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 .
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 claimed invention is directed to an abstract idea without significantly more.
Step 1:
According to the first part of the analysis, in the instant case, claims 1-11 are directed to a method, claims 12-18 are directed to using a conditional temporal diffusion model-based apparatus to perform the method, and claims 19-20 are directed to a non-transitory computer readable storage medium storing a computer program wherein the computer program is executed by a processor to perform the method. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Regarding claim 1:
A conditional temporal diffusion model-based method for generating a time series of an industrial device, comprising:
acquiring parameter indicator data for the time series of the industrial device, wherein the parameter indicator data is related to a type of the time series;
using a noise at a target time instant in a target Gaussian noise distribution as an initial variable of the time series;
inputting the parameter indicator data and the initial variable into a noise prediction model constructed based on a conditional temporal diffusion model, to obtain a predictive noise output by the noise prediction model;
denoising the predictive noise according to the initial variable, to obtain a target variable of the time series located at a previous time instant of the target time instant; and
inputting the target variable and the parameter indicator data into the noise prediction model for an iteration, to generate the time series of the industrial device.
Step 2A Prong 1:
“acquiring parameter indicator data for the time series of the industrial device, wherein the parameter indicator data is related to a type of the time series” is directed to mental step of data gathering.
“using a noise at a target time instant in a target Gaussian noise distribution as an initial variable of the time series” is directed to math because this relates directly to theory and stochastic calculus. Using a specific noise value at a particular time instant from a target Gaussian noise distribution to establish the initial condition for a time series is standard mathematical modeling.
“inputting the parameter indicator data and the initial variable into a noise prediction model constructed based on a conditional temporal diffusion model, to obtain a predictive noise output by the noise prediction model” is directed to math because a conditional temporal diffusion model is generative statistical models rooted in stochastic differential equations and Markov chains. They model data generation as a parameterized reverse calculus process. Mapping “parameter indicator data” and “initial variables” into a predictive output requires complex matrix transformations, high dimensional geometry, and time-series mathematical modeling.
“denoising the predictive noise according to the initial variable, to obtain a target variable of the time series located at a previous time instant of the target time instant” is directed to math because the concept is the core mechanism behind diffusion model applied to time series forecasting, reverse time stochastic differential equation and Markov chains.
“inputting the target variable and the parameter indicator data into the noise prediction model for an iteration, to generate the time series of the industrial device” is directed to math because noise prediction model acts as a mathematical function. It maps input variables (target variables and parameter indicators) to an output (predicted noise). Time series generation relies on probability distributions. The model predicts the statistical variance (noise) at each step to simulate realistic data.
Each limitation recites in the claim is a process that, under BRI covers performance of the limitation in the mind but for the recitation of a generic “measurement” which is a mere indication of the field of use. Nothing in the claim elements precludes the steps from practically being performed in the mind. Thus, the claim recites a mental process.
Further, the claim recites the step of "using a noise at a target time instant in a target Gaussian noise distribution as an initial variable of the time series;
inputting the parameter indicator data and the initial variable into a noise prediction model constructed based on a conditional temporal diffusion model, to obtain a predictive noise output by the noise prediction model; denoising the predictive noise according to the initial variable, to obtain a target variable of the time series located at a previous time instant of the target time instant; and inputting the target variable and the parameter indicator data into the noise prediction model for an iteration, to generate the time series of the industrial device” which as drafted, under BRI recites a mathematical calculation. The grouping of "mathematical concepts” in the 2019 PED includes "mathematical calculations" as an exemplar of an abstract idea. 2019 PEG Section |, 84 Fed. Reg. at 52. Thus, the recited limitation falls into the "mathematical concept" grouping of abstract ideas. This limitation also falls into the “mental process” group of abstract ideas, because the recited mathematical calculation is simple enough that it can be practically performed in the human mind, e.g., scientists and engineers have been solving the Arrhenius equation in their minds since it was first proposed in 1889.
