DETAILED ACTION
This Office Action is in response to Applicant's Communication received on 04/27/2026 for application number 17/812,312.
Claims 1-3, 5, 7-10,12,14-16,18, and 20-23 are presented for examination. Claims 1, 8, and 15 are independent claims.
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 .
Continued Examination under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/27/2026 has been entered.
Response to Amendment
The amendment filed on 03/27/2026 has been entered.
Claims 1, 8, and 15 have been amended. Claims 4, 6, 11, 13, 17, and 19 are canceled. Claims 21-23 are new. Claims 1-3, 5, 7-10,12,14-16,18, and 20-23 are pending in the application.
Claim Objection
Claim 15 is objected to because of the following informalities: Claim 15 recites “the set of motifs”, which has no antecedent basis. 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.
Claims 1-3, 5, 7-10,12,14-16,18, and 20-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-3, 5, 7, and 21-23 are directed to a method, Claims 8-10, 12, and 14 are directed to a system, and Claims 15-16, 18, and 20 are directed to medium. Thus, the claims fall within one of the statutory categories (process, machine, articles of manufacture) and are eligible under Step 1.
Step 2A Prong 1
Independent Claims
Claim 1 recites:
a method for selecting a forecasting algorithm for a motif comprising: calculating a matrix profile for the received time series data based on the received window size; determining a plurality of subsequences of the time series data using the calculated matrix profile; determining a set of motifs from the plurality of subsequences using the calculated matrix profile, wherein each motif of the set of motifs is associated with a plurality of the subsequences and each motif of the set of motifs has a size equal to the received window size; for each motif of the set of motifs, selecting a forecasting algorithm based on an associated forecast error when predicting the interval value for time intervals from the plurality of subsequences of the time series data associated with the motif; determining a motif of the set of motifs that is associated with the future time interval; using the forecasting algorithm selected for the motif to predict the interval value for the future time interval - these limitations encompasses a person to perform these steps mentally, such as a person making an evaluation/ judgement to identify patterns/ motifs having specific size/ duration from time series data using mathematical calculations, select a forecasting algorithm for a motif based on forecasting error (mathematical relationship), decide a motif associated with a future time interval and use the forecasting algorithm (mathematical calculations) associated with the motif to predict/ calculate interval value.
Claim 8 recites:
calculating a matrix profile for the received time series data based on the received window size; determining a plurality of subsequences of the time series data using the calculated matrix profile; determining a set of motifs from the plurality of subsequences using the calculated matrix profile, wherein each motif of the set of motifs is associated with a plurality of the subsequences and each motif of the set of motifs has a size equal to the received window size; for each motif of the set of motifs, selecting a forecasting algorithm based on an associated forecast error when predicting the interval value for time intervals from the plurality of subsequences of the time series data associated with the motif; determining a motif of the set of motifs that is associated with the future time interval; using the forecasting algorithm selected for the motif to predict the interval value for the future time interval - these limitations encompasses a person to perform these steps mentally, such as a person making an evaluation/ judgement to identify patterns/ motifs having specific size/ duration from time series data using mathematical calculations, select a forecasting algorithm for a motif based on forecasting error (mathematical relationship), decide a motif associated with a future time interval and use the forecasting algorithm (mathematical calculations) associated with the motif to predict/ calculate interval value.
Claim 15 recites:
calculating a matrix profile for the received time series data based on the received window size; determining a plurality of subsequences of the time series data using the calculated matrix profile; for each motif of the set of motifs, selecting a forecasting algorithm based on an associated forecast error when predicting the interval value for time intervals from the plurality of subsequences of the time series data associated with the motif; determining a motif of the set of motifs that is associated with the future time interval; using the forecasting algorithm selected for the motif to predict the interval value for the future time interval - these limitations encompasses a person to perform these steps mentally, such as a person making an evaluation/ judgement to identify subsequences from time series data using mathematical calculations, select a forecasting algorithm for a motif based on forecasting error (mathematical relationship), decide a motif associated with a future time interval and use the forecasting algorithm (mathematical calculations) associated with the motif to predict/ calculate interval value.
Accordingly, these claims recite an abstract idea that falls under the “mental process” and/ or “mathematical concepts” grouping.
Step 2A Prong 2
Independent Claims
Additional elements
Claims 1, 8, and 15:
receiving time series data by a computing device, wherein the time series data comprises a plurality of time intervals and each time interval is associated with an interval value; receiving a plurality of forecasting algorithms by the computing device; receiving a window size by the computing device; receiving a request to forecast the interval value at a future time interval by the computing device - these imitations amount to insignificant extra-solution activity of mere data gathering (see MPEP § 2106.05(g)).
by a computing device - this limitation is recited at a high-level of generality such that it amounts to no more than merely using computer as a tool, by using it in its ordinary capacity to perform tasks (see MPEP § 2106.05(f)). This limitation can also be viewed as generally linking the use of a judicial exception to the field of generic computer (see MPEP § 2106.05(h)).
providing the predicted interval value for the future time interval by the computing device - these imitations amount to insignificant extra-solution activity of mere data gathering/ outputting (see MPEP § 2106.05(g)).
scheduling one or more workers to work during the future time interval based on the predicted interval value - this limitation is recited at a high-level of generality such that it amounts to no more than merely indicating the field of use/ workforce planning in which to apply the judicial exception (see MPEP § 2106.05(h)).
