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. Information Disclosure Statement The information disclosure statements (IDS) submitted on FILLIN "Enter date IDS was filed" \* MERGEFORMAT 01 / 04 /202 4 and 0 1 / 12 /202 4 . The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner. Priority Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55 . Double Patenting The non - statutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A non - statutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg , 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman , 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi , 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum , 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel , 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington , 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on non - statutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The filing of a terminal disclaimer by itself is not a complete reply to a non - statutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA/25, or PTO/AIA/26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer . Claims 12, 13, 14, 16, 17, 18, 21, and 22 are provisionally rejected on the ground of non - statutory double patenting as being unpatentable over claims (10+11) , Claim (12+11), Claim 13, Claim 10, Claim 11, Claim 15, Claim 16, (Claim 17+claim 16), Claim 18, (Claim 19+ Claim 11+ Claim 16)) respectively of co - pending Application No. 18 / 521038 . Although the claims at issue are not identical, they are not patentably distinct from each other because they encompass substantially similar subject matter. The following table is presented for the purpose of a comparison of the conflicting claims between the application and the patent . Application No. 18/520721 Application No. 18/521038 Claim 1 2+1 6 Claim 1 0 +Claim 11 A computer-implemented method for predicting a state of a technical system, the method comprising the following steps: detecting a state of the technical system, and providing a time series which includes values which characterize a c ourse of the detected state of the technical system; determining, using a first filter, first filtered values for predicting a short-term behavior of the technical system, as a function of the values of the time series; determining, using a second filter, second filtered values for predicting a long- term behavior of the technical system, as a function of the values of the time series; determining a first value for the prediction as a function of the filtered first values; determining a second value for the prediction as a function of the filtered second values; and determining a value of the prediction as a function of the first value for the prediction and the second value for the prediction Claim 1 6 The method according to claim 15, wherein the first filtered values are mapped to the first value using a first model , wherein the sampled values are mapped to the second value using a second model A computer-implemented method for predicting a state of a technical system, comprising the following steps: detecting a state of the technical system; providing a time series which includes values which characterize a course of the detected state of the technical system; determining, using a learning-based model for predicting a short-term behavior of the technical system, a first value for the prediction, as a function of the values of the time series; determining, using a physical model for predicting a long-term behavior of the technical system, a second value for the prediction, as a function of the values of the time series; and determining a value of the prediction as a function of the first value for the prediction and the second value for the prediction. Claim 11 The method according to claim 10, wherein the first value for the prediction is determined as a function of values filtered using a first filter , the values filtered using the first filter being determined as a function of the time series using the learning-based model, wherein the second value for the prediction is determined as a function of values filtered using a second filter, the values filtered using the second filter being determined as a function of the time series using the physical model, wherein the first filter is a filter that is complementary to the second filter. Claim 13 Claim 12 + Claim 11 The method according to claim 12, wherein the first filter is a filter that is complementary to the second filter, the first filter being a high-pass filter and the second filter being a low-pass filter The method according to claim 11, wherein the first filter is a high-pass filter and the second filter is a low-pass filter. Claim 11 (Portion of claim 11) wherein the first filter is a filter that is complementary to the second filter Claim 1 4 Claim 1 3 The method according to claim 13, wherein: (i) the low-pass filter has a cut-off frequency, and (ii) the high-pass filter has the cut-off frequency or has a higher cut-off frequency than the low-pass filter. T he method according to claim 12, wherein the low-pass filter has a cut-off frequency, wherein the high-pass filter has the cut-off frequency or has a higher cut-off frequency than the low-pass filter. Claim 1 5 The method according to claim 12, wherein sampled values are determined by sampling a k-th of the filtered second values at a sampling rate k, wherein the second value for