DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Introduction
The following is a non-final Office Action in response to Applicant’s submission and preliminary amendment filed on April 25, 2023.
Currently claims 1-20 are pending, Claims 1, 8 and 15 are independent.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/25/2023 appears to be in compliance with the previsions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the Examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As per Step 1 of the subject matter eligibility analysis, it is to determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
In this case, claims 1-7 are directed to a method for generating forecasted value, which falls within the statutory category of a process. Claims 8-14 are directed to a system comprising one or more processors and a computer-readable medium, which falls within the statutory category of a machine. Claims 15-20 are directed to a non-transitory computer-readable storage medium storing computer instructions, which falls within the statutory category of a product.
In Step 2A of the subject matter eligibility analysis, it is to “determine whether the claim at issue is directed to a judicial exception (i.e., an abstract idea, a law of nature, or a natural phenomenon). Under this step, a two-prong inquiry will be performed to determine if the claim recites a judicial exception (an abstract idea enumerated in the 2019 Guidance), then determine if the claim recites additional elements that integrate the exception into a practical application of the exception. See 2019 Revised Patent Subject Matter Eligibility Guidance (2019 Guidance), 84 Fed. Reg. 50, 54-55 (January 7, 2019).
In Prong One, it is to determine if the claim recites a judicial exception (an abstract idea enumerated in the 2019 Guidance, a law of nature, or a natural phenomenon).
Taking the method as representative, claim 1 recites limitations of “determining a first change point of the first time series, determining a second change point of the first time series, generating a first truncated time series based at least in part on the first point, generating a second truncated time series, generating a first forecasted value using a first forecasting technique and the first truncated time series, generating a second forecasted value using a second forecasting technique and the second truncated time series, comparing the first forecasted value and the second forecasted value, selecting the first forecasting technique or the second forecasting technique to generate a final forecasted value, and generating the final forecasted value”, dependent claims 2-7 recite limitations of “determining a first confidence score for the first candidate change point, determining a first relative position score for the first candidate change point, determining a first category score of the first candidate change point, determining an average of the first confidence score, comparing the first overall score of for the first candidate change point to a second overall score of a second candidate change point, selecting the first candidate change point to be the first change point, determining the first time series and the second time series, selecting a data point of a third time series, splitting the third time series into the first time series and the second time series, splitting the first time series at the first change point, determining a final truncated time series, extracting input features from the first truncated time series, and forecasting the final forecasted value”. None of the limitations recites technological implementation details for any of these steps, but instead recite only results desired by any and all possible means. The limitations, as drafted, are directed to processes, under their broadest reasonable interpretation, cover performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “by a computing system” for performing the steps, nothing in the claim elements precludes the steps from practically being performed in the mind (including an observation, evaluation, judgment, opinion), or by a human using a pen and paper. For example, the claim encompasses a person can manually determining, generating, comparing, selecting which forecasting technique in the mind, or by a human using a pen and paper. Thus, the claims fall within the mental processes grouping. The mere nominal recitation of “by a computing system” does not take the claims out of the mental processes grouping. See Under the 2019 Guidance, 84 Fed. Reg. 52. Accordingly, the claims recite an abstract idea, and the analysis is proceeding to Prong Two.
Beyond the abstract idea, the claims recite the additional elements of “by a computing system”. The Specification describes that “a computing system including one or more processors and a computer-readable medium” (see ¶ 13), and “by way of example, personal computers and/or laptop computers running various versions of Microsoft Windows, Apple Macintosh, and/or Linux operating systems.”(See ¶ 1104). When given the broadest reasonable interpretation and in light of the Specification, the additional element is no more than generic computer and is recited at a high level of generality and amount to no more than adding the words “apply it” or using “a particular machine” with an abstract idea, or mere instructions to implement the abstract idea on a computer. Thus, merely adding a generic computer, generic computer components, or programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2358-59, 110 USPQ2d 1976, 1983-84 (2014). Again, automating an abstract process does not convert it into a practical application. See Credit Acceptance v. Westlake Servs., 859 F.3d 1044, 1055 (Fed. Cir. 2017) (“Our prior cases have made clear that mere automation of manual processes using generic computers does not constitute a patentable improvement in computer technology.”); see also Bancorp Servs., L.L.C. v. Sun Life Assurance Co. of Canada (U.S.), 687 F.3d 1266, 1278 (Fed. Cir. 2012) (A computer “employed only for its most basic function . . . does not impose meaningful limits on the scope of those claims.”). However, simply implementing the abstract idea on a generic computer does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Further, nothing in the claims that reflects an improvement to the functioning of a computer itself or another technology, effects a transformation or reduction of a particular article to a different state or thing, or applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. Therefore, the additional element does not integrate the judicial exception into a practical application. The claims are directed to an abstract idea, the analysis is proceeding to Step 2B.
