DETAILED ACTION
Claims 1-2 and 4-13 are presented for examination.
Claim 3 is withdrawn from consideration.
This Office Action is in response to submission of documents on March 2, 2026.
Objection to the claims for minor informalities.
Rejection of claims 1-2 and 4-13 under 35 U.S.C. 112(b) as being indefinite.
Rejection of claims 1-2 and 4-13 under 35 U.S.C. 101 for being directed to unpatentable subject matter.
Rejection of claims 1 and 4-13 under 35 U.S.C. 103 as being obvious over Hashimoto in view of Yang.
Rejection of claim 2 under 35 U.S.C. 103 as being obvious over Hashimoto in view of Yang and Froehlich.
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 .
Election/Restrictions
Claim 3 is withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected species (Species 2). Election was made without traverse in the reply filed on March 2, 2026. Claim 3, while it is not currently canceled, is withdrawn from further consideration by the Examiner by the election. In the next Response of the Applicant, any submitted claims that are withdrawn, cancelled, and/or amended should reflect the election of Species 1.
Claim Objections
Claims 1-2 and 4-13 are objected to because of the following informalities: the independent claims 1, 12, and 13 recite the term “fo” where “for” appears to be the intention of the Applicant (i.e., “the input variable fo the first model including at least a part of the first time series and at least a part of the second prediction). Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-2, and 4-13 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.
Claims 1, 12, and 13 recite the limitation "a second model" in both the “mapping the second time series using a second model” and “determining parameters of a second model.” Claims 2 and 4-11 are rejected for depending upon a rejected claim.
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 1-2 and 4-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exceptions without significantly more. The claims recite mathematical calculations. This judicial exception is not integrated into a practical application because the additional elements that are recited in the claims are extra-solution activities that do not integrate the judicial exceptions into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because courts have found that the steps of providing data are not significantly more than a judicial exception.
Claim 1
Step 1: The claim is directed to a process, falling under one of the four statutory categories of invention.
Step 2A, Prong 1: The claim 1 limitations include (bolded for abstract idea identification):
Claim 1
Mapping Under Step 2A Prong 1
A computer-implemented method for determining a variable of a technical system, the technical system being a fuel cell or an internal combustion engine, the method comprising the following steps:
determining an input variable for a first model for determining the variable of the technical system at a first temporal resolution, by:
providing a first time series at the first temporal resolution, the first time series including values which characterize an operating variable of the technical system,
providing a second time series at a second temporal resolution, the second time series including values which characterize the operating variable of the technical system, the first temporal resolution being different from the second temporal resolution,
mapping the second time series being mapped using a second model to determine a first prediction for the variable of the technical system at the second temporal resolution on the first prediction,
determining parameters of a second model using the second time series,
mapping the parameters of the second model on parameters of a third model at the first temporal resolution, and
mapping the first time series using the third model on a second prediction at the first temporal resolution, the input variable fo the first model including at least a part of the first time series and at least a part of the second prediction;
determining parameters of the first model using the input variable for the first model; and
mapping the input variable using the first model on the variable of the technical system.
Abstract Idea: Mathematical Calculations
The limitation, which is further elaborated upon in the subsequent sub-steps, involves mathematical concepts (using models characterized by equations, mapping variables, determining parameters). See MPEP § 2106.04(a)(2), Subsection I. Additional elements that are included in the step of “determining an input variable” are discussed at Step 2A, Prong 2, below.
Abstract Idea: Mathematical Calculations
Mapping a series of values (i.e., a “time series”) using a model is a mathematical concept that requires execution of one or more equations to perform the mapping. See MPEP § 2106.04(a)(2), Subsection I. See also, e.g., Specification at [0046]1, describing the mathematical equations that comprise the second model.
Abstract Idea: Mathematical Calculations
The process of determining the parameters of the second model is performed by performing one or more calculations. See MPEP § 2106.04(a)(2), Subsection I. See also, e.g., Specification at [0046], describing how the determination is performed.
Abstract Idea: Mathematical Calculations
Mapping a series of values (i.e., a “time series”) using a model is a mathematical concept that requires execution of one or more equations to perform the mapping. See MPEP § 2106.04(a)(2), Subsection I. See also, e.g., Specification at [0046], describing the mathematical equations that comprise the third model.