the prediction is determined as a function of the sampled values Claim 1 6 Claim 1 0 The method according to claim 15, wherein the first filtered values are mapped to the first value using a first model, wherein the sampled values are mapped to the second value using a second model. using a learning-based model for predicting a short-term behavior of the technical system, a first value for Claim 17 Claim 1 1 The method according to claim 16, wherein the first model is trained as a function of a first time series, wherein the first time series is determined, using the first filter, as a function of a time series which represents a temporal course of the state of the technical system, wherein the second model is trained is a function of a second time series, wherein a filtered time series is determined, using the second filter, as a function of the time series, wherein the second time series includes values sampled from the filtered time series at the sampling rate. The method according to claim 10, wherein the first value for the prediction is determined as a function of values filtered using a first filter, the values filtered using the first filter being determined as a function of the time series using the learning-based model, wherein the second value for the prediction is determined as a function of values filtered using a second filter, the values filtered using the second filter being determined as a function of the time series using the physical model, wherein the first filter is a filter that is complementary to the second filter. Claim 1 8 Claim 1 5 The method according to claim 12, the value of the prediction is determined as a function of a sum of the first value for the prediction and the second value for the prediction. The method according to claim 10, wherein the value of the prediction is determined as a function of a sum of the first value for the prediction and the second value for the prediction Claim 1 9 Claim 1 6 The method according to claim 12, wherein a parameter for operation of the technical system or a long-term behavior of the technical system is determined as a function of the prediction. The method according to claim 10, wherein a parameter for the operation of the technical system or a long-term behavior of the technical system is determined as a function of the prediction. Claim 20 Claim 17 +Claim 16 A device configured to predict a state of a technical system, comprising: at least one processor; and at least one memory; wherein the at least one processor is configured to execute machine-readable instructions for predicting a state of a technical system, the instructions, when executed by a processor, causes the processor to perform the following steps: detecting a state of the technical system, and providing a time series which includes values which characterize a course of the detected state of the technical system, determining, using a first filter, first filtered values for predicting a short-term behavior of the technical system, as a function of the values of the time series, determining, using a second filter, second filtered values for predicting a long-term behavior of the technical system, as a function of the values of the time series , determining a first value for the prediction as a function of the filtered first values, determining a second value for the prediction as a function of the filtered second values, and determining a value of the prediction as a function of the first value for the prediction and the second value for the prediction. A device for predicting a state of a technical system, comprising: at least one processor; and at least one memory; wherein the at least one processor is configured to execute machine-readable instructions for predicting a state of a technical system, the instructions, when executed by the at least one processor, causes the at least one processor to perform the following steps: detecting a state of the technical system; providing a time series which includes values which characterize a course of the detected state of the technical system; determining, using a learning-based model for predicting a short-term behavior of the technical system, a first value for the prediction, as a function of the values of the time series; d etermining, using a physical model for predicting a long-term behavior of the technical system, a second value for the prediction, as a function of the values of the time series; and determining a value of the prediction as a function of the first value for the prediction and the second value for the prediction. Claim 1 6 The method according to claim 10, wherein a parameter for the operation of the technical system or a long-term behavior of the technical system is determined as a function of the prediction Claim 21 Claim 18 The device according to claim 20, further comprising: a sensor configured to detect sensor data or an interface configured to communicate with the sensor for detecting the sensor data, wherein the sensor data include the course of the state of the technical system. The device according to claim 17, further comprising: a sensor for detecting sensor data, or an interface for communicating with a sensor for detecting sensor data; wherein the sensor data include the course of the state of the technical system. Claim 22 Claim 1 9 + Claim 11+ Claim 16 A non-transitory computer-readable medium on which is stored a program comprising computer-readable