DETAILED ACTION
Status of Claims
Claim(s) 1-20 are pending and are examined herein.
Claim(s) 1-20 are rejected under 35 U.S.C. §§§ 101, 102, and 103.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement IDS(s) submitted on July 27, 2022 is in compliance with the provisions of 37 CFR 1.97 and have been considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself. Applicant is advised to consult MPEP 2106 for more details of the analysis.
Under Step 1 analysis,
Claims 1-12 recite a system (representing a machine);
Claims 13-18 recite a system (representing a process); and
Claims 19-20 recite a non-transitory storage medium (representing an article of manufacture);
Therefore, each set of the claims falls into one of the four statutory categories (i.e., process, machine, article of manufacture, or composition of matter).
Claim(s)
1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more, and hence is not patent-eligible subject matter.
Regarding Claim 1,
Step 2A Prong 1: The claim recites an abstract idea enumerated in the 2019 PEG.
identify a first dataset comprising a plurality of time series having a plurality of characteristics, wherein a first time series of the plurality of time series comprises one or more characteristics of the plurality of characteristics that are different from characteristics of a second time series of the plurality of time series; (An abstract idea of a mental process. Examiner’s note: the “identify” step, as drafted, and under its broadest reasonable interpretation (BRI), covers concepts that can be practically performed in the human mind. The process of identifying the characteristics of timeseries dataset falls within the Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).)
select, based at least in part on the plurality of characteristics, a plurality of models; (An abstract idea of a mental process. Examiner’s note: the “selecting” step, as drafted, and under its broadest reasonable interpretation (BRI), covers concepts that can be practically performed in the human mind. The claimed step defines a decision-making process that can be performed in the human mind. For example, selecting the model based on the characteristics analysis identified for a given time-series data can be manually determined. This is an evaluation and judgment process that could be performed mentally without a computer. See MPEP § 2106.04(a)(2) (III).)
generate a model based at least in part on a combination of the plurality of models; (An abstract idea of a mental process. Examiner’s note: the “generating” step, as drafted, and under its broadest reasonable interpretation (BRI), covers concepts that can be practically performed in the human mind. This step is broadly interpreted as organizing or grouping trained models based on the selection characteristics criteria. The claim and the specification do not describe a specific technical implementation for “generating” a model from the plurality of trained models. Instead, they define the resulting combined model as collectively comprising the plurality of trained models ([0003], [0056], and [0154]). Therefore, under the BRI in light of the spec, the “generating” step is interpreted as assembling or grouping the trained models (i.e., deciding to use multiple models together or as a group).)
Step 2A Prong 2: Under this prong, we evaluate whether the claim recites additional elements that integrate the abstract idea into a practical application by considering the claim as a whole. The judicial exception is not integrated into a practical application.
Additional Elements Analysis:
The claim recite the additional element such as:
“train, via machine learning, the plurality of models with the first dataset;” (This amounts to no more than merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). In other words, the claim invokes computer and/or other machinery in its ordinary capacity merely as a tool to perform the abstract idea.)
“deploy the model to output one or more predictions responsive to a second dataset, different from the first dataset, having at least one of the plurality of characteristics.” (This amounts to no more than merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f). Examiner’s Note: High-level recitation of applying the trained model to perform predictions. This is a high-level application of machined learning model.)
Step 2B: Under this prong, the claim must include additional elements that amount to significantly more than the judicial exception. These elements must not be well-understood, routine, or conventional in the relevant field. When viewed individually and as an ordered combination, the claim does not include any such additional elements that are sufficient to amount to significantly more (i.e., inventive concept).
Additional Elements Analysis:
As explained above, the claimed additional elements merely represents generic computer components configured to execute computer instructions (i.e., conventional models) to perform the abstract ideas. As described in MPEP § 2106.05(f), additional elements that invoke computers or other machinery merely as a tool to perform an existing process will generally not amount to significantly more than a judicial exception.
Therefore, claim 1 does not recite patent-eligible subject matter.
Regarding Claim 2,
Step 2A Prong 1: Claim 2, which incorporates the rejection of claim 1, recites further limitation such as:
determine that multiple rows in the first dataset comprise a same timestamp;... determine to select the plurality of models based at least in part on the indication received from the computing device. (That is part of the abstract idea recited in claim 1. This merely defines the process of analyzing the dataset timestamp and selecting the model based on this identification (i.e., data analysis). These steps fall under the mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
provide, responsive to the determination, a prompt via a graphical user interface displayed on a display device coupled to a computing device; receive, via the prompt from the computing device, an indication that the first dataset comprises more than one time series; (The claimed steps amount to adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g). Examiner’s Note: The claimed steps represent displaying certain results of the collection and analysis and receiving user response, which represent generic data gathering and outputting steps in conjunction with the abstract idea. The recited “graphical user interface” amounts to using generic computer component to display the results of the analysis. Accordingly, the additional elements do not integrate the abstract idea into a practical application; they merely perform routine data gathering and generic model training.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As explained above, the additional elements identified above do not provide significantly more than the abstract idea. Providing and receiving information represents generic computer functions that have been recognized by the courts as well-understood, routine, conventional activities in the field. See MPEP § 2106.05(d).
Therefore, claim 2 is ineligible.
Regarding Claim 3,
Step 2A Prong 1: Claim 3, which incorporates the rejection of claim 1, recites further limitation such as:
split, responsive to the indication, the first dataset into segments. (An abstract idea of a mental process. The “splitting” step, as drafted and under its broadest reasonable interpretation, cover concepts that can be performed in the human mind with the aid of pen and paper. An individual can manually split the dataset into segments based on the characteristics analysis of the data. This falls under the mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
provide, for display via a graphical user interface presented on a display device coupled to a computing device, a prompt to split the first dataset by segments; receive, via the graphical user interface from the computing device, an indication to split the first dataset by segments; (The claimed steps amount to adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g). Examiner’s Note: The claimed steps represent displaying certain results of the collection and analysis and receiving user response, which represent generic data gathering and outputting steps in conjunction with the abstract idea. The recited “graphical user interface” amounts to using generic computer component to display the results of the analysis to split the dataset into segments (i.e., abstract idea). Accordingly, the additional elements do not integrate the abstract idea into a practical application; they merely perform routine data gathering and outputting steps.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As explained above, the additional elements identified above do not provide significantly more than the abstract idea. Providing and receiving information using GUI represents generic computer functions that have been recognized by the courts as well-understood, routine, conventional activities in the field. See MPEP § 2106.05(d).