Abstract Idea: Mathematical Calculations
Mapping a series of values (i.e., a “time series”) using a model is a mathematical concept that requires execution of one or more equations to perform the mapping. See MPEP § 2106.04(a)(2), Subsection I. See also, e.g., Specification at [0046], describing the mathematical equations that comprise the third model.
Abstract Idea: Mathematical Calculations
The process of determining the parameters of the first model is performed by performing one or more calculations. See MPEP § 2106.04(a)(2), Subsection I. See also, e.g., Specification at [0044]-[0045], describing how the determination is performed.
Abstract Idea: Mathematical Calculations
Mapping a variable using a model is a mathematical concept that requires execution of one or more equations to perform the mapping. See MPEP § 2106.04(a)(2), Subsection I. See also, e.g., Specification at [0044], describing the mathematical equations that comprise the first model.
Step 2A, Prong 2: The claim 1 limitations recite (bolded for additional element identification):
Claim 1
Mapping Under Step 2A Prong 2
A computer-implemented method for determining a variable of a technical system, the technical system being a fuel cell or an internal combustion engine, the method comprising the following steps:
determining an input variable for a first model for determining the variable of the technical system at a first temporal resolution, by:
providing a first time series at the first temporal resolution, the first time series including values which characterize an operating variable of the technical system,
providing a second time series at a second temporal resolution, the second time series including values which characterize the operating variable of the technical system, the first temporal resolution being different from the second temporal resolution,
mapping the second time series being mapped using a second model to determine a first prediction for the variable of the technical system at the second temporal resolution on the first prediction,
determining parameters of a second model using the second time series,
mapping the parameters of the second model on parameters of a third model at the first temporal resolution, and
mapping the first time series using the third model on a second prediction at the first temporal resolution, the input variable fo the first model including at least a part of the first time series and at least a part of the second prediction;
determining parameters of the first model using the input variable for the first model; and
mapping the input variable using the first model on the variable of the technical system.
Providing data is an extra-solution activity that does not integrate the judicial exception into a practical application. The limitation does not recite, with specificity, how the data is provided and therefore does not improve the functioning of a computer. See MPEP 2106.05(d)(II).
Providing data is an extra-solution activity that does not integrate the judicial exception into a practical application. The limitation does not recite, with specificity, how the data is provided and therefore does not improve the functioning of a computer. See MPEP 2106.05(d)(II).
Step 2B: Regarding Step 2B, the inquiry is whether any of the additional elements (i.e., the elements that are not the judicial exception) amount to significantly more than the recited judicial exception. Providing data is an additional element that courts have found does not amount to significantly more than the recited judicial exception. See, e.g., Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981); Bilski v. Kappos, 561 U.S. 593, 612, 95 USPQ2d 1001, 1010 (2010); Affinity Labs of Texas v. DirecTV, LLC, 838 F.3d 1253, 120 USPQ2d 1201 (Fed. Cir. 2016); Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (limiting use of abstract idea to the Internet); Electric Power, 830 F.3d at 1354, 119 USPQ2d at 1742 (limiting application of abstract idea to power grid data); Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d 1315, 1328-29, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017) (limiting use of abstract idea to use with XML tags).
Accordingly, claim 1 is rejected for being directed to unpatentable subject matter.
Claim 2
Claim 2 recites wherein the technical system is the internal combustion engine, (i) the operating variable characterizing a velocity or a load of the internal combustion engine, and/or (ii) the variable of the technical system characterizing a hydrocarbon emission of the internal combustion engine, or a nitrogen oxide emission of the internal combustion engine, or a temperature of the internal combustion engine, or a particle emission of the internal combustion engine, or an oxygen content of the internal combustion engine. The claim merely recites an application for the recited judicial exceptions and does not integrate the judicial exceptions into a practical application because the claim merely specifies the operating value used to perform the mathematical calculations recited in the claim. See MPEP 2106.05(b), Subsections II and III. Accordingly, claim 2 is rejected for being directed to unpatentable subject matter.