Claim 8:
a system comprising: one or more processors; and a memory storing instructions that when executed by the one or more processors cause the system to perform method - these limitations are recited at a high-level of generality such that it amounts to no more than merely using computer as a tool, by using it in its ordinary capacity to perform tasks (see MPEP § 2106.05(f)). This limitation can also be viewed as generally linking the use of a judicial exception to the field of generic computer (see MPEP § 2106.05(h)).
Claim 15:
a non-transitory computer-readable medium storing instructions that when executed by one or more processors of a system cause the system to perform method - these limitations are recited at a high-level of generality such that it amounts to no more than merely using computer as a tool, by using it in its ordinary capacity to perform tasks (see MPEP § 2106.05(f)). This limitation can also be viewed as generally linking the use of a judicial exception to the field of generic computer (see MPEP § 2106.05(h)).
Accordingly, these additional elements do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea. These claims are directed to the abstract idea.
Step 2B
Independent Claims
Additional elements
Claims 1, 8, and 15:
receiving time series data by a computing device, wherein the time series data comprises a plurality of time intervals and each time interval is associated with an interval value; receiving a plurality of forecasting algorithms by the computing device; receiving a window size by the computing device; receiving a request to forecast the interval value at a future time interval by the computing device - these imitations amount to insignificant extra-solution activity of mere data gathering, which is a well-understood, routine, and conventional activity (see MPEP § 2106.05(d), “receiving/ transmitting data”).
by a computing device - this limitation is recited at a high-level of generality such that it amounts to no more than merely using computer as a tool, by using it in its ordinary capacity to perform tasks (see MPEP § 2106.05(f)). This limitation can also be viewed as generally linking the use of a judicial exception to the field of generic computer (see MPEP § 2106.05(h)).
providing the predicted interval value for the future time interval by the computing device - these imitations amount to insignificant extra-solution activity of mere data gathering/ outputting, which is a well-understood, routine, and conventional activity (see MPEP § 2106.05(d), “receiving/ transmitting data”, “presenting offers”) .
scheduling one or more workers to work during the future time interval based on the predicted interval value - this limitation is recited at a high-level of generality such that it amounts to no more than merely indicating the field of use/ workforce planning in which to apply the judicial exception (see MPEP § 2106.05(h)).
Claim 8:
a system comprising: one or more processors; and a memory storing instructions that when executed by the one or more processors cause the system to perform method - these limitations are recited at a high-level of generality such that it amounts to no more than merely using computer as a tool, by using it in its ordinary capacity to perform tasks (see MPEP § 2106.05(f)). This limitation can also be viewed as generally linking the use of a judicial exception to the field of generic computer (see MPEP § 2106.05(h)).
Claim 15:
a non-transitory computer-readable medium storing instructions that when executed by one or more processors of a system cause the system to perform method - these limitations are recited at a high-level of generality such that it amounts to no more than merely using computer as a tool, by using it in its ordinary capacity to perform tasks (see MPEP § 2106.05(f)). This limitation can also be viewed as generally linking the use of a judicial exception to the field of generic computer (see MPEP § 2106.05(h)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, these claims are patent ineligible.
Step 2A Prong 1
Dependent Claims
Claims 2, 9, 16:
selecting the forecasting algorithm based on the associated forecast error when predicting the interval value for time intervals from the plurality of subsequences of the time series data associated with the motif comprises: for each forecasting algorithm of the plurality of forecasting algorithms: training the forecasting algorithm to predict the interval value using a portion of the time series data - these limitations encompass mathematical relationships, such as organizing information (time series data) and manipulating variables (interval values) by training forecasting algorithm (mathematical calculations).
for each time interval in the plurality of subsequences of the time series data that is associated with the motif and is not in the portion: using the forecasting algorithm to predict the interval value for the time interval - these limitations encompass mathematical calculations, such as using an algorithm to predict/ determine interval value (see MPEP 2106.04(a)(2) I.C - Example iv).
determining a difference between the interval value predicted for the time interval and the interval value associated with the time interval in the time series data; and updating the associated forecast error for the forecasting algorithm using the determined difference - these limitations encompass mathematical calculations.
selecting the forecasting algorithm for the motif based on the associated forecast error for the forecasting algorithm - this limitation encompasses a person to perform these steps mentally, such as a person making an evaluation/ judgement to select a forecasting algorithm for a motif based on forecasting error (mathematical relationship).