instructions for predicting a state of a technical system, the instruction, when executed by a computer, causing the computer to perform the following steps: detecting a state of the technical system, and providing a time series which includes values which characterize a course of the detected state of the technical system; determining, using a first filter, first filtered values for predicting a short-term behavior of the technical system, as a function of the values of the time series; determining, using a second filter, second filtered values for predicting a long- term behavior of the technical system, as a function of the values of the time series; determining a first value for the prediction as a function of the filtered first values; determining a second value for the prediction as a function of the filtered second values; and determining a value of the prediction as a function of the first value for the prediction and the second value for the prediction. . A non-transitory computer-readable medium on which is stored a program including computer-readable instructions for predicting a state of a technical system, the instructions, when executed by a processor, causing the processor to perform the following steps: detecting a state of the technical system; providing a time series which includes values which characterize a course of the detected state of the technical system; determining, using a learning-based model for predicting a short-term behavior of the technical system, a first value for the prediction, as a function of the values of the time series; determining, using a physical model for predicting a long-term behavior of the technical system, a second value for the prediction, as a function of the values of the time series; and determining a value of the prediction as a function of the first value for the prediction and the second value for the prediction. 7 Claim 11 The method according to claim 10, wherein the first value for the prediction is determined as a function of values filtered using a first filter, the values filtered using the first filter being determined as a function of the time series using the learning-based model, wherein the second value for the prediction is determined as a function of values filtered using a second filter, the values filtered using the second filter being determined as a function of the time series using the physical model, wherein the first filter is a filter that is complementary to the second filter Claim 1 6 The method according to claim 10, wherein a parameter for the operation of the technical system or a long-term behavior of the technical system is determined as a function of the prediction Claim Rejections- 35 USC §101 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 -2 2 are rejected under 35 U.S.C.§101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Regarding C laim 1 2 , A computer-implemented method for predicting a state of a technical system, the method comprising the following steps: detecting a state of the technical system, and providing a time series which includes values which characterize a course of the detected state of the technical system; determining, using a first filter, first filtered values for predicting a short-term behavior of the technical system, as a function of the values of the time series; determining, using a second filter, second filtered values for predicting a long- term behavior of the technical system, as a function of the values of the time series; determining a first value for the prediction as a function of the filtered first values; determining a second value for the prediction as a function of the filtered second values; and determining a value of the prediction as a function of the first value for the prediction and the second value for the prediction The claim limitations underlined above is abstract idea, and t he remaining limitations are “additional elements”. Step 1 (Statutory Category): Yes. we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category ( a mathematical manipulation ). Therefore , it is directed to a statutory category, i.e., a mathematical manipulation . Step 2 A, Prong-1 (the claim is evaluated to determine whether it is directed to a judicial-exception/abstract-idea): Yes. In the above claim 12 , the underlined portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exception. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation that covers mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations , a mathematical manipulation ). For example, steps of “ determining, using a first filter, first filtered values for predicting a short-term behavior of the technical system, as a function of the values of the time series ”, further steps of “ determining, second filtered values for predicting a long- term behavior of the technical system, as a function of the values of the time series ” ; determining a first value for the prediction as a function of the filtered first values; determining a second value for the prediction as a function of the filtered second values; and determining a value of the prediction as a function of the first value for the prediction and the second value for the prediction” represents filtering/ smoothing time series sensor data using model. Data filtered values are obtained by Model is trained as a function of the first time series, which is a mathematical manipulation process using algorithm/ model to