Claim 4
Claim 4 recites wherein the second time series includes values from the first time series, which are taken from the first time series at the second resolution. The limitation merely indicates the origin of provided data. As the providing of the time series has previously been identified as an additional limitation that does not integrate the abstract ideas into a practical application nor amount to significantly more, claim 4 is rejected for being directed to unpatentable subject matter for at least the same reasons as claim 1.
Claim 5
Claim 5 recites wherein the second time series includes values of a third prediction for the variable of the technical system, the third prediction being determined using a fourth model as a function of a third time series, which includes values of the operating variable in a third resolution, which is different from the first resolution and the second resolution. The limitation merely indicates the origin of provided data. As the providing of the time series has previously been identified as an additional limitation that does not integrate the abstract ideas into a practical application nor amount to significantly more. Accordingly, claim 5 is rejected for being directed to unpatentable subject matter.
Claim 6
Claim 6 recites wherein: (i) the parameters of the second model are determined in a training of the second model using training data which include the second time series and a reference for the first prediction at the second temporal resolution, and/or (ii) the parameters of the first model are determined in a training of the first model using training data which include the input variable and a reference for the variable of the technical system at the first temporal resolution. As previously asserted in the rejection of claim 1, the determination of parameters is a mathematical concept, as detailed further in the Specification. Accordingly, claim 6 is rejected for being directed to unpatentable subject matter.
Claim 7
Claim 7 recites wherein the third model includes a first linear transition model, in which a first matrix for mapping a state variable of the first linear transition model is determined by at least a part of the parameters of the third model, the second model includes a second linear transition model, in which a first matrix for mapping a state variable of the second linear transition model is determined by at least a part of the parameters of the second model, the first matrix of the first transition model being determined as a function of a root of the first matrix of the second transition model, an order of the root being determined as a function of a ratio of the first resolution to the second resolution. The claim recites the composition of the models. The claim recites the mathematical concepts and calculations that are required to execute the models, thus are directed to abstract ideas. See MPEP 2106.04(a)(2), Subsection I. Accordingly, claim 7 is rejected for being directed to unpatentable subject matter.
Claim 8
Claim 8 recites wherein the first linear transition model includes a second matrix for mapping the first time series, the second matrix being determined by at least a part of the parameters of the third model, the second linear transition model including a second matrix for mapping the second time series, the second matrix being determined by at least a part of the parameters of the second model, the second matrix of the first transition model being determined as a function of a product of an inverse of a sum of a number of summands with the second matrix of the second transition model, the sum including per summand a power of the first matrix of the first transition model, the number of summands being determined as a function of a ratio of the first resolution to the second resolution, the powers of order different from one another being from a set of integer numbers from 1 to the number. The claim recites the composition of the models. The claim recites the mathematical concepts and calculations that are required to execute the models, thus are directed to abstract ideas. See MPEP 2106.04(a)(2), Subsection I.
According, claim 8 is rejected for being directed to unpatentable subject matter.
Claim 9
Claim 9 recites wherein the third model includes an additive disturbance variable for the state variable of the first linear transition model, the disturbance variable of the third model being determined by a covariance matrix of a Gaussian distribution, the second model including an additive disturbance variable for the state variable of the second linear transition model, the disturbance variable of the second model being determined by a covariance matrix of a Gaussian distribution, the covariance matrix of the third model being determined so that a distance between the covariance matrix of the second model and a sum of a number of summands is minimal, the sum including per summand a product of a power of the first matrix of the third model with the covariance matrix of the third model and with a transpose of the power of the first matrix, the number of summands being determined as a function of a ratio of the first resolution to the second resolution, the powers of order different from one another being from a set of integer numbers from 1 to the number. The claim recites the composition of the models. The claim recites the mathematical concepts and calculations that are required to execute the models, thus are directed to abstract ideas. See MPEP 2106.04(a)(2), Subsection I.
Accordingly, claim 9 is directed to unpatentable subject matter.
Claim 10
Claim 10 recites wherein for a time series which represents the operating variable of the technical system in the first temporal resolution, the variable of the technical system is determined, when the variable of the technical system meets a condition: (i) an anomaly being recognized and/or the technical system being switched into a safe operating state, or (ii) the technical system otherwise being switched into an intended operating state. The claim merely indicates a time in which the method of claim 1 is performed. The claim does not integrate the judicial exceptions into a practical application because the type of variable that is determined is not directly a consequence of the state of the system. See Examiner Note on Subject Matter Eligibility. Accordingly, claim 10 is directed to unpatentable subject matter.