Therefore, claim 3 is ineligible.
Regarding Claim 4,
Step 2A Prong 1: Claim 4, which incorporates the rejection of claim 1, doesn’t recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
provide, via a graphical user interface presented by a display device of a computing device, a user interface element to adjust at least one of a first window used to derive one or more features from the first dataset or a second window over which to predict values for the one or more features. (This amounts to adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g). This represents a generic computer function (i.e., data gathering and/or outputting in conjunction with the abstract idea). This represents the concept of displaying certain results of the collection and analysis related to the timeseries data where the user can manually adjust the time window via interface. The additional element does not integrate the abstract idea into a practical application; it merely perform routine data outputting step.
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As explained above, the additional element identified above does not provide significantly more than the abstract idea. The recited graphical interface used to present/display the result of the data analysis amount to using a generic computer component to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry. See MPEP § 2106.05(d).
Therefore, claim 4 is ineligible.
Regarding Claim 5,
Step 2A Prong 1: Claim 5, which incorporates the rejection of claim 4, doesn’t recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
provide, via the graphical user interface, an indication of a forecast point at or between the first window and the second window. (This amounts to adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g). This describes providing information of the data analysis to the user via graphical user interface. This does not integrate the abstract idea into a practical application; it is a routine operation for providing the result of the data analysis. This additional element does not transform the abstract idea into a practical application and is considered generic data gathering/outputting operation.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As explained above, the additional element identified above does not provide significantly more than the abstract idea. Providing the result of the data analysis to the user via a graphical user interface represents a generic computer function that has been recognized by the courts as well-understood, routine, conventional activity. See MPEP § 2106.05(d).
Therefore, claim 5 is ineligible.
Regarding Claim 6,
Step 2A Prong 1: Claim 6, which incorporates the rejection of claim 5, recites further limitation such as:
identify a blind history gap between the first window and the forecast point presented via the graphical user interface; (That is part of the abstract idea recited in claim 1. Examiner’s note: the “identifying” step, as drafted, and under its broadest reasonable interpretation (BRI), covers concepts that can be practically performed in the human mind with the aid of pen and paper. But for the recitation of a graphical user interface, that is not other than using a computer component to perform the abstract idea. See MPEP § 2106.04(a)(2)(III). An individual can manually identify a blind history gap between two time windows using pen and paper or slide rule.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
provide an indication via the graphical user interface of the blind history gap. (This amounts to adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g). This describes providing information of the data analysis to the user via graphical user interface. This does not integrate the abstract idea into a practical application; it is a routine operation for providing the result of the data analysis. This additional element does not transform the abstract idea into a practical application and is considered generic data gathering/outputting operation.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As explained above, the additional element identified above does not provide significantly more than the abstract idea. Providing the result of the data analysis to the user via a graphical user interface represents a generic computer function that has been recognized by the courts as well-understood, routine, conventional activity. See MPEP § 2106.05(d).
Therefore, claim 6 is ineligible.
Regarding Claim 7,
Step 2A Prong 1: Claim 7, which incorporates the rejection of claim 5, recites further limitation such as:
identify, based at least on the forecast point and the second window, a gap for which the model is unable to make predictions. (That is part of the abstract idea recited in claim 1. Examiner’s note: the “identifying” step, as drafted, and under its broadest reasonable interpretation (BRI), covers concepts that can be practically performed in the human mind with the aid of pen and paper. See MPEP § 2106.04(a)(2)(III). An individual can manually identify a gap between two time windows using pen and paper or slide rule.)
Step 2A Prong 2: The claim does not recite additional element that integrates the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Therefore, claim 7 is ineligible.
Regarding Claim 8,
Step 2A Prong 1: Claim 8, which incorporates the rejection of claim 1, doesn’t recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
provide, for presentation by a graphical user interface via a display device coupled to a computing device, a user interface element to select a configuration for a backtest; receive, via the user interface element, a selection of the configuration for the backtest; and provide, for presentation by the graphical user interface, an indication of at least one of a validation portion for the backtest, a primary training data portion for the backtest, a gap for the backtest, or a holdout portion for the backtest. (The claimed steps amount to adding insignificant extra-solution activities to the judicial exception, as discussed in MPEP § 2106.05(g). These steps represents data gathering and outputting steps in conjunction with the abstract idea.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As explained above, the additional element identified above does not provide significantly more than the abstract idea. Providing the result of the data analysis to the user via a graphical user interface represents a generic computer function that has been recognized by the courts as well-understood, routine, conventional activity. See MPEP § 2106.05(d).
Therefore, claim 8 is ineligible.
Regarding Claim 9,
Step 2A Prong 1: Claim 9, which incorporates the rejection of claim 1, recites further limitation such as:
derive one or more features of the first dataset using the calendar of events. (That is part of the abstract idea recited in claim 1. Examiner’s note: the “deriving” step, as drafted, and under its broadest reasonable interpretation (BRI), covers concepts that can be practically performed in the human mind. See MPEP § 2106.04(a)(2)(III). An individual can manually identify features or characteristics from the dataset using calendar of events.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
provide, for presentation by a graphical user interface via a display device coupled to a computing device, a user interface element to input a calendar of events to generate a feature for the plurality of time series; receive, via the user interface element, the calendar of events; (The claimed additional limitations amount to adding insignificant extra-solution activities to the judicial exception, as discussed in MPEP § 2106.05(g). They represents providing information of the data analysis to the user via graphical user interface and receiving user response. These elements do not integrate the abstract idea into a practical application; they define generic interface receiving and outputting information.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As explained above, the additional elements identified above do not provide significantly more than the abstract idea. Providing and receiving information via a graphical user interface represents a generic computer function that has been recognized by the courts as well-understood, routine, conventional activity. See MPEP § 2106.05(d).