Note that even if most humans would use a physical aid (e.g., pen and paper, a slide rule, or a calculator) to help them complete the recited calculation, the use of such physical aid does not negate the mental nature of this limitation. See October Update at Section I(C)(i) and (iii).
Additional Elements:
Step 2A Prong 2:
“A conditional temporal diffusion model-based method for generating a time series of an industrial device, comprising” recited in the preamble does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“acquiring parameter indicator data for the time series of the industrial device, wherein the parameter indicator data is related to a type of the time series” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“using a noise at a target time instant in a target Gaussian noise distribution as an initial variable of the time series” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“inputting the parameter indicator data and the initial variable into a noise prediction model constructed based on a conditional temporal diffusion model, to obtain a predictive noise output by the noise prediction model” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“denoising the predictive noise according to the initial variable, to obtain a target variable of the time series located at a previous time instant of the target time instant” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“inputting the target variable and the parameter indicator data into the noise prediction model for an iteration, to generate the time series of the industrial device” is directed to insignificant activity and does not integrate the judicial exception into a practical application. See MPEP 2106.05(g).
The claim is merely selecting data, manipulating or analyzing the data using math and mental process, and displaying the results.
This is similar to electric power: MPEP 2106.05(h) vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
The claim as a whole does not meet any of the following criteria to integrate the judicial exception into a practical application:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Step 2B:
“A conditional temporal diffusion model-based method for generating a time series of an industrial device, comprising” recited in the preamble does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“acquiring parameter indicator data for the time series of the industrial device, wherein the parameter indicator data is related to a type of the time series” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“using a noise at a target time instant in a target Gaussian noise distribution as an initial variable of the time series” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“inputting the parameter indicator data and the initial variable into a noise prediction model constructed based on a conditional temporal diffusion model, to obtain a predictive noise output by the noise prediction model” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“denoising the predictive noise according to the initial variable, to obtain a target variable of the time series located at a previous time instant of the target time instant” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“inputting the target variable and the parameter indicator data into the noise prediction model for an iteration, to generate the time series of the industrial device” is directed to insignificant activity and does not amount to significantly more than the judicial exception in the claim. See MPEP 2106.05(g) and 2106.05(d)(ii), third list, (iv).
The claim is therefore ineligible under 35 USC 101.
Claim 12 is similar to claim 1 but recites conditional temporal diffusion model-based apparatus for generating a time series of an industrial device, comprising: a memory and a processor; where the memory is configured to store a computer instruction; and the processor is configured to run the computer instruction stored in the memory to perform the steps as in claim 1. These additional elements fail to integrate the abstract idea into a practical application. These limitations are recited at a high level of generality and do not add significantly more to the judicial exception. These elements are generic computing devices that perform generic functions. Using generic computer elements to perform an abstract idea does not integrate an abstract idea into a practical application. See 2019 Guidance, 84 Fed. Reg. at 55. Moreover, “the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.” Alice, 573 U.S. at 223; see also FairWarninglP, LLCv. latric SysInc., 839 F.3d 1089, 1096 (Fed. Cir. 2016) (citation omitted) (“[T]he use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter”).
On the record before us, we are not persuaded that the hardware of claim 12 integrates the abstract idea into a practical application. Nor are we persuaded that the additional elements are anything more than well-understood, routine, and conventional so as to impart subject matter eligibility to claim 12.
Claim 19 cites a non-transitory computer readable storage medium storing a computer program, wherein the computer program is executed by a processor to perform the seps as in claim 1. This amounts to nothing more than instructions to implement the abstract idea on a computer, which fails to integrate the abstract idea into a practical application. See 2019 Guidance, 84 Fed. Reg. at 55. Additionally, using instructions to implement an abstract idea on a generic computer “is not ‘enough’ to transform an abstract idea into a patent-eligible invention.” Alice, 573 U.S. at 226. Therefore, the rejection of claim 19 for the same reason discussed above with regard to the rejection of claim 1.