Claim 11 and 13
Claim 11 recites a computing unit and claim 13 recites a non-transitory computer-readable storage medium that performs a method that is substantially the same as the method of claim 1. Both of these limitations are additional elements that perform the judicial exceptions of each claim and therefore do not integrate the abstract ideas into a practical application.
Accordingly, for at least the same reasons as claim 1, claims 11 and 13 are rejected under 35 U.S.C. 101 for being directed to unpatentable subject matter.
Claim 12
Claim 12 recites a limitation that is already rejected in claim 2. Accordingly, for at least the same reasons, claim 12 is rejected under 35 U.S.C. 101.
Examiner Note on Subject Matter Eligibility
As indicated above, all pending claims are rejected under 35 U.S.C. 101 for being directed to abstract ideas with additional elements that integrate the judicial exceptions into a practical application and that do not amount to significantly more.
However, claim 10 appears to be close to limitations that may, depending on how claimed, integrate the judicial exceptions into a practical application. For example, an independent claim that first identifies the state of an engine (the (i) and (ii) of claim 10), selects the input variable, and provides the time series information based on measuring operation of the engine may overcome the rejection under 35 U.S.C. 101. The claim would further support integration into a practical application if the output of the process were then utilized in relation to the engine after the variable is determined.
Applicant is encouraged to schedule an interview to discuss potential amendments to overcome the rejection. Filing of an AIR Request and/or contacting the Examiner directly are both acceptable for coordinating an interview.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1 and 4-13 are rejected under 35 U.S.C. 103 as being obvious over Hashimoto, et al., (U.S. Pat. Pub. No. 2021/0255061, hereinafter “Hashimoto”) in view of Yang, et al., (“Learning Time Series from Scale Information,” hereinafter Yang).
Claim 1
Hashimoto discloses:
A computer-implemented method for determining a variable of a technical system, the technical system being a fuel cell or an internal combustion engine, the method comprising the following steps:
The technique described above determines the state of the internal combustion engine using the mapping learned through machine learning. Hashimoto at [0004].
The “state of the internal combustion engine” is a variable of the technical system.
determining an input variable for a first model for determining the variable of the technical system at a first temporal resolution, by:
The CPU 72 inputs the input variables x(1) to x(26) to a mapping that is specified by mapping data 76 a stored in the storage device 76 shown in FIG. 1 to calculate a probability P(i) that a misfire has occurred in each cylinder #i (i=1 to 4) (S16). The mapping data 76 a specifies a mapping configured to output the probability P(i) that a misfire has occurred in each cylinder #i in a period corresponding to minute rotation times T30(1) to T30(24) obtained by the process of S10. Hashimoto at [0032].
The “the probability P(i) that a misfire has occurred in each cylinder” is analogous to a “second prediction” at the resolution of the inputted time series resolution.
The training data includes “a misfire probability,” which is analogous to a “prediction” and further includes part of the time series of the operating variable (i.e., rotation times). Thus, the training data is analogous to the “input variable,” which includes a prediction and a time series.
providing a first time series at the first temporal resolution, the first time series including values which characterize an operating variable of the technical system,
The CPU 72 assigns the values obtained by the processes of S10 and S12 to input variables x(1) to x(26) of a mapping used to calculate a probability that a misfire has occurred (S14). More specifically, the CPU 72 assigns minute rotation time T30(s) to an input variable x(s) where s=1 to 24. That is, input variables x(1) to x(24) are time series data of minute rotation times T30. The CPU 72 assigns the rotation speed NE to an input variable x(25) and assigns the charging efficiency to an input variable x(26). Hashimoto at [0031].
The “time series data of minute rotation times” is analogous to an “operating variable.”
providing a second time series at a second temporal resolution, the second time series including values which characterize the operating variable of the technical system,
The CPU 72 assigns the values obtained by the processes of S10 and S12 to input variables x(1) to x(26) of a mapping used to calculate a probability that a misfire has occurred (S14). More specifically, the CPU 72 assigns minute rotation time T30(s) to an input variable x(s) where s=1 to 24. That is, input variables x(1) to x(24) are time series data of minute rotation times T30. The CPU 72 assigns the rotation speed NE to an input variable x(25) and assigns the charging efficiency to an input variable x(26). Hashimoto at [0031].