Therefore, claim 9 is ineligible.
Regarding Claim 10,
Step 2A Prong 1: Claim 10, which incorporates the rejection of claim 1, recites further limitation such as:
wherein the plurality of characteristics comprise at least one of seasonality, frequency content, average target values, maximum target values, minimum target values, or a number of zero values. (That is part of the abstract idea recited in claim 1. This limitation merely specifies the types of characteristics used to identify the timeseries dataset. The claim does not introduce any technical implementation of the abstract idea that would be considered as additional element and evaluated under Step 2A, Prong 2.)
Step 2A Prong 2: The claim does not recite additional element that integrates the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Therefore, claim 10 is ineligible.
Regarding Claim 11,
Step 2A Prong 1: Claim 11, which incorporates the rejection of claim 1, recites further limitation such as:
map each time series in the plurality of time series to at least one model in the plurality of models to select the plurality of models. (That is part of the abstract idea recited in claim 1. This limitation merely specifies the abstract idea of identifying the characteristic of the dataset and selecting the model corresponding to the type of dataset. The “mapping” step, as drafted and under its broadest reasonable interpretation, covers concept that can be practically performed in the human mind. This steps falls within the mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).)
Step 2A Prong 2: The claim does not recite additional element that integrates the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Therefore, claim 11 is ineligible.
Regarding Claim 12,
Step 2A Prong 1: Claim 12, which incorporates the rejection of claim 11, recites further limitation such as:
cluster the time series in the plurality of time series into a plurality of groups, wherein each group in the plurality of groups comprises common or similar characteristics from the characteristics; and assign each group to a respective model from the plurality of models to select the plurality of models. (That is part of the abstract idea recited in claim 1. The claim merely introduce the concepts of clustering time series data into groups and mapping a respective model to each group which can be practically performed in the human mind. These steps fall within the mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).)
Step 2A Prong 2: The claim does not recite additional element that integrates the judicial exception into a practical application.
Step 2B: The claim does not recite additional elements that amount to significantly more than the judicial exception.
Therefore, claim 12 is ineligible.
Regarding Claim 13,
The claim recites similar limitations as corresponding claim 1. Therefore, the same analysis (subject matter eligibility analysis) that was utilized for claim 1, as described above, is equally applicable to claim 13. The only difference is that claim 1 is drawn to a system, and claim 13 is drawn to a method.
Therefore, claim 13 is ineligible.
Regarding Claim 14,
The claim recites similar limitations as corresponding claim 2. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 2, as described above, is equally applicable to claim 14.
Therefore, claim 14 is ineligible.
Regarding Claim 15,
The claim recites similar limitations as corresponding claim 3. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 3, as described above, is equally applicable to claim 15.
Therefore, claim 15 is ineligible.
Regarding Claim 16,
The claim recites similar limitations as corresponding claim 4. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 4, as described above, is equally applicable to claim 16.
Therefore, claim 16 is ineligible.
Regarding Claim 17,
The claim recites similar limitations as corresponding claim 5. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 5, as described above, is equally applicable to claim 17.
Therefore, claim 17 is ineligible.
Regarding Claim 18,
The claim recites similar limitations as corresponding claim 8. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 8, as described above, is equally applicable to claim 18.
Therefore, claim 18 is ineligible.
Regarding Claim 17,
The claim recites similar limitations as corresponding claim 7. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 7, as described above, is equally applicable to claim 17.
Therefore, claim 17 is ineligible.
Regarding Claim 18,
The claim recites similar limitations as corresponding claim 8. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 8, as described above, is equally applicable to claim 18.
Therefore, claim 18 is ineligible.
Regarding Claim 19,
The claim recites similar limitations as corresponding claim 1. Therefore, the same analysis (subject matter eligibility analysis) that was utilized for claim 1, as described above, is equally applicable to claim 19. The only difference is that claim 1 is drawn to a system, and claim 19 is drawn to a non-transitory computer-readable medium. The recitation of “a non-transitory computer-readable medium storing processor executable instructions that, when executed by one or more processors...” merely defines computer component and instructions to implement a judicial exception, and hence the claimed additional elements listed above are merely generic elements and the implementation of the elements merely amount to no more than instructions to apply the abstract idea using generic computer components. Therefore, the additional elements do not integrate the judicial exception into a practical application or amount to significantly more. See MPEP 2106.05(f).
Therefore, claim 19 is ineligible.
Regarding Claim 20,
The claim recites similar limitations as corresponding claim 2. Therefore, the same subject matter eligibility analysis (including the abstract idea) that was utilized for claim 2, as described above, is equally applicable to claim 20.
Therefore, claim 20 is ineligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 10-13, and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Shashikant Rao et al., (Pub. No.: US 20200242483 A1), hereinafter Rao.
