URL: https://www.youtube.com/watch?v=O3loJOOSRGI, in the view of van Berkel, Koos, et al. "Topology and flywheel size optimization for mechanical hybrid powertrains." IEEE Transactions on Vehicular Technology 63.9 (2014): further in the view of Zhou, Xingyu, et al. "Representation, generation, and optimization methodology of hybrid electric vehicle powertrain architectures." Journal of Cleaner Production 256 (2020): further in the view of Kabalan, Bilal, et al. "Systematic methodology for architecture generation and design optimization of hybrid powertrains." IEEE Transactions on Vehicular Technology 69.12 (2020): 14846-14857, further in the view of Millo, Federico, Luciano Rolando, and Maurizio Andreata. "Numerical simulation for vehicle powertrain development." Numerical Analysis-Theory and Application. IntechOpen, 2011.
As of claim 4, the modified model teaches all the limitations of claim 3, but it does not explicitly teach the limitation of claim 4.
While Millo teaches wherein each configurable third component model comprises a first effort sum junction configurable to calculate a net effort input for the third component model based on at least one of: the effort output from one or more power source models, the effort output from one or more power sink models, and the effort output from one or more compliance based coupling models, wherein the flow output is calculated based on the net effort input (section 3.1, According to this approach the dynamic response of the tyre can be approximated by a first order delay and the maximum force generated at the road interface can be assumed to be proportional to the vertical load on the wheel. The first order delay is useful to avoid numerical issues at very low vehicle speed, and to simulate (although in a quite approximate way) the tyre damping. The brakes are modelled as an additional torque that reduces the net torque acting on the tyre. Therefore the net torque acting on the wheel is:
Twh=Tshaft−Tbrake
Where Twh is the wheel torque, Tshaft is the torque at the driveshaft, and Tbrake is the braking torque).
Millo is considered to be analogous to the claimed invention, since it focus on numerical simulation for vehicle powertrain development. Therefore it would be obvious for a person of ordinary skill in the art before the effective filing date to integrate Millo teaching of computing net torque on the wheel , shaft and brake into the third component model in the modified model.
The motivation would have been by applying numerical simulation to powertrain development and by performing evaluation of vehicle performance, paying particular attention to the engine behavior under transient conditions and assessment of the fuel economy it helps to create design of vehicle powertrain to achieve these targets or constraints (Millo, introduction).
As of claim 5, the modified model teaches all the limitations of claim 4, and Mike also teaches wherein each configurable third component model comprises an effort scaling module configurable to scale at least one of: the effort output from one or more power source models, the effort output from one or more power sink models, and the effort output from one or more compliance based coupling models based on a first scaling parameter and to scale the flow output calculated by the configurable third component model by a first complementary scaling parameter, ([7;38] – resign engine and recalibration controller, To automatically skill the engine, the default engine that comes with this reference application is 1.5 l engine, you can scale it down or up by an entire order of magnitude , so that is a way for you to very quickly get some reasonable engine data if you don’t have any dyno data to start with).
wherein the first scaling parameter is provided by the design tool when generating the model of each candidate powertrain architecture; and ( [7;39] bottom right automatically scale. – the first scaling parameter is volume of engine as 1.5l and it can be scaled down or up ).
Kabalan also teaches the design tool calculates an optimized first scaling parameter based on the cost associated with the candidate powertrain architecture and the candidate first, second, third, and fourth component parameters (page 14854 – 14855, Another output of the methodology is the possibility of examining closely the optimal sizing and the powertrain operation (energy management, components operation, modes choice…) for all the architectures on all the simulated driving cycles. An example is presented hereafter. The Pareto points corresponding to 28 Number of battery modules for architectures9 (one of the best),1(intermediate)and 2(worse) are considered in this example. The sizing is presented
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in Table IV, along with the fuel consumption in each of the driving conditions. Architecture 9 has the least fuel consumption in all driving conditions. It has also downsized MG1 and ICE compared to architecture 2. However, architecture 1 presents the most downsized electric powertrain components (MG1 and MG2), with a slightly upsized engine).