The “time series data of minute rotation times” is analogous to an “operating variable.”
mapping the second time series being mapped using a second model to determine a first prediction for the variable of the technical system at the second temporal resolution on the first prediction,
The CPU 72 inputs the input variables x(1) to x(26) to a mapping that is specified by mapping data 76 a stored in the storage device 76 shown in FIG. 1 to calculate a probability P(i) that a misfire has occurred in each cylinder #i (i=1 to 4) (S16). The mapping data 76 a specifies a mapping configured to output the probability P(i) that a misfire has occurred in each cylinder #i in a period corresponding to minute rotation times T30(1) to T30(24) obtained by the process of S10. Hashimoto at [0032].
The “the probability P(i) that a misfire has occurred in each cylinder” is analogous to a “first prediction.”
determining parameters of a second model using the second time series,
In a series of the steps shown in FIG. 5, the adaptation device 104 first obtains sets of minute rotation times T30(1) to T30(24), the rotation speed NE, the charging efficiency η, and a misfire true probability Pt(i) as training data determined based on detection results of the sensor group 102 (S50). Hashimoto at [0049].
The training data, which is time series data from the “operating variable” (i.e., rotation times) is used to train the model, which is analogous to “determining parameters” of a model.
mapping the first time series using the third model on a second prediction at the first temporal resolution, the input variable fo[r] the first model including at least a part of the first time series and at least a part of the second prediction;
The CPU 72 inputs the input variables x(1) to x(26) to a mapping that is specified by mapping data 76 a stored in the storage device 76 shown in FIG. 1 to calculate a probability P(i) that a misfire has occurred in each cylinder #i (i=1 to 4) (S16). The mapping data 76 a specifies a mapping configured to output the probability P(i) that a misfire has occurred in each cylinder #i in a period corresponding to minute rotation times T30(1) to T30(24) obtained by the process of S10. Hashimoto at [0032].
The “the probability P(i) that a misfire has occurred in each cylinder” is analogous to a “second prediction” at the resolution of the inputted time series resolution.
determining parameters of the first model using the input variable for the first model; and
In a series of the steps shown in FIG. 5, the adaptation device 104 first obtains sets of minute rotation times T30(1) to T30(24), the rotation speed NE, the charging efficiency η, and a misfire true probability Pt(i) as training data determined based on detection results of the sensor group 102 (S50). Hashimoto at [0049].
The training data includes “a misfire probability,” which is analogous to a “prediction” and further includes part of the time series of the operating variable (i.e., rotation times). Thus, the training data is analogous to the “input variable,” which includes a prediction and a time series.
Hashimoto does not appear to disclose:
the first temporal resolution being different from the second temporal resolution,
mapping the parameters of the second model on parameters of a third model at the first temporal resolution, and
mapping the input variable using the first model on the variable of the technical system.
Yang, which is analogous art, discloses:
the first temporal resolution being different from the second temporal resolution,
We include 1-day data, 2-day average data, 4-day average data, weekly (5-day) average data, 10-day average data, monthly (20 day) average data, six resolution levels in our algorithm. Yang at pg. 6, col. 2.
mapping the parameters of the second model on parameters of a third model at the first temporal resolution, and
The basic idea of Algo. 2 is to linearly combine the predictor estimated from each resolution weighted by their accumulated scores, and then update the score according to some carefully chosen loss function . The loss function takes the predictor and the revealed true value as arguments, and is used to penalize those resolution that does not perform well. Yang at pg. 3, col. 1.
mapping the input variable using the first model on the variable of the technical system.
The basic idea of Algo. 2 is to linearly combine the predictor estimated from each resolution weighted by their accumulated scores, and then update the score according to some carefully chosen loss function . The loss function takes the predictor and the revealed true value as arguments, and is used to penalize those resolution that does not perform well. Yang at pg. 3, col. 1.
