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 .
This action is in response to the application filed 2/22/2023. Claims 1-20 are pending
and have been examined. Claims 1-20 are rejected.
Drawings
The drawings are objected to under 37 CFR 1.83(a) because they fail to show a storage 230 as described in the specification in paragraph [0028]. Any structural detail that is essential for a proper understanding of the disclosed invention should be shown in the drawing. MPEP § 608.02(d).
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
Paragraph [0001] recites: “Resources predicted may then be allocated prior to the actual demand arrives to avoid potential problems”. If consistent with the original specification, Examiner suggests rephrasing the sentence to say “Resources predicted may then be allocated prior to when the actual demand arrives to avoid potential problems”, or otherwise for grammatical correctness.
Paragraph [0012] recites: “Fig. 4C shows exemplary autocorrelation result obtained” Examiner suggests rephrasing the sentence to say “Fig. 4C shows an exemplary autocorrelation result obtained”, or otherwise for grammatical correctness.
Paragraph [0020] recites: “The ensemble model generator 120 generates an ensemble mode 130” [sic] “…generates an ensemble model 130”.
Paragraph [0028] recites: “the model type determiner 220 may designate, at 235, some non-linear forecast models in the storage 230 as candidate base forecast models”. There is no storage 230 shown in the figures. If consistent with the original specification, Examiner suggests rephrasing the sentence to say “the model type determiner 220 may designate, at 235, some non-linear forecast models from Forecast Models 230 in as candidate base forecast models”, or otherwise corrected.
Paragraph [0029] recites: “for predicting the time series data of the remaining 6 moths” [sic] “for predicting the time series data of the remaining 6 months”.
Appropriate correction is required.
Claim Objections
Claims 15-20 are objected to because of the following informalities:
Independent Claim 15 recites: “and a resource use data collectors implemented by a processor and configured for…”. If consistent with the original specification, Examiner suggests rephrasing the limitation to say “and a resource use data collector implemented by…” or “and resource use data collectors implemented by…, for grammatical correctness. In addition, Claims 16-20, which each dep directly or indirectly from claim 15, are objected to based on their respective dependencies from claim 15.
Appropriate correction is required.
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.
Claim 1-20 are rejected under 35 U.S.C. 101 because the claims are directed to an abstract idea without significantly more.
Regarding Independent Claim 1:
Step 1: The claim is directed to a method, corresponding to a process, which is one of the
statutory categories.
Step 2A, Prong 1: The following limitations are directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind or with pen and paper (including an observation, evaluation, judgement, or opinion).
determining characteristics of historic time series data associated with a resource provider;
selecting a plurality of base forecast models from available forecast models based on the characteristics of the historic time series data;
determining a set of parameters to be used for generating the ensemble forecast models based on the costs associated respectively with the plurality of base forecast models,
Regarding the “determining characteristics of historic time series data”, this determination can be associated with the mental process of observation and evaluation/judgement/opinion of data values from a historic time series dataset. Given a sufficiently small historic time series dataset, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Regarding the “selecting a plurality of base forecast models”, this selection can be associated with the mental process of observation of base forecast models that meet a criteria and evaluation/judgement/opinion to decide which models are chosen. Given a sufficiently small number of base forecast models and characteristics of historic time series dataset, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Regarding the “determining a set of parameters to be used for generating the ensemble forecast models”, this determination can be associated with the mental process of observation and evaluation/judgement/opinion of data values from a set of parameters that fit certain cost constraints of the plurality of base forecast models. Given a sufficiently small dataset of parameters and cost values, nothing in the claim prohibits this process from being
performed mentally or with a pen and paper.
The following limitation is directed to the abstract idea of a mathematical concept [see MPEP 2106.04(a)(2) I. C.]. In particular, the claim recites limitations that encompass mathematical concepts (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations –including a mathematical operation of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation).
by computing a cost associated with each of the plurality of base forecast models,
Regarding the “computing a cost”, this this computation operation, in light of the specification, encompasses the mathematical concept of algebraic functions associated with the cost values of the base forecast models (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations).
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to integrate the judicial exception into a practical application.
generating an ensemble forecast model based on the selected plurality of base forecast models
and creating the ensemble forecast model based on the plurality of base forecast models in accordance with the set of parameters;
forecasting a resource need associated with the resource provider using the ensemble model,
The following additional element can be considered as generally linking the use of judicial exception to a particular technological environment or field of use [See MPEP § 2106.05(h)]. Therefore, the additional element does not integrate the abstract ideas into a practical application.
wherein the forecasted resource need is for allocating a resource to the resource provider;
The following additional elements add insignificant extra-solution activities (necessary data gathering and data storage) to the judicial exception [see MPEP 2106.05(g)].
collecting resource usage data associated with the resource provider;
and adding the resource usage data to the historic time series data
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
The following additional elements are directed to storing and retrieving information in memory. The courts (Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) have recognized storing and retrieving information in memory as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity to the judicial exception [see MPEP 2106.05(d) IV.].
collecting resource usage data associated with the resource provider;
and adding the resource usage data to the historic time series data
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations amount to no more than using generic computer components to implement the exception. Implementing the abstract idea by merely applying it using generic computer components, without more, does not amount to an inventive concept. Additionally, limiting the abstract idea to a particular technological context or field of use, does not render the claim patent eligible. Therefore, claim 1 is not patent eligible.
Regarding Claim 2:
Step 1: The claim is directed to the method of claim 1.
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 1.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional element can be considered as generally linking the use of judicial exception to a particular technological environment or field of use [See MPEP § 2106.05(h)]. Therefore, the additional element does not integrate the abstract ideas into a practical application.
wherein the characteristics of the historic time series data include seasonality or lack thereof exhibited in the historic time series data
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitation amounts to no more than using generic computer components to implement the exception. Limiting the abstract idea to a particular technological context or field of use, does not render the claim patent eligible. Therefore, claim 2 is not patent eligible.
Regarding Claim 3:
Step 1: The claim is directed to the method of claim 2.
Step 2A, Prong 1: The following limitations are directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind or with pen and paper (including an observation, evaluation, judgement, or opinion).
determining whether the historic time series data exhibits seasonality;
and identifying the plurality of base forecast models from the multiple candidate base forecast models based on forecast performance of each of the multiple candidate base forecast models
designating multiple candidate base forecast models from the available forecast models based on whether the historic time series data exhibits seasonality;
Regarding the “determining whether the historic time series data exhibits seasonality”, this determination can be associated with the mental process of observation and evaluation/judgement/opinion of data. Given a sufficiently small historic time series dataset, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Regarding the “identifying the plurality of base forecast models… based on forecast performance… of the… models”, this identifying can be associated with the mental process of observation of model performance values. Given a sufficiently small set of base forecast models and their respective performance datasets, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Regarding the “designating multiple candidate base forecast models… based on whether the historic time series data exhibits seasonality”, this designation can be associated with the mental process of observing data exhibiting seasonality and judgment/evaluation for labeling/classifying said models accordingly. Given a sufficiently small set of candidate base forecast models and their respective historic time series datasets, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding Claim 4:
Step 1: The claim is directed to the method of claim 3.
Step 2A, Prong 1: The following limitation is directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind or with pen and paper (including an observation, evaluation, judgement, or opinion).
and determining whether the historic time series data exhibits seasonality based on the auto-correlation results
Regarding the “determining whether the historic time series data exhibits seasonality”, this determination can be associated with the mental process of judgement/opinion of time series data. Given a sufficiently small historic time series dataset and set of auto-correlation results, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
The following limitation is directed to the abstract idea of a mathematical concept [see MPEP 2106.04(a)(2) I. C.]. In particular, the claim recites limitations that encompass mathematical concepts (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations –including a mathematical operation of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation).
wherein the determining whether the historic time series data exhibits seasonality comprises: performing linear regression on smoothed historic time series data to generate linear regression result;
Regarding the “determining whether the historic time series data exhibits seasonality comprises…” limitation, the ‘determining’ encompasses a mental process based on a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations). This determination can be associated with the mental process of observation and evaluation/judgement/opinion of the exhibition of seasonality in time series data based on a linear regression result.
Regarding the “performing linear regression on smoothed historic time series data”, this linear regression operation, in light of the specification, encompasses the mathematical concept of calculus/linear algebra functions (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations).
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to integrate the judicial exception into a practical application.
generating detrended historic time series data based on the smoothed historic time series data and the linear regression result;
performing auto-correlation on the smoothed time series data and the detrended historic time series data to generate auto-correlation results;
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations amount to no more than using generic computer components to implement the exception. Implementing the abstract idea by merely applying it using generic computer components, without more, does not amount to an inventive concept. Therefore, claim 4 is not patent eligible.
Regarding Claim 5:
Step 1: The claim is directed to the method of claim 4.
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 4.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements can be considered as generally linking the use of judicial exception to a particular technological environment or field of use [See MPEP § 2106.05(h)]. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
wherein the auto-correlation results include: a first auto-correlation metric obtained via auto-correlation on the smoothed historic time series data;
and a second auto-correlation metric obtained via auto-correlation on the detrended historic time series data
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations amount to no more than using generic computer components to implement the exception. Limiting the abstract idea to a particular technological context or field of use, does not render the claim patent eligible. Therefore, claim 5 is not patent eligible.
Regarding Claim 6:
Step 1: The claim is directed to the method of claim 3.
Step 2A, Prong 1: The following limitation is directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind or with pen and paper (including an observation, evaluation, judgement, or opinion).
selecting the plurality of base forecast models from the multiple candidate base forecast models based on the measures associated respectively with the multiple candidate base forecast models
Regarding the “selecting the plurality of base forecast models… based on the measures associated with the multiple candidate base forecast models”, this selection process can be associated with the mental process of observation of candidate base forecast model measurements and judgment/evaluation for selecting them based on a criteria. Given a sufficiently small number of base forecast models and their associated measurement datasets, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
The following limitation is directed to the abstract idea of a mathematical concept [see MPEP 2106.04(a)(2) I. C.]. In particular, the claim recites limitations that encompass mathematical concepts (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations –including a mathematical operation of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation).
computing a measure indicative of the performance of the candidate base forecast model based on the forecast result;
Regarding the “computing a measure indicative of the performance of the candidate base forecast” this computation operation, under the BRI, in light of the specification (see, e.g., paragraph [0029]), encompasses the mathematical concept of calculus/statistical functions (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations).
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to integrate the judicial exception into a practical application.
generating a forecast result based on the historic time series data using the candidate base forecast model,
The following additional element can be considered as generally linking the use of judicial exception to a particular technological environment or field of use [See MPEP § 2106.05(h)]. Therefore, the additional element does not integrate the abstract ideas into a practical application.
with respect to each of the multiple candidate base forecast models,
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations amount to no more than using generic computer components to implement the exception. Implementing the abstract idea by merely applying it using generic computer components, without more, does not amount to an inventive concept. Additionally, limiting the abstract idea to a particular technological context or field of use, does not render the claim patent eligible. Therefore, claim 6 is not patent eligible.
Regarding Claim 7:
Step 1: The claim is directed to the method of claim 1.
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 1.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements can be considered as generally linking the use of judicial exception to a particular technological environment or field of use [See MPEP § 2106.05(h)]. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
wherein the ensemble forecast model corresponds to a weighted sum of the plurality of base forecast models,
wherein the set of parameters correspond to weights to be applied to the respective base forecast models
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations amount to no more than using generic computer components to implement the exception. Limiting the abstract idea to a particular technological context or field of use, does not render the claim patent eligible. Therefore, claim 7 is not patent eligible.
Regarding Independent Claim 8:
Step 1: The claim is directed to a machine readable and non-transitory medium, corresponding to an article of manufacture, which is one of the statutory categories.
Step 2A, Prong 1: The following limitations are directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind or with pen and paper (including an observation, evaluation, judgement, or opinion).
determining characteristics of historic time series data associated with a resource provider;
selecting a plurality of base forecast models from available forecast models based on the characteristics of the historic time series data;
determining a set of parameters to be used for generating the ensemble forecast models based on the costs associated respectively with the plurality of base forecast models,
Regarding the “determining characteristics of historic time series data”, this determination can be associated with the mental process of observation and evaluation/judgement/opinion of data values from a historic time series dataset. Given a sufficiently small historic time series dataset, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Regarding the “selecting a plurality of base forecast models”, this selection can be associated with the mental process of observation of base forecast models that meet a criteria and evaluation/judgement/opinion to decide which models are chosen. Given a sufficiently small number of base forecast models and characteristics of historic time series dataset, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Regarding the “determining a set of parameters to be used for generating the ensemble forecast models”, this determination can be associated with the mental process of observation and evaluation/judgement/opinion of data values from a set of parameters that fit certain cost constraints of the plurality of base forecast models. Given a sufficiently small dataset of parameters and cost values, nothing in the claim prohibits this process from being
performed mentally or with a pen and paper.
The following limitation is directed to the abstract idea of a mathematical concept [see MPEP 2106.04(a)(2) I. C.]. In particular, the claim recites limitations that encompass mathematical concepts (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations –including a mathematical operation of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation).
by computing a cost associated with each of the plurality of base forecast models,
Regarding the “computing a cost”, this this computation operation, in light of the specification, encompasses the mathematical concept of algebraic functions associated with the cost values of the base forecast models (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations).
