DETAILED ACTION
This Non-Final Office action is in response to Applicant’s Amendment filed on 09/15/2023. Claims 1-14 are pending. The effective filing date of the claimed invention is 05/27/2021.
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 .
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 2-3 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 2 recites “wherein updating. . . .” and claim 3 recites “wherein physics-based parameter set. . . .” These limitations have been recited prior to this in claim 1. Accordingly, there is a lack of proper antecedent basis thereby rendering the claims indefinite. Appropriate correction is required.
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 1-14 are rejected under 35 U.S.C. 101 because the claims are directed to abstract idea.
Step 1 – Claims 1-12 are process claims. Claims 13-14 are machine/system claims. Step 1 is satisfied.
Step 2A Prong 1 – Exemplary claim 1 (and similarly 11, 13-14) recites the following abstract idea:
A computer-implemented method for automatically tuning a digital twin of a physical system (see MPEP 2106.04(a)(2)(I)(A) iv. organizing information and manipulating information through mathematical correlations, Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). The patentee in Digitech claimed methods of generating first and second data by taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form. The court explained that such claims were directed to an abstract idea because they described a process of organizing information through mathematical correlations, like Flook's method of calculating using a mathematical formula. 758 F.3d at 1350, 111 USPQ2d at 1721), the digital twin utilizing a physics-based model of the physical system defined by a physics-based parameter set, the method comprising:
obtaining a trained mapping between the physics-based parameter set and a filter co- efficient set of an adaptive filter applied to the physical system (e.g. MPEP 2106.04(a)(2)(III)),
initializing the physics-based parameter set and the filter coefficient set, and over a series of discrete time steps, iteratively performing at each time step (see e.g. MPEP 2106.04(a)(2)(III); for the iteratively performing aspect, see Step 2B):
computing, by the physics-based model, an output response to a measured physical input signal using current parameter values in the physics-based parameter set (MPEP 2106.04(a)(2)(I)),
measuring, from the physical system, an output signal produced in response to the physical input signal, and determining an error signal based on the measured out- put signal and the computed output response (see e.g. MPEP 2106.04(a)(2)(II)(C) citing Voter Verified),
updating the filter coefficient set as a linear function of the error signal (MPEP 2106.04(a)(2)(I)), and
determining updated parameter values in the physics-based parameter set from the updated filter coefficient set using the trained mapping (MPEP 2106.04(a)(2)(I)).
When viewed alone and in ordered combination, these limitations are found to be recite abstract idea.
Step 2A Prong 2 – Claim 1 (and 11, 13-14) is not found to integrate the above abstract idea into practical application. Exemplary claim 1 recites the additional limitations of computer-implemented method, iteratively performing at each time step, and the like. These additional limitations are found to fall under the “apply it” rationale of MPEP 2106.05(f). For the outputting of information, see also MPEP 2106.05(g), selecting a particular data source or type of data to be manipulated, data outputting. When viewed alone and in ordered combination, these limitations are found to be directed to abstract idea.
Step 2B – Exemplary claim 1 is not found to include significantly more than the underlying abstract idea. The additional limitations analysis of Step 2A Prong 2 is equally applied to Step 2B. Another consideration when determining whether a claim recites significantly more than a judicial exception is whether the additional element(s) are well-understood, routine, conventional activities previously known to the industry. This consideration is only evaluated in Step 2B of the eligibility analysis. See MPEP 2106.05(f).
For the claim limitations relating to receiving/transmitting data over a network, these limitations have been found to be WURC activity. See MPEP 2106.05(d)(II)(i).
For the claim limitations relating to iteratively performing calculations, this has been found to be WURC. See MPEP 2106.05(d)(II)(ii).
When these limitations are viewed alone and in ordered combination, the examiner finds these claims to be directed to abstract idea.
Dependent Claims – Claim 2 is more abstract idea. MPEP 2106.04(a)(2)(I). Claim 3 is more abstract idea. MPEP 2106.04(a)(2)(I). Claim 4 is more abstract idea. MPEP 2106.04(a)(2)(I). Claim 5 is more abstract idea. MPEP 2106.04(a)(2)(III). Claim 6 is more abstract idea in apply it manner. MPEP 2106.04(a)(2)(III). Claim 7-8 recite more abstract idea. MPEP 2106.04(a)(2)(I). Claim 9 is more abstract idea, retrieving data and making decision from data, and further WURC at MPPE 2106.05(d)(II)(iv). Claim 10 is more abstract idea performed in apply it manner. Claim 12 is abstract idea in apply it manner. Accordingly, all claims are found to be directed to abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-4, 11-14 are rejected under 35 U.S.C. 103 as being unpatentable over P. Aivaliotis et al. (2019). The use of digital twin for predictive maintenance in manufacturing. IJCIM (“Aivaliotis”) in view of Jelfs et a. (Jan. 7, 2021) All adaptive all-pass filter for time-varying delay estimation, in further view of Neri et al. (2016) FRF-Based model updating using neural networks, proceedings of ISMA 2016 (“Neri”).