Regarding Claim 1,
Rao discloses the following:
A system, comprising: (Rao, [0023] “In the specific illustrative example of FIG. 1, the production environment 100 includes a service provider computing environment 110 comprising a meta-model training system 120, a model selection system 130, a prediction system 140, and a data management system 150.”)
one or more processors, coupled to memory, to: (Rao, [0024] “In the specific illustrative example of FIG. 1, the production environment 100 includes a processor 111 and a memory 112. In one embodiment, the memory 112 includes instructions stored therein and which, when executed by the processor 111, performs a process.”)
identify a first dataset comprising a plurality of time series having a plurality of characteristics, wherein a first time series of the plurality of time series comprises one or more characteristics of the plurality of characteristics that are different from characteristics of a second time series of the plurality of time series; (Rao, [0005] “In one embodiment, sets of time series are distinguished from each other based on diverse sparsities, temporal scales and other time series attributes as discussed herein,..” [0030] “the service provider computing environment 110 includes real-time forecasting of millions of time series having differing characteristics. In one embodiment, characteristics of time series differ based on the volume of historical information.” [0113] “the plurality of time series training data 210 comprises a first time series data set and a second time series data set. In one embodiment, the first time series data set has a first categorization property that is diverse from a second categorization property of the second time series data set.”)
select, based at least in part on the plurality of characteristics, a plurality of models; (Rao, [0031] “In one embodiment, the service provider computing environment 110 selects the right type of prediction algorithm for the right kind of time series.” [0037] “In one embodiment, dynamic model selection includes choosing a model based on various properties of the data, such as sparsity. It is to be understood that a model is selected based on other properties in addition to sparsity, ...”)
train, via machine learning, the plurality of models with the first dataset; (Rao, [0031] “In one embodiment, the meta-model training system 120 trains meta-models for time series forecasting based on certain characteristics, such as sparsity of the time series.” [0069] “the meta-model decision engine training module 230 trains the meta-model.”)
generate a model based at least in part on a combination of the plurality of models; (Rao, [0031] “the meta-model training system 120 generates a meta-model decision graph based on time series training data. In one embodiment, the model selection system 130 selects the best possible forecasting algorithm based on the meta-model decision graph.” [0070] “... metrics are utilized to determine the best model combination of a prediction algorithm and a transform. In one embodiment, metrics inform the meta-model of the correct combination.” [008] “the meta-model decision engine training module 230 generates the meta-model artifact 240 based on the grouped time series training data 214... represented as a classification algorithm, a logistic regression approach, a decision graph, a decision tree, and other artifact representations.”) and
deploy the model to output one or more predictions responsive to a second dataset, different from the first dataset, having at least one of the plurality of characteristics. (Rao, [0040] “In one embodiment, the prediction system 140 applies the selected forecasting algorithm to current time series data.” [0085] “after the meta-model is trained, for a new time series that is not part of the training set, the new time series is compared to past time series as determined by the meta-model.” [0096] “In one embodiment, the prediction system 140 makes a prediction based on the selected model that was selected by the model selection system 130.” Further see [0121] and [0127].)
Regarding Claim 10, Rao teaches the elements of claim 1 as outlined above, and further teaches:
wherein the plurality of characteristics comprise at least one of seasonality, frequency content, average target values, maximum target values, minimum target values, or a number of zero values.. (Rao, [0037] “In one embodiment, dynamic model selection includes choosing a model based on various properties of the data, such as sparsity. It is to be understood that a model is selected based on other properties in addition to sparsity, including percent nonzero values, periodicity metrics, number of peaks, length of time series, seasonality metrics, anomalies detected, and other properties as discussed herein...” [0143] “In one embodiment, a feature includes sparsity, percent nonzero values, periodicity metrics, number of peaks, length of time series, seasonality metrics, anomalies detected, and other features...”)[Examiner’s Note: Rao teaches the plurality of characteristics including seasonality metrics (seasonality) and periodicity metric (frequency content).]
Regarding Claim 11, Rao teaches the elements of claim 1 as outlined above, and further teaches:
map each time series in the plurality of time series to at least one model in the plurality of models to select the plurality of models. (Rao, [0031] “As a specific illustrative example, if there are one hundred time series in a group, and there are five different applicable prediction algorithms, one of the five different algorithms is selected for each of the one hundred time series.” [0038] “In one embodiment, the time series are grouped based on one or more properties. In one embodiment, a prediction algorithm is selected for each time series group. It is to be understood that, in one embodiment, a selected prediction algorithm has better results for certain time series having certain properties.” [0093] “In one embodiment, the meta-model trained by the meta-model training system 120 is utilized to predict a label for the received time series. In one embodiment, the label represents a model comprising an input transform and a prediction algorithm. In one embodiment, a model is selected based on the predicted label. It is to be understood that, utilizing the meta-model, the model selection system 130 predicts a label based on the calculated labels of the meta-model training system 120.”)
Regarding Claim 12, Rao teaches the elements of claim 11 as outlined above, and further teaches:
cluster the time series in the plurality of time series into a plurality of groups, wherein each group in the plurality of groups comprises common or similar characteristics from the characteristics; (Rao, [0031] “the time series data sets have varying levels of sparsities... a first grouping may be low sparsity, a second grouping may be medium sparsity, and a third grouping may be high sparsity. In one embodiment, a model is chosen based on the appropriateness of a model to a grouping of a time series.” [0054] “the time series categorization module 212 receives the time series training data 210 and categorizes the degree of sparsities for each time series of the time series training data 210. In one embodiment, the time series categorization module 212 generates grouped time series training data 214 based on the categorizations.”) and assign each group to a respective model from the plurality of models to select the plurality of models. (Rao, [0038] “the time series are grouped based on one or more properties. In one embodiment, a prediction algorithm is selected for each time series group.” [0060] “a meta-model is generated for each group of the grouped time series training data 214.”)
Regarding Claim 13,
The claim recites substantially similar limitations as corresponding claim 1 and is rejected for similar reasons as claim 1 using similar teachings and rationale. Claim 1 is directed to a system, and claim 13 is directed to a method.
Rao also discloses method and system of dynamic model selection for time series forecasting.
Regarding Claim 19,
The claim recites substantially similar limitations as corresponding claim 1 and is rejected for similar reasons as claim 1 using similar teachings and rationale. Claim 1 is directed to a system, and claim 19 is directed to a non-transitory computer-readable medium storing processor executable instructions.
Rao also discloses [0167] “a computer program stored via a computer program product as defined herein that can be accessed by a computing system or other device to transform the computing system or other device into a specifically and specially programmed computing system or another device.
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.