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to integrate the judicial exception into a practical application.
generating an ensemble forecast model based on the selected plurality of base forecast models
and creating the ensemble forecast model based on the plurality of base forecast models in accordance with the set of parameters;
forecasting a resource need associated with the resource provider using the ensemble model,
The following additional element can be considered as generally linking the use of judicial exception to a particular technological environment or field of use [See MPEP § 2106.05(h)]. Therefore, the additional element does not integrate the abstract ideas into a practical application.
wherein the forecasted resource need is for allocating a resource to the resource provider;
The following additional elements add insignificant extra-solution activities (necessary data gathering and data storage) to the judicial exception [see MPEP 2106.05(g)].
collecting resource usage data associated with the resource provider;
and adding the resource usage data to the historic time series data
Claim 8 recites the additional element: “A machine readable and non-transitory medium having information recorded thereon, wherein the information, when read by the machine, causes the machine to perform the following steps:”, which is recited at a high level of generality as mere instructions to implement an abstract idea on a computer (i.e., a system, content repository, and a processor), or merely use a computer as a tool to perform an abstract idea (i.e., as generic computer components performing generic computer functions). See MPEP 2106.05(f).
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
The following additional elements are directed to storing and retrieving information in memory. The courts (Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) have recognized storing and retrieving information in memory as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity to the judicial exception [see MPEP 2106.05(d) IV.].
collecting resource usage data associated with the resource provider;
and adding the resource usage data to the historic time series data
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations amount to no more than using generic computer components to implement the exception. Implementing the abstract idea by merely applying it using generic computer components, without more, does not amount to an inventive concept. Additionally, limiting the abstract idea to a particular technological context or field of use, does not render the claim patent eligible. Therefore, claim 8 is not patent eligible.
Regarding Claim 9:
Step 1: The claim is directed to the machine readable and non-transitory medium of claim 8.
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 8.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional element can be considered as generally linking the use of judicial exception to a particular technological environment or field of use [See MPEP § 2106.05(h)]. Therefore, the additional element does not integrate the abstract ideas into a practical application.
wherein the characteristics of the historic time series data include seasonality or lack thereof exhibited in the historic time series data
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitation amounts to no more than using generic computer components to implement the exception. Limiting the abstract idea to a particular technological context or field of use, does not render the claim patent eligible. Therefore, claim 9 is not patent eligible.
Regarding Claim 10:
Step 1: The claim is directed to the machine readable and non-transitory medium of claim 9.
Step 2A, Prong 1: The following limitations are directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind or with pen and paper (including an observation, evaluation, judgement, or opinion).
determining whether the historic time series data exhibits seasonality;
and identifying the plurality of base forecast models from the multiple candidate base forecast models based on forecast performance of each of the multiple candidate base forecast models
designating multiple candidate base forecast models from the available forecast models based on whether the historic time series data exhibits seasonality;
Regarding the “determining whether the historic time series data exhibits seasonality”, this determination can be associated with the mental process of observation and evaluation/judgement/opinion of data. Given a sufficiently small historic time series dataset, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Regarding the “identifying the plurality of base forecast models… based on forecast performance… of the… models”, this identifying can be associated with the mental process of observation of model performance values. Given a sufficiently small set of base forecast models and their respective performance datasets, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Regarding the “designating multiple candidate base forecast models… based on whether the historic time series data exhibits seasonality”, this designation can be associated with the mental process of observing data exhibiting seasonality and judgment/evaluation for labeling/classifying said models accordingly. Given a sufficiently small set of candidate base forecast models and their respective historic time series datasets, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding Claim 11:
Step 1: The claim is directed to the machine readable and non-transitory medium of claim 10.
Step 2A, Prong 1: The following limitation is directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind or with pen and paper (including an observation, evaluation, judgement, or opinion).
and determining whether the historic time series data exhibits seasonality based on the auto-correlation results
Regarding the “determining whether the historic time series data exhibits seasonality”, this determination can be associated with the mental process of judgement/opinion of time series data. Given a sufficiently small historic time series dataset and set of auto-correlation results, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
The following limitation is directed to the abstract idea of a mathematical concept [see MPEP 2106.04(a)(2) I. C.]. In particular, the claim recites limitations that encompass mathematical concepts (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations –including a mathematical operation of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation).
wherein the determining whether the historic time series data exhibits seasonality comprises: performing linear regression on smoothed historic time series data to generate linear regression result;
Regarding the “determining whether the historic time series data exhibits seasonality comprises…” limitation, the ‘determining’ encompasses a mental process based on a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations). This determination can be associated with the mental process of observation and evaluation/judgement/opinion of the exhibition of seasonality in time series data based on a linear regression result.
Regarding the “performing linear regression on smoothed historic time series data”, this linear regression operation, in light of the specification, encompasses the mathematical concept of calculus/linear algebra functions (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations).
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to integrate the judicial exception into a practical application.
generating detrended historic time series data based on the smoothed historic time series data and the linear regression result;
performing auto-correlation on the smoothed time series data and the detrended historic time series data to generate auto-correlation results;
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations amount to no more than using generic computer components to implement the exception. Implementing the abstract idea by merely applying it using generic computer components, without more, does not amount to an inventive concept. Therefore, claim 11 is not patent eligible.
Regarding Claim 12:
Step 1: The claim is directed to the machine readable and non-transitory medium of claim 11.
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 11.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements can be considered as generally linking the use of judicial exception to a particular technological environment or field of use [See MPEP § 2106.05(h)]. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
wherein the auto-correlation results include: a first auto-correlation metric obtained via auto-correlation on the smoothed historic time series data;
and a second auto-correlation metric obtained via auto-correlation on the detrended historic time series data
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations amount to no more than using generic computer components to implement the exception. Limiting the abstract idea to a particular technological context or field of use, does not render the claim patent eligible. Therefore, claim 12 is not patent eligible.
Regarding Claim 13:
Step 1: The claim is directed to the machine readable and non-transitory medium of claim 10.
Step 2A, Prong 1: The following limitation is directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind or with pen and paper (including an observation, evaluation, judgement, or opinion).
selecting the plurality of base forecast models from the multiple candidate base forecast models based on the measures associated respectively with the multiple candidate base forecast models
Regarding the “selecting the plurality of base forecast models… based on the measures associated with the multiple candidate base forecast models”, this selection process can be associated with the mental process of observation of candidate base forecast model measurements and judgment/evaluation for selecting them based on a criteria. Given a sufficiently small number of base forecast models and their associated measurement datasets, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
The following limitation is directed to the abstract idea of a mathematical concept [see MPEP 2106.04(a)(2) I. C.]. In particular, the claim recites limitations that encompass mathematical concepts (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations –including a mathematical operation of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation).
computing a measure indicative of the performance of the candidate base forecast model based on the forecast result;
Regarding the “computing a measure indicative of the performance of the candidate base forecast” this computation operation, under the BRI, in light of the specification (see, e.g., paragraph [0029]), encompasses the mathematical concept of calculus/statistical functions (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations). Given a sufficiently dataset of forecast model performance values, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to integrate the judicial exception into a practical application.
generating a forecast result based on the historic time series data using the candidate base forecast model,
The following additional element can be considered as generally linking the use of judicial exception to a particular technological environment or field of use [See MPEP § 2106.05(h)]. Therefore, the additional element does not integrate the abstract ideas into a practical application.
with respect to each of the multiple candidate base forecast models,
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations amount to no more than using generic computer components to implement the exception. Implementing the abstract idea by merely applying it using generic computer components, without more, does not amount to an inventive concept. Additionally, limiting the abstract idea to a particular technological context or field of use, does not render the claim patent eligible. Therefore, claim 13 is not patent eligible.
Regarding Claim 14:
Step 1: The claim is directed to the machine readable and non-transitory medium of claim 8.
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 8.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements can be considered as generally linking the use of judicial exception to a particular technological environment or field of use [See MPEP § 2106.05(h)]. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
wherein the ensemble forecast model corresponds to a weighted sum of the plurality of base forecast models,
wherein the set of parameters correspond to weights to be applied to the respective base forecast models
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations amount to no more than using generic computer components to implement the exception. Limiting the abstract idea to a particular technological context or field of use, does not render the claim patent eligible. Therefore, claim 14 is not patent eligible.
Regarding Independent Claim 15:
Step 1: The claim is directed to a system, corresponding to a machine, which is one of the statutory categories.
Step 2A, Prong 1: The following limitations are directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind or with pen and paper (including an observation, evaluation, judgement, or opinion).
determining characteristics of historic time series data associated with a resource provider;
selecting a plurality of base forecast models from available forecast models based on the characteristics of the historic time series data;
determining a set of parameters to be used for generating the ensemble forecast models based on the costs associated respectively with the plurality of base forecast models,
Regarding the “determining characteristics of historic time series data”, this determination can be associated with the mental process of observation and evaluation/judgement/opinion of data values from a historic time series dataset. Given a sufficiently small historic time series dataset, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Regarding the “selecting a plurality of base forecast models”, this selection can be associated with the mental process of observation of base forecast models that meet a criteria and evaluation/judgement/opinion to decide which models are chosen. Given a sufficiently small number of base forecast models and characteristics of historic time series dataset, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Regarding the “determining a set of parameters to be used for generating the ensemble forecast models”, this determination can be associated with the mental process of observation and evaluation/judgement/opinion of data values from a set of parameters that fit certain cost constraints of the plurality of base forecast models. Given a sufficiently small dataset of parameters and cost values, nothing in the claim prohibits this process from being
performed mentally or with a pen and paper.
The following limitation is directed to the abstract idea of a mathematical concept [see MPEP 2106.04(a)(2) I. C.]. In particular, the claim recites limitations that encompass mathematical concepts (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations –including a mathematical operation of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation).
by computing a cost associated with each of the plurality of base forecast models,
Regarding the “computing a cost”, this this computation operation, in light of the specification, encompasses the mathematical concept of algebraic functions associated with the cost values of the base forecast models (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations).
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to integrate the judicial exception into a practical application.
generating an ensemble forecast model based on the selected plurality of base forecast models
and creating the ensemble forecast model based on the plurality of base forecast models in accordance with the set of parameters;
forecasting a resource need associated with the resource provider using the ensemble model,
The following additional element can be considered as generally linking the use of judicial exception to a particular technological environment or field of use [See MPEP § 2106.05(h)]. Therefore, the additional element does not integrate the abstract ideas into a practical application.
wherein the forecasted resource need is for allocating a resource to the resource provider;
The following additional elements add insignificant extra-solution activities (necessary data gathering and data storage) to the judicial exception [see MPEP 2106.05(g)].
collecting resource usage data associated with the resource provider;
and adding the resource usage data to the historic time series data
Claim 15 recites the additional element: “A system, comprising: a data preprocessor implemented by a processor and configured for…”, which is recited at a high level of generality as mere instructions to implement an abstract idea on a computer (i.e., a system, content repository, and a processor), or merely use a computer as a tool to perform an abstract idea (i.e., as generic computer components performing generic computer functions). See MPEP 2106.05(f).
Step 2B: There are no additional elements in this claim that amount to significantly more than the judicial exception.
The following additional elements are directed to storing and retrieving information in memory. The courts (Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)) have recognized storing and retrieving information in memory as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity to the judicial exception [see MPEP 2106.05(d) IV.].
collecting resource usage data associated with the resource provider;
and adding the resource usage data to the historic time series data
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations amount to no more than using generic computer components to implement the exception. Implementing the abstract idea by merely applying it using generic computer components, without more, does not amount to an inventive concept. Additionally, limiting the abstract idea to a particular technological context or field of use, does not render the claim patent eligible. Therefore, claim 15 is not patent eligible.
Regarding Claim 16:
Step 1: The claim is directed to the system of claim 15.
Step 2A, Prong 1: The following limitations are directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind or with pen and paper (including an observation, evaluation, judgement, or opinion).
determining whether the historic time series data exhibits seasonality;
and identifying the plurality of base forecast models from the multiple candidate base forecast models based on forecast performance of each of the multiple candidate base forecast models
designating multiple candidate base forecast models from the available forecast models based on whether the historic time series data exhibits seasonality;
Regarding the “determining whether the historic time series data exhibits seasonality”, this determination can be associated with the mental process of observation and evaluation/judgement/opinion of data. Given a sufficiently small historic time series dataset, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Regarding the “identifying the plurality of base forecast models… based on forecast performance… of the… models”, this identifying can be associated with the mental process of observation of model performance values. Given a sufficiently small set of base forecast models and their respective performance datasets, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Regarding the “designating multiple candidate base forecast models… based on whether the historic time series data exhibits seasonality”, this designation can be associated with the mental process of observing data exhibiting seasonality and judgment/evaluation for labeling/classifying said models accordingly. Given a sufficiently small set of candidate base forecast models and their respective historic time series datasets, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding Claim 17:
Step 1: The claim is directed to the system of claim 16.
Step 2A, Prong 1: The following limitation is directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind or with pen and paper (including an observation, evaluation, judgement, or opinion).
and determining whether the historic time series data exhibits seasonality based on the auto-correlation results
Regarding the “determining whether the historic time series data exhibits seasonality”, this determination can be associated with the mental process of judgement/opinion of time series data. Given a sufficiently small historic time series dataset and set of auto-correlation results, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
The following limitation is directed to the abstract idea of a mathematical concept [see MPEP 2106.04(a)(2) I. C.]. In particular, the claim recites mathematical processes that can be performed in the human mind or with pen and paper (including an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation).
wherein the determining whether the historic time series data exhibits seasonality comprises: performing linear regression on smoothed historic time series data to generate linear regression result;
Regarding the “determining whether the historic time series data exhibits seasonality comprises…” limitation, the ‘determining’ encompasses a mental process based on a mathematical concept (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations). This determination can be associated with the mental process of observation and evaluation/judgement/opinion of the exhibition of seasonality in time series data based on a linear regression result.
