CTNF 17/697,267 CTNF 100502 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. This action is in response to the RCE filed on Mar. 2 nd , 2026. The RCE and amendments are linked to the original application filed on Mar. 17 th , 2022. Continued Examination Under 37 CFR 1.114 07-42-04 AIA A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on Mar. 2 nd 2026 has been entered. Specification The title of the invention is not descriptive. In light of the remarks and reconsideration of the specification, the examiner believes the title is not clearly indicative of the invention. A new title is required that is clearly indicative of the invention to which the claims are directed. Appropriate actions are required. Response to Amendment The Examiner thanks the applicant for the remarks, edits and arguments. Regarding Claim Rejections – 35 USC 112(b) The applicant has amended claims 1, 8, and 15 in response to the examiner rejecting terms in these claims for being indefinite. The examiner has reviewed the amendments made and has found that claims 1, 8, and 15 recite subject matter which is not considered indefinite. Therefore, the examiner has withdrawn the rejection under 35 U.S.C. 112(b). Regarding Claim Rejections – 35 USC 101 The applicant discloses examples from the USPTO's 2019 Revised Patent Subject Matter Eligibility Guidance (the "2019 Guidance"), dated January 4th, 2019. Next, the applicant argues that the claims do not meet the requirements for Prong One or Prong Two and therefore are not directed to an abstract idea and recites amended claim 1 as an example. The examiner would like to first address Prong One. This Prong is designed to determine if the claims recite an Abstract idea, Law of Nature, or Natural Phenomenon. As it is clear to both parties, the current claims do not recite Laws of Nature or Natural Phenomenon. The claims are evaluated to ensure they do not disclose mental processes or mathematical concepts. Per MPEP 2106.04(a) a Mathematical Concepts is defined as: “mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);” and Mental process is defined as: “concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).”. Reviewing the amended claims the examiner has noted that some of the claim limitations disclose a mental process of an observation, evaluation, judgment, or an opinion. For example, Claim 1 recites, “ evaluating and ranking , by the one or more computer processors, the regular forecasting pipeline and the exogenous forecasting pipeline for a first data allocation;” (Emphasis added). Using the Broadest Reasonable Interpretation, BRI, of this claim and in light of the specification, the examiner believes that a human is able to evaluate functions such as regular forecasting pipelines. Further, a human is able to give judgement or an opinion based on the evaluation and rank the pipelines accordingly. Per the specification, “As an overview, a time-series forecasting pipeline predicts time-series data for one or more target variables according to a model trained using historic target and potentially exogenous variable data.” (Emphasis added). The examiner has noted this quotation is not an official definition of a “forecasting pipeline”, however, it is reasonable to an ordinary person of the art to recognize a forecasting pipeline as a function which is able to produce a predictions based on input data. The examiner believes that a human is able to observe and evaluate different sets of functions which produce predictions and apply a judgement or an opinion to rank the functions based on given criteria. The claim limitations also recites, “by the one or more computer processors”, which is also considered in the Alice/Mayo test. MPEP 2106.04(a)(2)(III)(C) states, “In evaluating whether a claim that requires a computer recites a mental process, examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept . In these situations, the claim is considered to recite a mental process.” (Emphasis added). As claimed, the limitations recites the use of computer processors to execute the function of evaluating and ranking forecasting pipelines. The examiner believes that this claim limitation recites an abstract idea of evaluation and applying an opinion while using a computer as a tool to perform the abstract function. This is one example of an abstract idea the examiner has located in the independent claim. The examiner believes other limitations recite mental processes and use a computer as a tool to carry out the abstract ideas. Therefore, further analysis of the claims using the Alice/Mayo test is required. Next, the applicant states that the specification recites improvements to technology which includes reducing computations and caching data. The applicant believes that the claims recite improvements from the specification. The applicant then recites claim limitations, “evaluating and ranking, by the one or more computer processors, the regular forecasting pipeline and the exogenous forecasting pipeline for a first data allocation; computing and caching, by the one or more computer processors, data transformations for a top ranked pipeline; evaluating and ranking, by the one or more computer processors, the regular forecasting pipeline and the exogenous forecasting pipeline for a subsequent data allocation; transferring, by the one or more computer processors, cached time-series variable data transformations from a first ranked pipeline to a second ranked pipeline” as examples of claimed improvements. The examiner would like note that the BRI of limitations provided is a process of evaluating and ranking forecasting pipelines, computing and storing data for a pipeline and providing data to a top pipeline. Per MPEP 2106.04(d)(1), “first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. … Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. The claim itself does not need to explicitly recite the improvement described in the specification (e.g., "thereby increasing the bandwidth of the channel")”. The examiner believes the current claims fails to recite improvements as stated in the specification. For example, but not limited to, the claims do not recite subject matter which discloses scoring pipelines, how the system improves prediction accuracy, how different pipelines can be mutated or altered, or how the computed data transformation reduce resources besides storing/retrieving data from a computer component which is known to improve retrieval speed. Claim 1, as a whole, recites a process of retrieving time series data, generically generating pipelines and comparing them to determine a top ranked pipeline, which will receive cached data. Finally, the claim as a whole, outputs a top ranked model. The examiner does not believe that a person of ordinary skill in the art would be able to recognize the improvements to technology with the given claims and specification. Therefore, the examiner does not believe that the claims recite a technical improvement to technology and further evaluate of the claims using the Alice/Mayo test is required. The examiner has considered the applicants remarks, amendments to the claims, and the specification and has applied the Alice/Mayo test to the claims. For the reasons stated above, and in the 101 rejection section, the examiner believes the current amened claims fail to comply with 35 USC 101 and recites patent ineligible subject matter. Therefore, the examiner has upheld the rejection under 35 U.S.C. 101, see 101 rejection below. Regarding Claim Rejections – 35 U.S.C. 103 The applicant discloses the legal standards of rejecting claims under 35 U.S.C. 103. The applicant then recites, The amended claims and states, “Applicant respectfully asserts that the cited portions of Sglavo and Liu do not disclose the features of claim 1, as amended. For example, the cited portions do not disclose the limitations of "evaluating and ranking, by the one or more computer processors, the regular forecasting pipeline and the exogenous forecasting pipeline for a first data allocation; computing and caching, by the one or more computer processors, data transformations for a top ranked pipeline; evaluating and ranking, by the one or more computer processors, the regular forecasting pipeline and the exogenous forecasting pipeline for a subsequent data allocation; transferring, by the one or more computer processors, the cached time-series variable data transformations to a top ranked pipeline of the subsequent data allocation; or evaluating and ranking, by the one or more computer processors, the first ranked pipeline and the second ranked pipeline for a final data allocation" as required by claim 1.” The examiner would like to note that the applicant has not provided any arguments, besides reciting the amended claims, as to why the combination of proposed arts fail to discloses the amended claims. The examiner does not find the recitation of the claims as a persuasive argument. Regardless, after each amendment the examiner is required to perform a complete and thorough search of the subject matter related to the amended claims. This done to ensure the claims comply with 35 U.S.C. 102/103 using the legal standards disclosed by the applicant and the MPEP. After completion of this search, the examiner has reconsidered the remarks, claims, and specification. The examiner believes the combination of current arts proposed are sufficient “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.”. Therefore, the current amended claims are still rejected under 35 U.S.C. 103 and the rejection is upheld, see 103 rejection below. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”). Claim 1 Step 1 – Is the claim to a process, machine, manufacture or composition of matter? Claim 1, recites "A computer implemented method for selecting a time-series forecasting pipeline, the method comprising:" therefore it is directed to the statutory category of a process. Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon? The claim recites, inter alia: " evaluating and ranking, by the one or more computer processors, the regular forecasting pipeline and the exogenous forecasting pipeline for a first data allocation; " Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and rank forecasting data or evaluations, with the assistance of pen and paper or using a generic computer as a tool. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c). “ computing and caching, by the one or more computer processors, time-series variable data transformations for a top ranked pipeline; ” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to observe and apply transformations to data using a generic computer and store values in commonly known computer components. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c). " evaluating and ranking, by the one or more computer processors, the regular forecasting pipeline and the exogenous forecasting pipeline for a subsequent data allocation; " Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and rank forecasting data or evaluations, with the assistance of pen and paper or using a generic computer as a tool. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c). " evaluating and ranking, by the one or more computer processors, a first ranked pipeline and a second ranked pipeline for a final data allocation; " Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and rank forecasting data or evaluations, with the assistance of pen and paper or using a generic computer as a tool. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c). " selecting, by the one or more computer processors, a highest ranked pipeline for the final data allocation according to the evaluation and ranking; and " Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and rank forecasting data or evaluations, this includes providing judgement of an evaluation to select the best fitting data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c). Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application? The claim recites the additional elements, " receiving, by one or more computer processors, target variable time-series data and exogenous variable time-series data; " is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible. " generating, by the one or more computer processors, a regular forecasting pipeline comprising a model according to the target variable time-series data and imputed data; " amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). " storing, in system cache memory, the imputed data in a sparse matrix; " is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible. " generating, by the one or more computer processors, an exogenous forecasting pipeline comprising a model according to the target variable time-series data, the stored imputed data, and the exogenous variable time-series data; " amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). “ transferring, by the one or more computer processors, the cached time-series variable data transformations to a top ranked pipeline of the subsequent data allocation; ” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible. " providing, by the one or more computer processors, the selected highest ranked pipeline. " is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible. Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception? Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, " receiving, by one or more computer processors, target variable time-series data and exogenous variable time-series data; " is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”. " generating, by the one or more computer processors, a regular forecasting pipeline comprising a model according to the target variable time-series data and imputed data; " amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). " storing, in system cache memory, the imputed data in a sparse matrix; " is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(iv); “Storing and retrieving information in memory”. " generating, by the one or more computer processors, an exogenous forecasting pipeline comprising a model according to the target variable time-series data, the stored imputed data, and the exogenous variable time-series data; " amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). “ transferring, by the one or more computer processors, the cached time-series variable data transformations to a top ranked pipeline of the subsequent data allocation; ” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”. " providing, by the one or more computer processors, the selected highest ranked pipeline. " is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”. Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible. Claim 2 Step 1- Is the claim to a process, machine, manufacture or composition of matter? A process, as above. Step 2A Prong 1- Does the claim recite an abstract idea, law of nature, or natural phenomenon? The claim recites the abstract ideas of the preceding claims from which it depends. Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? The claim recites the additional elements, " receiving, by the one or more computer processors, libraries for at least one of data imputation, data transformation, and pipeline generation; and " is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible. " generating, by the one or more computer processors, the regular forecasting pipeline according to the at least one of the data imputation, data transformation and pipeline generation library ." amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, " receiving, by the one or more computer processors, libraries for at least one of data imputation, data transformation, and pipeline generation; and " is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); "Receiving or transmitting data over a network, e.g., using the Internet to gather data". " generating, by the one or more computer processors, the regular forecasting pipeline according to the at least one of the data imputation, data transformation and pipeline generation library. " amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible. Claim 3 Step 1- Is the claim to a process, machine, manufacture or composition of matter? A process, as above. Step 2A Prong 1- Does the claim recite an abstract idea, law of nature, or natural phenomenon? The claim recites the abstract ideas of the preceding claims from which it depends. Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? The claim recites the additional elements, " providing, by the one or more computer processors, an explanation of forecast time-series data using information from at least one of the target variable time-series data and the exogenous variable time-series data. " is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible. Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, " providing, by the one or more computer processors, an explanation of forecast time-series data using information from at least one of the target variable time-series data and the exogenous variable timeseries data. " is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); "Receiving or transmitting data over a network, e.g., using the Internet to gather data". Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible. Claim 4 Step 1- Is the claim to a process, machine, manufacture or composition of matter? A process, as above. Step 2A Prong 1- Does the claim recite an abstract idea, law of nature, or natural phenomenon? The claim recites the abstract ideas of the preceding claims from which it depends. Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? The claim recites the additional elements, " providing, by the one or more computer processors, an explanation of forecast time-series data according to past and future exogenous variable data. " is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible. Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, " providing, by the one or more computer processors, an explanation of forecast time-series data according to past and future exogenous variable data. " is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); "Receiving or transmitting data over a network, e.g., using the Internet to gather data". Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible. Claim 5 Step 1- Is the claim to a process, machine, manufacture or composition of matter? A process, as above. Step 2A Prong 1- Does the claim recite an abstract idea, law of nature, or natural phenomenon? The claim recites, inter alia: " concurrently evaluating, by the one or more computer processors, the regular and exogenous pipelines under a common framework. " Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate different pipelines on a generic computing system. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c). Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? This claim does not recite any additional limitations which integrate the abstract idea into a practical application. Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible. Claim 6 Step 1- Is the claim to a process, machine, manufacture or composition of matter? A process, as above. Step 2A Prong 1- Does the claim recite an abstract idea, law of nature, or natural phenomenon? The claim recites the abstract ideas of the preceding claims from which it depends. Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? The claim recites the additional elements, " imputing, by the one or more computer processors, missing data for at least one of the target variable time-series data and the exogenous variable time-series data. " is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible. Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, " imputing, by the one or more computer processors, missing data for at least one of the target variable time-series data and the exogenous variable time-series data. " is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); "Receiving or transmitting data over a network, e.g., using the Internet to gather data". Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible. Claim 7 Step 1- Is the claim to a process, machine, manufacture or composition of matter? A process, as above. Step 2A Prong 1- Does the claim recite an abstract idea, law of nature, or natural phenomenon? The claim recites the abstract ideas of the preceding claims from which it depends. Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? The claim recites the additional elements, " masking, by the one or more computer processors, the imputed data. " amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, " masking, by the one or more computer processors, the imputed data. " amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible. Claim 8 Step 1 – Is the claim to a process, machine, manufacture or composition of matter? Claim 8, recites "A computer program product for selecting a time-series forecasting pipeline, the computer program product comprising one or more computer readable storage devices and collectively stored program instructions on the one or more computer readable storage devices, the stored program instructions comprising instructions, which when executed, cause a computing system to:" therefore it is directed to the statutory category of a machine. Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon? The claim recites, inter alia: " evaluate the regular forecasting pipeline and the exogenous forecasting pipeline for a first data allocation; " Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and rank forecasting data or evaluations, with the assistance of pen and paper or using a generic computer as a tool. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c). “ compute and cache and time-series variable data transformations for a top ranked pipeline; ” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to observe and apply transformations to data using a generic computer and store values in commonly known computer components. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c). " evaluate and rank the regular forecasting pipeline and the exogenous forecasting pipeline for a subsequent data allocation; " Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and rank forecasting data or evaluations, with the assistance of pen and paper or using a generic computer as a tool. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c). " evaluate and rank the first ranked pipeline and the second ranked pipeline for a final data allocation; " Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and rank forecasting data or evaluations, with the assistance of pen and paper or using a generic computer as a tool. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c). " select a highest ranked pipeline for the final data allocation according to the evaluation and ranking; and " Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and rank forecasting data or evaluations, this includes providing judgement of an evaluation to select the best fitting data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c). Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application? The claim recites the additional elements, " receive target variable time-series data and exogenous variable time-series data; " is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible. " generate a regular forecasting pipeline comprising a model according to the target variable time-series data and imputed data; " amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). " store the imputed data in a sparse matrix in system cache memory; " is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible. " generate an exogenous forecasting pipeline comprising a model according to the target variable time-series data, the stored imputed data, and the exogenous variable timeseries data; " amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). “ transfer the cached time-series variable and data transformations to a top ranked pipeline of the subsequent data allocation; ” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible. " provide the selected highest ranked pipeline. " is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible. Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception? Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, " receive target variable time-series data and exogenous variable time-series data; " is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”. " generate a regular forecasting pipeline comprising a model according to the target variable time-series data and imputed data; " amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). " store the imputed data in a sparse matrix in system cache memory; " is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(iv); “Storing and retrieving information in memory”. " generate an exogenous forecasting pipeline comprising a model according to the target variable time-series data, the stored imputed data, and the exogenous variable timeseries data; " amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). “ transfer the cached time-series variable and data transformations to a top ranked pipeline of the subsequent data allocation; ” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”. " provide the selected highest ranked pipeline. " is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”. Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible. Claim 9 Step 1- Is the claim to a process, machine, manufacture or composition of matter? A machine, as above. Step 2A Prong 1- Does the claim recite an abstract idea, law of nature, or natural phenomenon? The claim recites the abstract ideas of the preceding claims from which it depends. Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? The claim recites the additional elements, " receive libraries for at least one of data imputation, data transformation, and pipeline generation; and " is an insignificant extra solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible. " generate the regular forecasting pipeline according to the at least one of the data imputation, data transformation and pipeline generation library. " amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, " receive libraries for at least one of data imputation, data transformation, and pipeline generation; and " is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); "Receiving or transmitting data over a network, e.g., using the Internet to gather data". " generate the regular forecasting pipeline according to the at least one of the data imputation, data transformation and pipeline generation library. " amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible. Claim 10 Step 1- Is the claim to a process, machine, manufacture or composition of matter? A machine, as above. Step 2A Prong 1- Does the claim recite an abstract idea, law of nature, or natural phenomenon? The claim recites the abstract ideas of the preceding claims from which it depends. Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? The claim recites the additional elements, " the stored program instructions further causing the computing system to provide an explanation of forecast timeseries data using information from at least one of the target variable time-series data and the exogenous variable time-series data. " is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible. Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, " the stored program instructions further causing the computing system to provide an explanation of forecast time- series data using information from at least one of the target variable timeseries data and the exogenous variable time-series data. " is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); "Receiving or transmitting data over a network, e.g., using the Internet to gather data". Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible. Claim 11 Step 1- Is the claim to a process, machine, manufacture or composition of matter? A machine, as above. Step 2A Prong 1- Does the claim recite an abstract idea, law of nature, or natural phenomenon? The claim recites the abstract ideas of the preceding claims from which it depends. Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? The claim recites the additional elements, " the stored program instructions further causing the computing system to provide an explanation of forecast timeseries data according to past and future exogenous variable data. " is an insignificant extra solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible. Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, " the stored program instructions further causing the computing system to provide an explanation of forecast time- series data according to past and future exogenous variable data. " is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); "Receiving or transmitting data over a network, e.g., using the Internet to gather data". Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible. Claim 12 Step 1- Is the claim to a process, machine, manufacture or composition of matter? A machine, as above. Step 2A Prong 1- Does the claim recite an abstract idea, law of nature, or natural phenomenon? The claim recites, inter alia: " the stored program instructions further causing the computing system to concurrently evaluate the regular and exogenous pipelines under a common framework. " Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate different pipelines on a generic computing system. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c). Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? This claim does not recite any additional limitations which integrate the abstract idea into a practical application. Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible. Claim 13 Step 1- Is the claim to a process, machine, manufacture or composition of matter? A machine, as above. Step 2A Prong 1- Does the claim recite an abstract idea, law of nature, or natural phenomenon? The claim recites the abstract ideas of the preceding claims from which it depends. Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? The claim recites the additional elements, " the stored program instructions further causing the computing system to impute missing data for at least one of the target variable time-series data and the exogenous variable time-series data. " is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible. Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, " the stored program instructions further causing the computing system to impute missing data for at least one of the target variable time-series data and the exogenous variable time-series data. " is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); "Receiving or transmitting data over a network, e.g., using the Internet to gather data". Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible. Claim 14 Step 1- Is the claim to a process, machine, manufacture or composition of matter? A machine, as above. Step 2A Prong 1- Does the claim recite an abstract idea, law of nature, or natural phenomenon? The claim recites the abstract ideas of the preceding claims from which it depends. Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? The claim recites the additional elements, " the stored program instructions further causing the computing system to mask the imputed data. " amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, " the stored program instructions further causing the computing system to mask the imputed data. " amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible Claim 15 Step 1 – Is the claim to a process, machine, manufacture or composition of matter? Claim 15, recites "A computer system for selecting a time-series forecasting pipeline, the computer system comprising:" therefore it is directed to the statutory category of a machine. Step 2A Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon? The claim recites, inter alia: " evaluate the regular forecasting pipeline and the exogenous forecasting pipeline for a first data allocation; " Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and rank forecasting data or evaluations, with the assistance of pen and paper or using a generic computer as a tool. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c). “ compute and cache time-series variable data transformations for a top ranked pipeline; ” Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to observe and apply transformations to data using a generic computer and store values in commonly known computer components. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c). " evaluate and rank the regular forecasting pipeline and the exogenous forecasting pipeline for a subsequent data allocation; " Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and rank forecasting data or evaluations, with the assistance of pen and paper or using a generic computer as a tool. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c). " evaluate and rank the first ranked pipeline and the second ranked pipeline for a final data allocation; " Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and rank forecasting data or evaluations, with the assistance of pen and paper or using a generic computer as a tool. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c). " select a highest ranked pipeline for the final data allocation according to the evaluation and ranking; and " Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate and rank forecasting data or evaluations, this includes providing judgement of an evaluation to select the best fitting data. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c). Step 2A Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application? The claim recites the additional elements, “ one or more computer processors; one or more computer readable storage devices; and stored program instructions on the one or more computer readable storage devices for execution by the one or more computer processors, the stored program instructions comprising instructions, which when executed, cause the computer system to: ” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). " receive target variable time-series data and exogenous variable time-series data; " is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible. " generate a regular forecasting pipeline comprising a model according to the target variable time-series data and imputed data; " amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). " store the imputed data in a sparse matrix in system cache memory; " is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible. " generate an exogenous forecasting pipeline comprising a model according to the target variable time-series data, the stored imputed data, and the exogenous variable timeseries data; " amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). “ transfer the cached time-series variable data transformations to a top ranked pipeline of the subsequent data allocation; ” is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible. " provide the selected highest ranked pipeline. " is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible. Step 2B – Does the claim recite additional elements that amount to significantly more than the judicial exception? Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, “ one or more computer processors; one or more computer readable storage devices; and stored program instructions on the one or more computer readable storage devices for execution by the one or more computer processors, the stored program instructions comprising instructions, which when executed, cause the computer system to: ” amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). " receive target variable time-series data and exogenous variable time-series data; " is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”. " generate a regular forecasting pipeline comprising a model according to the target variable time-series data and imputed data; " amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). " store the imputed data in a sparse matrix in system cache memory; " is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(iv); “Storing and retrieving information in memory”. " generate an exogenous forecasting pipeline comprising a model according to the target variable time-series data, the stored imputed data, and the exogenous variable timeseries data; " amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). “ transfer the cached time-series variable data transformations to a top ranked pipeline of the subsequent data allocation; ” is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”. " provide the selected highest ranked pipeline. " is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network, e.g., using the Internet to gather data”. Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible. Claim 16 Step 1- Is the claim to a process, machine, manufacture or composition of matter? A machine, as above. Step 2A Prong 1- Does the claim recite an abstract idea, law of nature, or natural phenomenon? The claim recites the abstract ideas of the preceding claims from which it depends. Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? The claim recites the additional elements, " receive libraries for at least one of data imputation, data transformation, and pipeline generation; and " is an insignificant extra solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible. " generate the regular forecasting pipeline according to the at least one of the data imputation, data transformation and pipeline generation library. " amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, " receive libraries for at least one of data imputation, data transformation, and pipeline generation; and " is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); "Receiving or transmitting data over a network, e.g., using the Internet to gather data". " generate the regular forecasting pipeline according to the at least one of the data imputation, data transformation and pipeline generation library. " amounts to generic computer components used as a tool to perform an existing process. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible. Claim 17 Step 1- Is the claim to a process, machine, manufacture or composition of matter? A machine, as above. Step 2A Prong 1- Does the claim recite an abstract idea, law of nature, or natural phenomenon? The claim recites the abstract ideas of the preceding claims from which it depends. Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? The claim recites the additional elements, " the stored program instructions further causing the computer system to provide an explanation of forecast timeseries data using information from at least one of the target variable time-series data and the exogenous variable time-series data. " is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible. Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, " the stored program instructions further causing the computer system to provide an explanation of forecast time-series data using information from at least one of the target variable timeseries data and the exogenous variable time-series data. " is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); "Receiving or transmitting data over a network, e.g., using the Internet to gather data". Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible. Claim 18 Step 1- Is the claim to a process, machine, manufacture or composition of matter? A machine, as above. Step 2A Prong 1- Does the claim recite an abstract idea, law of nature, or natural phenomenon? The claim recites the abstract ideas of the preceding claims from which it depends. Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? The claim recites the additional elements, " the stored program instructions further causing the computer system to provide an explanation of forecast timeseries data according to past and future exogenous variable data. " is an insignificant extra solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible. Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, " the stored program instructions further causing the computer system to provide an explanation of forecast time-series data according to past and future exogenous variable data. " is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); "Receiving or transmitting data over a network, e.g., using the Internet to gather data". Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible. Claim 19 Step 1- Is the claim to a process, machine, manufacture or composition of matter? A machine, as above. Step 2A Prong 1- Does the claim recite an abstract idea, law of nature, or natural phenomenon? The claim recites, inter alia: " the stored program instructions further causing the computer system to concurrently evaluate the regular and exogenous pipelines under a common framework. " Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of evaluating and observing data, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper. A human is able to evaluate different pipelines on a generic computing system. The limitation is merely applying an abstract idea on generic computer system. See MPEP 2106.04(a)(2)(III)(c). Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? This claim does not recite any additional limitations which integrate the abstract idea into a practical application. Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea and thus the claim is subject-matter ineligible. Claim 20 Step 1- Is the claim to a process, machine, manufacture or composition of matter? A machine, as above. Step 2A Prong 1- Does the claim recite an abstract idea, law of nature, or natural phenomenon? The claim recites the abstract ideas of the preceding claims from which it depends. Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application? The claim recites the additional elements, " the stored program instructions further causing the computer system to impute missing data for at least one of the target variable time-series data and the exogenous variable time-series data. " is an insignificant extra-solution activity required for any uses of the mental processes (see MPEP § 2106.05(g)) As such, the claim is ineligible. Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception? Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea. The additional elements, " the stored program instructions further causing the computer system to impute missing data for at least one of the target variable time-series data and the exogenous variable time-series data ." is an insignificant extra-solution activity required for any uses of abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(i); "Receiving or transmitting data over a network, e.g., using the Internet to gather data". Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim s 1-6, 8-13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sglavo et al., (Sglavo et al., "PIPELINE SYSTEM FOR TIME-SERIES DATA FORECASTING", US20190394083A1, 2019, hereinafter "Sglavo") in view of Liu et al., (Liu et al., "TSCache: An Efficient Flash-based Caching Scheme for Time-series Data Workloads" 2021, hereinafter "Liu") Regarding claim 1 , Sglavo discloses, “ A computer implemented method for selecting a time-series forecasting pipeline, the method comprising :” (DETAILED DESCRIPTION, pp. 19, [0174]; "FIG. 17 is a flow chart of an example of a process 1700 for determining forecasts for time-series data based on forecast pipelines in a forecasting software environment according to some aspects. Some examples can include more steps than, fewer steps than, different steps than, or a different order of the steps shown in FIG. 17." Figure 17 shows the flowchart which displays the method used to generate a forecasting pipeline.) “ receiving, by one or more computer processors, target variable time-series data and exogenous variable time-series data; ” (DETAILED DESCRIPTION, pp. 19, [0175]; "In block 1702, a processing device obtains time series data for forecast. The time-series data can include multiple time series. The time-series data can be provided by a client device or be collected by the processing device from one or more third-party devices, such as being downloaded from one or more cloud computing servers or from a distributed network configured for measuring or otherwise collecting the time-series data." This model teaches time-series data from multiple sources are input into the model generation system. This data can be from the user or the system.) “ generating, by the one or more computer processors, a regular forecasting pipeline comprising a model according to the target variable time-series data and imputed data; ” (DETAILED DESCRIPTION, pp. 19, [0176]; "In block 1704, the processing device obtains one or more pipelines describing a sequence of operations for processing the time-series data to produce one or more forecasts. In one example, the pipelines are generated based on inputs sent from a client device. For example, the processing device can generate a GUI, such as the G UIS 1500-, and have the GUI presented on the client device. A user of the client device can operate in the GUI to build the, execute the pipelines, view the forecasts or perform any combination of these operations. In another example, the processing device can obtain the pipelines by accessing files defining existing pipelines that are used for other time -series data and importing the files into the current forecasting software environment." Once the data is input into the system one or more pipelines are used to generate a forecast of the data input by the user and/or system. Existing pipelines can also be used.) “ generating, by the one or more computer processors, an exogenous forecasting pipeline comprising a model according to the target variable time-series data, the stored imputed data, and the exogenous variable time-series data; ” (DETAILED DESCRIPTION, pp. 19, [0176]; "In block 1704, the processing device obtains one or more pipelines describing a sequence of operations for processing the time-series data to produce one or more forecasts. In one example, the pipelines are generated based on inputs sent from a client device. For example, the processing device can generate a GUI, such as the GUIS 1500-, and have the GUI presented on the client device. A user of the client device can operate in the GU I to build the, execute the pipelines, view the forecasts or perform any combination of these operations. In another example, the processing device can obtain the pipelines by accessing files defining existing pipelines that are used for other time-series data and importing the files into the