predict values by a computer. see specification, page 9, lines 5-25. and also pages 9-12, Figure 2A-2B, 3A- t hese steps represent a process (a mathematical manipulation) that, under its broadest reasonable interpretation, it encompasses using prediction algorithm with sensor measured time series data and prediction of a value based on evaluation/judgement see specification page 11, lines 1-3 “by means of interpolation as a function of the sampling rate k ” . There is no practical implementation. Predicting a value only but no subsequent implementation of this predict ed value to improve or make a difference in the field is abstract mathematical solution . Step 2A, Prong-2 (the claim is evaluated to determine whether the judicial exception/abstract-idea is integrated into a Practical Application): No. Claim 1 2 recites additional elements “ A computer-implemented method for predicting a state of a technical system ” and step of “ detecting a state of the technical system, and providing a time series which includes values which characterize a course of the detected state of the technical system ” ; are data gathering steps for the particular technological environment or field of use and describing type of data . Obtaining sensor data as a time series data at a particular point or position of time. These steps represent mere routine data gathering steps and only add an insignificant extra-solution activity to the judicial exception. The above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B . " characterize a course of the detected state " step merely represents insignificant post-solution activity. Furthermore, nothing in the claim reasonably indicates that the predicted value is displayed to user or implemented the abstract idea. in practical use. Step 2B (the claim is evaluated to determine whether recites additional elements that amount to an inventive concept, or also, the additional elements are significantly more than the recited the judicial-exception/abstract-idea): No. the additional element(s) are just insignificant extra-solution activity which are simply routine and conventional steps previously known to the pertinent industry that includes acquiring time series data from sensors . Therefore, the claim does not include additional element(s) significantly more, and/or, does not amount to more than the judicial-exception/abstract-idea itself and the claim is not patent eligible. claims 13 - 19 are rejected under 35 U.S.C. 101 because claims depend on claim 1 2 , therefore, has the abstract idea of claim 1 2 and also has the routine and conventional structure above of claim 1 2 . In addition, claims 13 - 19 further recite the elements which are simply more additional element (low pass, high pass filters) and description of data types and collection, and standard computational, mathematical-calculation to data gathering / generate data and/ or a prediction model, and. Furthermore, claims 2 13-19 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Regarding Claim 20, A device configured to predict a state of a technical system, comprising : at least one processor; and at least one memory; wherein the at least one processor is configured to execute machine-readable instructions for predicting a state of a technical system, the instructions, when executed by a processor, causes the processor to perform the following steps: detecting a state of the technical system, and providing a time series which includes values which characterize a course of the detected state of the technical system, determining, using a first filter, first filtered values for predicting a short-term behavior of the technical system, as a function of the values of the time series, determining, using a second filter, second filtered values for predicting a long-term behavior of the technical system, as a function of the values of the time series, determining a first value for the prediction as a function of the filtered first values, determining a second value for the prediction as a function of the filtered second values, and determining a value of the prediction as a function of the first value for the prediction and the second value for the prediction . The claim limitations underlined above is abstract idea, and t he remaining limitations are “additional elements”. Step 1 (Statutory Category): Yes. we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (a mathematical manipulation). Therefore, it is directed to a statutory category, i.e., a mathematical manipulation . Step 2 A, Prong-1 (the claim is evaluated to determine whether it is directed to a judicial-exception/abstract-idea): Yes. In the above claim 20 , the underlined portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exception. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation that covers mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations, a mathematical manipulation). For example, steps of “ determining, using a first filter, first filtered values for predicting a short-term behavior of the technical system, as a function of the values of the time series ”, further steps of “ determining, second filtered values for predicting a long- term behavior of the technical system, as a function of the values of the time series”; determining a first value for the prediction as a function of the filtered first values; determining a second value for the prediction as a function of the filtered second values; and determining a value of the prediction as a function of the first value for the prediction and the second value for the prediction” represents filtering/ smoothing time series sensor data using model. Data filtered values are obtained by Model is trained as a function of the first time series, which is a mathematical manipulation process using algorithm/ model to predict values by a computer. see specification, page 9, lines 5-25.and also pages 9-12, Figure 2A-2B,3A- t hese steps represent a process (a mathematical manipulation) that, under its broadest reasonable interpretation, it encompasses using prediction algorithm with sensor measured time series data and prediction of a value based on evaluation/judgement see specification page 11, lines 1-3 “by means of interpolation as a function of the sampling rate k ”. There is no practical implementation. Predicting a value only but no subsequent implementation of this predicted value to improve or make a difference in the field is abstract mathematical solution. Step 2A, Prong-2 (the claim is evaluated to determine whether the judicial exception/abstract-idea is integrated into a Practical Application): No. Claim 20 recites additional elements “ A device configured to predict a state of a technical system, comprising: at least one processor; and at least one memory; wherein the at least one processor is configured to execute machine-readable instructions for predicting a state of a technical system, the instructions, when executed by a processor, causes the processor to perform the following steps ” represent “processor/computer”, "computer program product" which is a mere information in the form of software/code/ data . T he elements recited (e.g. memory, processor ) state that the elements would reasonably be interpreted as components of a general purpose computer which fail to amount to significantly more than the judicial exception . S tep of “detecting a state of the technical system, and providing a time series which includes values which characterize a course of the detected state of the technical system” ; are data gathering steps for the particular technological environment or field of use and describing type of data. Obtaining sensor data as a time series data at a particular point or position of time. These steps represent mere routine data gathering steps and only add an insignificant extra-solution activity to the judicial exception. The above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B. " characterize a course of the detected state " step merely represents insignificant post-solution activity. Furthermore, nothing in the claim reasonably indicates that the predicted value is displayed to user or implemented the abstract idea. in practical use. Step 2B (the claim is evaluated to determine whether recites additional elements that amount to an inventive concept, or also, the additional elements are significantly more than the recited the judicial-exception/abstract-idea): No. the additional element(s) are just insignificant extra-solution activity which are simply routine and conventional steps previously known to the pertinent industry that includes acquiring time series data from sensors. Therefore, the claim does not include additional element(s) significantly more, and/or, does not amount to more than the judicial-exception/abstract-idea itself and the claim is not patent eligible. Claim 21 is rejected under 35 U.S.C. 101 because claims depend on claim 20, therefore, has the abstract idea of claim 20 and also has the routine and conventional structure above of claim 20. In addition, claim 20 further recite the additional elements (interface to communicate with sensor) which is simply a standard data gathering means, and. Furthermore, claim 20 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Regarding Claim 22, A non-transitory computer-readable medium on which is stored a program comprising computer-readable instructions for predicting a state of a technical system, the instruction, when executed by a computer, causing the computer to perform the following steps: detecting a state of the technical system, and providing a time series which includes values which characterize a course of the detected state of the technical system; determining, using a first filter, first filtered values for predicting a short-term behavior of the technical system, as a function of the values of the time series; determining, using a second filter, second filtered values for predicting a long- term behavior of the technical system, as a function of the values of the time series; determining a first value for the prediction as a function of the filtered first values; determining a second value for the prediction as a function of the filtered second values; and determining a value of the prediction as a function of the first value for the prediction and the second value for the prediction . The claim limitations underlined above is abstract idea, and t he remaining limitations are “additional elements”. Step 1 (Statutory Category): Yes. we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (a mathematical manipulation). Therefore, it is directed to a statutory category, i.e., a mathematical manipulation . Step 2 A, Prong-1 (the claim is evaluated to determine whether it is directed to a judicial-exception/abstract-idea): Yes. In the above claim 2 2 , the underlined portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exception. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim limitation that covers mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion and mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations, a mathematical manipulation). For example, steps of “ determining, using a first filter, first filtered values for predicting a short-term behavior of the technical system, as a function of the values of the time series ”, further steps of “ determining, second filtered values for predicting a long- term behavior of the technical system, as a function of the values of the time series”; determining a first value for the prediction as a function of the filtered first values; determining a second value for the prediction as a function of the filtered second values; and determining a value of the prediction as a function of the first value for the prediction and the second value for the prediction” represents filtering/ smoothing time series sensor data using model. Data filtered values are obtained by Model is trained as a function of the first time series, which is a mathematical manipulation process using algorithm/ model to predict values by a computer. see specification, page 9, lines 5-25.and also pages 9-12, Figure 2A-2B,3A- t hese steps represent a process (a mathematical manipulation) that, under its broadest reasonable interpretation, it encompasses using prediction algorithm with sensor measured time series data and prediction of a value based on evaluation/judgement see specification page 11, lines 1-3 “by means of interpolation as a function of the sampling rate k ”. There is no practical implementation. Predicting a value only but no subsequent implementation of this predicted value to improve or make a difference in the field is abstract mathematical solution. Step 2A, Prong-2 (the claim is evaluated to determine whether the judicial exception/abstract-idea is integrated into a Practical Application): No. Claim 2 2 recites additional elements “ A non-transitory computer-readable medium on which is stored a program comprising computer-readable instructions for predicting a state of a technical system, the instruction, when executed by a computer, causing the computer to perform the following steps ” represent “processor/computer”, "computer program product" which is a mere information in the form of software/code/ data . T he elements recited (e.g. memory, processor ) state that the elements would reasonably be interpreted as components of a general-purpose computer which fail to amount to significantly more than the judicial exception . Step of “detecting a state of the technical system, and providing a time series which includes values which characterize a course of the detected state of the technical system” ; are data gathering steps for the particular technological environment or field of use and describing type of data. Obtaining sensor data as a time series data at a particular point or position of time. These steps represent mere routine data gathering steps and only add an insignificant extra-solution activity to the judicial exception. The above additional elements, considered individually and in combination with the other claim elements do not reflect an improvement to other technology or technical field, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B. " characterize a course of the detected state " step merely represents insignificant post-solution activity. Furthermore, nothing in the claim reasonably indicates that the predicted value is displayed to user or implemented the abstract idea. in practical use. Step 2B (the claim is evaluated to determine whether recites additional elements that amount to an inventive concept, or also, the additional elements are significantly more than the recited the judicial-exception/abstract-idea): No. the additional element(s) are just insignificant extra-solution activity which are simply routine and conventional steps previously known to the pertinent industry that includes acquiring time series data from sensors. Therefore, the claim does not include additional element(s) significantly more, and/or, does not amount to more than the judicial-exception/abstract-idea itself and the claim is not patent eligible. Claim Rejections - 35 USC § 103 The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action: A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made . Claims 12 -2 2 are rejected under 35 U.S.C. 103 as being unpatentable over O kawa et al. ( US 2022/0230076 A 1 , hereinafter , Okawa ) and in view of Z hang H ang . ( CN 114529099 A , hereinafter Zhang , an original copy combined with translation is uploaded by the examiner . Ref. paragraph no used from translation, and figure no are from original ). Regarding Claim 12, Okawa teaches, A computer-implemented method for predicting a state of a technical system (Okawa, Figure 13 , prediction unit 1 34 , The prediction unit 134 generates a prediction result indicating a class to which the input data belongs and stores the prediction result in the prediction result storage unit 126 ” . [0071] The machine learning apparatus 100 enters the input data 158 into the model 142 to obtain a prediction result”) the method could be applied for state prediction of any system. For example , “wind power” system. see [0005]. For example, there has been proposed a wind power prediction method of predicting future wind power production using past wind power