In Step 2B of Alice, it is "a search for an ‘inventive concept’—i.e., an element or combination of elements that is ‘sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the [ineligible concept’ itself.’” Id. (alternation in original) (quoting Mayo Collaborative Servs. v. Prometheus Labs., Inc., 132 S. Ct. 1289, 1294 (2012)).
The claims as described in Prong Two above, nothing in the claims that integrates the abstract idea into a practical application. The same analysis applies here in Step 2B.
Beyond the abstract idea, the claims recite the additional elements of “by a computing system”. The Specification describes that “a computing system including one or more processors and a computer-readable medium” (see ¶ 13), and “by way of example, personal computers and/or laptop computers running various versions of Microsoft Windows, Apple Macintosh, and/or Linux operating systems.”(See ¶ 1104). When given the broadest reasonable interpretation and in light of the Specification, the additional element is no more than generic computer and is recited at a high level of generality and merely invoked as tools to perform the generic computer functions. Taking the claim elements separately and as an ordered combination, the computing system (one or more processors), at best, may perform the generic computer functions including receiving, manipulating, and transmitting information over a network. However, generic computer for performing generic computer functions have been recognized by the courts as merely well-understood, routine, and conventional functions of generic computers. Again, reciting the additional element of a processor is merely adding the words “apply it” or using “a particular machine” with an abstract idea, or mere instructions to implement an abstract idea on a computer do not amount to significantly more than the abstract idea. See buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); Collecting information, analyzing it, and displaying certain results of the collection and analysis, Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1351-52, 119 USPQ2d 1739, 1740 (Fed. Cir. 2016); RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1326-27, 122 USPQ2d 1377, 1379-80 (Fed. Cir. 2017) (the manipulation of information through a series of mental steps and a mathematical calculation, was held directed to an abstract idea)). Thus, simply implementing the abstract idea on a generic computer for performing generic computer functions do not amount to significantly more than the abstract idea. (MPEP 2106.05(a)-(c), (e-f) & (h)).
For the foregoing reasons, claims 1-7 cover subject matter that is judicially-excepted from patent eligibility under § 101 as discussed above, the other system claims 8-14 and medium claims 15-20 parallel claims 1-7—similarly cover claimed subject matter that is judicially excepted from patent eligibility under § 101.
Therefore, the claims as a whole, viewed individually and as a combination, do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. The claims are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Willemain et al., (US 2022/00050763, hereinafter: Willemain), and in view of Sahaf (US 2022/0317674), and further in view of Cao et al., (US 2022/0065785, hereinafter: Cao).
Regarding claim 1, Willemain discloses a method, comprising:
generating, by the computing system (see Fig. 5, # 103; ¶ 45, ¶ 51), a first truncated time series based at least in part on the first change point, the first truncated time series comprising a first subset of data points of the first time series ranging from the first change point to a youngest data point of the first time series (see ¶ 5-6, ¶ 21, ¶ 28);
generating, by the computing system, a second truncated time series based at least in part on the second change point, the second truncated time series comprising a second subset of data points of the first time series ranging from the second change point to the youngest data point of the first time series (see ¶ 7, ¶ 25-27, ¶ 28);
generating, by the computing system, a first forecasted value using a first forecasting technique and the first truncated time series (see ¶ 3, ¶ 21, ¶ 26);
generating, by the computing system, a second forecasted value using a second forecasting technique and the second truncated time series (see ¶ 22, 42-44);
comparing, by the computing system, the first forecasted value and the second forecasted value using a second time series (see ¶ 7-9, ¶ 22, ¶ 27, ¶ 40);
selecting, by the computing system, the first forecasting technique or the second forecasting technique to generate a final forecasted value based at least in part on the comparison (see ¶ 24, ¶ 27, ¶ 42); and
based, at least in part, on the first truncated time series and the second truncated time series, generating, by the computing system and using the selected first forecasting technique or second forecasting technique, the final forecasted value (see ¶ 28, ¶ 42-44, ¶ 130).