current forecasting software environment." Once the data is input into the system one or more pipelines are used to generate a forecast of the data input by the user and/or system. Existing pipelines can also be used.) and (DETAILED DESCRIPTION, [0156], pp. 17; “The sequence of operations included in the segmented pipeline 1300 further includes a segment operation 1304 involving splitting the time-series data up into data segments (groups) that can be operated on in parallel. Each data segment has a common characteristic. For example, the segment operation 1304 can split up the time-series data into data segments by type, source, quantity, associated entities, or any combination of these.”) and (DETAILED DESCRIPTION, [0157], pp. 17; “Each of the data segments can be assigned to its own pipeline segment (e.g., branch) and operated on in parallel to the data segments. For example, each of the pipeline segments 1310 corresponds to one of the data segments and can include a group of operations. The group of operations can include a data collection and processing operation 1306, one or more model strategy operations 1308, and a strategy comparison operation 1310.” Further, these two paragraphs disclose that each of the pipelines may be segments based on the data types and the operations on the pipelines. This teaches the generation of pipelines containing different data types as disclosed. This is stated above be each of the segments or branches can be treated as parts of a pipeline performing actions on different forms of data.) “ evaluating and ranking, by the one or more computer processors, the regular forecasting pipeline and the exogenous forecasting pipeline for a first data allocation; ” (DETAILED DESCRIPTION, [0173], PP. 18; “Referring now to FIG. 16, in the example user interface 1600, two pipelines are compared by showing a table summarizing metrics (e.g., WMAE, WMAPE, WMASE, WRMSE, WAPE, and the WASE) calculated based on the pipelines. Based on these metrics, a champion pipeline is selected and indicated in the field 1604.” This system includes a visual comparison of different pipelines for the users. The pipelines are compared and a champion pipeline is selected. Using the broadest reasonable interpretation, a champion pipeline would indicate a ranking structure, as in one pipeline if ranked or scored higher than the other. This teaches evaluation and ranking of different pipelines.) and (DETAILED DESCRIPTION, pp. 19, [0177]; "In block 1706, the processing device accesses one of the pipelines for evaluation. This pipeline is referred to below as the current pipeline," since this is the pipeline to be currently evaluated and executed." One of the many pipelines are evaluated so a "champion" pipeline can be selected.) “ evaluating and ranking, by the one or more computer processors, the regular forecasting pipeline and the exogenous forecasting pipeline for a subsequent data allocation; ” (DETAILED DESCRIPTION, [0171], pp. 18; “A champion pipeline can be determined from among the multiple pipelines in a project, e.g., by determining the pipeline having the smallest aggregate error. A marker or other visual indicator can then be depicted, in the "Pipeline Comparison" user interface 1600, proximate to the champion pipeline to indicate that it is the champion pipeline. Similarly, multiple projects can also be established and compared. For example, multiple projects can be compared by comparing the pipelines within the projects. A champion pipeline can be determined for each of the multiple projects. The champion pipelines for these projects can be compared with one another to determine the champion project, which may be the project containing the champion pipeline having the smallest aggregate error.” As stated above, multiple pipelines can be evaluated, this includes evaluating pipelines from different projects and containing different types of data. This model will select a champion pipelines based on an evaluation. The Broadest reasonable interpretation would lead one to recognize that a champion pipeline is an evaluated pipeline and is ranked or scored to be the best or top pipeline.) And (DETAILED DESCRIPTION, pp. 19, [0177]; "In block 1706, the processing device accesses one of the pipelines for evaluation. This pipeline is referred to below as the current pipeline," since this is the pipeline to be currently evaluated and executed." One of the many pipelines are evaluated so a "champion" pipeline can be selected. Once the champion pipeline is selected it can be used to gather data or be altered by the method.) “ transferring, by the one or more computer processors, the cached time-series variable data transformations to a top ranked pipeline of the subsequent data allocation; ” (DETAILED DESCRIPTION, pp. 15, [0144]; "As one particular example, the model strategy operation 1104A can include three forecast models (e.g., as part of the model strategy 1112A) configured to produce Forecast 1, Forecast 2, and Forecast 3, respectively, for Time Series X in the time-series data 1101. The model strategy 1112A can also be configured to apply the same three forecast models, or a different set of forecast models, to produce Forecast 4, Forecast 5, and Forecast 6, respectively, for Time Series Yin the time-series data 1101. Once these forecasts are generated, the model strategy operation 1104A can be configured to analyze Forecasts 1-3 to determine a champion model for Time Series X and analyze Forecasts 4-6 to determine a champion model for Time Series Y. The analysis for Time Series X can be performed by calculating a performance metric (e.g., a forecast error) for each of the three forecasts and comparing the performance metrics against one another." This model is able to generate different forecasts using time series data and data from other pipelines. This model is able to draw data from another pipeline to better develop and produce a "champion" pipeline.) and (DETAILED DESCRIPTION, pp. 3, [0038]; “Likewise, the pipeline generated for a set of time series can be saved and reused for another set of time series. The reusability of the model strategy and the pipeline significantly increases the flexibility of the forecasting system and also the efficiency of performing a forecasting task.” This discloses that pipelines can be saved, including the champion pipeline, and reused on different sets of data. This increases the flexibility of the pipelines.) “ evaluating and ranking, by the one or more computer processors, a first ranked pipeline and a second ranked pipeline for a final data allocation; ” (DETAILED DESCRIPTION, [0171], pp. 18; “A champion pipeline can be determined from among the multiple pipelines in a project, e.g., by determining the pipeline having the smallest aggregate error. A marker or other visual indicator can then be depicted, in the "Pipeline Comparison" user interface 1600, proximate to the champion pipeline to indicate that it is the champion pipeline. Similarly, multiple projects can also be established and compared. For example, multiple projects can be compared by comparing the pipelines within the projects. A champion pipeline can be determined for each of the multiple projects. The champion pipelines for these projects can be compared with one another to determine the champion project, which may be the project containing the champion pipeline having the smallest aggregate error.” As stated above, multiple pipelines can be evaluated, this includes evaluating pipelines from different projects and containing different types of data. This model will select a champion pipelines based on an evaluation. The Broadest reasonable interpretation would lead one to recognize that a champion pipeline is an evaluated pipeline and is ranked or scored to be the best or top pipeline.) And (DETAILED DESCRIPTION, pp. 19, [0177]; "In block 1706, the processing device accesses one of the pipelines for evaluation. This pipeline is referred to below as the current pipeline," since this is the pipeline to be currently evaluated and executed." One of the many pipelines are evaluated so a "champion" pipeline can be selected. Once the champion pipeline is selected it can be used to gather data or be altered by the method.) “ selecting, by the one or more computer processors, a highest ranked pipeline for the final data allocation according to the evaluation and ranking; and ” (DETAILED DESCRIPTION, pp. 19, [0185]; "Once there are no more pipelines to be evaluated, the process 1700 proceeds to block 1720. In block 1720, the processing device determines a champion pipeline (assuming that there are multiple pipelines for the time-series data). As discussed in detail above, the comparison can be performed by comparing distributions of performance metrics between the pipelines. Based on the distribution comparison, the processing device can select a champion pipeline." After all of the generated pipelines are evaluated a "champion" pipeline is selected.) “ providing, by the one or more computer processors, the selected highest ranked pipeline. ” (DETAILED DESCRIPTION, pp. 19, [0186]; "In some examples, the processing device may further receive an override instruction from a user to instead use another pipeline as the champion pipeline, rather than the system-designated champion pipeline. Subsequent to receiving the override instruction, the processing device can specify the other pipeline as the champion pipeline for the time-series data." The evaluated pipelines are given to the user. The user can use a GUI to either keep the system selected champion pipeline or override the system recommendations and select a different pipeline. However, the system is able to provide the user through a GUI, the system selected champion pipeline or model of pipelines.) Sglavo fails to explicitly disclose, “ storing, in system cache memory, the imputed data in a sparse matrix; ” and “ computing and caching, by the one or more computer processors, time-series variable data transformations for a top ranked pipeline; ”. However, Liu discloses, “ storing, in system cache memory, the imputed data in a sparse matrix; ” (Architecture Overview, pp. 3255; "As illustrated in Figure 2, TSCache has five major components: (1) Time-range based interface: A general, simplified interface is provided for clients to store and retrieve time-series data, which is typically a collection of data points returned from a time-series database for a query. (2) Slab manager: TSCache adopts a slabbased management to accommodate incoming data in large, write-once-read-many chunks as a sequence of continuous data points in the time order. (3) Data index: A two-layered data indexing structure is used to quickly filter out irrelevant data points and accelerate time-range based searches. (4) Cache manager: An adaptive cache replacement scheme is designed to identify the most valuable data for caching, which is optimized for the unique access patterns in time-series workloads. (5) Compaction module: A low-cost compaction process runs in the background to remove duplicate data points for optimizing cache space utilization." The model proposed in this article handles time series data for computing. This system utilizes a caching scheme to allow for faster time series data allocation. This is a smart caching system.) and (Time-range based Interface, pp. 3256; “Interface design. Inspired by key-value caches, TSCache provides a Time-range based, Key-value-like Interface for clients to store and retrieve data. Specifically, we use the data query statement excluding the time range part to calculate a 160-bit SHA-1 hash value [1] as a key to represent the query.” This system stores information from a database in a matrix format. The system stores data according to the database entry and may be dense or sparse depending on the populated fields.) “ computing and caching, by the one or more computer processors, time-series variable data transformations for a top ranked pipeline; ” (Time-range base Interface, pp. 3256; "TSCache provides two basic operations for users to set (store) and get (retrieve) data in the cache, as follows. Set(key, value, time_start, time_end) stores the client supplied data to the cache server. The key is a 160-bit digest calculated using the SHA-1 cryptographic hash function based on the data query statement. The value is a JavaScript Object Notation (JSON) [23] file with a collection of data points encoded, which is shareable across different platforms. The time range of the data points is specified by time_start and time_end. The key and the time range pair together uniquely identify the value. The client is responsible for guaranteeing that the supplied data points are the complete result of a database query for a specified time range. Get(key, time_start, time_end) retrieves data points from the cache server according to the provided key and time range. The key represents the query, and the pair of time_start and time_end specifies the requested time range. To retrieve data points from the cache server, it needs to meet two conditions: The key should be matched, and based on that, the requested time range should be covered by the cached data. The data points returned to the client are encoded as a JSON file to guarantee its integrity." The main concept of this article is the ability for the system to store and retrieve time series data from a cache. This model will determine what data and when that data will be stored in a system cache. This teaches the use of storing different data features in cache to be used by a system at a later time. This will also transform the data into a format which it can be stored and retrieved by the user.) It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Sglavo and Liu. Sglavo teaches a method which is able to generate forecast pipelines using time series data and select the best generated pipeline and provide it to the user. Liu teaches a method which is able to uses different caching methods of time series data to assist with time series data I/0. One of ordinary skill would have motivation to combine a forecast generation Al model which uses times series data with a system that is able to cache data more effectively and for faster computing of times series data, "Overall comparison. Figure 9 shows the overall performance of the four system solutions. Compared to the two database-only systems, TSCache achieves the best overall performance across all the five workloads. By caching only 10% of the dataset, TSCache improves the bandwidth by a factor of 2.7-6.7 and reduces the average latency by 63.6%-84.2% than Single DB. If comparing with Dual-DB, the two-server setup, TSCache increases the bandwidth by a factor of 1.7-3.4 and reduces the latency by 42.3%-68.6%. TSCache is also much more efficient than simply adopting a general-purpose cache. In this test, we use a 4-hour time unit to chunk time-series data in Fatcache. In Figure 9(a), we can see that TSCache achieves a significantly higher hit ratio than Fatcache. TSCache achieves a hit ratio of 88%-92.1% under the five workloads, which is an increase of 30.8-59.9 percentage points (p.p.) than Fatcache. As a result, TSCache improves the bandwidth by a factor of 1.4-3 and reduces the average latency by 22%- 66.9%." (Liu, Overall Comparison, pp. 3260). Regarding claim 2 , Sglavo discloses, “ receiving, by the one or more computer processors, libraries for at least one of data imputation, data transformation, and pipeline generation; and ” (DETAILED