Claim(s) 2-6, 14-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Rao et al., (Pub. No.: US 20200242483 A1) in view of Achin et al., (Pub. No.: US 20180046926 A1).
Regarding Claim 2,
Rao teaches the elements of claim 1 as outlined above:
Rao also teaches:
determine that multiple rows in the first dataset comprise a same timestamp; (Rao, [0106]-[0107] “The user computing environments 502 correspond to computing environments of the various users of the data management system 150. The user computing environments 502 may include user systems 503. The users of the data management system 150 utilize the user computing environments 502 to interact with the data management system 150. The users of the data management system 150 can use the user computing environments 502 to provide data to the data management system 150 and to receive data, including data management services, from the data management system 150.... The time series data 521 may be derived from data indicating the current status of all of the accounts of all of the users of the data management system 150. Thus, the data management database 520 can include a vast amount of data related to the data management services provided to users.” [0141] “each row of the table 1200 represents a time series, and each time series has an identifier to uniquely identify the time series. In one embodiment, the table 1200 has a row 1221 represented by a first time series identifier, a row 1231 represented by a second time series identifier, and a row 1241 represented by an Nth time series identifier.”) provide, responsive to the determination, a prompt via a graphical user interface displayed on a display device coupled to a computing device; (Rao, [0107] “the user interface module 530 provides interface content 534 to the user computing environments 502. The interface content 534 can include data enabling a user to obtain the current status of the user's accounts... The interface content 534 can also enable a user to select among the various options in the data management system 150 in order to fully utilize the services of the data management system 150.”) receive, via the prompt from the computing device, an indication that the first dataset comprises more than one time series; (Rao, [0107] “the interface content 534 can enable the user to select among the user's accounts in order to view transactions associated with the user's accounts. The interface content 534 can enable a user to view the overall state of many accounts. The interface content 534 can also enable a user to select among the various options in the data management system 150 in order to fully utilize the services of the data management system 150. [0109] “...when the user utilizes the user interface module 530 to view interface content 534, the interface content 534 includes the time series data 521 retrieved from the data management database 520. In one embodiment, the time series data 521 represents at least one transaction. In one embodiment, a transaction includes fields for user, merchant, category, description, date, amount, and other transaction fields as discussed herein,..”) and determine to select the plurality of models based at least in part on the indication received from the computing device. (Rao, [0030]-[0031] “the service provider computing environment 110 includes real-time forecasting of millions of time series having differing characteristics. In one embodiment, characteristics of time series differ based on the volume of historical information... different types of prediction algorithms are applied to these different types of time series. In one embodiment, the service provider computing environment 110 selects the right type of prediction algorithm for the right kind of time series.”
While Rao teaches multi-time series dataset, GUI interaction with the user, and model selection based on characteristics, Rao is salient on determining same timestamp time-series dataset, define a prompt, and receiving user indication that is used for model selection. However, it would have been obvious in view of Achin. Hereinafter, Rao in view of Achin teaches:
determine that multiple rows in the first dataset comprise a same timestamp; (Achin, [0182] “exploration engine 110 evaluates the dataset. This evaluation may include calculating the characteristics of the dataset. In some embodiments, this evaluation includes performing an analysis of the dataset, which may help the user better understand the prediction problem.” [0336]-[0337] “When the exploration engine 110 loads a dataset (e.g., at step 404 of the method 400 illustrated in FIG. 4), it may automatically detect whether the dataset appears to contain time series data and, if so, what the time index appears to be... In cases where the dataset contains time series data with a mixture of time resolutions, the engine 110 may use the most common resolution as part of the time index,..”) provide, responsive to the determination, a prompt via a graphical user interface displayed on a display device coupled to a computing device; (Achin, [0179] “the exploration engine 110 prompts the user to select the dataset for the predictive modeling problem to be solved. The user can chose from previously loaded datasets or create a new dataset, either from a file or instructions for retrieving data from other information systems.” [0187] “Predictive modeling system 100 presents the results of the dataset evaluation (e.g., the results of the dataset analysis, the characteristics of the dataset, and/or the results of the dataset transformations) to the user. In some embodiments, the results of the dataset evaluation are presented via user interface 120 (e.g., using graphs and/or tables).” [0340] “the presentation illustrates the dependencies of each time series variable's current value on past values at one or more time lags. In cases where a dataset is a panel dataset, containing a mixture of time-series and cross-sectional variables, the presentation may illustrate dependencies between cross-sectional variables either at the same time period or at different lags from each other.”) receive, via the prompt from the computing device, an indication that the first dataset comprises more than one time series; (Achin, [0188] “the user may refine the dataset (e.g., based on the results of the dataset evaluation).”[0338]-[0339] “a user may override (e.g., at step 406 of the method 400) the exploration engine's determination that the dataset is or is a not a time series dataset, either blocking or forcing treatment of the dataset as a time series dataset. If the user chooses to force treatment of a dataset as a time series dataset, the user may specify which variables are time-based, how to interpret their data format, and/or a time index... For automatically detected time series datasets, the user may accept the suggested time index, ...”) and determine to select the plurality of models based at least in part on the indication received from the computing device. (Achin, [0190]-[0191] “The determination of which modeling techniques are available may depend on the selected modeling methodology... Prioritizing the modeling techniques may include determining the suitabilities of the modeling techniques for the prediction problem, and selecting at least a subset of the modeling techniques for execution based on their determined suitabilities.” [0377] “selecting a predictive model for a prediction problem may be used to select a time-series predictive model for a time-series prediction problem.”) [Examiner Note: Under BRI, Rao in view of Achin describes an interactive process between user and the system to refine and select predictive modeling. Achin teaches the evaluating dataset and automatic detection using exploration engine of the characteristics of data, prompting/presenting the user the analysis and allowing the user to refine the dataset or adjusting modeling techniques, and predictive modeling selection/prioritization based at least in part of the user input. This reads on the claimed process of identifying same timestamps, prompting the user, receiving an indication related to the time-series dataset, and using user input to select models.]