Willemain discloses collecting time series data associated with resources and analyzing each of a plurality of time series to detect a change point occurred (see ¶ 7).
Willemain does not explicitly disclose a first time series and a second time series; however, Sahaf in an analogous art for identifying subcomponent failure discloses
determining, by a computing system, a first time series comprising a first set of data points (see ¶ 100, ¶ 124, ¶ 216, claim 2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Willemain to include the teaching of Sahaf in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more specific time data set, and enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Willemain discloses evaluate all possible change points can be considered a multiple comparison test (see ¶ 31).
Willemain and Sahaf do not explicitly the following limitations; however, Cao in an analogous art for detecting change points discloses
determining, by the computing system, a first change point of the first time series based at least in part on a first relative position of the first change point in the first time series and a category of the first change point (see ¶ 56-57, ¶ 60-62, ¶ 95-97, ¶ 105, ¶ 145-148);
determining, by the computing system, a second change point of the first time series based at least in part on a second relative position of the second change point in the first time series and a category of the second change point (see ¶ 56-57, ¶ 95-97, ¶ 105, ¶ 113-115, ¶ 146).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Willemain and in view of Sahaf to include teaching of Cao in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing computational efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 2, Willemain does not explicitly the following limitations; however, Sahaf discloses the method of claim 1, wherein determining the first change point of the first time series comprises:
determining a first confidence score for a first candidate change point (see ¶ 167, ¶ 179, ¶ 202);
determining a first relative position score for the first candidate change point based at least in part on the relative position of the first candidate change point in the first time series (see ¶ 39, ¶ 51-52, ¶ 57-59);
determining a first category score of the first candidate change point based at least in part on a first change point category (see ¶ 59-62, ¶ 113-116);
determining an average of the first confidence score, the first relative position score, and the first category score to generate a first overall score of the first candidate change point (see ¶ 39, ¶ 64, ¶ 117-118);
comparing the first overall score of the first candidate change point to a second overall score of a second candidate change point (see ¶ 27-29, ¶ 68, ¶ 115).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Willemain to include the teaching of Sahaf in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more specific time data, and enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Willemain and Sahaf do not explicitly disclose the following limitations; however, Cao discloses
determining a first category score of the first candidate change point based at least in part on a first change point category (see ¶ 59-62, ¶ 113-116);
selecting the first candidate change point to be the first change point based at least in part on the comparison (see ¶ 105).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Willemain and in view of Sahaf to include teaching of Cao in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing computational efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 3, Willemain does not explicitly the following limitations; however, Sahaf discloses the method of claim 2, wherein the first overall score is normalized overall score with respect to a second overall score (see ¶ 68-69).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Willemain to include the teaching of Sahaf in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more specific time data, and enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 4, Willemain discloses the method of claim 1, wherein method further comprises:
determining the first time series and the second time series by: selecting a data point of a third time series based at least in part on avoidance of a forecasting bias (see Fig. 2, #S3; ¶ 27); and
splitting the third time series into the first time series and the second time series based at least in part on the selected data point, the first time series comprising a training set of data points, the second time series comprising a testing set of data points (see Fig. 2, #S4; ¶ 8, ¶ 20, ¶ 26, claim 2).
Regarding claim 5, Willemain discloses the method of claim 1, wherein generating the first truncated time series comprises splitting the first time series at the first change point (see ¶ 7, ¶ 21).
Regarding claim 6, Willemain discloses the method of claim 1, wherein the first forecasting technique is selected, and wherein generating the final forecasted value comprises:
determining a final truncated time series based at least in part on combining the selected first truncated time series with the second time series (see ¶ 7, ¶ 25-27);
extracting input features from the final truncated time series (see ¶ 27, claim 17) ; and
forecasting the final forecasted value based at least in part on the input features (see ¶ 22, ¶ 44).
Regarding claim 7, Willemain discloses the method of claim 1, wherein the first forecasting technique comprises Prophet, autoregressive integrated moving average (ARIMA), deep learning-based forecasting, and machine learning-based forecasting (see ¶ 7, ¶ 42).