Regarding the “performing linear regression on smoothed historic time series data”, this linear regression operation, in light of the specification, encompasses the mathematical concept of calculus/linear algebra functions (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations).
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to integrate the judicial exception into a practical application.
generating detrended historic time series data based on the smoothed historic time series data and the linear regression result;
performing auto-correlation on the smoothed time series data and the detrended historic time series data to generate auto-correlation results;
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations amount to no more than using generic computer components to implement the exception. Implementing the abstract idea by merely applying it using generic computer components, without more, does not amount to an inventive concept. Therefore, claim 17 is not patent eligible.
Regarding Claim 18:
Step 1: The claim is directed to the system of claim 17.
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 17.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements can be considered as generally linking the use of judicial exception to a particular technological environment or field of use [See MPEP § 2106.05(h)]. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
wherein the auto-correlation results include: a first auto-correlation metric obtained via auto-correlation on the smoothed historic time series data;
and a second auto-correlation metric obtained via auto-correlation on the detrended historic time series data
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations amount to no more than using generic computer components to implement the exception. Limiting the abstract idea to a particular technological context or field of use, does not render the claim patent eligible. Therefore, claim 18 is not patent eligible.
Regarding Claim 19:
Step 1: The claim is directed to the system of claim 16.
Step 2A, Prong 1: The following limitation is directed to the abstract idea of a mental process [see MPEP 2106.04(a)(2) III. C.]. In particular, the claim recites mental processes that are concepts performed in the human mind or with pen and paper (including an observation, evaluation, judgement, or opinion).
selecting the plurality of base forecast models from the multiple candidate base forecast models based on the measures associated respectively with the multiple candidate base forecast models
Regarding the “selecting the plurality of base forecast models… based on the measures associated with the multiple candidate base forecast models”, this selection process can be associated with the mental process of observation of candidate base forecast model measurements and judgment/evaluation for selecting them based on a criteria. Given a sufficiently small number of base forecast models and their associated measurement datasets, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
The following limitation is directed to the abstract idea of a mathematical concept [see MPEP 2106.04(a)(2) I. C.]. In particular, the claim recites mathematical processes that can be performed in the human mind or with pen and paper (including an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation).
computing a measure indicative of the performance of the candidate base forecast model based on the forecast result;
Regarding the “computing a measure indicative of the performance of the candidate base forecast” this computation operation, under the BRI, in light of the specification (see, e.g., paragraph [0029]), encompasses the mathematical concept of calculus/statistical functions (i.e., mathematical relationships, mathematical formulas or equations, and mathematical calculations). Given a sufficiently dataset of forecast model performance values, nothing in the claim prohibits this process from being performed mentally or with a pen and paper.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements are adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea [see MPEP 2106.05(f)] and therefore fails to integrate the judicial exception into a practical application.
generating a forecast result based on the historic time series data using the candidate base forecast model,
The following additional element can be considered as generally linking the use of judicial exception to a particular technological environment or field of use [See MPEP § 2106.05(h)]. Therefore, the additional element does not integrate the abstract ideas into a practical application.
with respect to each of the multiple candidate base forecast models,
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations amount to no more than using generic computer components to implement the exception. Implementing the abstract idea by merely applying it using generic computer components, without more, does not amount to an inventive concept. Additionally, limiting the abstract idea to a particular technological context or field of use, does not render the claim patent eligible. Therefore, claim 19 is not patent eligible.
Regarding Claim 20:
Step 1: The claim is directed to the system of claim 15.
Step 2A, Prong 1: The claim recites the same abstract ideas as in claim 15.
Step 2A, Prong 2: There are no additional elements in this claim that integrate the judicial exception into a practical application.
The following additional elements can be considered as generally linking the use of judicial exception to a particular technological environment or field of use [See MPEP § 2106.05(h)]. Therefore, the additional elements do not integrate the abstract ideas into a practical application.
wherein the ensemble forecast model corresponds to a weighted sum of the plurality of base forecast models,
wherein the set of parameters correspond to weights to be applied to the respective base forecast models
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations amount to no more than using generic computer components to implement the exception. Limiting the abstract idea to a particular technological context or field of use, does not render the claim patent eligible. Therefore, claim 20 is not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 7-9, 14-15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Narayanaswamy (US 20150253463 A1; hereinafter Nara.) in view of Wu (US 20220004941 A1; hereinafter Wu).
Regarding Independent Claim 1, Nara. teaches A method, comprising… selecting a plurality of base forecast models from available forecast models based on the characteristics of the historic time series data (see e.g., Nara. paragraphs [0014-0015]: "FIG. 1 depicts a model recommendation system 106, which includes an event classifier component 108, a model and parameter selection engine 110, and an estimation component 112 which includes an accuracy estimation engine 114 and a resource and cost estimation engine 116. As illustrated in FIG. 1" [i.e., the model recommendation system functions perform parameter selection], paragraph [0015]: "Additionally, the estimation component 112 receives input in the form of models and parameters 118 as well as relevant historical data 120. The parameters noted via component 118… " [i.e., available models are fed to the estimation component which estimates accuracy and resource costs using estimation engines 114 and 116 as well as historical data 120], paragraphs [0021-0022]: “At least one embodiment of the invention also includes extracting a time series of vector values of a different set of (possibly overlapping) variables prior to a given event… During run-time, the SVM takes inputs at each time step of the same time series of historical values” [i.e., time series data (historic time series data) characteristics are processed by the model recommendation system], paragraph [0031]: "In further reference to FIG. 1, and as detailed herein, the model and parameter selection engine 110 determines, based on the identified event (from the event classifier 108), the estimated accuracy for the event (from the accuracy estimation engine 114) and the estimated resource requirement and cost (from the resource and cost estimation engine 116), an ensemble of models and parameterizations that are suited for the given event, as well as the availability of budget and/or computational resources" [i.e., the model and parameter selection engine coupled in the model recommendation system determines/selects the ensemble of models based on the optimally estimated models and parameters from the associated estimation components 114 and 116] and paragraph [0035]: "Step 208 includes determining an ensemble of one or more of the multiple forecasting models to apply to the given environmental event based on… (ii) said cost and said one or more resource requirements for each of the multiple forecasting models" [i.e., forecasting models are used in the selection/generation of the ensemble forecast model]);
generating an ensemble forecast model based on the selected plurality of base forecast models by computing a cost associated with each of the plurality of base forecast models (see, e.g., Nara. paragraph [0006]: "In one aspect of the present invention, techniques for generating an ensemble of forecasting models are provided", paragraph [0016]: "the resource and cost estimation engine 116 provide input to the model and parameter selection engine 110, which also receives input in the form of budget information, computational resource information, and cost information 104" and paragraph [0031]: "In further reference to FIG. 1, and as detailed herein, the model and parameter selection engine 110 determines, based on… an ensemble of models and parameterizations that are suited for the given event, as well as the availability of budget and/or computational resources" [i.e., the model and parameter selection engine generates an ensemble forecast model based on computed costs associated with the forecasting models it receives]),
determining a set of parameters to be used for generating the ensemble forecast models based on the costs associated respectively with the plurality of base forecast models (see, e.g., Nara. paragraph [0024]: "The resource and cost estimation engine 116, as depicted in FIG. 1, can be used to decide on the feasibility of running a configuration based on user-specified constraints. Examples of user-specified constraints can include the available number of processors, total memory available, model output required in under two hours, etc. Based on historical data pertaining, for example, to computational run-times, memory requirements, and hardware resource information, at least one embodiment of the invention includes creating models for hardware configuration and/or computation time/memory cost for a particular model configuration… This can include categorical features corresponding to various parameters such as physics options, cumulus parameterization, etc., or valued features such as number of grid points, time-step used, clock frequency of the processors, etc." and paragraph [0026]: "The model and parameter selection engine 110 determines the final set of model configurations for a particular event. This selection depends on multiple factors such as socio-economic cost associated with the event and the lead times required for an accurate forecast, as well as the user-specified constraints on cost and computational resource availability. As noted, the resource and cost estimation engine 116 computes the computational time and/or memory and resource requirements for each model configuration" [i.e., a set of model configuration parameters associated with the constraints on operating costs and resources are determined by the resource and cost estimation engine, which feeds the parameters into the model and parameter selection engine, for generating the ensemble forecast models]),
and creating the ensemble forecast model based on the plurality of base forecast models in accordance with the set of parameters (see, e.g., Nara. paragraph [0031]: "In further reference to FIG. 1, and as detailed herein, the model and parameter selection engine 110 determines, based on… the estimated resource requirement and cost (from the resource and cost estimation engine 116), an ensemble of models and parameterizations that are suited for the given event, as well as the availability of budget and/or computational resources" [i.e., the parameters from the resource and cost estimation engine are fed into the model and the parameter selection engine to generate an ensemble forecast model] and paragraph [0035]: "Step 208 includes determining an ensemble of one or more of the multiple forecasting models to apply to the given environmental event based on (i) said estimated accuracy value for each of the multiple forecasting models, and (ii) said cost and said one or more resource requirements for each of the multiple forecasting models" [i.e., forecasting models are used in the selection/generation of the ensemble forecast model]);
Although Nara. substantially teaches the claimed invention, Nara. is not relied upon to explicitly teach the limitation determining characteristics of historic time series data associated with a resource provider;
forecasting a resource need associated with the resource provider using the ensemble model,
wherein the forecasted resource need is for allocating a resource to the resource provider;
collecting resource usage data associated with the resource provider,
and adding the resource usage data to the historic time series data
In the same field, analogous art Wu teaches determining characteristics of historic time series data associated with a resource provider (see, e.g., Wu paragraph [0036]: “In the case of electric load forecasting, the input layer 202 may receive historical load consumption (e.g., the lagged load consumption data for the last three hours)” [i.e., historical load consumption used for electric load forecasting is a form of historic time series data associated with a resource (electricity) provider]);
forecasting a resource need associated with the resource provider using the ensemble model (see, e.g., Wu paragraph [0031]: "The server computer 114 may also operate in a cloud computing service model, such as Software as a Service (SaaS), Platform as a Service (PaaS), or Infrastructure as a Service (IaaS), as discussed below with respect to FIGS. 5 and 6. The server computer 114 may also be located in a cloud computing deployment model, such as a private cloud, community cloud, public cloud, or hybrid cloud" and paragraphs [0043-0044]: "At 306, the method 3001 includes determining an ensemble model from the learned set of forecasting models based on gradient boosting… At 308, the method 300 includes allocating an available resource based on the ensemble model. We can use the forecasting results to guide the allocate the same resource allocation, e.g., allocate the same resource that can meet the requirements of predicted results. The available resource may correspond to an electric load, a network bandwidth, and a transportation route" [i.e., the ensemble model forecasts a resource need (electric load, network bandwidth, transportation route) functioning as an ensemble model based forecaster]),
wherein the forecasted resource need is for allocating a resource to the resource provider (see, e.g., Wu paragraph [0031]: "The server computer 114 may also operate in a cloud computing service model, such as Software as a Service (SaaS), Platform as a Service (PaaS), or Infrastructure as a Service (IaaS)", paragraph [0044]: "At 308, the method 3002 includes allocating an available resource based on the ensemble model. We can use the forecasting results to guide the allocate the same resource allocation, e.g., allocate the same resource that can meet the requirements of predicted results. The available resource may correspond to an electric load, a network bandwidth, and a transportation route" and paragraph [0051]: "The software program 108 (FIG. 1) and the Load Forecasting Program 116 (FIG. 1) on the server computer 114 (FIG. 1) can be downloaded to the computer 102 (FIG. 1) and server computer 114 from an external computer via a network (for example, the Internet, a local area network or other, wide area network) and respective network adapters or interfaces 836" [i.e., the Load Forecasting Program of Fig. 3 that performs the method 300 is implemented on a server computer 114 a resource/cloud provider and allocates resources of the electric load, a network bandwidth, and transportation route of said provider]);
collecting resource usage data associated with the resource provider (see, e.g., Wu paragraph [0031]: "The server computer 114 may also operate in a cloud computing3 service model, such as Software as a Service (SaaS), Platform as a Service (PaaS), or Infrastructure as a Service (IaaS)" and paragraph [0060]: "Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the utilized service" [i.e., resource usage is collected for transparent reporting to the resource provider]);
and adding the resource usage data to the historic time series data (see, e.g., Wu paragraph [0036]: "Referring now to FIG. 2, an exemplary load forecasting system 200 according to one or more embodiments is depicted. The load forecasting system 2004 may include, among other things, an input layer 202… the input layer 202 may receive historical load consumption (e.g., the lagged load consumption data for the last three hours), historical temperature information (e.g., the lagged temperature data for the last three 166 hours), and the corresponding weekday/weekend information (e.g., a binary variable set to 1 for weekday and 0 for weekend)" [i.e., the load forecasting system uses historical load consumption (historic time series data)] and paragraph [0060]: "Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the utilized service" [i.e., resource usage is collected for transparent reporting to the resource provider]).
Nara. and Wu are analogous art because they are both directed to ensemble forecast models (see, e.g., Nara., paragraph [0004], Wu, paragraph [0027]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Nara. to incorporate the teachings of Wu to use a method that determines characteristics of historic time series data associated with a resource provider for forecasting a resource need associated with the resource provider using the ensemble model, such that the need for the forecasted resource is to allocate a resource to the resource provider, collecting resource usage data associated with the resource provider, and finally adding the resource usage data to the historic time series data. Doing so would have allowed Nara. to use Wu’s system for “improving the load forecasting accuracy, [so] even a small utility company, could save hundreds of thousands of dollars annually in operation costs, and many millions for larger utility companies”, as suggested by Wu (see, e.g., Wu, paragraph [0016]).