DESCRIPTION, pp. 2, [0031]; "Specifically, some examples of the present disclosure include a graphical user interface (GUI) in which a pipeline can be built by dragging and dropping user interface components representing different operations in a pipeline or by importing an existing pipeline. For example, the GUI can provide a library of model strategies from which a user can select and position an appropriate model strategy in the pipeline. The pipeline system can also automatically add operations to, or remove operations from, the pipeline depending on the user's selections. Examples of such operations can include pre-processing, model strategy comparison, and pipeline segmentation operations. Automatically adding and removing dependent operations can prevent failures and inaccuracies." Through the user interface a library of model strategies can be display to the user. The user can select from one or more of these strategies for pipeline model generation.) “ generating, by the one or more computer processors, the regular forecasting pipeline according to the at least one of the data imputation, data transformation and pipeline generation library. ” (DETAILED DESCRIPTION, pp. 2, [0031]; "Examples of such operations can include pre-processing, model strategy comparison, and pipeline segmentation operations. Automatically adding and removing dependent operations can prevent failures and inaccuracies. The pipeline system can store the pipelines in files, which can be subsequently edited, copied, and transferred among users." The user can select a pipeline generation strategy and it can be used later in the generation of a pipeline.) and (DETAILED DESCRIPTION, pp. 18, [0172]; “Although the above description focuses on a user manually creating a pipeline in the GUI 1400 or 1500, a pipeline or a portion there of may be automatically generated based on a rule set. For example, a user can specify high level parameters that the user wants in the pipeline, such as the number of segments, the number of model strategies, the default model strategies to be included in the pipeline, or a combination thereof. Based on the ruleset, the computing device can automatically generate the pipeline and its association graphical visualization.” A user can have a pipeline automatically generated based on a set of rules stored by the system or defined by the user.) Regarding claim 3 , Sglavo discloses, “ further comprising providing, by the one or more computer processors, an explanation of forecast time-series data using information from at least one of the target variable time-series data and the exogenous variable time-series data. ” (DETAILED DESCRIPTION, pp. 18, [0173]; "Referring now to FIG. 16, in the example user interface 1600, two pipelines are compared by showing a table summarizing metrics (e.g., WMAE, WMAPE, WMASE, WRMSE, WAPE, and the WASE) calculated based on the pipelines. Based on these metrics, a champion pipeline is selected and indicated in the field 1604." In the user interface, the different pipelines are displayed to the user. The user is also shown different metrics about the different pipelines so they can choose the correct one.) Regarding claim 4 , Sglavo discloses, “ further comprising providing, by the one or more computer processors, an explanation of forecast time-series data according to past and future exogenous variable data. ” (DETAILED DESCRIPTION, pp. 18, [0176]; "In block 1704, the processing device obtains one or more pipelines describing a sequence of operations for processing the time-series data to produce one or more forecasts. In one example, the pipelines are generated based on inputs sent from a client device. For example, the processing device can generate a GUI, such as the GUis 1500-1600, and have the GUI presented on the client device. A user of the client device can operate in the GUI to build the pipelines, execute the pipelines, view the forecasts or perform any combination of these operations. In another example, the processing device can obtain the pipelines by accessing files defining existing pipelines that are used for other time-series data and importing the files into the current forecasting software environment." In the user interface, the different pipelines are displayed to the user. These past or imported pipelines can be compared and evaluated based on external datasets. A user is able to view and create pipelines which uses different time series data.) and (DETAILED DESCRIPTION, pp. 18, [0173]; "Referring now to FIG. 16, in the example user interface 1600, two pipelines are compared by showing a table summarizing metrics (e.g., WMAE, WMAPE, WMASE, WRMSE, WAPE, and the WASE) calculated based on the pipelines. Based on these metrics, a champion pipeline is selected and indicated in the field 1604." This model will show the user through a GUI the different scores each of the pipelines which represent an explanation of the accuracy of the models based on past data to generate future prediction.) Regarding claim 5 , Sglavo discloses, “ further comprising concurrently evaluating, by the one or more computer processors, the regular and exogenous pipelines under a common framework. ” (DETAILED DESCRIPTION, pp. 18, [0171]; “A champion pipeline can be determined from among the multiple pipelines in a project, e.g., by determining the pipeline having the smallest aggregate error. A marker or other visual indicator can then be depicted, in the "Pipeline Comparison" user interface 1600, proximate to the champion pipeline to indicate that it is the champion pipeline. Similarly, multiple projects can also be established and compared. For example, multiple projects can be compared by comparing the pipelines within the projects. A champion pipeline can be determined for each of the multiple projects. The champion pipelines for these projects can be compared with one another to determine the champion project, which may be the project containing the champion pipeline having the smallest aggregate error.” This section discloses that the different pipelines and sets of pipelines can be evaluated using a common framework by computing the aggregate error of the different pipelines.) and (DETAILED DESCRIPTION, pp. 19, [0184]- [0185]; "In block 1718, the processing device determines whether there are more pipelines for the time-series data. If so, the processing device accesses the next pipeline for evaluation and repeats steps 1708-1718 for the next pipeline. [0185] Once there are no more pipelines to be evaluated, the process 1700 proceeds to block 1720. In block 1720, the processing device determines a champion pipeline (assuming that there are multiple pipelines for the time-series data). As discussed in detail above, the comparison can be per formed by comparing distributions of performance metrics between the pipelines. Based on the distribution comparison, the processing device can select a champion pipeline." Each of the pipelines are evaluated and compared. The results of the pipelines among other values are evaluated and output by the system to determine a "champion" pipeline to display to the user.) Regarding claim 6 , Sglavo discloses, “ further comprising imputing, by the one or more computer processors, missing data for at least one of the target variable time-series data and the exogenous variable time-series data. ” (DETAILED DESCRIPTION, pp. 16, [0155]; "Similar to the pipeline 1100, the segmented pipeline 1300 includes a pre-processing operation 1302 which can be implemented similarly as the preprocessing operation 1102 described above with regard to FIG. 11. For example, the preprocessing operation 1302 can preprocess the time-series data to, for example, normalize, clean, add, remove, or reformat the timeseries data." When evaluating the pipeline the data can be preprocessed. This process can perform many different actions including add or modify time-series data as needed.) Regarding claim 8 , Sglavo discloses, " A computer program product for selecting a timeseries forecasting pipeline, the computer program product comprising one or more computer readable storage devices and collectively stored program instructions on the one or more computer readable storage devices, the stored program instructions comprising instructions, which when executed, cause a computing system to: " (SUMMARY, pp. 1, [0004]; "One example of the present disclosure includes a system. The system can include a processing device and a memory device comprising instructions that are executable by the processing device. The instructions can cause the processing device to access a pipe line for forecasting a plurality of time series. The pipeline represents a sequence of operations for processing the plurality of time series to produce forecasts." This invention uses a system which contains memory, processors and other devices to store and execute instructions stored within.) “ receive target variable time-series data and exogenous variable time-series data; ” (DETAILED DESCRIPTION, pp. 19, [0175]; "In block 1702, a processing device obtains time series data for forecast. The time-series data can include multiple time series. The time-series data can be provided by a client device or be collected by the processing device from one or more third-party devices, such as being downloaded from one or more cloud computing servers or from a distributed network configured for measuring or otherwise collecting the time-series data." This model teaches time-series data from multiple sources are input into the model generation system. This data can be from the user or the system.) “ generate a regular forecasting pipeline comprising a model according to the target variable time-series data and imputed data; ” (DETAILED DESCRIPTION, pp. 19, [0176]; "In block 1704, the processing device obtains one or more pipelines describing a sequence of operations for processing the time-series data to produce one or more forecasts. In one example, the pipelines are generated based on inputs sent from a client device. For example, the processing device can generate a GUI, such as the G UIS 1500-, and have the GUI presented on the client device. A user of the client device can operate in the GUI to build the, execute the pipelines, view the forecasts or perform any combination of these operations. In another example, the processing device can obtain the pipelines by accessing files defining existing pipelines that are used for other time -series data and importing the files into the current forecasting software environment." Once the data is input into the system one or more pipelines are used to generate a forecast of the data input by the user and/or system. Existing pipelines can also be used.) “ generate an exogenous forecasting pipeline comprising a model according to the target variable time-series data, the stored imputed data, and the exogenous variable timeseries data; ” (DETAILED DESCRIPTION, pp. 19, [0176]; "In block 1704, the processing device obtains one or more pipelines describing a sequence of operations for processing the time-series data to produce one or more forecasts. In one example, the pipelines are generated based on inputs sent from a client device. For example, the processing device can generate a GUI, such as the GUIS 1500-, and have the GUI presented on the client device. A user of the client device can operate in the GU I to build the, execute the pipelines, view the forecasts or perform any combination of these operations. In another example, the processing device can obtain the pipelines by accessing files defining existing pipelines that are used for other time-series data and importing the files into the current forecasting software environment." Once the data is input into the system one or more pipelines are used to generate a forecast of the data input by the user and/or system. Existing pipelines can also be used.) and (DETAILED DESCRIPTION, [0156], pp. 17; “The sequence of operations included in the segmented pipeline 1300 further includes a segment operation 1304 involving splitting the time-series data up into data segments (groups) that can be operated on in parallel. Each data segment has a common characteristic. For example, the segment operation 1304 can split up the time-series data into data segments by type, source, quantity, associated entities, or any combination of these.”) and (DETAILED DESCRIPTION, [0157], pp. 17; “Each of the data segments can be assigned to its own pipeline segment (e.g., branch) and operated on in parallel to the data segments. For example, each of the pipeline segments 1310 corresponds to one of the data segments and can include a group of operations. The group of operations can include a data collection and processing operation 1306, one or more model strategy operations 1308, and a strategy comparison operation 1310.” Further, these two paragraphs disclose that each of the pipelines may be segments based on the data types and the operations on the pipelines. This teaches the generation of pipelines containing different data types as disclosed. This is stated above be each of the segments or branches can be treated as parts of a pipeline performing actions on different forms of data.) “ evaluate the regular forecasting pipeline and the exogenous forecasting pipeline for a first data allocation; ” (DETAILED DESCRIPTION, [0173], PP. 18; “Referring now to FIG. 16, in the example user interface 1600, two pipelines are compared by showing a table summarizing metrics (e.g., WMAE, WMAPE, WMASE, WRMSE, WAPE, and the WASE) calculated based on the pipelines. Based on these metrics, a champion pipeline is selected and indicated in the field 1604.” This system includes a visual comparison of different pipelines for the users. The pipelines are compared and a champion pipeline is selected. Using the broadest reasonable interpretation, a champion pipeline would indicate a ranking structure, as in one pipeline if ranked or scored higher than the other. This teaches evaluation and ranking of different pipelines.) and (DETAILED DESCRIPTION, pp. 19, [0177]; "In block 1706, the processing device accesses one of the pipelines for evaluation. This pipeline is referred to below as the current pipeline," since this is the pipeline to be currently evaluated and executed." One of the many pipelines are evaluated so a "champion" pipeline can be selected.) “ evaluate and rank the regular forecasting pipeline and the exogenous forecasting pipeline for a subsequent data allocation; ” (DETAILED DESCRIPTION, [0171], pp. 18; “A champion pipeline can be determined from among the multiple pipelines in a project, e.g., by determining the pipeline having the smallest aggregate error. A marker or other visual indicator can then be depicted, in the "Pipeline Comparison" user interface 1600, proximate to the champion pipeline to indicate that it is the champion pipeline. Similarly, multiple projects can also be established and compared. For example, multiple projects can be compared by comparing the pipelines within the projects. A champion pipeline can be determined for each of the multiple projects. The champion pipelines for these projects can be compared with one another to determine the champion project, which may be the project containing the champion pipeline having the smallest aggregate error.” As stated above, multiple pipelines can be evaluated, this includes evaluating pipelines from different projects and containing different types of data. This model will select a champion pipelines