With regard to claims 1, 12-14, Aivaliotis discloses the claimed method for automatically tuning a digital twin of a physical system, the digital twin utilizing a physics-based model of the physical system defined by a physics-based parameter set (see Aivaliotis at pg. 1067, 1068, 1070, digital twin concept for manufacturing machinery, defining modelling parameters that are editable, associated with synchronous tuning, and used to update the physical model), the method comprising:
obtaining a trained mapping between the physics-based parameter set and a filter co- efficient set of an adaptive filter applied to the physical system (Aviliotis page 1071, says the modeling parameters are periodically estimated to reduce the deviation between actual and simulated behavior, but Avilitis does not explain that estimation through adaptive-filter coefficients.; Aviliotis is missing 1) adaptive filter, 2) filter coefficient set, 3) mapping between filter coefficients and physics parameters, 4) the mapping being trained. see Jelf, e.g. page 1-2, a relationship between adaptive FIR coefficients and an underlying physical quantity, explaining that a time delay is equivalent to all-pass filtering and that the delay can be obtained by estimating the filter and extracting the delay. See Equation 6, directly calculates estimated delay from FIR coefficientsJelf’s mapping is not trained. See third reference, Neri, page 1, teaches a trained mapping from response information to physical-model parameters.),
initializing the physics-based parameter set and the filter coefficient set, and over a series of discrete time steps, iteratively performing at each time step (Aviliotis, page 1070 teaches initial creation of digital model and definition of its modeling parameters before synchronous tuning; Aviliotis does not teach initialing an adaptive filtering set. See Jelf, page 2-3, Eq. 11, 13, 16):
computing, by the physics-based model, an output response to a measured physical input signal using current parameter values in the physics-based parameter set (Aiviliotis, page 1071 actual data provided as input for simulations, the same tasks performed by real machines are used as inputs to the simulation, the simulation operates using then-current modeling parameters. Aiviliotis does not teach where this modeled output is also processed through, or related to, an adaptive filter. See Jelfs at page 2, Eq 11.),
measuring, from the physical system, an output signal produced in response to the physical input signal, and determining an error signal based on the measured out- put signal and the computed output response (see Aivilitis, page 1067, 1069, 1071 expressly gathers actual data from machine controllers and external sensors, using those actual signals to characterize real machine’s behavior and compare it with the simulated behavior; See Jelf’s page 2, expressly defines the error between measured samples and the estimate obtained from the current filter coefficients),
updating the filter coefficient set as a linear function of the error signal (Aiviliotis does not teach this. See Jelf, page 3, Eq 13-18), and
determining updated parameter values in the physics-based parameter set from the updated filter coefficient set using the trained mapping (Aivioltis, page 1071, teaches periodically estimating updated modeling parameters, tuning the physics-based model, decreasing the deviation, and then providing new modeling parameters to the digital model. Aivilitis does not derive those parameters from updated adaptive-filter coefficients or use a trained mapping. See Jelf, as shown above, page 1-2. For the “trained mapping” see third reference, Neri.).
A person of ordinary skill implementing Aivaliotis’s synchronously tuned physics-based digital twin using Jelfs’s adaptive-filter identification technique would have been motivated to use Neri’s trained neural-network model-updating method to convert the identified dynamic-response representation into updated physical-model parameters. Neri expressly teaches that a trained feed-forward neural network can receive measured dynamic-response information and output physical mass and stiffness parameters used to update a finite-element model, thereby reducing the difference between experimental and numerical behavior. Because Jelfs’s updated FIR coefficients define the estimated impulse response, and therefore the corresponding frequency response, of the physical system, using the response represented by those coefficients as the input to Neri’s trained parameter mapping would predictably produce updated physics-based model parameters for Aivaliotis’s digital twin.
Neri itself identifies several reasons for using the neural-network mapping:
finding unknown physical parameters from experimental dynamic properties;
avoiding repeated direct nonlinear inverse calculations;
reducing modeled-versus-measured discrepancies;
improving the accuracy of the initial FE model; and
obtaining a model more representative of the actual physical structure.
With regard to claim 2, Aivioltis does not disclose this. See Jelfs at page 2, defining the optimization objective (14)and then differentiates that squared error cost with respect to the filter coefficeints and derives the adaptive update in Eg. 15-18. Jelfs explains that the resulting update has “precisely the form of the stated LMS algorithm.” Then states, Therefore, following the standard analysis for the LMS al gorithm [18], [19] we can show (see Appendix A) the mean square error ξ(n) converges asymptotically to ξ(∞) = ξmin. Therefore, it would have been obvious to one of ordinary skill in the art to modify Aivilitis to include such features of Jelfs, as the advantage is this is both accurate and capable of tracking time-varying delays. See Jelfs, abstract.
With regard to claim 3, wherein physics-based parameter set comprises at least one physics-based parameter that is not directly measurable from the physical system (See Neri, pg 3244, the neural networks are used to find unknown parameters. See combination above.).
With regard to claim 4, see Neri page 3244-3246. See combination above.
Claims 5-10 are Distinguished over the Prior Art
The examiner has been unable to find claims 5-10 in the prior art. These claims are distinguished over the prior art.
Conclusion
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/PETER LUDWIG/Primary Examiner, Art Unit 3627
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