Regarding Claim 2, as discussed above, Nara. in view of Wu teaches the method of claim 1.
Nara. further teaches wherein the characteristics of the historic time series data include seasonality or lack thereof exhibited in the historic time series data (see, e.g., Nara. paragraph [0023]: “With respect to the accuracy estimation engine 114, as depicted in FIG. 1, relevant historical data can include, for example, weather forecast output results (precipitation, humidity, etc.) from previous runs of the forecasting models, and actual observed values for weather parameters (precipitation, humidity, etc.) as obtained from a meteorological department, sensors, or other sources” [i.e., past weather forecasts and observed weather values inherently reflect patterns over time that correspond to seasonality or immediate weather changes (absence of seasonality) in the historic data (historic time series data)]).
Regarding claim 7, as discussed above, Nara. in view of Wu teaches the method of claim 1.
Nara. further teaches wherein the ensemble forecast model corresponds to a weighted sum of the plurality of base forecast models (see, e.g., Nara. paragraph [0028]: “Parameterization, as used herein, includes weights and/or reliabilities associated with different models, which can be used to combine forecasts from multiple models" [i.e., the ensemble forecast is formed by combining multiple base models according to specific weights, effectively forming a weighted sum]),
wherein the set of parameters correspond to weights to be applied to the respective base forecast models (see, e.g., Nara. paragraph [0035]: “Step 208 includes determining an ensemble of one or more of the multiple forecasting models… The determining step can include determining an ensemble of one or more of the multiple forecasting models and a parameterization for each of the one or more of the multiple forecasting models to apply to the given environmental event" [i.e., the ensemble is formed by selecting specific base forecasting models and separate weights are applied to each selected model's forecast output to combine them into a single ensemble prediction]).
Regarding Independent Claim 8, Nara. teaches A machine readable and non-transitory medium having information recorded thereon, wherein the information, when read by the machine, causes the machine to perform the following steps (see, e.g., Nara. paragraph [0008]: “Another aspect of the invention or elements thereof can be implemented in the form of an article of manufacture tangibly embodying computer readable instructions which, when implemented, cause a computer to carry out a plurality of method steps, as described herein” ):
selecting a plurality of base forecast models from available forecast models based on the characteristics of the historic time series data (see e.g., Nara. paragraphs [0021-0022]: “At least one embodiment of the invention also includes extracting a time series of vector values of a different set of (possibly overlapping) variables prior to a given event… During run-time, the SVM takes inputs at each time step of the same time series of historical values” [i.e., time series data (historic time series data) characteristics are processed by the model recommendation system], paragraph [0031]: "In further reference to FIG. 1, and as detailed herein, the model and parameter selection engine 110 determines, based on the identified event (from the event classifier 108), the estimated accuracy for the event (from the accuracy estimation engine 114) and the estimated resource requirement and cost (from the resource and cost estimation engine 116), an ensemble of models and parameterizations that are suited for the given event, as well as the availability of budget and/or computational resources" [i.e., the model and parameter selection engine coupled in the model recommendation system determines/selects the ensemble of models based on the optimally estimated models and parameters from the associated estimation components 114 and 116] and paragraph [0035]: "Step 208 includes determining an ensemble of one or more of the multiple forecasting models to apply to the given environmental event based on… (ii) said cost and said one or more resource requirements for each of the multiple forecasting models" [i.e., forecasting models are used in the selection/generation of the ensemble forecast model]);
generating an ensemble forecast model based on the selected plurality of base forecast models by computing a cost associated with each of the plurality of base forecast models (see, e.g., Nara. paragraph [0006]: "In one aspect of the present invention, techniques for generating an ensemble of forecasting models are provided", paragraph [0016]: "the resource and cost estimation engine 116 provide input to the model and parameter selection engine 110, which also receives input in the form of budget information, computational resource information, and cost information 104" and paragraph [0031]: "In further reference to FIG. 1, and as detailed herein, the model and parameter selection engine 110 determines, based on… an ensemble of models and parameterizations that are suited for the given event, as well as the availability of budget and/or computational resources" [i.e., the model and parameter selection engine generates an ensemble forecast model based on computed costs associated with the forecasting models it receives]),
determining a set of parameters to be used for generating the ensemble forecast models based on the costs associated respectively with the plurality of base forecast models (see, e.g., Nara. paragraph [0024]: "The resource and cost estimation engine 116, as depicted in FIG. 1, can be used to decide on the feasibility of running a configuration based on user-specified constraints. Examples of user-specified constraints can include the available number of processors, total memory available, model output required in under two hours, etc. Based on historical data pertaining, for example, to computational run-times, memory requirements, and hardware resource information, at least one embodiment of the invention includes creating models for hardware configuration and/or computation time/memory cost for a particular model configuration… This can include categorical features corresponding to various parameters such as physics options, cumulus parameterization, etc., or valued features such as number of grid points, time-step used, clock frequency of the processors, etc." and paragraph [0026]: "The model and parameter selection engine 110 determines the final set of model configurations for a particular event. This selection depends on multiple factors such as socio-economic cost associated with the event and the lead times required for an accurate forecast, as well as the user-specified constraints on cost and computational resource availability. As noted, the resource and cost estimation engine 116 computes the computational time and/or memory and resource requirements for each model configuration" [i.e., a set of model configuration parameters associated with the constraints on operating costs and resources are determined by the resource and cost estimation engine, which feeds the parameters into the model and parameter selection engine, for generating the ensemble forecast models]),
and creating the ensemble forecast model based on the plurality of base forecast models in accordance with the set of parameters (see, e.g., Nara. paragraph [0031]: "In further reference to FIG. 1, and as detailed herein, the model and parameter selection engine 110 determines, based on… the estimated resource requirement and cost (from the resource and cost estimation engine 116), an ensemble of models and parameterizations that are suited for the given event, as well as the availability of budget and/or computational resources" [i.e., the parameters from the resource and cost estimation engine are fed into the model and the parameter selection engine to generate an ensemble forecast model] and paragraph [0035]: "Step 208 includes determining an ensemble of one or more of the multiple forecasting models to apply to the given environmental event based on (i) said estimated accuracy value for each of the multiple forecasting models, and (ii) said cost and said one or more resource requirements for each of the multiple forecasting models" [i.e., forecasting models are used in the selection/generation of the ensemble forecast model]);
Although Nara. substantially teaches the claimed invention, Nara. is not relied upon to explicitly teach the limitation determining characteristics of historic time series data associated with a resource provider;
forecasting a resource need associated with the resource provider using the ensemble model,
wherein the forecasted resource need is for allocating a resource to the resource provider;
collecting resource usage data associated with the resource provider,
and adding the resource usage data to the historic time series data
In the same field, analogous art Wu teaches determining characteristics of historic time series data associated with a resource provider (see, e.g., Wu paragraph [0036]: “In the case of electric load forecasting, the input layer 202 may receive historical load consumption (e.g., the lagged load consumption data for the last three hours)” [i.e., historical load consumption used for electric load forecasting is a form of historic time series data associated with a resource (electricity) provider]);
forecasting a resource need associated with the resource provider using the ensemble model (see, e.g., Wu paragraph [0031]: "The server computer 114 may also operate in a cloud computing service model, such as Software as a Service (SaaS), Platform as a Service (PaaS), or Infrastructure as a Service (IaaS), as discussed below with respect to FIGS. 5 and 6. The server computer 114 may also be located in a cloud computing deployment model, such as a private cloud, community cloud, public cloud, or hybrid cloud" and paragraphs [0043-0044]: "At 306, the method 3005 includes determining an ensemble model from the learned set of forecasting models based on gradient boosting… At 308, the method 300 includes allocating an available resource based on the ensemble model. We can use the forecasting results to guide the allocate the same resource allocation, e.g., allocate the same resource that can meet the requirements of predicted results. The available resource may correspond to an electric load, a network bandwidth, and a transportation route" [i.e., the ensemble model forecasts a resource need (electric load, network bandwidth, transportation route) functioning as an ensemble model based forecaster]),
wherein the forecasted resource need is for allocating a resource to the resource provider (see, e.g., Wu paragraph [0031]: "The server computer 114 may also operate in a cloud computing service model, such as Software as a Service (SaaS), Platform as a Service (PaaS), or Infrastructure as a Service (IaaS)", paragraph [0044]: "At 308, the method 3006 includes allocating an available resource based on the ensemble model. We can use the forecasting results to guide the allocate the same resource allocation, e.g., allocate the same resource that can meet the requirements of predicted results. The available resource may correspond to an electric load, a network bandwidth, and a transportation route" and paragraph [0051]: "The software program 108 (FIG. 1) and the Load Forecasting Program 116 (FIG. 1) on the server computer 114 (FIG. 1) can be downloaded to the computer 102 (FIG. 1) and server computer 114 from an external computer via a network (for example, the Internet, a local area network or other, wide area network) and respective network adapters or interfaces 836" [i.e., the Load Forecasting Program of Fig. 3 that performs the method 300 is implemented on a server computer 114 a resource/cloud provider and allocates resources of the electric load, a network bandwidth, and transportation route of said provider]);
collecting resource usage data associated with the resource provider (see, e.g., Wu paragraph [0031]: "The server computer 114 may also operate in a cloud computing7 service model, such as Software as a Service (SaaS), Platform as a Service (PaaS), or Infrastructure as a Service (IaaS)" and paragraph [0060]: "Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the utilized service" [i.e., resource usage is collected for transparent reporting to the resource provider]);
and adding the resource usage data to the historic time series data (see, e.g., Wu paragraph [0036]: "Referring now to FIG. 2, an exemplary load forecasting system 200 according to one or more embodiments is depicted. The load forecasting system8 200 may include, among other things, an input layer 202… the input layer 202 may receive historical load consumption (e.g., the lagged load consumption data for the last three hours), historical temperature information (e.g., the lagged temperature data for the last three 166 hours), and the corresponding weekday/weekend information (e.g., a binary variable set to 1 for weekday and 0 for weekend)" [i.e., the load forecasting system uses historical load consumption (historic time series data)] and paragraph [0060]: "Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the utilized service" [i.e., resource usage is collected for transparent reporting to the resource provider]).
Nara. and Wu are analogous art because they are both directed to ensemble forecast models (see, e.g., Nara., paragraph [0004], Wu, paragraph [0027]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Nara. to incorporate the teachings of Wu to use a method that determines characteristics of historic time series data associated with a resource provider for forecasting a resource need associated with the resource provider using the ensemble model, such that the need for the forecasted resource is to allocate a resource to the resource provider, collecting resource usage data associated with the resource provider, and finally adding the resource usage data to the historic time series data. Doing so would have allowed Nara. to use Wu’s system for “improving the load forecasting accuracy, [so] even a small utility company, could save hundreds of thousands of dollars annually in operation costs, and many millions for larger utility companies”, as suggested by Wu (see, e.g., Wu, paragraph [0016]).
Regarding Claim 9, as discussed above, Nara. in view of Wu teaches the non-transitory medium of claim 8.
Nara. further teaches wherein the characteristics of the historic time series data include seasonality or lack thereof exhibited in the historic time series data (see, e.g., Nara. paragraph [0023]: “With respect to the accuracy estimation engine 114, as depicted in FIG. 1, relevant historical data can include, for example, weather forecast output results (precipitation, humidity, etc.) from previous runs of the forecasting models, and actual observed values for weather parameters (precipitation, humidity, etc.) as obtained from a meteorological department, sensors, or other sources” [i.e., past weather forecasts and observed weather values inherently reflect patterns over time that correspond to seasonality or immediate weather changes (absence of seasonality) in the historic data (historic time series data)]).
Regarding claim 14, as discussed above, Nara. in view of Wu teaches the non-transitory medium of claim 8.
Nara. further teaches wherein the ensemble forecast model corresponds to a weighted sum of the plurality of base forecast models (see, e.g., Nara. paragraph [0028]: “Parameterization, as used herein, includes weights and/or reliabilities associated with different models, which can be used to combine forecasts from multiple models" [i.e., the ensemble forecast is formed by combining multiple base models according to specific weights, effectively forming a weighted sum]),
wherein the set of parameters correspond to weights to be applied to the respective base forecast models (see, e.g., Nara. paragraph [0035]: “Step 208 includes determining an ensemble of one or more of the multiple forecasting models… The determining step can include determining an ensemble of one or more of the multiple forecasting models and a parameterization for each of the one or more of the multiple forecasting models to apply to the given environmental event" [i.e., the ensemble is formed by selecting specific base forecasting models and separate weights are applied to each selected model's forecast output to combine them into a single ensemble prediction]).