Rao and Achin are from the same field of endeavor and their disclosure generally relates to (time-series predictive data analytics).
Accordingly, at the effective filing date, it would have been prima facie obvious to one ordinarily skilled in the art to modify the combination of Rao and Achin to incorporate the predictive modeling techniques used for time-series data as taught by Achin. One would have been motivated to make such a combination in order to rigorously and efficiently exploring the modeling search space for time-series models. Doing so would significantly improve the productivity of analysts at any skill level and/or significantly increase the accuracy of predictive models achievable with a given amount of resources (Achin [0248]).
Regarding Claim 3, Rao teaches the elements of claim 1 as outlined above:
While Rao teaches the process of separating/categorizing timeseries dataset into different groups based on their characteristics and providing interface content of the data management analysis to enable user interaction and selection (see Rao e.g., [0053] and [0107]), Rao does not appear to explicitly suggest an indication to split the first dataset by segments; and split, responsive to the indication, the first dataset into segments. However, it would have been obvious in view of Achin.
Hereinafter, Rao in view of Achin teaches:
provide, for display via a graphical user interface presented on a display device coupled to a computing device, a prompt to split the first dataset by segments; (Achin, [0191]-[0192] “In automatic mode, the exploration engine 110 partitions the dataset (step 418) using a default sampling algorithm and prioritizes the modeling techniques (step 420) using a default prioritization algorithm... In manual mode, the exploration engine 110 suggests data partitions (step 422)...” [0029] “the actions of the method further include partitioning the time-series data into a plurality of partitions.”) receive, via the graphical user interface from the computing device, an indication to split the first dataset by segments; and split, responsive to the indication, the first dataset into segments. (Achin, [0192]-[0194] “The user may accept the suggested data partition or specify custom partitions (step 426). ... predictive modeling system may partition the dataset into K folds, where the number of folds K is a default parameter. In step 426, the user may change the number of folds K or cancel the use of cross-validation altogether... predictive modeling system 100 may partition the dataset such that a default percentage of the dataset is reserved for the holdout set... the user may change the percentage of the dataset reserved for the holdout set, or cancel the use of a holdout set altogether.”) [Examiner’ Note: Achin teaches presenting partitioning options and receiving user selection or modification of the partitions, and partitioning the dataset into folds.]
The same motivation that was utilized for combining Rao and Achin as set forth in claim 2 is equally applicable to claim 3.
Regarding Claim 4, Rao teaches the elements of claim 1 as outlined above:
While Rao teaches the interface module providing interface content to the user of the data management analysis to enable user interaction and selection (see Rao e.g., [0053] and [0107]), Rao does not appear to explicitly suggest a user interface element to adjust at least one of a first window used to derive one or more features from the first dataset or a second window over which to predict values for the one or more features.
However, Rao in view of Achin teaches the limitation:
provide, via a graphical user interface presented by a display device of a computing device, a user interface element to adjust at least one of a first window used to derive one or more features from the first dataset or a second window over which to predict values for the one or more features. (Achin, [0340]-[0342] “In cases where a dataset is a panel dataset, containing a mixture of time-series and cross-sectional variables, the presentation may illustrate dependencies between cross-sectional variables either at the same time period or at different lags from each other. The user may indicate a “skip range” in the data, which is a gap between the end of a training window (e.g., a time range of data used for training) and the start of a validation window (e.g., a time range of data used for validation)... The user may indicate a desired forecast range (e.g., the number of future time periods to be predicted by the model...” [0353] “The user interface may allow the user to pick a particular fitted model or group of fitted models, and adjust the training and validation windows so that they include any observations not in the holdout window.”) [Examiner Note: Achin discloses a GUI element enabling users to adjust both the training window (first window, used to derive features/fit models) and forecast/validation window (second window, over which values are predicted).]
The same motivation that was utilized for combining Rao and Achin as set forth in claim 2 is equally applicable to claim 4.
Regarding Claim 5, Rao in view of Achin teaches the elements of claim 4 as outlined above, and further teaches:
provide, via the graphical user interface, an indication of a forecast point at or between the first window and the second window. (Achin, [0341] “The user may indicate a “skip range” in the data, which is a gap between the end of a training window... and the start of a validation window... the practical goal may be to begin predicting after a certain gap from the last available historical observation.” [0355] “The skip range may indicate a temporal lag between a time associated with an earliest prediction in the forecast range and a time associated with a latest observation upon which predictions in the forecast range are to be based.” [0356] “The skip range separates an end of the testing-input time range from a beginning of the testing-validation time range. A duration of the testing-validation time range is at least as long as the forecast range.”) [Examiner’s Note: Achin defines a forecast point (temporal gap/lag) situated between the first window and the second window, and presented to the user via GUI.]
Regarding Claim 6,
Rao in view of Achin teaches the elements of claim 5 as outlined above, and further teaches:
identify a blind history gap between the first window and the forecast point presented via the graphical user interface; and provide an indication via the graphical user interface of the blind history gap. (Achin, [0341] “The user may indicate a “skip range” in the data, which is a gap between the end of a training window (e.g., a time range of data used for training) and the start of a validation window (e.g., a time range of data used for validation) or a holdout window (e.g., a time range of data used for holdout testing).... the practical goal may be to begin predicting after a certain gap from the last available historical observation. The skip range permits the engine 110 to evaluate candidate predictive model with this gap in place.”) [Examiner’s Note: Under the BRI, the skip range and the temporal lag reads on the blind history gap.]
Regarding Claim 14,
The claim recites substantially similar limitations as corresponding claim 2 and is rejected for similar reasons as claim 2 using similar teachings and rationale.
Regarding Claim 15,
The claim recites substantially similar limitations as corresponding claim 3 and is rejected for similar reasons as claim 3 using similar teachings and rationale.
Regarding Claim 16,
The claim recites substantially similar limitations as corresponding claim 4 and is rejected for similar reasons as claim 4 using similar teachings and rationale.