based on an evaluation. The Broadest reasonable interpretation would lead one to recognize that a champion pipeline is an evaluated pipeline and is ranked or scored to be the best or top pipeline.) And (DETAILED DESCRIPTION, pp. 19, [0177]; "In block 1706, the processing device accesses one of the pipelines for evaluation. This pipeline is referred to below as the current pipeline," since this is the pipeline to be currently evaluated and executed." One of the many pipelines are evaluated so a "champion" pipeline can be selected. Once the champion pipeline is selected it can be used to gather data or be altered by the method.) “ transfer the cached time-series variable and data transformations to a top ranked pipeline of the subsequent data allocation; ” (DETAILED DESCRIPTION, pp. 15, [0144]; "As one particular example, the model strategy operation 1104A can include three forecast models (e.g., as part of the model strategy 1112A) configured to produce Forecast 1, Forecast 2, and Forecast 3, respectively, for Time Series X in the time-series data 1101. The model strategy 1112A can also be configured to apply the same three forecast models, or a different set of forecast models, to produce Forecast 4, Forecast 5, and Forecast 6, respectively, for Time Series Yin the time-series data 1101. Once these forecasts are generated, the model strategy operation 1104A can be configured to analyze Forecasts 1-3 to determine a champion model for Time Series X and analyze Forecasts 4-6 to determine a champion model for Time Series Y. The analysis for Time Series X can be performed by calculating a performance metric (e.g., a forecast error) for each of the three forecasts and comparing the performance metrics against one another." This model is able to generate different forecasts using time series data and data from other pipelines. This model is able to draw data from another pipeline to better develop and produce a "champion" pipeline.) and (DETAILED DESCRIPTION, pp. 3, [0038]; “Likewise, the pipeline generated for a set of time series can be saved and reused for another set of time series. The reusability of the model strategy and the pipeline significantly increases the flexibility of the forecasting system and also the efficiency of performing a forecasting task.” This discloses that pipelines can be saved, including the champion pipeline, and reused on different sets of data. This increases the flexibility of the pipelines.) “ evaluate and rank the first ranked pipeline and the second ranked pipeline for a final data allocation; ” (DETAILED DESCRIPTION, [0171], pp. 18; “A champion pipeline can be determined from among the multiple pipelines in a project, e.g., by determining the pipeline having the smallest aggregate error. A marker or other visual indicator can then be depicted, in the "Pipeline Comparison" user interface 1600, proximate to the champion pipeline to indicate that it is the champion pipeline. Similarly, multiple projects can also be established and compared. For example, multiple projects can be compared by comparing the pipelines within the projects. A champion pipeline can be determined for each of the multiple projects. The champion pipelines for these projects can be compared with one another to determine the champion project, which may be the project containing the champion pipeline having the smallest aggregate error.” As stated above, multiple pipelines can be evaluated, this includes evaluating pipelines from different projects and containing different types of data. This model will select a champion pipelines based on an evaluation. The Broadest reasonable interpretation would lead one to recognize that a champion pipeline is an evaluated pipeline and is ranked or scored to be the best or top pipeline.) And (DETAILED DESCRIPTION, pp. 19, [0177]; "In block 1706, the processing device accesses one of the pipelines for evaluation. This pipeline is referred to below as the current pipeline," since this is the pipeline to be currently evaluated and executed." One of the many pipelines are evaluated so a "champion" pipeline can be selected. Once the champion pipeline is selected it can be used to gather data or be altered by the method.) “ select a highest ranked pipeline for the final data allocation according to the evaluation and ranking; and ” (DETAILED DESCRIPTION, pp. 19, [0185]; "Once there are no more pipelines to be evaluated, the process 1700 proceeds to block 1720. In block 1720, the processing device determines a champion pipeline (assuming that there are multiple pipelines for the time-series data). As discussed in detail above, the comparison can be performed by comparing distributions of performance metrics between the pipelines. Based on the distribution comparison, the processing device can select a champion pipeline." After all of the generated pipelines are evaluated a "champion" pipeline is selected.) “ provide the selected highest ranked pipeline. ” (DETAILED DESCRIPTION, pp. 19, [0186]; "In some examples, the processing device may further receive an override instruction from a user to instead use another pipeline as the champion pipeline, rather than the system-designated champion pipeline. Subsequent to receiving the override instruction, the processing device can specify the other pipeline as the champion pipeline for the time-series data." The evaluated pipelines are given to the user. The user can use a GUI to either keep the system selected champion pipeline or override the system recommendations and select a different pipeline. However, the system is able to provide the user through a GUI, the system selected champion pipeline or model of pipelines.) Sglavo fails to explicitly disclose, “ store the imputed data in a sparse matrix in system cache memory; ” and “ compute and cache and time-series variable data transformations for a top ranked pipeline; ”. However, Liu discloses, “ store the imputed data in a sparse matrix in system cache memory; ” (Architecture Overview, pp. 3255; "As illustrated in Figure 2, TSCache has five major components: (1) Time-range based interface: A general, simplified interface is provided for clients to store and retrieve time-series data, which is typically a collection of data points returned from a time-series database for a query. (2) Slab manager: TSCache adopts a slabbased management to accommodate incoming data in large, write-once-read-many chunks as a sequence of continuous data points in the time order. (3) Data index: A two-layered data indexing structure is used to quickly filter out irrelevant data points and accelerate time-range based searches. (4) Cache manager: An adaptive cache replacement scheme is designed to identify the most valuable data for caching, which is optimized for the unique access patterns in time-series workloads. (5) Compaction module: A low-cost compaction process runs in the background to remove duplicate data points for optimizing cache space utilization." The model proposed in this article handles time series data for computing. This system utilizes a caching scheme to allow for faster time series data allocation. This is a smart caching system.) and (Time-range based Interface, pp. 3256; “Interface design. Inspired by key-value caches, TSCache provides a Time-range based, Key-value-like Interface for clients to store and retrieve data. Specifically, we use the data query statement excluding the time range part to calculate a 160-bit SHA-1 hash value [1] as a key to represent the query.” This system stores information from a database in a matrix format. The system stores data according to the database entry and may be dense or sparse depending on the populated fields.) “ compute and cache and time-series variable data transformations for a top ranked pipeline; ” (Time-range base Interface, pp. 3256; "TSCache provides two basic operations for users to set (store) and get (retrieve) data in the cache, as follows. Set(key, value, time_start, time_end) stores the client supplied data to the cache server. The key is a 160-bit digest calculated using the SHA-1 cryptographic hash function based on the data query statement. The value is a JavaScript Object Notation (JSON) [23] file with a collection of data points encoded, which is shareable across different platforms. The time range of the data points is specified by time_start and time_end. The key and the time range pair together uniquely identify the value. The client is responsible for guaranteeing that the supplied data points are the complete result of a database query for a specified time range. Get(key, time_start, time_end) retrieves data points from the cache server according to the provided key and time range. The key represents the query, and the pair of time_start and time_end specifies the requested time range. To retrieve data points from the cache server, it needs to meet two conditions: The key should be matched, and based on that, the requested time range should be covered by the cached data. The data points returned to the client are encoded as a JSON file to guarantee its integrity." The main concept of this article is the ability for the system to store and retrieve time series data from a cache. This model will determine what data and when that data will be stored in a system cache. This teaches the use of storing different data features in cache to be used by a system at a later time. This will also transform the data into a format which it can be stored and retrieved by the user.) Regarding claim 9 , Sglavo discloses, “ receive libraries for at least one of data imputation, data transformation, and pipeline generation; and ” (DETAILED DESCRIPTION, pp. 2, [0031]; "Specifically, some examples of the present disclosure include a graphical user interface (GUI) in which a pipeline can be built by dragging and dropping user interface components representing different operations in a pipeline or by importing an existing pipeline. For example, the GUI can provide a library of model strategies from which a user can select and position an appropriate model strategy in the pipeline. The pipeline system can also automatically add operations to, or remove operations from, the pipeline depending on the user's selections. Examples of such operations can include pre-processing, model strategy comparison, and pipeline segmentation operations. Automatically adding and removing dependent operations can prevent failures and inaccuracies." Through the user interface a library of model strategies can be display to the user. The user can select from one or more of these strategies for pipeline model generation.) “ generate the regular forecasting pipeline according to the at least one of the data imputation, data transformation and pipeline generation library. ” (DETAILED DESCRIPTION, pp. 2, [0031]; "Examples of such operations can include pre-processing, model strategy comparison, and pipeline segmentation operations. Automatically adding and removing dependent operations can prevent failures and inaccuracies. The pipeline system can store the pipelines in files, which can be subsequently edited, copied, and transferred among users." The user can select a pipeline generation strategy and it can be used later in the generation of a pipeline.) and (DETAILED DESCRIPTION, pp. 18, [0172]; “Although the above description focuses on a user manually creating a pipeline in the GUI 1400 or 1500, a pipeline or a portion there of may be automatically generated based on a rule set. For example, a user can specify high level parameters that the user wants in the pipeline, such as the number of segments, the number of model strategies, the default model strategies to be included in the pipeline, or a combination thereof. Based on the ruleset, the computing device can automatically generate the pipeline and its association graphical visualization.” A user can have a pipeline automatically generated based on a set of rules stored by the system or defined by the user.) Regarding claim 10 , Sglavo discloses, “ the stored program instructions further causing the computing system to provide an explanation of forecast timeseries data using information from at least one of the target variable time-series data and the exogenous variable time-series data. ” (DETAILED DESCRIPTION, pp. 18, [0173]; "Referring now to FIG. 16, in the example user interface 1600, two pipelines are compared by showing a table summarizing metrics (e.g., WMAE, WMAPE, WMASE, WRMSE, WAPE, and the WASE) calculated based on the pipelines. Based on these metrics, a champion pipeline is selected and indicated in the field 1604." In the user interface, the different pipelines are displayed to the user. The user is also shown different metrics about the different pipelines so they can choose the correct one.) Regarding claim 11 , Sglavo discloses, “ the stored program instructions further causing the computing system to provide an explanation of forecast timeseries data according to past and future exogenous variable data. ” (DETAILED DESCRIPTION, pp. 18, [0176]; "In block 1704, the processing device obtains one or more pipelines describing a sequence of operations for processing the time-series data to produce one or more forecasts. In one example, the pipelines are generated based on inputs sent from a client device. For example, the processing device can generate a GUI, such as the GUis 1500-1600, and have the GUI presented on the client device. A user of the client device can operate in the GUI to build the pipelines, execute the pipelines, view the forecasts or perform any combination of these operations. In another example, the processing device can obtain the pipelines by accessing files defining existing pipelines that are used for other time-series data and importing the files into the current forecasting software environment." In the user interface, the different pipelines are displayed to the user. These past or imported pipelines can be compared and evaluated based on external datasets. A user is able to view and create pipelines which uses different time series data.) and (DETAILED DESCRIPTION, pp. 18, [0173]; "Referring now to FIG. 16, in the example user interface 1600, two pipelines are compared by showing a table summarizing metrics (e.g., WMAE, WMAPE, WMASE, WRMSE, WAPE, and the WASE) calculated based on the pipelines. Based on these metrics, a champion pipeline is selected and indicated in the field 1604." This model will show the user through a GUI the different scores each of the pipelines which represent an explanation of the accuracy of the models based on past data to generate future prediction.) Regarding claim 12 , Sglavo discloses, “ the stored program instructions further causing the computing system to concurrently evaluate the regular and exogenous pipelines under a common framework. ” (DETAILED DESCRIPTION, pp. 18, [0171]; “A champion pipeline can be determined from among the multiple pipelines in a project, e.g., by determining the pipeline having the smallest aggregate error. A marker or other visual indicator can then be depicted, in the "Pipeline Comparison" user interface 1600, proximate to the champion pipeline to indicate that it is the champion pipeline. Similarly, multiple projects can also be established and compared. For example, multiple projects can be compared by comparing the pipelines within the projects. A champion pipeline can be determined for each of the multiple projects. The champion pipelines for these projects can be compared with one another to determine