Regarding Independent Claim 15, Nara. teaches A system, comprising… a performance based model selector implemented by a processor and configured for selecting a plurality of base forecast models from available forecast models based on the characteristics of the historic time series data (see e.g., Nara. paragraphs [0014-0015]: "FIG. 1 depicts a model recommendation system 106, which includes an event classifier component 108, a model and parameter selection engine 110, and an estimation component 112 which includes an accuracy estimation engine 114 and a resource and cost estimation engine 116. As illustrated in FIG. 1" [i.e., the model recommendation system functions as a unified performance based model selector that includes components for performance based model and parameter selection], paragraph [0015]: "Additionally, the estimation component 112 receives input in the form of models and parameters 118 as well as relevant historical data 120. The parameters noted via component 118… " [i.e., available models are fed to the estimation component which estimates accuracy and resource costs using estimation engines 114 and 116 as well as historical data 120], paragraphs [0021-0022]: “At least one embodiment of the invention also includes extracting a time series of vector values of a different set of (possibly overlapping) variables prior to a given event… During run-time, the SVM takes inputs at each time step of the same time series of historical values” [i.e., time series data (historic time series data) characteristics are processed by the model recommendation system], paragraph [0031]: "In further reference to FIG. 1, and as detailed herein, the model and parameter selection engine 110 determines, based on the identified event (from the event classifier 108), the estimated accuracy for the event (from the accuracy estimation engine 114) and the estimated resource requirement and cost (from the resource and cost estimation engine 116), an ensemble of models and parameterizations that are suited for the given event, as well as the availability of budget and/or computational resources" [i.e., the model and parameter selection engine coupled in the model recommendation system determines/selects the ensemble of models based on the optimally estimated models and parameters from the associated estimation components 114 and 116] and paragraph [0035]: "Step 208 includes determining an ensemble of one or more of the multiple forecasting models to apply to the given environmental event based on… (ii) said cost and said one or more resource requirements for each of the multiple forecasting models" [i.e., forecasting models are used in the selection/generation of the ensemble forecast model]);
an integrated model ensemble unit implemented by a processor and configured for generating an ensemble forecast model based on the selected plurality of base forecast models by computing a cost associated with each of the plurality of base forecast models (see, e.g., Nara. paragraph [0006]: "In one aspect of the present invention, techniques for generating an ensemble of forecasting models are provided", paragraph [0016]: "the resource and cost estimation engine 116 provide input to the model and parameter selection engine 110, which also receives input in the form of budget information, computational resource information, and cost information 104" and paragraph [0031]: "In further reference to FIG. 1, and as detailed herein, the model and parameter selection engine 110 determines, based on… an ensemble of models and parameterizations that are suited for the given event, as well as the availability of budget and/or computational resources" [i.e., the model and parameter selection engine functions as an integrated model ensemble unit that generates an ensemble forecast model based on computed costs associated with the forecasting models it receives]),
determining a set of parameters to be used for generating the ensemble forecast models based on the costs associated respectively with the plurality of base forecast models (see, e.g., Nara. paragraph [0024]: "The resource and cost estimation engine 116, as depicted in FIG. 1, can be used to decide on the feasibility of running a configuration based on user-specified constraints. Examples of user-specified constraints can include the available number of processors, total memory available, model output required in under two hours, etc. Based on historical data pertaining, for example, to computational run-times, memory requirements, and hardware resource information, at least one embodiment of the invention includes creating models for hardware configuration and/or computation time/memory cost for a particular model configuration… This can include categorical features corresponding to various parameters such as physics options, cumulus parameterization, etc., or valued features such as number of grid points, time-step used, clock frequency of the processors, etc." and paragraph [0026]: "The model and parameter selection engine 110 determines the final set of model configurations for a particular event. This selection depends on multiple factors such as socio-economic cost associated with the event and the lead times required for an accurate forecast, as well as the user-specified constraints on cost and computational resource availability. As noted, the resource and cost estimation engine 116 computes the computational time and/or memory and resource requirements for each model configuration" [i.e., a set of model configuration parameters associated with the constraints on operating costs and resources are determined by the resource and cost estimation engine, which feeds the parameters into the model and parameter selection engine, for generating the ensemble forecast models]),
and creating the ensemble forecast model based on the plurality of base forecast models in accordance with the set of parameters (see, e.g., Nara. paragraph [0031]: "In further reference to FIG. 1, and as detailed herein, the model and parameter selection engine 110 determines, based on… the estimated resource requirement and cost (from the resource and cost estimation engine 116), an ensemble of models and parameterizations that are suited for the given event, as well as the availability of budget and/or computational resources" [i.e., the parameters from the resource and cost estimation engine are fed into the model and the parameter selection engine to generate an ensemble forecast model] and paragraph [0035]: "Step 208 includes determining an ensemble of one or more of the multiple forecasting models to apply to the given environmental event based on (i) said estimated accuracy value for each of the multiple forecasting models, and (ii) said cost and said one or more resource requirements for each of the multiple forecasting models" [i.e., forecasting models are used in the selection/generation of the ensemble forecast model]);
Although Nara. substantially teaches the claimed invention, Nara. is not relied upon to explicitly teach the limitations a data preprocessor implemented by a processor and configured for determining characteristics of historic time series data associated with a resource provider;
an ensemble model based forecaster implemented by a processor and configured for forecasting a resource need associated with the resource provider using the ensemble model,
wherein the forecasted resource need is for allocating a resource to the resource provider;
and a resource use data collectors implemented by a processor and configured for collecting resource usage data associated with the resource provider,
and adding the resource usage data to the historic time series data
In the same field, analogous art Wu teaches A system, comprising: a data preprocessor implemented by a processor and configured for determining characteristics of historic time series data associated with a resource provider (see, e.g., Wu paragraph [0047]: “Computer 102 (FIG. 1) and server computer 114 (FIG. 1) may include respective sets of internal components 800A,B and external components 900A,B illustrated in FIG. 4. Each of the sets of internal components 800 include one or more processors 820, one or more computer-readable RAMs 822 and one or more computer-readable ROMs 824 on one or more buses 826, one or more operating systems 828, and one or more computer-readable tangible storage devices 830”, paragraph [0048]: “Processor 820 is implemented in hardware, firmware, or a combination of hardware and software. Processor 820 is a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), a microprocessor, a microcontroller, a digital signal processor (DSP), a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or another type of processing component. In some implementations, processor 820 includes one or more processors capable of being programmed to perform a function” [i.e., the combination of the processors executing software act as pre-processors implemented on a processor] and paragraph [0036]: “In the case of electric load forecasting, the input layer 202 may receive historical load consumption (e.g., the lagged load consumption data for the last three hours)” [i.e., historical load consumption used for electric load forecasting is a form of historic time series data associated with a resource (electricity) provider]);
an ensemble model based forecaster implemented by a processor and configured for forecasting a resource need associated with the resource provider using the ensemble model (see, e.g., Wu paragraph [0031]: "The server computer 114 may also operate in a cloud computing service model, such as Software as a Service (SaaS), Platform as a Service (PaaS), or Infrastructure as a Service (IaaS), as discussed below with respect to FIGS. 5 and 6. The server computer 114 may also be located in a cloud computing deployment model, such as a private cloud, community cloud, public cloud, or hybrid cloud" and paragraphs [0043-0044]: "At 306, the method 3009 includes determining an ensemble model from the learned set of forecasting models based on gradient boosting… At 308, the method 300 includes allocating an available resource based on the ensemble model. We can use the forecasting results to guide the allocate the same resource allocation, e.g., allocate the same resource that can meet the requirements of predicted results. The available resource may correspond to an electric load, a network bandwidth, and a transportation route" [i.e., the ensemble model forecasts a resource need (electric load, network bandwidth, transportation route) functioning as an ensemble model based forecaster]),
wherein the forecasted resource need is for allocating a resource to the resource provider (see, e.g., Wu paragraph [0031]: "The server computer 114 may also operate in a cloud computing service model, such as Software as a Service (SaaS), Platform as a Service (PaaS), or Infrastructure as a Service (IaaS)", paragraph [0044]: "At 308, the method 30010 includes allocating an available resource based on the ensemble model. We can use the forecasting results to guide the allocate the same resource allocation, e.g., allocate the same resource that can meet the requirements of predicted results. The available resource may correspond to an electric load, a network bandwidth, and a transportation route" and paragraph [0051]: "The software program 108 (FIG. 1) and the Load Forecasting Program 116 (FIG. 1) on the server computer 114 (FIG. 1) can be downloaded to the computer 102 (FIG. 1) and server computer 114 from an external computer via a network (for example, the Internet, a local area network or other, wide area network) and respective network adapters or interfaces 836" [i.e., the Load Forecasting Program of Fig. 3 that performs the method 300 is implemented on a server computer 114 a resource/cloud provider and allocates resources of the electric load, a network bandwidth, and transportation route of said provider]);
and a resource use data collectors implemented by a processor and configured for collecting resource usage data associated with the resource provider (see, e.g., [] paragraph [0031]: "The server computer 114 may also operate in a cloud computing11 service model, such as Software as a Service (SaaS), Platform as a Service (PaaS), or Infrastructure as a Service (IaaS)" and paragraph [0060]: "Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the utilized service" [i.e., resource usage is collected for transparent reporting to the resource provider]),
and adding the resource usage data to the historic time series data (see, e.g., Wu paragraph [0036]: "Referring now to FIG. 2, an exemplary load forecasting system 200 according to one or more embodiments is depicted. The load forecasting system12 200 may include, among other things, an input layer 202… the input layer 202 may receive historical load consumption (e.g., the lagged load consumption data for the last three hours), historical temperature information (e.g., the lagged temperature data for the last three 166 hours), and the corresponding weekday/weekend information (e.g., a binary variable set to 1 for weekday and 0 for weekend)" [i.e., the load forecasting system uses historical load consumption (historic time series data)] and paragraph [0060]: "Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the utilized service" [i.e., resource usage is collected for transparent reporting to the resource provider]).
Nara. and Wu are analogous art because they are both directed to ensemble forecast models (see, e.g., Nara., paragraph [0004], Wu, paragraph [0027]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Nara. to incorporate the teachings of Wu to use a system that determines characteristics of historic time series data associated with a resource provider that creates an ensemble model based forecaster for forecasting a resource need associated with the resource provider using the ensemble model, such that the need for the forecasted resource is to allocate a resource to the resource provider, and utilizing a resource use data collector for collecting resource usage data associated with the resource provider and finally adding the resource usage data to the historic time series data. Doing so would have allowed Nara. to use Wu’s system for “improving the load forecasting accuracy, [so] even a small utility company, could save hundreds of thousands of dollars annually in operation costs, and many millions for larger utility companies”, as suggested by Wu (see, e.g., Wu, paragraph [0016]).
Regarding claim 20, as discussed above, Nara. in view of Wu teaches the system of claim 15.
Nara. further teaches wherein the ensemble forecast model corresponds to a weighted sum of the plurality of base forecast models (see, e.g., Nara. paragraph [0028]: “Parameterization, as used herein, includes weights and/or reliabilities associated with different models, which can be used to combine forecasts from multiple models" [i.e., the ensemble forecast is formed by combining multiple base models according to specific weights, effectively forming a weighted sum]),
wherein the set of parameters correspond to weights to be applied to the respective base forecast models (see, e.g., Nara. paragraph [0035]: “Step 208 includes determining an ensemble of one or more of the multiple forecasting models… The determining step can include determining an ensemble of one or more of the multiple forecasting models and a parameterization for each of the one or more of the multiple forecasting models to apply to the given environmental event" [i.e., the ensemble is formed by selecting specific base forecasting models and separate weights are applied to each selected model's forecast output to combine them into a single ensemble prediction]).
Claims 3, 6, 10, 13, 16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Nara. in view of Wu, and further in view of Ames (US 20150227859 A1; hereinafter Ames).
Regarding Claim 3, as discussed above, Nara. in view of Wu teaches the method of claim 2.
Nara. further teaches and identifying the plurality of base forecast models from the multiple candidate base forecast models based on forecast performance of each of the multiple candidate base forecast models (see, e.g., Nara. paragraph [0033]: “Estimating can include estimating the accuracy value for each of the multiple forecasting models across multiple parameterizations” [i.e., accuracy values (a performance metric) of models are estimated] and paragraph [0035]: “Step 208 includes determining an ensemble of one or more of the multiple forecasting models… based on (i) said estimated accuracy value for each of the multiple forecasting models” [i.e., models are identified based on their accuracy values corresponding to performance of the models]).
Although Nara. in view of Wu substantially teaches the claimed invention, Nara in view of Wu is not relied upon to explicitly teach wherein the selecting a plurality of base forecast models comprises: determining whether the historic time series data exhibits seasonality;
designating multiple candidate base forecast models from the available forecast models based on whether the historic time series data exhibits seasonality;
In the same field, analogous art Ames teaches wherein the selecting a plurality of base forecast models comprises: determining whether the historic time series data exhibits seasonality (see, e.g., Ames paragraphs [0018]: “Additionally, a determination may be made regarding which forecasting models will be utilized. These may include… twelve month weighted seasonality, additive smoothing, multiplicative smoothing, exponential smoothing, Holt's Method for double exponential smoothing, Holt-Winters Method for additive smoothing, Holt-Winters Method for multiplicative smoothing, seasonality and trend,… etc. Each of these models can have from zero to many variables, which can be optimized to give the best fit for that model to the collected historic time series data” [i.e., a determination is made as to which forecasting models will be utilized between seasonality and non-seasonality metrics of historic time series data]and paragraph [0030]: “As illustrated, the computing device 104 utilizes the time series values from the time series column 440 into the equations in the forecasting model section 450. From this, the computing device 104 can determine values for level, seasonality, forecast, and APE. These values may be determined for the first 24 time series values (historical time series data)” [i.e., seasonality values are determined for historical time series data which exhibits a degree of seasonality]);
designating multiple candidate base forecast models from the available forecast models based on whether the historic time series data exhibits seasonality (see, e.g., Ames paragraph [0017]: “One embodiment disclosed herein includes a method for weighting a plurality of forecasting methods while simultaneously optimizing one or more variables of each forecasting method in the ensemble. Specifically, a user may determine which time series is going to be forecasted… embodiments may be configured for collecting historic time series data. The observations of each series should be substantially evenly spaced e.g., monthly, weekly, daily, etc. The potential periodicity of the time series should be noted. For example, monthly data could have a 12 observation periodicity, etc.” and paragraph [0018]: “Additionally, a determination may be made regarding which forecasting models will be utilized. These may include… twelve month weighted seasonality, additive smoothing, multiplicative smoothing, exponential smoothing, Holt's Method for double exponential smoothing, Holt-Winters Method for additive smoothing, Holt-Winters Method for multiplicative smoothing, seasonality and trend,… etc. Each of these models can have from zero to many variables, which can be optimized to give the best fit for that model to the collected historic time series data” [i.e., a determination is made as to which forecasting models will be utilized based on seasonality metrics of historic time series data]);
Nara., Wu and Ames are analogous art because they are each directed to ensemble forecast models (see, e.g., Nara., paragraph [0004], Wu, paragraph [0027], and Ames, paragraph [0003]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Nara., in view of Wu to incorporate the teachings of Ames to determine whether historic time series data exhibits seasonality and designate base forecast models based on whether historic time series data exhibits seasonality. Doing so would have allowed Nara. in view of Wu to use Ames method in order to “predict future events with accuracy significantly greater than any of the models utilized in the combination”, as suggested by Ames (see, e.g., Ames, paragraph [0015]).