Yang is analogous art to the claimed invention because both are directed to making predictions of a system using time series data at different resolutions. It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the application, to combine the models disclosed in Hashimoto, which receive time series data and output a prediction, with the sequential multi-resolution analysis of Yang to result in a system that determines a first prediction at a first resolution and determines a second prediction at a second resolution, and then sequentially processes the predictions to determine another variable of the system. Motivation to combine includes improving the accuracy of the output by taking into account both low resolution data and high resolution data, which improves accuracy by modeling both short-term and long-term fluctuations in the system variable that is determined.
Claim 4
Hashimoto does not appear to disclose:
wherein the second time series includes values from the first time series, which are taken from the first time series at the second resolution.
Yang discloses:
wherein the second time series includes values from the first time series, which are taken from the first time series at the second resolution.
For the purpose of displaying weights change clearly, we keep the lag order fixed as the true value 2, and only change the resolution from every one data point to every five data points. Yang at pg. 5, cols. 1-2.
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the application, to generate time series data from a single series, as disclosed in Yang, such that a first model can process a first resolution data and a second model can process a second resolution data, the models comprised as disclosed in Hashimoto. Motivation to combine includes reducing time and expense of separate data streams for each resolution. Further, by using two time series from the same time period, accuracy of the system is improved to ensure that the first and second predictions are based on the same events.
Claim 5
Hashimoto discloses:
wherein the second time series includes values of a third prediction for the variable of the technical system, the third prediction being determined using a fourth model as a function of a third time series, which includes values of the operating variable in a third resolution, which is different from the first resolution and the second resolution.
We include 1-day data, 2-day average data, 4-day average data, weekly (5-day) average data, 10-day average data, monthly (20 day) average data, six resolution levels in our algorithm. The lag order d is from one to five. There are 30 models in total. Yang at pg. 6, col. 2.
Claim 6
Hashimoto discloses:
wherein: (i) the parameters of the second model are determined in a training of the second model using training data which include the second time series and a reference for the first prediction at the second temporal resolution, and/or
We use the first 800 data points as the training set and last 200 data points as the test set. If we luckily specify the correct parametric model and the unknown parameters A1,w1,A2,w2 can be estimated via methods such as MLE, then the prediction error is only from the noise part t, with a mean square error (MSE) converging to 1. Yang at pg. 4, col. 1.
(ii) the parameters of the first model are determined in a training of the first model using training data which include the input variable and a reference for the variable of the technical system at the first temporal resolution.
We use the first 800 data points as the training set and last 200 data points as the test set. If we luckily specify the correct parametric model and the unknown parameters A1,w1,A2,w2 can be estimated via methods such as MLE, then the prediction error is only from the noise part t, with a mean square error (MSE) converging to 1. Yang at pg. 4, col. 1.
Data in Year 2006 to 2013 is the training set, and data in Year 2014 and 2015 is the test set. Its prediction RMSE as a function of number of predictors is shown by the blue line in Figure 10. The horizontal axis is the number of past days we use to predict the next day. The optimal RMSE is around 0.0081 when using past 5 days data to make prediction. Yang at pg. 5, col. 2-pg. 6, col. 1.
The “input variable” includes time series information, just as the training data of Yang includes time series data and additionally an outcome related to the time series data, the outcome being a reference for the variable that is being predicted.
Claim 10
Hashimoto discloses:
wherein for a time series which represents the operating variable of the technical system in the first temporal resolution, the variable of the technical system is determined, when the variable of the technical system meets a condition: (i) an anomaly being recognized and/or the technical system being switched into a safe operating state, or (ii) the technical system otherwise being switched into an intended operating state.
In a series of the steps shown in FIG. 5, the adaptation device 104 first obtains sets of minute rotation times T30(1) to T30(24), the rotation speed NE, the charging efficiency η, and a misfire true probability Pt(i) as training data determined based on detection results of the sensor group 102 (S50). Minute rotation times T30(1) to T30(24), the rotation speed NE, and the charging efficiency η are internal combustion engine state variables, which indicate states of the internal combustion engine. Hashimoto at [0049].
Claim 11
Hashimoto discloses:
A device for determining a variable of a technical system, comprising: a computing unit configured to…
In a method for learning a mapping used by a computer, the mapping calculates a probability related to a categorization result using an internal combustion engine state variable as an input. Hashimoto at Abstract.