Regarding claims 2 and 13, “ wherein the inputting the parameter indicator data and the initial variable into the noise prediction model constructed based on the conditional temporal diffusion model, to obtain the predictive noise output by the noise prediction model comprises: inputting the initial variable into a convolutional layer of an embedding module of the noise prediction model, and performing convolution processing on the initial variable to obtain first data; inputting the parameter indicator data into a fully connected layer of the embedding module of the noise prediction model, and performing a data transformation on the parameter indicator data to obtain a parameter indicator vector; and inputting the first data and the parameter indicator vector into a UNet module of the noise prediction model, and performing reconstruction processing on the first data and the parameter indicator vector to obtain the predictive noise” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
.
Regarding claims 3 and 14, “wherein the UNet module comprises an encoder layer, a temporal decomposition reconstruction layer, a decoder layer, and a convolutional layer, and the performing the reconstruction processing on the first data and the parameter indicator vector to obtain the predictive noise comprises: embedding the parameter indicator vector into the encoder layer and the decoder layer; inputting the first data into the encoder layer for encoding processing, to obtain second data; inputting the second data into the temporal decomposition reconstruction layer for temporal decomposition reconstruction processing, to obtain third data; and inputting the third data into the decoder layer for decoding processing to obtain fourth data, and inputting the fourth data into the convolutional layer for convolution processing, to obtain the predictive noise” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claims 4 and 15, “wherein the temporal decomposition reconstruction layer comprises: a pooling layer, a convolutional layer, and an attention layer; and the inputting the second data into the temporal decomposition reconstruction layer for the temporal decomposition reconstruction processing, to obtain the third data comprises: inputting the second data into the pooling layer for pooling processing, to obtain target feature data; wherein the target feature data comprises peak feature data and trend feature data; and inputting after concatenating the peak feature data and the trend feature data into the convolutional layer and the attention layer for processing, to obtain the third data” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claims 5 and 16, “wherein the inputting the second data into the pooling layer for the pooling processing, to obtain the target feature data comprises: performing average pooling processing on the second data to obtain the trend feature data; and performing maximum pooling processing on the second data to obtain the peak feature data” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claims 6 and 17, “acquiring a training sample, wherein the training sample comprises a sample time series of at least one industrial device, parameter indicator data for the sample time series, a time step of the sample time series, and a label noise; inputting the training sample into the noise prediction model to obtain a target noise output by the noise prediction model; acquiring, according to the label noise and the target noise, an objective loss function of the noise prediction model by means of maximum mean discrepancy (MMD); and training, according to the objective loss function, the noise prediction model by means of back propagation” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claims 7, “wherein the inputting the training sample into the noise prediction model to obtain the target noise output by the noise prediction model comprises: inputting the sample time series into a diffusion layer of an embedding module of the noise prediction model for noise diffusion, to obtain a latent variable of the sample time series; inputting the latent variable of the sample time series into a convolutional layer of the embedding module of the noise prediction model, and performing convolution processing on the latent variable to obtain fifth data; inputting the parameter indicator data for the sample time series and the time step into a fully connected layer of the embedding module of the noise prediction model for data processing respectively to obtain sixth data; and inputting the fifth data and the sixth data into a UNet module of the noise prediction model, and performing reconstruction processing on the fifth data and the sixth data to obtain the target noise” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claims 8, “wherein the UNet module comprises an encoder layer, a temporal decomposition reconstruction layer, a decoder layer, and a convolutional layer, and the performing the reconstruction processing on the fifth data and the sixth data to obtain the target noise comprises: embedding the sixth data into the encoder layer and the decoder layer; inputting the fifth data into the encoder layer for encoding processing, to obtain seventh data; inputting the seventh data into the temporal decomposition reconstruction layer for temporal decomposition reconstruction processing, to obtain eighth data; and inputting the eighth data into the decoder layer for decoding processing to obtain ninth data, and inputting the ninth data into the convolutional layer for convolution processing, to obtain the target noise” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claims 9, “wherein the acquiring, according to the label noise and the target noise, the objective loss function of the noise prediction model by means of the maximum mean discrepancy (MMD) comprises: acquiring a noise estimation loss function according to the label noise and the target noise; mapping the label noise and the target noise to a target dimension space, to obtain a similarity function between the label noise and the target noise; and obtaining the objective loss function according to the noise estimation loss function and the similarity function” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claims 10, “wherein the obtaining the objective loss function according to the noise estimation loss function and the similarity function comprises: performing additive processing on the noise estimation loss function and the similarity function to obtain the objective loss function” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claims 11, 18, and 20, “wherein the parameter indicator data is used to indicate a type of the generated time series, and the parameter indicator data comprises at least one of: health indicator data of an engine, health indicator data of a gearbox, health indicator data of a bearing, health indicator data of a milling cutter, and health indicator data of a turbine” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Hence the claims 1-20 are treated as ineligible subject matter under 35 U.S.C. § 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.