Regarding claim 6, as discussed above, Nara. in view of Wu and further in view of Ames teaches the method of claim 3.
Nara. further teaches wherein the identifying the plurality of base forecast models comprises: with respect to each of the multiple candidate base forecast models, generating a forecast result based on the historic time series data using the candidate base forecast model (see, e.g., Nara. paragraph [0023]: “The accuracy estimation engine 114 uses such historical data to determine the accuracy of the forecasting models... To do this, the accuracy estimation engine 114, for every model and event type, scans previous forecasts and filters those runs for which the corresponding observed values indicate occurrence of the selected event” [i.e., the candidate base forecast models are input into the accuracy estimation engine] and paragraph [0033]: “Step 204 includes estimating an accuracy value for each of multiple forecasting models applied to an environmental event related to the given environmental event based on historical data. Estimating can include estimating the accuracy value for each of the multiple forecasting models across multiple parameterizations. Additionally, estimating can be carried out online and/or offline” [i.e., each forecasting model generates a forecast output result based on the historic time series data, that are each evaluated for an accuracy value]),
computing a measure indicative of the performance of the candidate base forecast model based on the forecast result (see, e.g., Nara. paragraph [0033]: “Estimating can include estimating the accuracy value for each of the multiple forecasting models across multiple parameterizations” [i.e., the accuracy value is a measure indicative of each forecasting model based on the forecast output results]);
and selecting the plurality of base forecast models from the multiple candidate base forecast models based on the measures associated respectively with the multiple candidate base forecast models (see, e.g., Nara. paragraph [0035]: “Step 208 includes determining an ensemble of one or more of the multiple forecasting models… based on (i) said estimated accuracy value for each of the multiple forecasting models, and (ii) said cost and said one or more resource requirements for each of the multiple forecasting models. The determining step can include determining an ensemble of one or more of the multiple forecasting models and a parameterization for each of the one or more of the multiple forecasting models” and paragraph [0037]: “determining an ensemble of one or more of the multiple forecasting models and a parameterization for each of the one or more of the multiple forecasting models… wherein said determining is based on (i) said estimated accuracy value for each of the multiple forecasting models, (ii) said computational cost and said one or more resource requirements for each of the multiple forecasting models, (iii) availability of budget and one or more computational resources, and (iv) a specified forecast lead time parameter” [i.e., the selection of models from candidate forecasting models uses their associated performance measures]).
Regarding Claim 10, as discussed above, Nara. in view of Wu teaches non-transitory medium of claim 9.
Nara. further teaches and identifying the plurality of base forecast models from the multiple candidate base forecast models based on forecast performance of each of the multiple candidate base forecast models (see, e.g., Nara. paragraph [0033]: “Estimating can include estimating the accuracy value for each of the multiple forecasting models across multiple parameterizations” [i.e., accuracy values (a performance metric) of models are estimated] and paragraph [0035]: “Step 208 includes determining an ensemble of one or more of the multiple forecasting models… based on (i) said estimated accuracy value for each of the multiple forecasting models” [i.e., models are identified based on their accuracy values corresponding to performance of the models]).
Although Nara. in view of Wu substantially teaches the claimed invention, Nara in view of Wu is not relied upon to explicitly teach wherein the selecting a plurality of base forecast models comprises: determining whether the historic time series data exhibits seasonality;
designating multiple candidate base forecast models from the available forecast models based on whether the historic time series data exhibits seasonality;
In the same field, analogous art Ames teaches wherein the selecting a plurality of base forecast models comprises: determining whether the historic time series data exhibits seasonality (see, e.g., Ames paragraphs [0018]: “Additionally, a determination may be made regarding which forecasting models will be utilized. These may include… twelve month weighted seasonality, additive smoothing, multiplicative smoothing, exponential smoothing, Holt's Method for double exponential smoothing, Holt-Winters Method for additive smoothing, Holt-Winters Method for multiplicative smoothing, seasonality and trend,… etc. Each of these models can have from zero to many variables, which can be optimized to give the best fit for that model to the collected historic time series data” [i.e., a determination is made as to which forecasting models will be utilized between seasonality and non-seasonality metrics of historic time series data]and paragraph [0030]: “As illustrated, the computing device 104 utilizes the time series values from the time series column 440 into the equations in the forecasting model section 450. From this, the computing device 104 can determine values for level, seasonality, forecast, and APE. These values may be determined for the first 24 time series values (historical time series data)” [i.e., seasonality values are determined for historical time series data which exhibits a degree of seasonality]);
designating multiple candidate base forecast models from the available forecast models based on whether the historic time series data exhibits seasonality (see, e.g., Ames paragraph [0017]: “One embodiment disclosed herein includes a method for weighting a plurality of forecasting methods while simultaneously optimizing one or more variables of each forecasting method in the ensemble. Specifically, a user may determine which time series is going to be forecasted… embodiments may be configured for collecting historic time series data. The observations of each series should be substantially evenly spaced e.g., monthly, weekly, daily, etc. The potential periodicity of the time series should be noted. For example, monthly data could have a 12 observation periodicity, etc.” and paragraph [0018]: “Additionally, a determination may be made regarding which forecasting models will be utilized. These may include… twelve month weighted seasonality, additive smoothing, multiplicative smoothing, exponential smoothing, Holt's Method for double exponential smoothing, Holt-Winters Method for additive smoothing, Holt-Winters Method for multiplicative smoothing, seasonality and trend,… etc. Each of these models can have from zero to many variables, which can be optimized to give the best fit for that model to the collected historic time series data” [i.e., a determination is made as to which forecasting models will be utilized based on seasonality metrics of historic time series data]);
Nara., Wu and Ames are analogous art because they are each directed to ensemble forecast models (see, e.g., Nara., paragraph [0004], Wu, paragraph [0027], and Ames, paragraph [0003]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Nara., in view of Wu to incorporate the teachings of Ames to determine whether historic time series data exhibits seasonality and designate base forecast models based on whether historic time series data exhibits seasonality. Doing so would have allowed Nara. in view of Wu to use Ames method in order to “predict future events with accuracy significantly greater than any of the models utilized in the combination”, as suggested by Ames (see, e.g., Ames, paragraph [0015]).
Regarding claim 13, as discussed above, Nara. in view of Wu and further in view of Ames teaches the non-transitory medium of claim 10.
Nara. further teaches wherein the identifying the plurality of base forecast models comprises: with respect to each of the multiple candidate base forecast models, generating a forecast result based on the historic time series data using the candidate base forecast model (see, e.g., Nara. paragraph [0023]: “The accuracy estimation engine 114 uses such historical data to determine the accuracy of the forecasting models... To do this, the accuracy estimation engine 114, for every model and event type, scans previous forecasts and filters those runs for which the corresponding observed values indicate occurrence of the selected event” [i.e., the candidate base forecast models are input into the accuracy estimation engine] and paragraph [0033]: “Step 204 includes estimating an accuracy value for each of multiple forecasting models applied to an environmental event related to the given environmental event based on historical data. Estimating can include estimating the accuracy value for each of the multiple forecasting models across multiple parameterizations. Additionally, estimating can be carried out online and/or offline” [i.e., each forecasting model generates a forecast output result based on the historic time series data, that are each evaluated for an accuracy value]),
computing a measure indicative of the performance of the candidate base forecast model based on the forecast result (see, e.g., Nara. paragraph [0033]: “Estimating can include estimating the accuracy value for each of the multiple forecasting models across multiple parameterizations” [i.e., the accuracy value is a measure indicative of each forecasting model based on the forecast output results]);
and selecting the plurality of base forecast models from the multiple candidate base forecast models based on the measures associated respectively with the multiple candidate base forecast models (see, e.g., Nara. paragraph [0035]: “Step 208 includes determining an ensemble of one or more of the multiple forecasting models… based on (i) said estimated accuracy value for each of the multiple forecasting models, and (ii) said cost and said one or more resource requirements for each of the multiple forecasting models. The determining step can include determining an ensemble of one or more of the multiple forecasting models and a parameterization for each of the one or more of the multiple forecasting models” and paragraph [0037]: “determining an ensemble of one or more of the multiple forecasting models and a parameterization for each of the one or more of the multiple forecasting models… wherein said determining is based on (i) said estimated accuracy value for each of the multiple forecasting models, (ii) said computational cost and said one or more resource requirements for each of the multiple forecasting models, (iii) availability of budget and one or more computational resources, and (iv) a specified forecast lead time parameter” [i.e., the selection of models from candidate forecasting models uses their associated performance measures]).
Regarding Claim 16, as discussed above, Nara. in view of Wu teaches the system of claim 15.
Nara. further teaches and identifying the plurality of base forecast models from the multiple candidate base forecast models based on forecast performance of each of the multiple candidate base forecast models (see, e.g., Nara. paragraph [0033]: “Estimating can include estimating the accuracy value for each of the multiple forecasting models across multiple parameterizations” [i.e., accuracy values (a performance metric) of models are estimated] and paragraph [0035]: “Step 208 includes determining an ensemble of one or more of the multiple forecasting models… based on (i) said estimated accuracy value for each of the multiple forecasting models” [i.e., models are identified based on their accuracy values corresponding to performance of the models]).
Although Nara. in view of Wu substantially teaches the claimed invention, Nara in view of Wu is not relied upon to explicitly teach wherein the selecting a plurality of base forecast models comprises: determining whether the historic time series data exhibits seasonality;
designating multiple candidate base forecast models from the available forecast models based on whether the historic time series data exhibits seasonality;
In the same field, analogous art Ames teaches wherein the selecting a plurality of base forecast models comprises: determining whether the historic time series data exhibits seasonality (see, e.g., Ames paragraphs [0018]: “Additionally, a determination may be made regarding which forecasting models will be utilized. These may include… twelve month weighted seasonality, additive smoothing, multiplicative smoothing, exponential smoothing, Holt's Method for double exponential smoothing, Holt-Winters Method for additive smoothing, Holt-Winters Method for multiplicative smoothing, seasonality and trend,… etc. Each of these models can have from zero to many variables, which can be optimized to give the best fit for that model to the collected historic time series data” [i.e., a determination is made as to which forecasting models will be utilized between seasonality and non-seasonality metrics of historic time series data]and paragraph [0030]: “As illustrated, the computing device 104 utilizes the time series values from the time series column 440 into the equations in the forecasting model section 450. From this, the computing device 104 can determine values for level, seasonality, forecast, and APE. These values may be determined for the first 24 time series values (historical time series data)” [i.e., seasonality values are determined for historical time series data which exhibits a degree of seasonality]);
designating multiple candidate base forecast models from the available forecast models based on whether the historic time series data exhibits seasonality (see, e.g., Ames paragraph [0017]: “One embodiment disclosed herein includes a method for weighting a plurality of forecasting methods while simultaneously optimizing one or more variables of each forecasting method in the ensemble. Specifically, a user may determine which time series is going to be forecasted… embodiments may be configured for collecting historic time series data. The observations of each series should be substantially evenly spaced e.g., monthly, weekly, daily, etc. The potential periodicity of the time series should be noted. For example, monthly data could have a 12 observation periodicity, etc.” and paragraph [0018]: “Additionally, a determination may be made regarding which forecasting models will be utilized. These may include… twelve month weighted seasonality, additive smoothing, multiplicative smoothing, exponential smoothing, Holt's Method for double exponential smoothing, Holt-Winters Method for additive smoothing, Holt-Winters Method for multiplicative smoothing, seasonality and trend,… etc. Each of these models can have from zero to many variables, which can be optimized to give the best fit for that model to the collected historic time series data” [i.e., a determination is made as to which forecasting models will be utilized based on seasonality metrics of historic time series data]);
Nara., Wu and Ames are analogous art because they are each directed to ensemble forecast models (see, e.g., Nara., paragraph [0004], Wu, paragraph [0027], and Ames, paragraph [0003]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Nara., in view of Wu to incorporate the teachings of Ames to determine whether historic time series data exhibits seasonality and designate base forecast models based on whether historic time series data exhibits seasonality. Doing so would have allowed Nara. in view of Wu to use Ames method in order to “predict future events with accuracy significantly greater than any of the models utilized in the combination”, as suggested by Ames (see, e.g., Ames, paragraph [0015]).
Regarding claim 19, as discussed above, Nara. in view of Wu and further in view of Ames teaches the system of claim 16.