the champion project, which may be the project containing the champion pipeline having the smallest aggregate error.” This section discloses that the different pipelines and sets of pipelines can be evaluated using a common framework by computing the aggregate error of the different pipelines.) and (DETAILED DESCRIPTION, pp. 19, [0184]- [0185]; "In block 1718, the processing device determines whether there are more pipelines for the time-series data. If so, the processing device accesses the next pipeline for evaluation and repeats steps 1708-1718 for the next pipeline. [0185] Once there are no more pipelines to be evaluated, the process 1700 proceeds to block 1720. In block 1720, the processing device determines a champion pipeline (assuming that there are multiple pipelines for the time-series data). As discussed in detail above, the comparison can be per formed by comparing distributions of performance metrics between the pipelines. Based on the distribution comparison, the processing device can select a champion pipeline." Each of the pipelines are evaluated and compared. The results of the pipelines among other values are evaluated and output by the system to determine a "champion" pipeline to display to the user.) Regarding claim 13 , Sglavo discloses, “ the stored program instructions further causing the computing system to impute missing data for at least one of the target variable time-series data and the exogenous variable time-series data. ” (DETAILED DESCRIPTION, pp. 16, [0155]; "Similar to the pipeline 1100, the segmented pipeline 1300 includes a pre-processing operation 1302 which can be implemented similarly as the preprocessing operation 1102 described above with regard to FIG. 11. For example, the preprocessing operation 1302 can preprocess the time-series data to, for example, normalize, clean, add, remove, or reformat the timeseries data." When evaluating the pipeline the data can be preprocessed. This process can perform many different actions including add or modify time-series data as needed.) Regarding claim 15 , Sglavo discloses, " A computer system for selecting a time-series forecasting pipeline, the computer system comprising: one or more computer processors; one or more computer readable storage devices; and stored program instructions on the one or more computer readable storage devices for execution by the one or more computer processors, the stored program instructions comprising instructions, which when executed, cause the computer system to: " (SUMMARY, pp. 1, [0004]; "One example of the present disclosure includes a system. The system can include a processing device and a memory device comprising instructions that are executable by the processing device. The instructions can cause the processing device to access a pipe line for forecasting a plurality of time series. The pipeline represents a sequence of operations for processing the plurality of time series to produce forecasts." This invention is comprised of a computer system which contains memory, processors and other devices to store and execute instructions stored within.) “ receive target variable time-series data and exogenous variable time-series data; ” (DETAILED DESCRIPTION, pp. 19, [0175]; "In block 1702, a processing device obtains time series data for forecast. The time-series data can include multiple time series. The time-series data can be provided by a client device or be collected by the processing device from one or more third-party devices, such as being downloaded from one or more cloud computing servers or from a distributed network configured for measuring or otherwise collecting the time-series data." This model teaches time-series data from multiple sources are input into the model generation system. This data can be from the user or the system.) “ generate a regular forecasting pipeline comprising a model according to the target variable time-series data and imputed data; ” (DETAILED DESCRIPTION, pp. 19, [0176]; "In block 1704, the processing device obtains one or more pipelines describing a sequence of operations for processing the time-series data to produce one or more forecasts. In one example, the pipelines are generated based on inputs sent from a client device. For example, the processing device can generate a GUI, such as the G UIS 1500-, and have the GUI presented on the client device. A user of the client device can operate in the GUI to build the, execute the pipelines, view the forecasts or perform any combination of these operations. In another example, the processing device can obtain the pipelines by accessing files defining existing pipelines that are used for other time -series data and importing the files into the current forecasting software environment." Once the data is input into the system one or more pipelines are used to generate a forecast of the data input by the user and/or system. Existing pipelines can also be used.) “ generate an exogenous forecasting pipeline comprising a model according to the target variable time-series data, the stored imputed data, and the exogenous variable timeseries data; ” (DETAILED DESCRIPTION, pp. 19, [0176]; "In block 1704, the processing device obtains one or more pipelines describing a sequence of operations for processing the time-series data to produce one or more forecasts. In one example, the pipelines are generated based on inputs sent from a client device. For example, the processing device can generate a GUI, such as the GUIS 1500-, and have the GUI presented on the client device. A user of the client device can operate in the GU I to build the, execute the pipelines, view the forecasts or perform any combination of these operations. In another example, the processing device can obtain the pipelines by accessing files defining existing pipelines that are used for other time-series data and importing the files into the current forecasting software environment." Once the data is input into the system one or more pipelines are used to generate a forecast of the data input by the user and/or system. Existing pipelines can also be used.) and (DETAILED DESCRIPTION, [0156], pp. 17; “The sequence of operations included in the segmented pipeline 1300 further includes a segment operation 1304 involving splitting the time-series data up into data segments (groups) that can be operated on in parallel. Each data segment has a common characteristic. For example, the segment operation 1304 can split up the time-series data into data segments by type, source, quantity, associated entities, or any combination of these.”) and (DETAILED DESCRIPTION, [0157], pp. 17; “Each of the data segments can be assigned to its own pipeline segment (e.g., branch) and operated on in parallel to the data segments. For example, each of the pipeline segments 1310 corresponds to one of the data segments and can include a group of operations. The group of operations can include a data collection and processing operation 1306, one or more model strategy operations 1308, and a strategy comparison operation 1310.” Further, these two paragraphs disclose that each of the pipelines may be segments based on the data types and the operations on the pipelines. This teaches the generation of pipelines containing different data types as disclosed. This is stated above be each of the segments or branches can be treated as parts of a pipeline performing actions on different forms of data.) “ evaluate the regular forecasting pipeline and the exogenous forecasting pipeline for a first data allocation; ” (DETAILED DESCRIPTION, [0173], PP. 18; “Referring now to FIG. 16, in the example user interface 1600, two pipelines are compared by showing a table summarizing metrics (e.g., WMAE, WMAPE, WMASE, WRMSE, WAPE, and the WASE) calculated based on the pipelines. Based on these metrics, a champion pipeline is selected and indicated in the field 1604.” This system includes a visual comparison of different pipelines for the users. The pipelines are compared and a champion pipeline is selected. Using the broadest reasonable interpretation, a champion pipeline would indicate a ranking structure, as in one pipeline if ranked or scored higher than the other. This teaches evaluation and ranking of different pipelines.) and (DETAILED DESCRIPTION, pp. 19, [0177]; "In block 1706, the processing device accesses one of the pipelines for evaluation. This pipeline is referred to below as the current pipeline," since this is the pipeline to be currently evaluated and executed." One of the many pipelines are evaluated so a "champion" pipeline can be selected.) “ evaluate and rank the regular forecasting pipeline and the exogenous forecasting pipeline for a subsequent data allocation; ” (DETAILED DESCRIPTION, [0171], pp. 18; “A champion pipeline can be determined from among the multiple pipelines in a project, e.g., by determining the pipeline having the smallest aggregate error. A marker or other visual indicator can then be depicted, in the "Pipeline Comparison" user interface 1600, proximate to the champion pipeline to indicate that it is the champion pipeline. Similarly, multiple projects can also be established and compared. For example, multiple projects can be compared by comparing the pipelines within the projects. A champion pipeline can be determined for each of the multiple projects. The champion pipelines for these projects can be compared with one another to determine the champion project, which may be the project containing the champion pipeline having the smallest aggregate error.” As stated above, multiple pipelines can be evaluated, this includes evaluating pipelines from different projects and containing different types of data. This model will select a champion pipelines based on an evaluation. The Broadest reasonable interpretation would lead one to recognize that a champion pipeline is an evaluated pipeline and is ranked or scored to be the best or top pipeline.) And (DETAILED DESCRIPTION, pp. 19, [0177]; "In block 1706, the processing device accesses one of the pipelines for evaluation. This pipeline is referred to below as the current pipeline," since this is the pipeline to be currently evaluated and executed." One of the many pipelines are evaluated so a "champion" pipeline can be selected. Once the champion pipeline is selected it can be used to gather data or be altered by the method.) “ transfer the cached time-series variable data transformations to a top ranked pipeline of the subsequent data allocation; ” (DETAILED DESCRIPTION, pp. 15, [0144]; "As one particular example, the model strategy operation 1104A can include three forecast models (e.g., as part of the model strategy 1112A) configured to produce Forecast 1, Forecast 2, and Forecast 3, respectively, for Time Series X in the time-series data 1101. The model strategy 1112A can also be configured to apply the same three forecast models, or a different set of forecast models, to produce Forecast 4, Forecast 5, and Forecast 6, respectively, for Time Series Yin the time-series data 1101. Once these forecasts are generated, the model strategy operation 1104A can be configured to analyze Forecasts 1-3 to determine a champion model for Time Series X and analyze Forecasts 4-6 to determine a champion model for Time Series Y. The analysis for Time Series X can be performed by calculating a performance metric (e.g., a forecast error) for each of the three forecasts and comparing the performance metrics against one another." This model is able to generate different forecasts using time series data and data from other pipelines. This model is able to draw data from another pipeline to better develop and produce a "champion" pipeline.) and (DETAILED DESCRIPTION, pp. 3, [0038]; “Likewise, the pipeline generated for a set of time series can be saved and reused for another set of time series. The reusability of the model strategy and the pipeline significantly increases the flexibility of the forecasting system and also the efficiency of performing a forecasting task.” This discloses that pipelines can be saved, including the champion pipeline, and reused on different sets of data. This increases the flexibility of the pipelines.) “ evaluate and rank the first ranked pipeline and the second ranked pipeline for a final data allocation; ” (DETAILED DESCRIPTION, [0171], pp. 18; “A champion pipeline can be determined from among the multiple pipelines in a project, e.g., by determining the pipeline having the smallest aggregate error. A marker or other visual indicator can then be depicted, in the "Pipeline Comparison" user interface 1600, proximate to the champion pipeline to indicate that it is the champion pipeline. Similarly, multiple projects can also be established and compared. For example, multiple projects can be compared by comparing the pipelines within the projects. A champion pipeline can be determined for each of the multiple projects. The champion pipelines for these projects can be compared with one another to determine the champion project, which may be the project containing the champion pipeline having the smallest aggregate error.” As stated above, multiple pipelines can be evaluated, this includes evaluating pipelines from different projects and containing different types of data. This model will select a champion pipelines based on an evaluation. The Broadest reasonable interpretation would lead one to recognize that a champion pipeline is an evaluated pipeline and is ranked or scored to be the best or top pipeline.) And (DETAILED DESCRIPTION, pp. 19, [0177]; "In block 1706, the processing device accesses one of the pipelines for evaluation. This pipeline is referred to below as the current pipeline," since this is the pipeline to be currently evaluated and executed." One of the many pipelines are evaluated so a "champion" pipeline can be selected. Once the champion pipeline is selected it can be used to gather data or be altered by the method.) “ select a highest ranked pipeline for the final data allocation according to the evaluation and ranking; and ” (DETAILED DESCRIPTION, pp. 19, [0185]; "Once there are no more pipelines to be evaluated, the process 1700 proceeds to block 1720. In block 1720, the processing device determines a champion pipeline (assuming that there are multiple pipelines for the time-series data). As discussed in detail above, the comparison can be performed by comparing distributions of performance metrics between the pipelines. Based on the distribution comparison, the processing device can select a champion pipeline." After all of the generated pipelines are evaluated a "champion" pipeline is selected.) “ provide the selected highest ranked pipeline. ” (DETAILED DESCRIPTION, pp. 19, [0186]; "In some examples, the processing device may further receive an override instruction from a user to instead use another pipeline as the champion pipeline, rather than the system-designated champion pipeline. Subsequent to receiving the override instruction, the processing device can specify the other pipeline as the champion pipeline for the time-series data." The evaluated pipelines are given to the user. The user can use a GUI to either keep the system selected champion pipeline or override the system recommendations and select a different pipeline. However, the system is able to provide the user through a GUI, the system selected champion pipeline or model of pipelines.) Sglavo fails to explicitly disclose, “ store the imputed data in a sparse matrix in system cache memory; ” and “ compute and