Regarding Claim 17,
The claim recites substantially similar limitations as corresponding claim 5 and is rejected for similar reasons as claim 5 using similar teachings and rationale.
Regarding Claim 20,
The claim recites substantially similar limitations as corresponding claim 2 and is rejected for similar reasons as claim 2 using similar teachings and rationale.
Claim(s) 7 is rejected under 35 U.S.C. 103 as being unpatentable over Rao in view of Achin as outlined above and further in view of Driverless AI (NPL: “Time Series in Driverless AI — Using Driverless AI.” (2019)).
Regarding Claim 7,
Rao in view of Achin teaches the elements of claim 5 as outlined above, and further teaches:
While Rao in view of Achin teaches the concept of determining a forecast range and a skip range associated with a prediction problem represented by the time-series data and providing graphic information presented via a user interface that indicate a temporal lag between changes, Rao in view of Achin does not appear to explicitly teach: identify, based at least on the forecast point and the second window, a gap for which the model is unable to make predictions.
However, it would have been obvious the NPL paper Driverless AI paper. Hereinafter, Driverless AI, in combination with Rao and Achin, teaches the limitation:
identify, based at least on the forecast point and the second window, a gap for which the model is unable to make predictions. (Driverless AI, [Pp. 2-3] “Given a training dataset, gap and forecast horizon are parameters that determine how to split the training dataset into training samples and validation samples. Gap is the amount of missing time bins between the end of a training set and the start of test set (with regards to time). For example: .... The previous day (5/1/2019) does not belong to the train data. It is a day that cannot be used for training (i.e., because information from that day may not be available at scoring time). This day cannot be used to derive information (such as historical lags) for the test data either... Quite often, it is not possible to have the most recent data available when applying a model... models need to be built accounting for a “future gap”... Not specifying a gap and predicting 7 days ahead with the data as it is 7 days ahead is unrealistic (and can cannot happen... Forecast Horizon (or prediction length) is the period that the test data spans for (for example, one day, one week, etc.). In other words it is the future period that the model can make predictions for (or the number of units out that the model should be optimized to predict).”) [Examiner’s Note: the paper clearly defines and computes a “gap” as a temporal period between the end of available data (training window) and forecast horizon (prediction start). This gap is determined using the forecast horizon (forecast point) and the period separation between training and test set (i.e., second window). The paper notes that the data within the gap is not available at prediction time, and therefore cannot be used to generate predictions.]
Accordingly, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, having the combination of Rao and Achin, to incorporate the time-series forecasting techniques as taught by Driverless AI paper. One would have been motivated to make such a combination in order to possibly improve the model by changing the features used, choosing a different algorithm, and/or selecting different parameters (Driverless AI [p. 12]).
Claim(s) 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Rao as outlined above in view of et al., Achin et al., (Pub. No.: US 20180046926 A1) and Haddad et al., (Pub. No.: US 20200265012 A1).
Regarding Claim 8, Rao teaches the elements of claim 1 as outlined above:
While Rao teaches the interface module providing interface content to the user of the data management analysis to enable user interaction and selection (see Rao e.g., [0053] and [0107]), Rao does not appear to explicitly teach:
provide, for presentation by a graphical user interface via a display device coupled to a computing device, a user interface element to select a configuration for a backtest; receive, via the user interface element, a selection of the configuration for the backtest; and provide, for presentation by the graphical user interface, an indication of at least one of a validation portion for the backtest, a primary training data portion for the backtest, a gap for the backtest, or a holdout portion for the backtest.
However, Rao in view of Achin teaches the following:
provide, for presentation by a graphical user interface via a display device coupled to a computing device, a user interface element to select a configuration for a backtest; receive, via the user interface element, a selection of the configuration for the backtest; (Achin, [0192]-[0194] “... the exploration engine 110 suggests data partitions (step 422) and suggests a prioritization of the modeling techniques (step 424). The user may accept the suggested data partition or specify custom partitions (step 426)... To facilitate cross-validation, predictive modeling system 100 may partition the dataset (or suggest a partitioning of the dataset) into K “folds”... into a training set and a “holdout” test set... the training set is further partitioned into K folds for cross-validation.”) and provide, for presentation by the graphical user interface, an indication of at least one of a validation portion for the backtest, a primary training data portion for the backtest, a gap for the backtest, or a holdout portion for the backtest. (Achin, [0345] “For time series data, the engine 110 may implement cross validation and holdout (e.g., at steps 418 and 422 of the method 400) with a set of training ranges, a set of corresponding validation ranges offset by the skip range, and a holdout range.” [0354] “the user may refit any subset of the models using any combination of data from the training, validation, and holdout windows.” [0376] “Cross-validation ... and holdout techniques may be used for fitting and/or testing the predictive model. For purposes of cross-validation, the time-series data may be partitioned cross-sectionally and/or temporally.”
Accordingly, at the effective filing date, it would have been prima facie obvious to one ordinarily skilled in the art to modify the combination of Rao and Achin to incorporate the predictive modeling techniques used for time-series data as taught by Achin. One would have been motivated to make such a combination in order to rigorously and efficiently exploring the modeling search space for time-series models. Doing so would significantly improve the productivity of analysts at any skill level and/or significantly increase the accuracy of predictive models achievable with a given amount of resources (Achin [0248]).