Nara. further teaches wherein the identifying the plurality of base forecast models comprises: with respect to each of the multiple candidate base forecast models, generating a forecast result based on the historic time series data using the candidate base forecast model (see, e.g., Nara. paragraph [0023]: “The accuracy estimation engine 114 uses such historical data to determine the accuracy of the forecasting models... To do this, the accuracy estimation engine 114, for every model and event type, scans previous forecasts and filters those runs for which the corresponding observed values indicate occurrence of the selected event” [i.e., the candidate base forecast models are input into the accuracy estimation engine] and paragraph [0033]: “Step 204 includes estimating an accuracy value for each of multiple forecasting models applied to an environmental event related to the given environmental event based on historical data. Estimating can include estimating the accuracy value for each of the multiple forecasting models across multiple parameterizations. Additionally, estimating can be carried out online and/or offline” [i.e., each forecasting model generates a forecast output result based on the historic time series data, that are each evaluated for an accuracy value]),
computing a measure indicative of the performance of the candidate base forecast model based on the forecast result (see, e.g., Nara. paragraph [0033]: “Estimating can include estimating the accuracy value for each of the multiple forecasting models across multiple parameterizations” [i.e., the accuracy value is a measure indicative of each forecasting model based on the forecast output results]);
and selecting the plurality of base forecast models from the multiple candidate base forecast models based on the measures associated respectively with the multiple candidate base forecast models (see, e.g., Nara. paragraph [0035]: “Step 208 includes determining an ensemble of one or more of the multiple forecasting models… based on (i) said estimated accuracy value for each of the multiple forecasting models, and (ii) said cost and said one or more resource requirements for each of the multiple forecasting models. The determining step can include determining an ensemble of one or more of the multiple forecasting models and a parameterization for each of the one or more of the multiple forecasting models” and paragraph [0037]: “determining an ensemble of one or more of the multiple forecasting models and a parameterization for each of the one or more of the multiple forecasting models… wherein said determining is based on (i) said estimated accuracy value for each of the multiple forecasting models, (ii) said computational cost and said one or more resource requirements for each of the multiple forecasting models, (iii) availability of budget and one or more computational resources, and (iv) a specified forecast lead time parameter” [i.e., the selection of models from candidate forecasting models uses their associated performance measures]).
Claims 4-5, 11-12 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Nara., Wu and Ames, and further in view of Cheng (US 20210357402 A1; hereinafter Cheng).
Regarding Claim 4, as discussed above, the combination of Nara., Wu and Ames teaches the method of claim 3.
Although the combination of Nara., Wu and Ames substantially teaches the claimed invention, said combination is not relied upon for explicitly teaching wherein the determining whether the historic time series data exhibits seasonality comprises: performing linear regression on smoothed historic time series data to generate linear regression result;
generating detrended historic time series data based on the smoothed historic time series data and the linear regression result;
performing auto-correlation on the smoothed time series data and the detrended historic time series data to generate auto-correlation results;
and determining whether the historic time series data exhibits seasonality based on the auto-correlation results.
In the same field, analogous art Cheng teaches wherein the determining whether the historic time series data exhibits seasonality comprises: performing linear regression on smoothed historic time series data to generate linear regression result (see, e.g., Cheng paragraph [0034]: “The preprocess stage 510 receives an input time series 502 and performs data frequency handling 512, NULL imputation 514 (i.e., determining and/or rejecting any nulls in the input time series 502), holiday effect modeling 516, and anomaly detection 518” [i.e., the preprocessing operations provide a cleaned and smoothed input time series] and paragraph [0036]: “After preprocessing, the training stage 520 begins with a seasonal and trend decomposition using local regression (STL) module 522 which generates a de-seasoned component 523 a and a seasonal component 523 b” [i.e., the STL module uses local regression to smooth the input time series, producing a de-seasoned (smoothed historic time series data) component as it represents the non-seasonal variations in the data], "The STL module 522 estimates nonlinear relationships and decomposes a time series into multiple components 412 a-c (FIG. 4)" [i.e., the de-seasoned component from the STL module serves as a regression estimate of the smoothed series]);
generating detrended historic time series data based on the smoothed historic time series data and the linear regression result (see, e.g., Cheng paragraph [0036]: “After preprocessing, the training stage 520 begins with a seasonal and trend decomposition using local regression (STL) module 522 which generates a de-seasoned component 523” [i.e., the STL module produces a smoothed/de-seasoned component 523a which inherently generates detrended historic time series data]);
performing auto-correlation on the smoothed time series data and the detrended historic time series data to generate auto-correlation results (see, e.g., Cheng paragraph [0024]: “The model trainer 210 may generate and/or train each model 212 with different parameters, for example, the model trainer 210 may generate and train a plurality of autoregressive integrated moving average (ARIMA) models with different orders of the autoregressive models (i.e., the number of time lags and commonly represented as the parameter p)” [i.e., the autoregressive order parameter p depends on the auto-correlation of input data, and is used in encoding correlation patterns in the time series data] and paragraph [0036]: “The de-seasoned component 523 a is processed via a Kwtatkowsi-Phillips-Schmidt-Shin (KPSS) test module 524 and generates a plural of ARIMA models in parallel at 526” [i.e., the de-seasoned (smoothed) component is processed via KPSS and used for generating ARIMA models in parallel; these steps inherently involve the auto-correlation calculations on the smoothed and de-trended data of the input time series (historic time series data) generating auto-correlation results]);
and determining whether the historic time series data exhibits seasonality based on the auto-correlation results (see, e.g., Cheng paragraph [0024]: “The model trainer 210 may generate and/or train each model 212 with different parameters, for example, the model trainer 210 may generate and train a plurality of autoregressive integrated moving average (ARIMA) models with different orders of the autoregressive models (i.e., the number of time lags and commonly represented as the parameter p)” [i.e., ARIMA's autoregressive parameter p and lag structure captures temporal correlations of de-seasoned series], paragraph [0025]: “As discussed in more detail below, the model trainer 210 may generate and train forecasting models 212 capable of modeling many different aspects of time series. For example, the forecast models 212 may account for seasonal effects” and paragraph [0036]: “The de-seasoned component 523 a is processed via a Kwtatkowsi-Phillips-Schmidt-Shin (KPSS) test module 524 and generates a plural of ARIMA models in parallel at 526” [i.e., the ARIMA models are trained on the de-seasoned component and yields autocorrelation coefficients that quantify repeating/seasonal patterns in the input time series data, inherently determining whether the data exhibits seasonality]).
Nara., Wu, Ames and Cheng are analogous art because they are each directed to multiple forecast models (see, e.g., Nara., paragraph [0004], Wu, paragraph [0027], Ames, paragraph [0003], and Cheng paragraph [0007]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Nara., Wu, and Ames to incorporate the teachings of Cheng to perform linear regression on smoothed historic time series data to generate linear regression result, generate detrended historic time series data based on the smoothed historic time series data and the linear regression result, perform auto-correlation on the smoothed time series data and the detrended historic time series data to generate auto-correlation results and determine whether the historic time series data exhibits seasonality based on the auto-correlation results. Doing so would have allowed the combination of Nara., Wu, and Ames to use Cheng’s method in order to “accurately forecast future trends incorporating the time component”, as suggested by Cheng (see, e.g., Cheng, paragraph [0003]).
Regarding Claim 5, as discussed above, the combination of Nara., Wu and Ames teaches the method of claim 4.
Although the combination of Nara., Wu and Ames substantially teaches the claimed invention, said combination is not relied upon for explicitly teaching wherein the auto-correlation results include: a first auto-correlation metric obtained via auto-correlation on the smoothed historic time series data;
and a second auto-correlation metric obtained via auto-correlation on the detrended historic time series data
In the same field, analogous art Cheng teaches wherein the auto-correlation results include: a first auto-correlation metric obtained via auto-correlation on the smoothed historic time series data; and a second auto-correlation metric obtained via auto-correlation on the detrended historic time series data (see, e.g., Cheng paragraph [0024]: “The model trainer 210 may generate and/or train each model 212 with different parameters, for example, the model trainer 210 may generate and train a plurality of autoregressive integrated moving average (ARIMA) models with different orders of the autoregressive models (i.e., the number of time lags and commonly represented as the parameter p)” [i.e., the lag-order (p) and resulting coefficients from ARIMA model generation encode the specific temporal correlation patterns that are identified in the de-seasoned/smoothed time series input data], paragraph [0034]: “Referring now to FIG. 5… The preprocess stage 510 receives an input time series 502 and performs data frequency handling 512, NULL imputation 514 (i.e., determining and/or rejecting any nulls in the input time series 502), holiday effect modeling 516, and anomaly detection 518” [i.e., the input time series undergoes a form of smoothing via NULL imputation and anomaly detection] and paragraph [0036]: “The de-seasoned component 523 a is processed via a Kwtatkowsi-Phillips-Schmidt-Shin (KPSS) test module 524 and generates a plural of ARIMA models in parallel at 526” [i.e., the generation of ARIMA models with a specific lag order produces corresponding auto-regressive coefficients which represent the strength of the temporal correlation identified at first and second time lags (first and second auto-correlation metrics) within the de-seasoned component (encompassing detrended & smoothed input time series data)]).
Nara., Wu, Ames and Cheng are analogous art because they are each directed to multiple forecast models (see, e.g., Nara., paragraph [0004], Wu, paragraph [0027], and Ames, paragraph [0003], and Cheng paragraph [0007]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Nara., Wu, and Ames to incorporate the teachings of Cheng to include first and second auto-correlation metrics obtained via auto-correlation on smoothed historic time series data and detrended historic time series data, respectively. Doing so would have allowed the combination of Nara., Wu, and Ames to use Cheng’s method in order to “accurately forecast future trends incorporating the time component”, as suggested by Cheng (see, e.g., Cheng, paragraph [0003]).
Regarding Claim 11, as discussed above, the combination of Nara., Wu and Ames teaches the non-transitory medium of claim 10.
Although the combination of Nara., Wu and Ames substantially teaches the claimed invention, said combination is not relied upon for explicitly teaching wherein the determining whether the historic time series data exhibits seasonality comprises: performing linear regression on smoothed historic time series data to generate linear regression result;
generating detrended historic time series data based on the smoothed historic time series data and the linear regression result;
performing auto-correlation on the smoothed time series data and the detrended historic time series data to generate auto-correlation results;
and determining whether the historic time series data exhibits seasonality based on the auto-correlation results.
In the same field, analogous art Cheng teaches wherein the determining whether the historic time series data exhibits seasonality comprises: performing linear regression on smoothed historic time series data to generate linear regression result (see, e.g., Cheng paragraph [0034]: “The preprocess stage 510 receives an input time series 502 and performs data frequency handling 512, NULL imputation 514 (i.e., determining and/or rejecting any nulls in the input time series 502), holiday effect modeling 516, and anomaly detection 518” [i.e., the preprocessing operations provide a cleaned and smoothed input time series] and paragraph [0036]: “After preprocessing, the training stage 520 begins with a seasonal and trend decomposition using local regression (STL) module 522 which generates a de-seasoned component 523 a and a seasonal component 523 b” [i.e., the STL module uses local regression to smooth the input time series, producing a de-seasoned (smoothed historic time series data) component as it represents the non-seasonal variations in the data], "The STL module 522 estimates nonlinear relationships and decomposes a time series into multiple components 412 a-c (FIG. 4)" [i.e., the de-seasoned component from the STL module serves as a regression estimate of the smoothed series]);
generating detrended historic time series data based on the smoothed historic time series data and the linear regression result (see, e.g., Cheng paragraph [0036]: “After preprocessing, the training stage 520 begins with a seasonal and trend decomposition using local regression (STL) module 522 which generates a de-seasoned component 523” [i.e., the STL module produces a smoothed/de-seasoned component 523a which inherently generates detrended historic time series data]);
performing auto-correlation on the smoothed time series data and the detrended historic time series data to generate auto-correlation results (see, e.g., Cheng paragraph [0024]: “The model trainer 210 may generate and/or train each model 212 with different parameters, for example, the model trainer 210 may generate and train a plurality of autoregressive integrated moving average (ARIMA) models with different orders of the autoregressive models (i.e., the number of time lags and commonly represented as the parameter p)” [i.e., the autoregressive order parameter p depends on the auto-correlation of input data, and is used in encoding correlation patterns in the time series data] and paragraph [0036]: “The de-seasoned component 523 a is processed via a Kwtatkowsi-Phillips-Schmidt-Shin (KPSS) test module 524 and generates a plural of ARIMA models in parallel at 526” [i.e., the de-seasoned (smoothed) component is processed via KPSS and used for generating ARIMA models in parallel; these steps inherently involve the auto-correlation calculations on the smoothed and de-trended data of the input time series (historic time series data) generating auto-correlation results]);
and determining whether the historic time series data exhibits seasonality based on the auto-correlation results (see, e.g., Cheng paragraph [0024]: “The model trainer 210 may generate and/or train each model 212 with different parameters, for example, the model trainer 210 may generate and train a plurality of autoregressive integrated moving average (ARIMA) models with different orders of the autoregressive models (i.e., the number of time lags and commonly represented as the parameter p)” [i.e., ARIMA's autoregressive parameter p and lag structure captures temporal correlations of de-seasoned series], paragraph [0025]: “As discussed in more detail below, the model trainer 210 may generate and train forecasting models 212 capable of modeling many different aspects of time series. For example, the forecast models 212 may account for seasonal effects” and paragraph [0036]: “The de-seasoned component 523 a is processed via a Kwtatkowsi-Phillips-Schmidt-Shin (KPSS) test module 524 and generates a plural of ARIMA models in parallel at 526” [i.e., the ARIMA models are trained on the de-seasoned component and yields autocorrelation coefficients that quantify repeating/seasonal patterns in the input time series data, inherently determining whether the data exhibits seasonality]).