As of claim 6, the modified model teaches all the limitation of claim 4 and Mike also teaches wherein the net effort input calculated by the first effort sum junction is based on efforts in the same energy domain ([14:30], during low power low speed the motor drives the wheel directly as shown on bottom left and a common energy source is used – battery).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Mike, MATLAB, "How to Model and Simulate Automotive Systems Using Powertrain Blockset", April 17, 2018, XP055865500, URL: https://www.youtube.com/watch?v=O3loJOOSRGI, in the view of van Berkel, Koos, et al. "Topology and flywheel size optimization for mechanical hybrid powertrains." IEEE Transactions on Vehicular Technology 63.9 (2014): further in the view of Zhou, Xingyu, et al. "Representation, generation, and optimization methodology of hybrid electric vehicle powertrain architectures." Journal of Cleaner Production 256 (2020): further in the view of Shubhamhegade, "Bond graph", Wikipedia, September 7, 2020, XP055865508, URL: https://en.wikipedia.org/w/index.php?title=Bond_graph&oldid=977168675
As of claim 7, the modified model teaches all the limitations of claim 1, but it does not explicitly teach the limitation of claim 7.
While Shubhamhegade teaches wherein each configurable fourth component model comprises a first flow sum junction configured to calculate the net flow input for the fourth component model based at least one of: the flow output from one or more power source models, the flow output from one or more power sink models, and the flow output from one or more inertance coupling models ( section , “Bond graph” The bond graph is composed of the "bonds" which link together "single port", "double-port" and "multi-port" elements (see below for details). Each bond represents the instantaneous flow of energy (dE/dt) or power… Section, “component” If an engine is connected to a wheel through a shaft, the power is being transmitted in the rotational mechanical domain, meaning the effort and the flow are torque (τ) and angular velocity (ω) respectively. A word bond graph is a first step towards a bond graph, in which words define the components. As a word bond graph, this system would look like:
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Shubhamhegade is considered to be analogous to the claimed invention since it focus on linking ports to compute flow of energy. Therefore it would be obvious to try for a person of ordinary skill in the art before the effective filing date to integrate Shubhamhegade teaching of bond graph to compute the net flow input and output into the modified model for optimized powertrain architecture.
The motivation would have been by using a bond graph we can compute flow and effort in the powertrain. For example, for the bond of an electrical system, the flow is the current, while the effort is the voltage. By multiplying current and voltage in this example you can get the instantaneous power of the bond (Shubhamhegade, Section “bond graph”).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Mike, MATLAB, "How to Model and Simulate Automotive Systems Using Powertrain Blockset", April 17, 2018, XP055865500, URL: https://www.youtube.com/watch?v=O3loJOOSRGI, in the view of van Berkel, Koos, et al. "Topology and flywheel size optimization for mechanical hybrid powertrains." IEEE Transactions on Vehicular Technology 63.9 (2014): further in the view of Zhou, Xingyu, et al. "Representation, generation, and optimization methodology of hybrid electric vehicle powertrain architectures." Journal of Cleaner Production 256 (2020): further in the view of Shubhamhegade, "Bond graph", Wikipedia, September 7, 2020, XP055865508, URL: https://en.wikipedia.org/w/index.php?title=Bond_graph&oldid=977168675 further in the view of Kabalan, Bilal, et al. "Systematic methodology for architecture generation and design optimization of hybrid powertrains." IEEE Transactions on Vehicular Technology 69.12 (2020): 14846-14857
As of claim 8, the modified model teaches all the limitations of claim 7, and Mike also teaches wherein each configurable fourth component model comprises a flow scaling module configurable to scale at least one of: the flow output from one or more power source models, the flow output from one or more power sink models, and the flow output from one or more effort based coupling models based on a second scaling parameter and to scale the effort output calculated by the configurable fourth component model by a second complementary scaling parameter, ([7;38] – resign engine and recalibration controller, To automatically skill the engine, the default engine that comes with this reference application is 1.5 l engine, you can scale it down or up by an entire order of magnitude , so that is a way for you to very quickly get some reasonable engine data if you don’t have any dyno data to start with )
wherein the second scaling parameter is provided by the design tool when generating the model of each candidate powertrain architecture; and ([7;39] bottom right automatically scale. – the first scaling parameter is volume of engine as 1.5l and it can be scaled down or up).