production and weather Prediction”). the method comprising the following steps: detecting a state of the technical system (Okawa, predicting results, Prediction unit 19) , and providing a time series (Okawa, Figure 14, Time series data) which includes values which characterize a course of the detected state of the technical system (Okawa, Figure 16, obtain normal measurement data, Figure 1, Measurement data 15, 16 [0027] Data that is used in machine learning may be measurement data that is obtained by a measurement device, such as time-series signal data or image data” [0037] The measurement data 15 may be time-series signal data representing”); determining, using a first filter (Okawa, Figure 4, 141 filters), determining, using a second filter (Okawa, Figure 4, 143 filters), determining a first value for the prediction as a function of the filtered first values (Okawa, Figure 4, prediction result [00 60 ] [0060] The machine learning apparatus 100 enters the measurement data 151 into a preprocessing filter 141 to generate training data 152. The preprocessing filter 141 is designed to remove noise from the measurement data 151 ” outputs a prediction result regarding a class to which the input data 154 belongs ) determining a second value for the prediction as a function of the filtered second values (Okawa, Figure s 3- 4, prediction result [00 70 ] “ , the preprocessing filter 141 is changed to a preprocessing filter 143 having a different parameter from the preprocessing filter 141. For example, the cutoff frequency of the low-pass filter is changed. After that, the machine learning apparatus 100 obtains measurement data 157. The measurement data 157 contains the same tendency of noise as the measurement data 155. The machine learning apparatus 100 enters the measurement data 157 into the preprocessing filter 143 to convert the measurement data 157 into input data 158. It is expected that the input data 158 is obtained by removing noise from the measurement data 157. The characteristics of the input data 158 match those of the training data 152. [0071] The machine learning apparatus 100 enters the input data 158 into the model 142 to obtain a prediction result ” ), and Okawa teaches time series data filtered by first and second filter and processed the time series data with first and second model to predict the state, however, Okawa is silent on first filtered values for predicting a short-term behavior of the technical system, as a function of the values of the time series second filtered values for predicting a long- term behavior of the technical system, as a function of the values of the time series ; determining a value of the prediction as a function of the first value for the prediction and the second value for the prediction However, Zhang teaches first filtered values for predicting a short-term behavior of the technical system, as a function of the values of the time series (Zhang, [0009]-[0010], filtering the target order associated data corresponding to the target order data from the historical order associated data according to the weight data.[n0010] “O btaining the preconstructed first type of time series model, the second type of time series model, and the third type of time series model; inputting the target order data into the first type of time series model, the second type of time series model, and the third type of time series model respectively to obtain a first short-term forecasting result, a second short-term forecasting result, and a third short-term forecasting result; and integrating the first short-term forecasting result, the second short-term forecasting result, and the third short-term forecasting result to obtain the short-term demand data forecasting result ”) second filtered values for predicting a long- term behavior of the technical system, as a function of the values of the time series; ( Z hang, [0013] the step of outputting long-term demand data prediction results based on the long-term prediction model according to the target prediction time data includes: performing stage division processing on the target prediction time data to determine the long-term prediction duration data in the target prediction time data; and inputting the long-term prediction duration data into the long-term prediction model to obtain the long-term demand data prediction results ”) . determining a value of the prediction as a function of the first value for the prediction and the second value for the prediction (Zhang, [0014], the step of fusing the short-term demand data prediction result and the long-term demand data prediction result to obtain a demand data prediction result includes: (…) fusing the granularized short-term demand data prediction result and the long-term demand data prediction result to obtain a target demand data prediction resul t”) It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Okawa ’s method for predicting state to incorporate a a method of predicting long term and short term prediction result as taught by Zhang and obtain an accurate short term and long term prediction by incorporating model and correlation of data and generate prediction ( Zhang, [0009]-[0014] ). It would have been obvious to a person of ordinary skill to include the well-known machine learning models and method of predicting short and/or long-term dynamics using time series data , in order to yield the accurate state predict ion results, yet with higher