In addition, claim 7 merely describing the characteristics of the first forecasting technique is directed to nonfunctional descriptive material because they cannot exhibit any functional interrelationship with the way the steps are performed. Therefore, it has been held that nonfunctional descriptive material will not distinguish the invention from prior art in term of patentability. (In re Gulack, 217 USPQ 401 (Fed. Cir. 1983), In re Ngai, 70 USPQ2d (Fed. Cir. 2004), In re Lowry, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2111.05).
Regarding claim 8, Willemain discloses a computing system, comprising:
one or more processors (see Fig. 5, # 103; ¶ 45, ¶ 51); and
a computer-readable medium including instructions that, when executed by the one or more processors (see ¶ 46, ¶ 51), cause the one or more processors to perform operations comprising:
generating a first truncated time series based at least in part on the first change point, the first truncated time series comprising a first subset of data points of the first time series ranging from the first change point to a youngest data point of the first time series (see ¶ 5-6, ¶ 21, ¶ 28);
generating a second truncated time series based at least in part on the second change point, the second truncated time series comprising a second subset of data points of the first time series ranging from the second change point to the youngest data point of the first time series (see ¶ 7, ¶ 25-27, ¶ 28);
generating a first forecasted value using a first forecasting technique and the first truncated time series (see ¶ 3, ¶ 21, ¶ 26);
generating a second forecasted value using a second forecasting technique and the second truncated time series (see ¶ 22, 42-44);
comparing the first forecasted value and the second forecasted value using a second time series (see ¶ 7-9, ¶ 22, ¶ 27, ¶ 40);
selecting the first forecasting technique or the second forecasting technique to generate a final forecasted value based at least in part on the comparison (see ¶ 24, ¶ 27, ¶ 42); and
based, at least in part, on the first truncated time series and the second truncated time series, generating, using the selected first forecasting technique or second forecasting technique, the final forecasted value (see ¶ 28, ¶ 42-44, ¶ 130).
Willemain does not explicitly disclose a first time series and a second time series; however, Sahaf in an analogous art for identifying subcomponent failure discloses
determining a first time series comprising a first set of data points (see ¶ 100, ¶ 124, ¶ 216, claim 2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Willemain to include the teaching of Sahaf in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more specific time data set, and enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Willemain discloses evaluate all possible change points can be considered a multiple comparison test (see ¶ 31).
Willemain and Sahaf do not explicitly the following limitations; however, Cao in an analogous art for detecting change points discloses
determining a first change point of the first time series based at least in part on a first relative position of the first change point in the first time series and a category of the first change point (see ¶ 56-57, ¶ 60-62, ¶ 95-97, ¶ 105, ¶ 145-148);
determining a second change point of the first time series based at least in part on a second relative position of the second change point in the first time series and a category of the second change point (see ¶ 56-57, ¶ 95-97, ¶ 105, ¶ 113-115, ¶ 146).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Willemain and in view of Sahaf to include teaching of Cao in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing computational efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 9, Willemain does not explicitly the following limitations; however, Sahaf discloses the computing system of claim 8, wherein determining the first change point of the first time series comprises:
determining a first confidence score for a first candidate change point (see ¶ 167, ¶ 179, ¶ 202);
determining a first relative position score for the first candidate change point based at least in part on the relative position of the first candidate change point in the first time series (see ¶ 39, ¶ 51-52, ¶ 57-59);
determining an average of the first confidence score, the first relative position score, and the first category score to generate a first overall score of the first candidate change point (see ¶ 39, ¶ 64, ¶ 117-118);
comparing the first overall score of the first candidate change point to a second overall score of a second candidate change point (see ¶ 27-29, ¶ 68, ¶ 115).
Willemain and Sahaf do not explicitly disclose the following limitations; however, Cao discloses
determining a first category score of the first candidate change point based at least in part on a first change point category (see ¶ 59-62, ¶ 113-116);
selecting the first candidate change point to be the first change point based at least in part on the comparison (see ¶ 105).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Willemain and in view of Sahaf to include teaching of Cao in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing computational efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 10, Willemain does not explicitly the following limitations; however, Sahaf discloses the computing system of claim 9, wherein the first overall score is normalized overall score with respect to a second overall score (see ¶ 68-69).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Willemain to include the teaching of Sahaf in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more specific time data, and enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 11, Willemain discloses the computing system of claim 8, wherein the instructions that, when executed by the one or more processors, further cause performance of operations comprising:
determining the first time series and the second time series by: selecting a data point of a third time series based at least in part on avoidance of a forecasting bias (see Fig. 2, #S3; ¶ 27); and
splitting the third time series into the first time series and the second time series based at least in part on the selected data point, the first time series comprising a training set of data points, the second time series comprising a testing set of data points (see Fig. 2, #S4; ¶ 8, ¶ 20, ¶ 26, claim 2).