Nara., Wu, Ames and Cheng are analogous art because they are each directed to multiple forecast models (see, e.g., Nara., paragraph [0004], Wu, paragraph [0027], and Ames, paragraph [0003], and Cheng paragraph [0007]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Nara., Wu, and Ames to incorporate the teachings of Cheng to perform linear regression on smoothed historic time series data to generate linear regression result, generate detrended historic time series data based on the smoothed historic time series data and the linear regression result, perform auto-correlation on the smoothed time series data and the detrended historic time series data to generate auto-correlation results and determine whether the historic time series data exhibits seasonality based on the auto-correlation results. Doing so would have allowed the combination of Nara., Wu, and Ames to use Cheng’s method in order to “accurately forecast future trends incorporating the time component”, as suggested by Cheng (see, e.g., Cheng, paragraph [0003]).
Regarding Claim 12, as discussed above, the combination of Nara., Wu, Ames and Cheng teaches the non-transitory medium of claim 11.
Although the combination of Nara., Wu and Ames substantially teaches the claimed invention, said combination is not relied upon for explicitly teaching wherein the auto-correlation results include: a first auto-correlation metric obtained via auto-correlation on the smoothed historic time series data;
and a second auto-correlation metric obtained via auto-correlation on the detrended historic time series data
In the same field, analogous art Cheng teaches wherein the auto-correlation results include: a first auto-correlation metric obtained via auto-correlation on the smoothed historic time series data; and a second auto-correlation metric obtained via auto-correlation on the detrended historic time series data (see, e.g., Cheng paragraph [0024]: “The model trainer 210 may generate and/or train each model 212 with different parameters, for example, the model trainer 210 may generate and train a plurality of autoregressive integrated moving average (ARIMA) models with different orders of the autoregressive models (i.e., the number of time lags and commonly represented as the parameter p)” [i.e., the lag-order (p) and resulting coefficients from ARIMA model generation encode the specific temporal correlation patterns that are identified in the de-seasoned/smoothed time series input data], paragraph [0034]: “Referring now to FIG. 5… The preprocess stage 510 receives an input time series 502 and performs data frequency handling 512, NULL imputation 514 (i.e., determining and/or rejecting any nulls in the input time series 502), holiday effect modeling 516, and anomaly detection 518” [i.e., the input time series undergoes a form of smoothing via NULL imputation and anomaly detection] and paragraph [0036]: “The de-seasoned component 523 a is processed via a Kwtatkowsi-Phillips-Schmidt-Shin (KPSS) test module 524 and generates a plural of ARIMA models in parallel at 526” [i.e., the generation of ARIMA models with a specific lag order produces corresponding auto-regressive coefficients which represent the strength of the temporal correlation identified at first and second time lags (first and second auto-correlation metrics) within the de-seasoned component (encompassing detrended & smoothed input time series data)]).
Nara., Wu, Ames and Cheng are analogous art because they are each directed to multiple forecast models (see, e.g., Nara., paragraph [0004], Wu, paragraph [0027], and Ames, paragraph [0003], and Cheng paragraph [0007]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Nara., Wu, and Ames to incorporate the teachings of Cheng to include first and second auto-correlation metrics obtained via auto-correlation on smoothed historic time series data and detrended historic time series data, respectively. Doing so would have allowed the combination of Nara., Wu, and Ames to use Cheng’s method in order to “accurately forecast future trends incorporating the time component”, as suggested by Cheng (see, e.g., Cheng, paragraph [0003]).
Regarding Claim 17, as discussed above, the combination of Nara., Wu and Ames teaches the system of claim 16.
Although the combination of Nara., Wu and Ames substantially teaches the claimed invention, said combination is not relied upon for explicitly teaching wherein the determining whether the historic time series data exhibits seasonality comprises: performing linear regression on smoothed historic time series data to generate linear regression result;
generating detrended historic time series data based on the smoothed historic time series data and the linear regression result;
performing auto-correlation on the smoothed time series data and the detrended historic time series data to generate auto-correlation results;
and determining whether the historic time series data exhibits seasonality based on the auto-correlation results.
In the same field, analogous art Cheng teaches wherein the determining whether the historic time series data exhibits seasonality comprises: performing linear regression on smoothed historic time series data to generate linear regression result (see, e.g., Cheng paragraph [0034]: “The preprocess stage 510 receives an input time series 502 and performs data frequency handling 512, NULL imputation 514 (i.e., determining and/or rejecting any nulls in the input time series 502), holiday effect modeling 516, and anomaly detection 518” [i.e., the preprocessing operations provide a cleaned and smoothed input time series] and paragraph [0036]: “After preprocessing, the training stage 520 begins with a seasonal and trend decomposition using local regression (STL) module 522 which generates a de-seasoned component 523 a and a seasonal component 523 b” [i.e., the STL module uses local regression to smooth the input time series, producing a de-seasoned (smoothed historic time series data) component as it represents the non-seasonal variations in the data], "The STL module 522 estimates nonlinear relationships and decomposes a time series into multiple components 412 a-c (FIG. 4)" [i.e., the de-seasoned component from the STL module serves as a regression estimate of the smoothed series]);
generating detrended historic time series data based on the smoothed historic time series data and the linear regression result (see, e.g., Cheng paragraph [0036]: “After preprocessing, the training stage 520 begins with a seasonal and trend decomposition using local regression (STL) module 522 which generates a de-seasoned component 523” [i.e., the STL module produces a smoothed/de-seasoned component 523a which inherently generates detrended historic time series data]);
performing auto-correlation on the smoothed time series data and the detrended historic time series data to generate auto-correlation results (see, e.g., Cheng paragraph [0024]: “The model trainer 210 may generate and/or train each model 212 with different parameters, for example, the model trainer 210 may generate and train a plurality of autoregressive integrated moving average (ARIMA) models with different orders of the autoregressive models (i.e., the number of time lags and commonly represented as the parameter p)” [i.e., the autoregressive order parameter p depends on the auto-correlation of input data, and is used in encoding correlation patterns in the time series data] and paragraph [0036]: “The de-seasoned component 523 a is processed via a Kwtatkowsi-Phillips-Schmidt-Shin (KPSS) test module 524 and generates a plural of ARIMA models in parallel at 526” [i.e., the de-seasoned (smoothed) component is processed via KPSS and used for generating ARIMA models in parallel; these steps inherently involve the auto-correlation calculations on the smoothed and de-trended data of the input time series (historic time series data) generating auto-correlation results]);
and determining whether the historic time series data exhibits seasonality based on the auto-correlation results (see, e.g., Cheng paragraph [0024]: “The model trainer 210 may generate and/or train each model 212 with different parameters, for example, the model trainer 210 may generate and train a plurality of autoregressive integrated moving average (ARIMA) models with different orders of the autoregressive models (i.e., the number of time lags and commonly represented as the parameter p)” [i.e., ARIMA's autoregressive parameter p and lag structure captures temporal correlations of de-seasoned series], paragraph [0025]: “As discussed in more detail below, the model trainer 210 may generate and train forecasting models 212 capable of modeling many different aspects of time series. For example, the forecast models 212 may account for seasonal effects” and paragraph [0036]: “The de-seasoned component 523 a is processed via a Kwtatkowsi-Phillips-Schmidt-Shin (KPSS) test module 524 and generates a plural of ARIMA models in parallel at 526” [i.e., the ARIMA models are trained on the de-seasoned component and yields autocorrelation coefficients that quantify repeating/seasonal patterns in the input time series data, inherently determining whether the data exhibits seasonality]).
Nara., Wu, Ames and Cheng are analogous art because they are each directed to multiple forecast models (see, e.g., Nara., paragraph [0004], Wu, paragraph [0027], and Ames, paragraph [0003], and Cheng paragraph [0007]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Nara., Wu, and Ames to incorporate the teachings of Cheng to perform linear regression on smoothed historic time series data to generate linear regression result, generate detrended historic time series data based on the smoothed historic time series data and the linear regression result, perform auto-correlation on the smoothed time series data and the detrended historic time series data to generate auto-correlation results and determine whether the historic time series data exhibits seasonality based on the auto-correlation results. Doing so would have allowed the combination of Nara., Wu, and Ames to use Cheng’s method in order to “accurately forecast future trends incorporating the time component”, as suggested by Cheng (see, e.g., Cheng, paragraph [0003]).
Regarding Claim 18, as discussed above, the combination of Nara., Wu and Ames teaches the system of claim 17.
Although the combination of Nara., Wu and Ames substantially teaches the claimed invention, said combination is not relied upon for explicitly teaching wherein the auto-correlation results include: a first auto-correlation metric obtained via auto-correlation on the smoothed historic time series data;
and a second auto-correlation metric obtained via auto-correlation on the detrended historic time series data
In the same field, analogous art Cheng teaches wherein the auto-correlation results include: a first auto-correlation metric obtained via auto-correlation on the smoothed historic time series data; and a second auto-correlation metric obtained via auto-correlation on the detrended historic time series data (see, e.g., Cheng paragraph [0024]: “The model trainer 210 may generate and/or train each model 212 with different parameters, for example, the model trainer 210 may generate and train a plurality of autoregressive integrated moving average (ARIMA) models with different orders of the autoregressive models (i.e., the number of time lags and commonly represented as the parameter p)” [i.e., the lag-order (p) and resulting coefficients from ARIMA model generation encode the specific temporal correlation patterns that are identified in the de-seasoned/smoothed time series input data], paragraph [0034]: “Referring now to FIG. 5… The preprocess stage 510 receives an input time series 502 and performs data frequency handling 512, NULL imputation 514 (i.e., determining and/or rejecting any nulls in the input time series 502), holiday effect modeling 516, and anomaly detection 518” [i.e., the input time series undergoes a form of smoothing via NULL imputation and anomaly detection] and paragraph [0036]: “The de-seasoned component 523 a is processed via a Kwtatkowsi-Phillips-Schmidt-Shin (KPSS) test module 524 and generates a plural of ARIMA models in parallel at 526” [i.e., the generation of ARIMA models with a specific lag order produces corresponding auto-regressive coefficients which represent the strength of the temporal correlation identified at first and second time lags (first and second auto-correlation metrics) within the de-seasoned component (encompassing detrended & smoothed input time series data)]).
Nara., Wu, Ames and Cheng are analogous art because they are each directed to multiple forecast models (see, e.g., Nara., paragraph [0004], Wu, paragraph [0027], and Ames, paragraph [0003], and Cheng paragraph [0007]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Nara., Wu, and Ames to incorporate the teachings of Cheng to include first and second auto-correlation metrics obtained via auto-correlation on smoothed historic time series data and detrended historic time series data, respectively. Doing so would have allowed the combination of Nara., Wu, and to use Cheng’s method in order to “accurately forecast future trends incorporating the time component”, as suggested by Cheng (see, e.g., Cheng, paragraph [0003]).
Conclusion
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/JEREMY HALZEL/Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
1 Under the broadest reasonable interpretation (BRI), method 300 can be executed by server computer 114 as a cloud provider, using its SaaS infrastructure to dynamically allocate resources based on the ensemble model.
2 Under the broadest reasonable interpretation (BRI), method 300 can be executed by server computer 114 as a cloud provider, using its SaaS infrastructure to dynamically allocate resources based on the ensemble model.
3 Under the BRI, a cloud-computing system that uses a measured service that monitors, controls and reports resource usage, functions as a 'resource use data collector' as it inherently collects resource usage associated which directly relates to the cloud service provider of server computer 114 which operates within a cloud service model.
Under the BRI the load forecasting system receives historical load consumption data and because server computer 114 operates as the service provider, its metered usage forms time-indexed data of historic load consumption, it is reasonably expected that the historic resource usage data collected by the server computer is appended to the historic load consumption inputs described in the load forecasting system.
5 Under the broadest reasonable interpretation (BRI), method 300 can be executed by server computer 114 as a cloud provider, using its SaaS infrastructure to dynamically allocate resources based on the ensemble model.
6 Under the broadest reasonable interpretation (BRI), method 300 can be executed by server computer 114 as a cloud provider, using its SaaS infrastructure to dynamically allocate resources based on the ensemble model.
7 Under the BRI, a cloud-computing system that uses a measured service that monitors, controls and reports resource usage, functions as a 'resource use data collector' as it inherently collects resource usage associated which directly relates to the cloud service provider of server computer 114 which operates within a cloud service model.
Under the BRI the load forecasting system receives historical load consumption data and because server computer 114 operates as the service provider, its metered usage forms time-indexed data of historic load consumption, it is reasonably expected that the historic resource usage data collected by the server computer is appended to the historic load consumption inputs described in the load forecasting system.
9 Under the broadest reasonable interpretation (BRI), method 300 can be executed by server computer 114 as a cloud provider, using its SaaS infrastructure to dynamically allocate resources based on the ensemble model.
10 Under the broadest reasonable interpretation (BRI), method 300 can be executed by server computer 114 as a cloud provider, using its SaaS infrastructure to dynamically allocate resources based on the ensemble model.
11 Under the BRI, a cloud-computing system that uses a measured service that monitors, controls and reports resource usage, functions as a 'resource use data collector' as it inherently collects resource usage associated which directly relates to the cloud service provider of server computer 114 which operates within a cloud service model.
Under the BRI the load forecasting system receives historical load consumption data and because server computer 114 operates as the service provider, its metered usage forms time-indexed data of historic load consumption, it is reasonably expected that the historic resource usage data collected by the server computer is appended to the historic load consumption inputs described in the load forecasting system.
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