The modified model does not explicitly teach teaches the design tool calculates an optimized second scaling parameter based on the cost associated with the candidate powertrain architecture and the candidate first, second, third, and fourth component parameters.
While Kabalan teaches the design tool calculates an optimized second scaling parameter based on the cost associated with the candidate powertrain architecture and the candidate first, second, third, and fourth component parameters (page 14854 -14855, Another output of the methodology is the possibility of examining closely the optimal sizing and the powertrain operation (energy management, components operation, modes choice…) for all the architectures on all the simulated driving cycles. An example is presented hereafter. The Pareto points corresponding to 28 Number of battery modules for architectures9 (one of the best),1(intermediate)and 2(worse) are considered in this example. The sizing is presented
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in Table IV, along with the fuel consumption in each of the driving conditions. Architecture 9 has the least fuel consumption in all driving conditions. It has also downsized MG1 and ICE compared to architecture 2. However, architecture 1 presents the most downsized electric powertrain components (MG1 and MG2), with a slightly upsized engine).
Examiner note: a flow scaling module is not represented on the drawing in the fourth component model (see drawing objection above) but it does on the third component model.
While as it is cited above Mike and Kabalan teaches a scaling of engine and parameters and Kabalan is considered to be analogous to the claimed invention since it focus on architecture generation and design optimization of hybrid powertrains, therefore it would be obvious to try by a person of ordinary skill in the art to include scaling model in the fourth component model as well. The motivation would have been by starting from a set of chosen components, the methodology automatically generates all the possible graphs of architectures using constraint-programming techniques and by automatically filtered the architectures the most promising ones are selected ( Kabalan, abstract).
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mike, MATLAB, "How to Model and Simulate Automotive Systems Using Powertrain Blockset", April 17, 2018, XP055865500, URL: https://www.youtube.com/watch?v=O3loJOOSRGI, in the view of van Berkel, Koos, et al. "Topology and flywheel size optimization for mechanical hybrid powertrains." IEEE Transactions on Vehicular Technology 63.9 (2014): further in the view of Zhou, Xingyu, et al. "Representation, generation, and optimization methodology of hybrid electric vehicle powertrain architectures." Journal of Cleaner Production 256 (2020): further in the view of Millo, Federico, Luciano Rolando, and Maurizio Andreata. "Numerical simulation for vehicle powertrain development." Numerical Analysis-Theory and Application. IntechOpen, 2011.
As of claim 10, the modified model teaches all the limitation of claim 1, but it does not explicitly teach wherein generating a connections model of the N power source models, M power sink models and X coupling models for each candidate powertrain architecture comprises generating a causal relationship between the N power source models, M power sink models and X coupling models.
While Millo teaches wherein generating a connections model of the N power source models, M power sink models and X coupling models for each candidate powertrain architecture comprises generating a causal relationship between the N power source models, M power sink models and X coupling models (section 2.2
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).As shown on the figure, it shows a step-by-step flow of torque and speed in the powertrain architecture from engine to wheel and wheel to engine.
Millo is considered to be analogous to the claimed invention, since it focus on numerical simulation for vehicle powertrain development. Therefore it would be obvious for a person of ordinary skill in the art before the effective filing date to integrate Millo teaching of a step-by-step flow of torque and speed in the powertrain architecture to the modified model.
The motivation would have been by applying numerical simulation to powertrain development and by performing evaluation of vehicle performance, paying particular attention to the engine behavior under transient conditions and assessment of the fuel economy it helps to create design of vehicle powertrain to achieve these targets or constraints (Millo, introduction).
Claim 20 is in the same scope as that of claim 10, and claim 20 is rejected under the same rational as of claim 10.
Claims 11-12 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Mike, MATLAB, "How to Model and Simulate Automotive Systems Using Powertrain Blockset", April 17, 2018, XP055865500, URL: https://www.youtube.com/watch?v=O3loJOOSRGI, in the view of van Berkel, Koos, et al. "Topology and flywheel size optimization for mechanical hybrid powertrains." IEEE Transactions on Vehicular Technology 63.9 (2014): further in the view of Zhou, Xingyu, et al. "Representation, generation, and optimization methodology of hybrid electric vehicle powertrain architectures." Journal of Cleaner Production 256 (2020): further in the view of Tran, Dai-Duong, et al. "Thorough state-of-the-art analysis of electric and hybrid vehicle powertrains: Topologies and integrated energy management strategies." Renewable and sustainable energy reviews 119 (2020):
As of claim 11, the modified model teaches all the limitation of claim 1, but it does not explicitly teach wherein the design tool optimizes the candidate first, second, third, and fourth component parameters using a stratified sampling strategy.