Regarding claim 12, Willemain discloses the computing system of claim 8, wherein generating the first truncated time series comprises splitting the first time series at the first change point (see ¶ 7, ¶ 21).
Regarding claim 13, Willemain discloses the computing system of claim 8, wherein the first forecasting technique is selected, and wherein generating the final forecasted value comprises:
determining a final truncated time series based at least in part on combining the selected first truncated time series with the second time series (see ¶ 7, ¶ 25-27);
extracting input features from the final truncated time series (see ¶ 27, claim 17); and
forecasting the final forecasted value based at least in part on the input features (see ¶ 22, ¶ 44).
Regarding claim 14, Willemain discloses the computing system of claim 8, wherein the first forecasting technique comprises Prophet, autoregressive integrated moving average (ARIMA), deep learning-based forecasting, and machine learning-based forecasting (see ¶ 7, ¶ 42).
In addition, claim 7 merely describing the characteristics of the first forecasting technique is directed to nonfunctional descriptive material because they cannot exhibit any functional interrelationship with the way the steps are performed. Therefore, it has been held that nonfunctional descriptive material will not distinguish the invention from prior art in term of patentability. (In re Gulack, 217 USPQ 401 (Fed. Cir. 1983), In re Ngai, 70 USPQ2d (Fed. Cir. 2004), In re Lowry, 32 USPQ2d 1031 (Fed. Cir. 1994); MPEP 2111.05).
Regarding claim 15, Willemain discloses a non-transitory computer-readable medium including stored thereon a sequence of instructions that, when executed by one or more processors (see ¶ 26-28), causes performance of operations comprising:
generating a first truncated time series based at least in part on the first change point, the first truncated time series comprising a first subset of data points of the first time series ranging from the first change point to a youngest data point of the first time series (see ¶ 5-6, ¶ 21, ¶ ¶ 28);
generating a second truncated time series based at least in part on the second change point, the second truncated time series comprising a second subset of data points of the first time series ranging from the second change point to the youngest data point of the first time series (see ¶ 7, ¶ 25-27, ¶ 28);
generating a first forecasted value using a first forecasting technique and the first truncated time series (see ¶ 3, ¶ 21, ¶ 26);
generating a second forecasted value using a second forecasting technique and the second truncated time series (see ¶ 22, 42-44);
comparing the first forecasted value and the second forecasted value using a second time series (see ¶ 7-9, ¶ 22, ¶ 27, ¶ 40);
selecting the first forecasting technique or the second forecasting technique to generate a final forecasted value based at least in part on the comparison (see ¶ 24, ¶ 27, ¶ 42); and
based, at least in part, on the first truncated time series and the second truncated time series, generating, using the selected first forecasting technique or second forecasting technique, the final forecasted value (see ¶ 28, ¶ 42-44, ¶ 130).
Willemain discloses collecting time series data associated with resources and analyzing each of a plurality of time series to detect a change point occurred (see ¶ 7).
Willemain does not explicitly disclose a first time series and a second time series; however, Sahaf in an analogous art for identifying subcomponent failure discloses
determining a first time series comprising a first set of data points (see ¶ 100, ¶ 124, ¶ 216, claim 2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Willemain to include the teaching of Sahaf in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more specific time data set, and enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Willemain discloses evaluate all possible change points can be considered a multiple comparison test (see ¶ 31).