Claim(s) 1-10, 12-17, and 19 is/are rejected under 35 U.S.C. 103 as obvious over Zhang et al. (CN 117076931A).
Regarding claims 1, 12, and 19, Zhang et al. discloses a conditional diffusion model-based apparatus, computer, and method for generating time series, comprising in particular (see paragraphs 0033-0096 of the specification):
S1, collecting and preprocessing historical data, the historical data comprising dynamic time-sequential data that varies over time and corresponding static data that does not vary over time; the dynamic timing data comprises mainly data of oil well production, but also dynamic data of oil pressure such as oil tip over time (parameter index data corresponding to the acquisition time series, the parameter index data relating to the type of time series);
preprocessing the historical data includes, but is not limited to, splitting the historical data according to different mining processes, according to respective processes.
S2, dividing the pre-processed historical data into a training set and a validation set;
S3, building a feature processing model comprising a feature encoder and a feature decoder;
S4, building a conditional diffusion model with conditional input Transformer as a dry network, inputting the conditional diffusion model with high-dimensional dynamic temporal data of the day within the training set as noisy data of the conditional diffusion model and the high-dimensional dynamic temporal data of the day fused with corresponding static data as conditional features; the conditional input Transformer, extracting and fusing conditional features with feature information of data with Gaussian noise through a multi-head self-attention layer, and learning a mapping relationship from feature information to noise distribution through a training process of a conditional diffusion model, outputting predicted Gaussian noise (corresponding to inputting the parameter indicator data and the initial variables into a noise prediction model constructed based on the diffusion model, resulting in predicted noise output by the noise prediction model);
S5, merging high-dimensional dynamic timing data up dimensionally first dynamic timing data to be predicted with corresponding first static data as conditional features, the trained conditional diffusion model is input, a de-noising operation is performed, resulting in predicted high-dimensional dynamic timing features, which are input S3 to the trained feature decoder, resulting in final predicted second dynamic timing data.
The inverse generation process is performed by inputting into the trained conditional diffusion model a random Gaussian noise sequence as an initial and the conditional features Xc, the conditional inputting into Transformer a prediction of added noise (corresponding to the noise at a target instant in a target Gaussian noise distribution as an initial variable of the time sequence); the conditional input Transformer predicts added noise, a denoising calculation by equation (1.2) (corresponding to denoising the prediction noise from the initial variable, resulting in a target variable in the time series located at a previous time of the target time), as input for the next iteration, repeating the iterative process T times results in predicted high-dimensional dynamic timing features, training the completed feature decoder in input S3 to obtain final predicted second dynamic timing data (corresponding to inputting the target variable and the parameter indicator data into the noisy prediction model for iteration, generating a time series).
Zhang teaches a time series of oil well production data or oil pressure in the oil mouth, whereas the present application is a time series of industrial equipment. Based on the above distinctions, it can be determined that the technical problem actually solved by this invention is how to generate a time series of industrial devices. Whereas Zhang discloses a time series, reflecting index data of an oil well, one skilled in the art would easily think of generating a time series of industrial equipment with diffusion models.