While Rao in view of Achin teaches the user interface allowing selection of validation and defines the dataset components to perform validation including training portion, validation portion, skip range/gap, and holdout portion, Rao in view of Achin does not appear to clearly define the process as backtesting. However, Haddad, in combination with Rao and Achin teaches the limitation:
provide, for presentation by a graphical user interface via a display device coupled to a computing device, a user interface element to select a configuration for a backtest; receive, via the user interface element, a selection of the configuration for the backtest; and provide, for presentation by the graphical user interface, an indication of at least one of a validation portion for the backtest, a primary training data portion for the backtest, a gap for the backtest, or a holdout portion for the backtest. (Haddad, [0181]-[0213] “Referring now to FIG. 10, a schematic representation of a backtest configuration graphical user interface 1002 is shown. The backtest configuration graphical user interface 1002 may allow a user to select one or more securities 1004 and apply one or more of the filtering and/or conditions 945 described above with reference to FIG. 9A... a user may select one or more securities,.. The securities dropdown menu 1006 may allow a user to select, with for example, a mouse cursor or touch input, one or more securities to analyze... The backtest configuration interface 1002 may also allow a user to apply one or more of the filtering and/or conditions 945 to the selected securities. For example, a user may select a date range 1008. The user may select a specific date range from the present day through a date range dropdown menu 1010... The user may select a specific lookback period using a lookback period slider bar 1024... The backtest configuration interface 1002 may allow a user to... run 1078 the backtesting utility 999 described above.... The results dashboard 1102 may also display backtest details 1114. The backtest details 1114 may include the backtesting data used in the modeling steps... The backtesting report 2102 may display a type 2112 of securities analyzed and a date range 2114 for observations. The backtesting report 2102 may include a summary graph 2104 that may show one or more of the graphs described above in a single window, which may allow a user to review multiple types of data in one place.”)
Accordingly, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, having the combination of Rao and Achin, to incorporate the user-interactive backtesting methodologies using machine learning as taught by Haddad. One would have been motivated to make such a combination in order to ensure that only pertinent data and information is used in the statistical calculations, thereby improving the accuracy of any resulting calculations while at the same time reducing the amount of data and information that must be modeled (Haddad [0042]).
Regarding Claim 18,
The claim recites substantially similar limitations as corresponding claim 8 and is rejected for similar reasons as claim 8 using similar teachings and rationale.
Claim(s) 9 is rejected under 35 U.S.C. 103 as being unpatentable over Rao as outlined above in view of SAS (NPL: “Looking Inside SAS® Forecast Studio.” (2015)).
Regarding Claim 9, Rao teaches the elements of claim 1 as outlined above:
Rao teaches the time series data derived from data indicating current status of data management system, where time series data represents description, date, amount, and other transaction fields, and the interface content enable a user to view the overall state of many accounts, and select the possible forecasting algorithm based on a meta-model decision graph. See Rao [0040], [0107], and [0109]. Rao does not appear to explicitly teach:
provide, for presentation by a graphical user interface via a display device coupled to a computing device, a user interface element to input a calendar of events to generate a feature for the plurality of time series; receive, via the user interface element, the calendar of events;
However, it would have been obvious in view of SAS paper. Hereinafter, Rao in view of SAS teaches:
provide, for presentation by a graphical user interface via a display device coupled to a computing device, a user interface element to input a calendar of events to generate a feature for the plurality of time series; (SAS, [P. 4, Col. 1] “As the graphical user interface for SAS Forecast Server, SAS Forecast Studio has been designed to speed the work of both novice and advanced analysts: .. An Events dialog box simplifies the addition of events – for example, holidays and promotions – that might influence the forecast.” [P. 6, Col. 2] “The Project Wizard’s Event Manager guides the user through selecting, creating, editing, combining or deleting events. SAS Forecast Studio includes a large collection of predefined events (mostly public holidays). In addition, externally generated tables of events can be imported easily into SAS Forecast Studio.”) receive, via the user interface element, the calendar of events; (SAS, [p. 6, Col. 2] “In addition, externally generated tables of events can be imported easily into SAS Forecast Studio. To define a custom event within SAS Forecast Studio, the user specifies when the event occurred or will occur, the frequency of recurrence and the appropriate shape.” [p. 6, Col. 1] “Defining Events Some models may be improved by the inclusion of events, which are occurrences out of the ordinary that may disturb the underlying time series. Some events are unplanned (such as strikes or storms); their impact should be isolated so that it is not propagated in future forecasts. Other events are planned (holidays, promotions, price changes) and may be recurring; their impact should be assessed and included in future forecasts if appropriate.”) and derive one or more features of the first dataset using the calendar of events. (SAS, [p. 6, Col. 2] “Events can be added to regression, ARIMAX and unobserved components models (UCM) as event variables: variables that indicate when something out of the ordinary occurred in the past or will occur again in the future. Once specified, SAS Forecast Server statistically estimates the impact of the event in the past and uses the estimated impact to calculate future forecasts where the event recurs.” [p. 8, Col. 1] “When selecting the best-fitting model, SAS Forecast Studio automatically tests candidate independent variables and events – identified during the forecast setup process – and determines how they should be used in the forecast models. In addition to examining the contemporaneous relationships between independent and dependent variables, lagged and dynamic relationships are explored. If appropriate, variable transformations, lags and transfer function definitions are calculated.”) [Examiner’s Note: the SAS forecast studio GUI provides events dialog box and event manger that represents interface element that allow user to input calendar of events. The calendar events are converted into event variables (i.e., derived features) for the time series dataset.]
Therefore, it would have been prima facie obvious to one of ordinary skill in the art, before the effective date of the claimed invention, having the combination of Rao and SAS to incorporate the SAS Forecast Studio graphical interface as taught by SAS paper. One would have been motivated to make such a combination in order to facilitates and speeds the forecasting process by providing a convenient, user-friendly interface to the large-scale automatic forecasting, model building and time series exploration capabilities (SAS [Abstract]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
(Pub. No.: US 20220121336 A1) – “Pieter Joris Verhoeven” relates to “Interactive graphical user-interface for building networks of time series.”
(Pub. No.: US 20220180207 A1) – “Chen Liang” relates to “Automated Machine Learning for Time Series Prediction.”
(Pub. No.: US 20220172038 A1) – “Bei Chen” relates to “Automated deep learning architecture selection for time series prediction with user interaction.”
NPL: Shah, Syed Yousaf, et al. "AutoAI-TS: AutoAI for Time Series Forecasting." (2021).
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/S.A.A./Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121
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