While Tran teaches wherein the design tool optimizes the candidate first, second, third, and fourth component parameters using a stratified sampling strategy ( section 3.3.2.2. Model predictive control-based strategies. The MPC was introduced to tackle the issue of the DP algorithm, as shown in Fig. 17. In the DP, the global optimal control can be achieved when all future information including the road shape, state of the vehicle, and the road loads are known in advance. Such conditions are impractical to obtain in advance for real-time applications. Therefore, the MPC operates based on a receding-horizon control strategy with a predictive scheme using three main steps [237]: (i) calculating the optimal inputs over a prediction horizon to minimize the objective function subject to the constraints, (ii) implementing the first element of the derived optimal inputs to the physical plant, and (iii) moving the entire prediction horizon forward and repeating from step (i). The optimal control problem in the finite domain is solved at each sampling instant, and control actions are obtained based on an online rolling optimization). DP algorithm to compute global optimal is interpreted as stratified sampling strategy.
Tran is considered to be analogous to the claimed invention since it focus on analysis and topology of electric and hybrid vehicle powertrains. Therefore it would be obvious for a person of ordinary skill in the art before the effective filing date to apply to integrate Tran teaching stratified sampling strategy on the modified model parameters to get optimized model parameters.
The motivation would have been to improved fuel economy and performance of by analyzing several powertrain topology strategies to address control objectives to reducing fuel consumption and emissions ESS charge maintenance, and enhancing the drivability and vehicle performance, and by selection of the topology that fits the requirement( Tran, abstract and conclusion).
Claim 17 is in the same scope as that of claim 11, and claim 17 is rejected under the same rational as of claim 11.
As of claim 12, The modified model teaches all the limitations of claim 11 and Tran also teaches wherein the design tool further optimizes the candidate first, second, third, and fourth component parameters following the stratified sampling strategy using a line search strategy (section , 3.3.2.2, Therefore, the MPC operates based on a receding-horizon control strategy with a predictive scheme using three main steps [237]: (i) calculating the optimal inputs over a prediction horizon to minimize the objective function subject to the constraints…. section 3.3.1.3. Gradient algorithms. Vehicle powertrains have become more sophisticated with nonlinear models of the ICE, EM, battery, and complex constraints. To reduce the calculation time and increase the robustness of the optimization solution, the powertrain systems or objective functions need to be efficiently simplified as analytical equations for use in the gradient algorithms. Such algorithms use the derivative information of an objective function, which is under mathematic conditions, such as the continuity or differentiability, or satisfy the Lipschitz condition to solve the optimization problem. Gradient algorithm-based EMSs are mainly classified into linear programming (LP), quadratic programming (QP), sequential quadratic programming (SQP), and convex programming (CP)) according to [0224], line search strategy is interpreted as Gradient algorithm of linear programming.
Claim 18 is in the same scope as that of claim 12, and claim 18 is rejected under the same rational as of claim 12.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
YAN JIFEI (WO 2017223524 A1, Date Published 2017-12-28) is similar to the claimed invention since it teaches design optimization for a hybrid electric vehicle with the flywheel as a third energy storage system and the powertrain comprises three different propulsion systems including: an internal combustion engine (ICE); an electric motor (EM) with battery; and a flywheel and continuously variable transmission (CVT) that comprise the SESS.
Duty; Mark J. (US 20080262712 A1, Date Published 2008-10-23) is similar to the claimed invention since it teaches methods and systems for powertrain optimization and improved fuel economy including multiple displacement engine modeling and control optimization, automotive powertrain matching for fuel economy, cycle-based automotive shift and lock-up scheduling for fuel economy, and engine performance requirements based on vehicle attributes and drive cycle characteristics
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/ABRHAM ALEHEGN TAMIRU/Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188
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