…perform a method that is substantially the same as the method disclosed in claim 1. Accordingly, for at least the same reasons and based on the same prior art as claim 1, claim 11 is rejected under 35 U.S.C. 103 as being obvious over Hashimoto in view of Yang.
Claim 12
Hashimoto discloses:
wherein the technical system is
In a method for learning a mapping used by a computer, the mapping calculates a probability related to a categorization result using an internal combustion engine state variable as an input. Hashimoto at Abstract.
Claim 13
Hashimoto discloses:
A non-transitory computer-readable storage medium on which is stored a computer program including computer-readable instructions…
The controller 70 is a computer that includes a mapping and an arithmetic unit. The controller 70 includes a central processing unit (CPU) 72, which is the arithmetic unit, a read-only memory (ROM) 74, an electrically rewritable nonvolatile memory (storage device 76), and a peripheral circuit 77, which are configured to communicate with each other through a local network 78. Hashimoto at [0025].
…to perform a method that is substantially the same as the method disclosed in claim 1. Accordingly, for at least the same reasons and based on the same prior art as claim 1, claim 13 is rejected under 35 U.S.C. 103 as being obvious over Hashimoto in view of Yang.
Claim 2 is rejected under 35 U.S.C. 103 as being obvious over Hashimoto in view of Yang and further in view of Froehlich, et al., (U.S. Pat. No 12,158,734, hereinafter Froehlich).
Claim 2
Hashimoto discloses:
wherein the technical system is the internal combustion engine,
In a method for learning a mapping used by a computer, the mapping calculates a probability related to a categorization result using an internal combustion engine state variable as an input. Hashimoto at Abstract.
(i) the operating variable characterizing a velocity or a load of the internal combustion engine, and/or
In a series of the processes shown in FIG. 2, the CPU 72 first obtains minute (i.e. very short) rotation times T30(1), T30(2), . . . T30(24), which are moment speed parameters (S10). Hashimoto at [0028].
Hashimoto does not appear to disclose:
(ii) the variable of the technical system characterizing a hydrocarbon emission of the internal combustion engine, or a nitrogen oxide emission of the internal combustion engine, or a temperature of the internal combustion engine, or a particle emission of the internal combustion engine, or an oxygen content of the internal combustion engine.
Froehlich, which is analogous art, discloses:
(ii) the variable of the technical system characterizing a hydrocarbon emission of the internal combustion engine, or a nitrogen oxide emission of the internal combustion engine, or a temperature of the internal combustion engine, or a particle emission of the internal combustion engine, or an oxygen content of the internal combustion engine.
In an exemplary embodiment, control unit 3 is used to control an internal combustion engine as technical system. For this purpose, a throttle valve position, a fuel supply, and/or the like can be specified as input variables to the throttle valve positioner or to the controlling for injection valves, and corresponding state variables, such as a rotational speed, a load, an engine temperature, can be received. Froehlich at col. 6, lines 55-61.
Froehlich is analogous art because both are directed to using models to predict operation of a system, such as an internal combustion engine. It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the application, to combine Froehlich with Hashimoto and Yang to result in a system that predicts hydrocarbon emissions based on known operation of the engine. Motivation to combine includes improved versatility of the system by allowing for additional types of system variables to be tested.
Examiner’s Note on Rejection Under 35 U.S.C. 103
Claims 7-9 have not been rejected under 35 U.S.C. 103, but are rejected under 35 U.S.C. 101. Therefore, if the independent claims were amended to overcome the rejection under 35 U.S.C. 101 (see suggestion in previous Examiner’s Not, above) and further amended to incorporate all of the limitations of claim 7, the amended claims would be allowed.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Siami-Namini, et al., “A Comparison of ARIMA and LSTM in Forecasting Time Series.”
Schmeichen, DE 102016115331, “Method and device for determining a model of a technical system.”
Communication
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JOSEPH MORRIS
Examiner
Art Unit 2188
/JOSEPH P MORRIS/Examiner, Art Unit 2188
/RYAN F PITARO/Supervisory Patent Examiner, Art Unit 2188
1 For convenience, references to the Specification herein are to the corresponding printed publication (U.S. Pat. Pub. No. 2023/0041825).
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