DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Preliminary Amendment
The preliminary amendment filed 6/12/2023 has been entered. Claims 1-9 have been amended. Claim 11 has been added.
Claim Objections
Claim 5 is objected to because of the following informalities: Claim 5 recites the limitation “using a likelihood function is as an error function…”, should be amended as “using a likelihood function as an error function…”. Appropriate correction is required.
Claim 6 is objected to because of the following informalities: Claim 6 recites the limitation “using a mean value, a variance, a standard deviation, a probability value and/or a distribution type of the statistical distribution are characteristic values”, should be amended as “using a mean value, a variance, a standard deviation, a probability value and/or a distribution type of the statistical distribution as characteristic values”. Appropriate correction is required.
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.
Claims 1-11 are rejected under 35 U.S.C. 103 as being unpatentable over Tresp et al. (US 5,806,053, Applicant’s submitted IDS filed 6/12/2023), hereinafter “Tresp”, and in view of Schmitt et al. (US 2021/0110262 A1), hereinafter “Schmitt”.
As per claim 1, Tresp teaches a method for reproducing noise components of lossy recorded operating signals of a technical system comprising:
“(a) lossy recording an input operating signal for a control device of the technical system and
(Tresp teaches time series of measured values supplied to a neural network, wherein the time series includes missing measured value in order to train the neural network)
“(b) on the basis of the recorded operating signals, training a neural network to reproduce a recorded target operating signal and a statistical distribution of a stochastic component of the recorded target operating signal on the basis of a recorded input operating signal” at Col. 3 line 65 to Col. 6 line 60;
(Tresp teaches on the basis of the time series, training the neural network to predict the missing measured values based on the existing information from the remaining values. When a further measured value of the time series is to be simulated, the method provides an iterative approximation of the probability distribution of the lacking values)
“(c) supplying a current input operating signal of the technical system to the trained neural network” at Col. 7 lines 15-65 and Fig. 2;
(Tresp teaches supplying time series data from the technical system f to the trained neural network NNW)
“(d) generating an output signal having a noise component modelled on the statistical distribution on the basis of the supplied current input operating signal and a noise signal” at Col. 7 lines 15-65
(Tresp teaches the manipulated variable Ut-1 is emitted by the neural network NNW as an output to the technical system f via the connecting line 150. In the line 150, this value is superimposed with noise having a known noise distribution ϵ)
“(e) outputting the output signal as the current target operating signal for controlling the technical system” at Col. 7 lines 15-65.
(Tresp teaches this value Ut-1 + ϵ is supplied to the technical system f together with the value yt-1. The technical system f reacts to this manipulated variable by generating a control variable yt)
Tresp does not explicitly lossy record “a target operating signal for controlling the technical system” to be used as input to a neural network as recited in the claims. However, Schmitt teaches a method for training a neural language using two different input signals: the first signal is data samples characterizing anomalous operation and the second signal is data samples characterizing normal operation (i.e., “target operating signal for controlling the technical system”) at [0055]-[0058] and Fig. 1. Thus, it would have been obvious to an ordinary skilled in the art to combine Schmitt with Tresp’s teaching in order to train the neural network model to discriminate between regular operating states and anomalous operating states, as suggested by Schmitt at [0015].
As per claim 2, Tresp and Schmitt teach the method of claim 1 discussed above. Tresp also teaches: wherein “using a Bayesian neural network having latent parameters representing the statistical distribution as the neural network, that the latent parameters are inferred by the training; feeding the noise signal into an input layer of the Bayesian neural network; and generating the output signal by the Bayesian neural network, which has been trained with the inferred latent parameters, from the current input operating signal and the noise signal” at Col. 3 line 65 to Col. 6 line 60
As per claim 3, Tresp and Schmitt teach the method of claim 2 discussed above. Tresp also teaches: wherein “carrying the inference out by a variational inference method and/or by a Markov chain Monte Carlo method” at Col. 3 line 65 to Col. 6 line 60.
As per claim 4, Tresp and Schmitt teach the method of claim 1 discussed above. Tresp also teaches: wherein “training the neural network to reproduce statistical characteristic values of the statistical distribution on the basis of a recorded input operating signal, determining the statistical characteristic value for the supplied current input operating signal by the trained neural network; and generating the noise signal depending on the determined statistical characteristic values, and output as an output signal” at Col. 3 line 65 to Col. 6 line 60, Col. 7 lines 15-65.
As per claim 5, Tresp and Schmitt teach the method of claim 4 discussed above. Tresp also teaches: wherein “using a likelihood function as an error function to be minimized for the training” at Col. 3 line 65 to Col. 6 line 60.
As per claim 6, Tresp and Schmitt teach the method of claim 4 discussed above. Tresp also teaches: “wherein using a mean value, a variance, a standard deviation, a probability value and/or a distribution type of the statistical distribution as characteristic values” at Col. 3 line 65 to Col. 6 line 60.
As per claim 7, Tresp and Schmitt teach the method of claim 1 discussed above. Schmitt also teaches: wherein “continuously detecting and feeding the current input operating signal to the neural network, and a concurrent simulator” at [0055]-[0058] and Fig. 1.
As per claim 11, Tresp and Schmitt teach the method of claim 7 discussed above. Schmitt also teaches: wherein “operating a digital twin of the technical system, by the neural network” at [0055]-[0058] and Fig. 1.
Claims 8-10 recite similar limitations as in claim 1 and are therefore rejected by the same reasons.
Conclusion
Examiner's Note: Examiner has cited particular columns and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KHANH B PHAM whose telephone number is (571)272-4116. The examiner can normally be reached Monday - Friday, 8am to 4pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sanjiv Shah can be reached at (571)272-4098. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/KHANH B PHAM/Primary Examiner, Art Unit 2166
January 20, 2026