cache time-series variable data transformations for a top ranked pipeline; ”. However, Liu discloses, “ store the imputed data in a sparse matrix in system cache memory; ” (Architecture Overview, pp. 3255; "As illustrated in Figure 2, TSCache has five major components: (1) Time-range based interface: A general, simplified interface is provided for clients to store and retrieve time-series data, which is typically a collection of data points returned from a time-series database for a query. (2) Slab manager: TSCache adopts a slabbased management to accommodate incoming data in large, write-once-read-many chunks as a sequence of continuous data points in the time order. (3) Data index: A two-layered data indexing structure is used to quickly filter out irrelevant data points and accelerate time-range based searches. (4) Cache manager: An adaptive cache replacement scheme is designed to identify the most valuable data for caching, which is optimized for the unique access patterns in time-series workloads. (5) Compaction module: A low-cost compaction process runs in the background to remove duplicate data points for optimizing cache space utilization." The model proposed in this article handles time series data for computing. This system utilizes a caching scheme to allow for faster time series data allocation. This is a smart caching system.) and (Time-range based Interface, pp. 3256; “Interface design. Inspired by key-value caches, TSCache provides a Time-range based, Key-value-like Interface for clients to store and retrieve data. Specifically, we use the data query statement excluding the time range part to calculate a 160- bit SHA-1 hash value [1] as a key to represent the query.” This system stores information from a database in a matrix format. The system stores data according to the database entry and may be dense or sparse depending on the populated fields.) “ compute and cache time-series variable data transformations for a top ranked pipeline; ” (Time-range base Interface, pp. 3256; "TSCache provides two basic operations for users to set (store) and get (retrieve) data in the cache, as follows. Set(key, value, time_start, time_end) stores the client supplied data to the cache server. The key is a 160-bit digest calculated using the SHA-1 cryptographic hash function based on the data query statement. The value is a JavaScript Object Notation (JSON) [23] file with a collection of data points encoded, which is shareable across different platforms. The time range of the data points is specified by time_start and time_end. The key and the time range pair together uniquely identify the value. The client is responsible for guaranteeing that the supplied data points are the complete result of a database query for a specified time range. Get(key, time_start, time_end) retrieves data points from the cache server according to the provided key and time range. The key represents the query, and the pair of time_start and time_end specifies the requested time range. To retrieve data points from the cache server, it needs to meet two conditions: The key should be matched, and based on that, the requested time range should be covered by the cached data. The data points returned to the client are encoded as a JSON file to guarantee its integrity." The main concept of this article is the ability for the system to store and retrieve time series data from a cache. This model will determine what data and when that data will be stored in a system cache. This teaches the use of storing different data features in cache to be used by a system at a later time. This will also transform the data into a format which it can be stored and retrieved by the user.) Regarding claim 16 , Sglavo discloses, “ receive libraries for at least one of data imputation, data transformation, and pipeline generation; and ” (DETAILED DESCRIPTION, pp. 2, [0031]; "Specifically, some examples of the present disclosure include a graphical user interface (GUI) in which a pipeline can be built by dragging and dropping user interface components representing different operations in a pipeline or by importing an existing pipeline. For example, the GUI can provide a library of model strategies from which a user can select and position an appropriate model strategy in the pipeline. The pipeline system can also automatically add operations to, or remove operations from, the pipeline depending on the user's selections. Examples of such operations can include pre-processing, model strategy comparison, and pipeline segmentation operations. Automatically adding and removing dependent operations can prevent failures and inaccuracies." Through the user interface a library of model strategies can be display to the user. The user can select from one or more of these strategies for pipeline model generation.) “ generate the regular forecasting pipeline according to the at least one of the data imputation, data transformation and pipeline generation library. ” (DETAILED DESCRIPTION, pp. 2, [0031]; "Examples of such operations can include pre-processing, model strategy comparison, and pipeline segmentation operations. Automatically adding and removing dependent operations can prevent failures and inaccuracies. The pipeline system can store the pipelines in files, which can be subsequently edited, copied, and transferred among users." The user can select a pipeline generation strategy and it can be used later in the generation of a pipeline.) and (DETAILED DESCRIPTION, pp. 18, [0172]; “Although the above description focuses on a user manually creating a pipeline in the GUI 1400 or 1500, a pipeline or a portion there of may be automatically generated based on a rule set. For example, a user can specify high level parameters that the user wants in the pipeline, such as the number of segments, the number of model strategies, the default model strategies to be included in the pipeline, or a combination thereof. Based on the ruleset, the computing device can automatically generate the pipeline and its association graphical visualization.” A user can have a pipeline automatically generated based on a set of rules stored by the system or defined by the user.) Regarding claim 17 , Sglavo discloses, “ the stored program instructions further causing the computer system to provide an explanation of forecast time-series data using information from at least one of the target variable time-series data and the exogenous variable time-series data. ” (DETAILED DESCRIPTION, pp. 18, [0173]; "Referring now to FIG. 16, in the example user interface 1600, two pipelines are compared by showing a table summarizing metrics (e.g., WMAE, WMAPE, WMASE, WRMSE, WAPE, and the WASE) calculated based on the pipelines. Based on these metrics, a champion pipeline is selected and indicated in the field 1604." In the user interface, the different pipelines are displayed to the user. The user is also shown different metrics about the different pipelines so they can choose the correct one.) Regarding claim 18 , Sglavo discloses, “ the stored program instructions further causing the computer system to provide an explanation of forecast time-series data according to past and future exogenous variable data. ” (DETAILED DESCRIPTION, pp. 18, [0176]; "In block 1704, the processing device obtains one or more pipelines describing a sequence of operations for processing the time-series data to produce one or more forecasts. In one example, the pipelines are generated based on inputs sent from a client device. For example, the processing device can generate a GUI, such as the GUis 1500-1600, and have the GUI presented on the client device. A user of the client device can operate in the GUI to build the pipelines, execute the pipelines, view the forecasts or perform any combination of these operations. In another example, the processing device can obtain the pipelines by accessing files defining existing pipelines that are used for other time-series data and importing the files into the current forecasting software environment." In the user interface, the different pipelines are displayed to the user. These past or imported pipelines can be compared and evaluated based on external datasets. A user is able to view and create pipelines which uses different time series data.) and (DETAILED DESCRIPTION, pp. 18, [0173]; "Referring now to FIG. 16, in the example user interface 1600, two pipelines are compared by showing a table summarizing metrics (e.g., WMAE, WMAPE, WMASE, WRMSE, WAPE, and the WASE) calculated based on the pipelines. Based on these metrics, a champion pipeline is selected and indicated in the field 1604." This model will show the user through a GUI the different scores each of the pipelines which represent an explanation of the accuracy of the models based on past data to generate future prediction.) Regarding claim 19 , Sglavo discloses, “ the stored program instructions further causing the computer system to concurrently evaluate the regular and exogenous pipelines under a common framework. ” (DETAILED DESCRIPTION, pp. 18, [0171]; “A champion pipeline can be determined from among the multiple pipelines in a project, e.g., by determining the pipeline having the smallest aggregate error. A marker or other visual indicator can then be depicted, in the "Pipeline Comparison" user interface 1600, proximate to the champion pipeline to indicate that it is the champion pipeline. Similarly, multiple projects can also be established and compared. For example, multiple projects can be compared by comparing the pipelines within the projects. A champion pipeline can be determined for each of the multiple projects. The champion pipelines for these projects can be compared with one another to determine the champion project, which may be the project containing the champion pipeline having the smallest aggregate error.” This section discloses that the different pipelines and sets of pipelines can be evaluated using a common framework by computing the aggregate error of the different pipelines.) and (DETAILED DESCRIPTION, pp. 19, [0184]- [0185]; "In block 1718, the processing device determines whether there are more pipelines for the time-series data. If so, the processing device accesses the next pipeline for evaluation and repeats steps 1708-1718 for the next pipeline. [0185] Once there are no more pipelines to be evaluated, the process 1700 proceeds to block 1720. In block 1720, the processing device determines a champion pipeline (assuming that there are multiple pipelines for the time-series data). As discussed in detail above, the comparison can be per formed by comparing distributions of performance metrics between the pipelines. Based on the distribution comparison, the processing device can select a champion pipeline." Each of the pipelines are evaluated and compared. The results of the pipelines among other values are evaluated and output by the system to determine a "champion" pipeline to display to the user.) Regarding claim 20 , Sglavo discloses, “ the stored program instructions further causing the computer system to impute missing data for at least one of the target variable time-series data and the exogenous variable time-series data. ” (DETAILED DESCRIPTION, pp. 16, [0155]; "Similar to the pipeline 1100, the segmented pipeline 1300 includes a pre-processing operation 1302 which can be implemented similarly as the preprocessing operation 1102 described above with regard to FIG. 11. For example, the preprocessing operation 1302 can preprocess the time-series data to, for example, normalize, clean, add, remove, or reformat the timeseries data." When evaluating the pipeline the data can be preprocessed. This process can perform many different actions including add or modify time-series data as needed.) 07-21-aia AIA Claim s 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Sglavo and Liu in view of Barker et al., (Barker et al., "Secure and Automated Enterprise Revenue Forecasting", 2018, hereinafter "Barker") . Regarding claim 7 , Barker discloses, “ further comprising masking, by the one or more computer processors, the imputed data. ” (Automated Workflow on Azure, pp. 7661; "Data in transit should be over a secure and encrypted channel (SSL/TLS etc.). Data at rest should be encrypted. Any keys used for encryption should be securely managed and regularly rotated to defend against potential future attacks." This model uses a form of cybersecurity to help protect against attacks. In this model data encryption and security protocols are used. Encrypting data is interpreted as applying a mask to that data.) It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Sglavo, Liu and Barker. Sglavo teaches a method which is able to generate forecast pipelines using time series data and select the best generated pipeline and provide it to the user. Liu teaches a method which is able to uses different caching of time series data to assist with data I/0. Barker teaches the use of generating forecasting pipelines which handles sensitive and/or private data. One of ordinary skill would have motivation to combine a forecast generation Al model which uses times series data with a system that is able to cache data more effectively and for faster computing of times series data and with a system which able to also generate forecasting pipeline but is able to do this with secure data, "The secure, automated revenue forecasting pipeline described in this paper shows a strong track record of high accuracy and proven value in a high business impact application. This work creates a general platform which can be deployed for many forecasting applications." (Barker, Conclusions, pp. 7664). Regarding claim 14 , Barker discloses, “ the stored program instructions further causing the computing system to mask the imputed data. ” (Automated Workflow on Azure, pp. 7661; "Data in transit should be over a secure and encrypted channel (SSL/TLS etc.). Data at rest should be encrypted. Any keys used for encryption should be securely managed and regularly rotated to defend against potential future attacks." This model uses a form of cybersecurity to help protect against attacks. In this model data encryption and security protocols are used. Encrypting data is interpreted as applying a mask to that data.) Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL MICHAEL GALVIN-SIEBENALER whose telephone number is (571)272-1257. The examiner can normally be reached Monday - Friday 8AM to 5PM. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Viker Lamardo can be reached at (571) 270-5871. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /PAUL M GALVIN-SIEBENALER/Examiner, Art Unit 2147 /ERIC NILSSON/Primary Examiner, Art Unit 2151 Application/Control Number: 17/697,267 Page 2 Art Unit: 2147 Application/Control Number: 17/697,267 Page 3 Art Unit: 2147 Application/Control Number: 17/697,267 Page 4 Art Unit: 2147 Application/Control Number: 17/697,267 Page 5 Art Unit: 2147 Application/Control Number: 17/697,267 Page 6 Art Unit: 2147 Application/Control Number: 17/697,267 Page 7 Art Unit: 2147 Application/Control Number: 17/697,267 Page 8 Art Unit: 2147 Application/Control Number: 17/697,267 Page 9 Art Unit: 2147 Application/Control Number: 17/697,267 Page 10 Art Unit: 2147 Application/Control Number: 17/697,267 Page 11 Art Unit: 2147 Application/Control Number: 17/697,267 Page 12 Art Unit: 2147 Application/Control Number: 17/697,267 Page 13 Art Unit: 2147 Application/Control Number: 17/697,267 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