accuracy (KSR). Regarding Claim 13, combination of Okawa and Zhang teaches the method according to claim 12, Okawa further teaches wherein the first filter (Okawa, Figure 4 , processing filter 141 ) is a filter that is complementary to the second filter, (Okawa, Figure 4 , processing filter 14 3) the first filter being a high-pass filter and the second filter being a low-pass filter. (Okawa, Figure 1, Figure 3, [0034] “ For example, the preprocessing 13 functions as a noise filter to remove noise from the measurement data 15 and 16. The preprocessing 13 may function as a low-pass filter to remove high-frequency components, as a high-pass filter to remove low-frequency components ” NOTE: it is a design choice to have first filter as high pass filter and second filter as low pass filter ). Regarding Claim 14, combination of Okawa and Zhang teaches the method according to claim 1 3, Okawa further teaches wherein: (i) the low-pass filter has a cut-off frequency, and (ii) the high-pass filter has the cut-off frequency or has a higher cut-off frequency than the low-pass filter. (Okawa, Figure 1 5 , Cutoff frequency, [ 0034] “ The parameter 13a may be set to specify a cutoff frequency indicating a boundary for cutting off Frequencies ”. [0060 ] “ The cutoff frequency is adjusted by trial and error by an operator at the training stage ”. Figure 16, [0132] (S12) The preprocessing unit 131 passes each of the plurality of normal samples through a low-pass filter. The parameters set in the low-pass filter, such as a cutoff frequency and a filter order, are specified by a user. In this connection, it may be so designed as not to pass the normal samples through the low-pass filter. Alternatively, the lowpass filter may substantially be deactivated by adjusting the parameters of the low-pass filter, for example, by setting a sufficiently high cutoff frequency ”) Regarding Claim 15, combination of Okawa and Zhang teaches the method according to claim 1 2, Okawa further teaches wherein sampled values are determined by sampling a k-th of the filtered second values at a sampling rate k, wherein the second value for the prediction is determined as a function of the sampled values . (Okawa, Figure 4, [0080] FIG. 7 illustrates an example of abnormality detection by a k-nearest neighbor model. The machine learning apparatus 100 creates a k-nearest neighbor model of classifying input samples into normal and abnormal with the k-nearest neighbor algorithm, using the normal samples 161-1, 161-2, 161-3, ... that are training data ” [0127] The signal level of the time-series data is measured at a predetermined sampling rate. A label indicates a correct classification class to which the time-series data belongs. For example, the label indicates normal or abnormal ”). Regarding Claim 16, combination of Okawa and Zhang teaches the method according to claim 1 5, Okawa further teaches wherein the first filtered values (Okawa, Figure 3, step 141) are mapped to the first value using a first model ( Okawa, Figure 3 , Step 142 ) , wherein the sampled values are mapped to the second value using a second model . (Okawa, Figure 3 , [ 0063] The machine learning apparatus 100 enters the measurement data 153 into the preprocessing filter 141 to generate input data 154. The machine learning apparatus 100 enters the input data 154 into the model 142 and outputs a prediction result regarding a class to which the input data 154 belongs [0035] the model 14 may be a k-nearest neighbor model that classifies the input data 18 with the k-nearest neighbor algorithm ” ) . Regarding Claim 17, combination of Okawa and Zhang teaches the method according to claim 1 6, Okawa further teaches wherein the first model is trained as a function of a first time series wherein the first time series is determined, (Okawa figure 4, step 155) , using the first filter (Okawa figure 4, step 141) , as a function of a time series which represents a temporal course of the state of the technical system (Okawa figure 4, step 1 56 ) , , wherein the second model is trained is a function of a second time series, (Okawa figure 4, step 157) wherein a filtered time series is determined, using the second filter, (Okawa figure 4,step 1 43 ) as a function of the time series (Okawa figure 4, step 158) , wherein the second time series includes values sampled from the filtered time series at the sampling rate. (Okawa, [0 127 ] The signal level of the time-series data is measured at a predetermined sampling rate ”) . Regarding Claim 18, combination of Okawa and Zhang teaches the me thod according to claim 12, Okawa is silent on the value of the prediction is determined as a function of a sum of the first value for the prediction and the second value for the prediction . However, Zhang teaches the value of the prediction is determined as a function of a sum of the first value for the prediction and the second value for the prediction ( Zhang , [0014] “ fusing the granularized short-term demand data prediction result and the long-term demand data prediction result to obtain a target demand data prediction result ” ) . It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Okawa ’s method for predicting state to incorporate a a method of predicting long term and short term prediction result as taught by Zhang and obtain an accurate short term and long term prediction by incorporating model and correlation of data and generate prediction ( Zhang, [0009]-[0014]