Willemain and Sahaf do not explicitly the following limitations; however, Cao in an analogous art for detecting change points discloses
determining a first change point of the first time series based at least in part on a first relative position of the first change point in the first time series and a category of the first change point (see ¶ 56-57, ¶ 60-62, ¶ 95-97, ¶ 105, ¶ 145-148);
determining a second change point of the first time series based at least in part on a second relative position of the second change point in the first time series and a category of the second change point (see ¶ 56-57, ¶ 95-97, ¶ 105, ¶ 113-115, ¶ 146).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Willemain and in view of Sahaf to include teaching of Cao in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing computational efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 16, Willemain does not explicitly the following limitations; however, Sahaf discloses the non-transitory computer-readable medium of claim 15, wherein determining the first change point of the first time series comprises:
determining a first confidence score for a first candidate change point (see ¶ 167, ¶ 179, ¶ 202);
determining a first relative position score for the first candidate change point based at least in part on the relative position of the first candidate change point in the first time series (see ¶ 39, ¶ 51-52, ¶ 57-59);
determining an average of the first confidence score, the first relative position score, and the first category score to generate a first overall score of the first candidate change point (see ¶ 39, ¶ 64, ¶ 117-118);
comparing the first overall score of the first candidate change point to a second overall score of a second candidate change point(see ¶ 27-29, ¶ 68, ¶ 115).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Willemain to include the teaching of Sahaf in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more specific time data, and enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Willemain and Sahaf do not explicitly disclose the following limitations; however, Cao discloses
determining a first category score of the first candidate change point based at least in part on a first change point category (see ¶ 59-62, ¶ 113-116);
selecting the first candidate change point to be the first change point based at least in part on the comparison (see ¶ 105).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Willemain and in view of Sahaf to include teaching of Cao in order to gain the commonly understood benefit of such adaption, such as providing the benefit of enhancing computational efficiency, in turn of operational efficiency. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 17, Willemain does not explicitly the following limitations; however, Sahaf discloses the non-transitory computer-readable medium of claim 16, wherein the first overall score is normalized overall score with respect to a second overall score (see ¶ 68-69).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Willemain to include the teaching of Sahaf in order to gain the commonly understood benefit of such adaption, such as providing the benefit of a more specific time data, and enabling better decision making. Since the combination of each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 18, Willemain discloses the non-transitory computer-readable medium of claim 15, wherein the instructions that, when executed by the one or more processors, further cause performance of operations comprising:
determining the first time series and the second time series by: selecting a data point of a third time series based at least in part on avoidance of a forecasting bias (see Fig. 2, #S3; ¶ 27); and
splitting the third time series into the first time series and the second time series based at least in part on the selected data point, the first time series comprising a training set of data points, the second time series comprising a testing set of data points (see Fig. 2, #S4; ¶ 8, ¶ 20, ¶ 26, claim 2).
Regarding claim 19, Willemain discloses the non-transitory computer-readable medium of claim 15, wherein generating the first truncated time series comprises splitting the first time series at the first change point (see ¶ 7, ¶ 21).
Regarding claim 20, Willemain discloses the non-transitory computer-readable medium of claim 15, wherein the first forecasting technique is selected, and wherein generating the final forecasted value comprises:
determining a final truncated time series based at least in part on combining the selected first truncated time series with the second time series (see ¶ 7, ¶ 25-27);
extracting input features from the final truncated time series (see ¶ 27, claim 17); and
forecasting the final forecasted value based at least in part on the input features (see ¶ 22, ¶ 44).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hehn et al., (US 2024/0354370) discloses a system for determining change point within received time series data include a first candidate change point comprising an earlier time and a first value, and a second candidate change point comprising a later time and a second value.
Nakamura, (WO 2022003969) discloses a data processing device comprising a time series acquisition unit for acquiring time series data, a feature extraction unit for extracting the feature amount of the time series data, a first division unit for dividing the time series data into a plurality of items of first partial time series data, and a second division unit for dividing each of the plurality of items of first partial time series data.
Xia et al., (CN 112651539) discloses an information processing apparatus for detecting a plurality of change points from the history characteristic data sequence of the object using a first data dividing unit, a first data partition unit, and a training unit configured to train the predictor of the object based on a plurality of data segments.
Xu et al., (US 11651271 B1) discloses a system for detecting a future change point in time series data used as input to a machine learning model to generate a forecast for the time series data and extract data features from individual segments in the time series data.
Lattanzi et al., “A change-point approach for the identification of financial extreme regimes”, Brazilian Journal of Probability and Statistics 2021, Vol. 35, No. 4, 811-837. Brazilian Statistical Association, 2021.
Plasse et al., “Multiple Changepoint detection in categorical data streams”, Department of Mathematics, Imperial College London, London, UK. Statistics and Computing (2019) 29:1109-1125. Published online: 15 February 2019.
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/PAN G CHOY/Primary Examiner, Art Unit 3624