From the above, It would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention that the technical solutions claimed by this claim will be obtained by judicious reasoning and analysis in combination with the person skilled in the art on the basis of Zhang.
Regarding claims 2-5 and 13-16, Zhang disclose conditional diffusion models built with conditional input Transformer into a main dry network and inputting the noised data and the dynamic timing data fused with the static data as conditional features to a conditional diffusion model for training to obtain predicted Gaussian noise, those skilled in the art will readily appreciate that inputting initial variables and parameter indicators into a noise prediction model and deriving a prediction noise, while the initial variable and parameter indicator data are convolved and data transformed respectively through different modules of the model are conventional means of processing data during model training in the art; in addition, Zhang discloses utilizing Transformer as backbone network and encoder decoder, uNet also belongs to a network model common in the art, both of which can handle time series prediction problems, so the selection of a network model can be made by those skilled in the art in conjunction with actual needs, and the specific structure of UNet and data processing such as encoding, decoding, pooling, etc. of data are conventional techniques in the art.
Regarding claims 6 and 17, Zhang discloses that historical data is divided into a training set and a validation set and inputted into a conditional diffusion model with plus noise data for training, iteratively optimizing model parameters with KL divergence as a loss function, outputting the predicted noise, in light of Zhang, the skilled person will readily envision obtaining training samples and inputting into the noise prediction model a target noise output by the noise prediction model, obtaining a loss function and training the noise prediction model by means of back propagation according to the loss function; while the specifics of the training samples and the determination of the target loss function are selectable and configurable by those skilled in the art in conjunction with actual needs.
Regarding claims 7, Zhang discloses inputting a dynamic timing sequence into a conditional diffusion model, and model training is divided into a forward plus noise process and a noisy prediction learning process in which noise is added stepwise at corresponding times to obtain predicted values of noise added at corresponding time steps. In light of Zhang, one skilled in the art will readily recognize that inputting a sample time series into a diffusion layer of the embedding module for noise diffusion results in the latent variables of the time series and further processing among the different structures of the model results in the target noise.
Regrading claims 8-10, Zhang disclose inputting a training set into a conditional diffusion model for training and iteratively optimizing the model parameters using the KL divergence as a loss function, outputting the predicted noise, in the light of Zhang, one skilled in the art will readily envision obtaining a loss function and training said noise prediction model according to the loss function, while the determination of the target loss function can be selected and set by one skilled in the art in combination with actual needs.
Claim(s) 11, 18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (CN) in view of Kandemir et al. (US 11,868,887).
Regarding claims 11, 18, and 20, Zhang fails to disclose the parameter indicator data comprises at least one of: health indicator data of an engine, health indicator data of a gearbox, health indicator data of a bearing, health indicator data of a milling cutter, and health indicator data of a turbine.
Kandemir et al. teach the parameter indicator data comprises at least one of: health indicator data of an engine, health indicator data of a gearbox, health indicator data of a bearing, health indicator data of a milling cutter, and health indicator data of a turbine (Col.3, line 51-Col.4, line 8: the trainable part of the drift component may be optimized to provide a drift contribution that best combines with the drift contribution of the predefined part of the drift component to provide an optimal overall drift for the SDE., Col.8, lines 42-50: the output device is an actuator associated with the computer-controlled, and the processor subsystem is configured to control the computer-controlled system by providing control data to the actuator which is based on the determined time-series prediction. For example, the actuator may be used to control a vehicle, such as an autonomous or semi-autonomous vehicle, a robot, a manufacturing machine, a building, etc.). Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Kandemir with the teaching of Zhang in order to provide a computer-implemented method of training a model for making time-series predictions of a computer-controlled system (Kandemir , abstract).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN H LE whose telephone number is (571)272-2275. The examiner can normally be reached on Monday-Friday from 7:00am – 3:30pm Eastern Time.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby A. Turner can be reached on (571) 272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN H LE/Primary Examiner, Art Unit 2857
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