Claims 3, 10:
the interval value is one of a communication volume, an average handling time, or a shrinkage – these limitations merely furthers the mathematical concepts by specifying the predicted interval value.
Claims 5, 12, 20:
selecting the forecasting algorithm based on the associated forecast error comprises selecting the forecasting algorithm with a minimum forecast error - these limitations encompass a mental process (which is observing, evaluating, and judging that can be practically performed in the human mind or by a human using a pen and paper) of selecting forecasting algorithm from the plurality of forecasting algorithms based on the minimum associated error (mathematical relationship).
Claims 7, 14, 18:
selecting the forecasting algorithm based on the associated forecast error when predicting the interval value for time intervals from the plurality of subsequences of the time series data associated with the motif comprises: for each forecasting algorithm of the plurality of forecasting algorithms: for each subsequence of the plurality of subsequences of the time series data associated with the motif: training the forecasting algorithm to predict the interval value using a portion of the time series data that is before the subsequence in the time series data - these limitations encompass mathematical relationships, such as organizing information (time series data) and manipulating variables (interval values) by training forecasting algorithm (mathematical calculations).
for each time interval in the subsequence: using the forecasting algorithm to predict the interval value for the time interval - these limitations encompass mathematical calculations, such as using an algorithm to predict/ determine interval value (see MPEP 2106.04(a)(2) I.C - Example iv).
determining a difference between the interval value predicted for the time interval and the interval value associated with the time interval in the time series data; and updating the associated forecast error for the forecasting algorithm using the determined difference - these limitations encompass mathematical calculations.
selecting the forecasting algorithm for the motif based on the forecast error - this limitation encompasses a person to perform these steps mentally, such as a person making an evaluation/ judgement to select a forecasting algorithm for a motif based on forecasting error (mathematical relationship).
Claim 21:
selecting the forecasting algorithm for a motif comprises calculating a forecast error using absolute differences between predicted interval values and observed interval values averaged across subsequences associated with the motif - these limitations merely furthers the mathematical concepts by specifying the mathematical calculations used in selecting the forecasting algorithm for the motif.
Claim 22:
the forecast error comprises a mean absolute percentage error - these limitations merely furthers the mathematical concepts by specifying the forecast error in selecting the forecasting algorithm for the motif.
Claim 23:
generating a mapping that associates each motif of the set of motifs with a corresponding selected forecasting algorithm - these limitations recite a mental process of writing down the mapping of motifs and algorithms.
Thus, the claims recite the abstract idea.
Step 2A Prong 2
Dependent Claims
Additional elements
Claims 9-10, 12, and 14:
the system - these limitations are recited at a high-level of generality such that it amounts to no more than merely using computer as a tool, by using it in its ordinary capacity to perform tasks (see MPEP § 2106.05(f)). This limitation can also be viewed as generally linking the use of a judicial exception to the field of generic computer (see MPEP § 2106.05(h)).
Claims 16, 18, and 20:
the non-transitory computer-readable medium - these limitations are recited at a high-level of generality such that it amounts to no more than merely using computer as a tool, by using it in its ordinary capacity to perform tasks (see MPEP § 2106.05(f)). This limitation can also be viewed as generally linking the use of a judicial exception to the field of generic computer (see MPEP § 2106.05(h)).
Accordingly, these additional elements do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea. These claims are directed to the abstract idea.
Step 2B
Dependent Claims
Additional elements
Claims 9-10, 12, and 14:
the system - these limitations are recited at a high-level of generality such that it amounts to no more than merely using computer as a tool, by using it in its ordinary capacity to perform tasks (see MPEP § 2106.05(f)). This limitation can also be viewed as generally linking the use of a judicial exception to the field of generic computer (see MPEP § 2106.05(h)).
Claims 16, 18, and 20:
the non-transitory computer-readable medium - these limitations are recited at a high-level of generality such that it amounts to no more than merely using computer as a tool, by using it in its ordinary capacity to perform tasks (see MPEP § 2106.05(f)). This limitation can also be viewed as generally linking the use of a judicial exception to the field of generic computer (see MPEP § 2106.05(h)).
Accordingly, these additional elements do not amount to significantly more than the judicial exception. As such, these claims are patent ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3, 5, and 21-23 are rejected under 35 U.S.C. 103 as being unpatentable over Yocum et al. (US 2021/0271925 A1 hereinafter Yocum) in view of Law (US 2020/0258157 A1).
Regarding Claim 1, Yocum teaches a method for selecting a forecasting algorithm for a motif ([0023] for a given schedule of individual days/ points in time for which predictions are to be made, the system may select the best model by forecast group, prediction horizon, and model class for a particular day (i.e., motif)) comprising:
receiving time series data by a computing device, wherein the time series data comprises a plurality of time intervals and each time interval is associated with an interval value ([0013] models are trained to predict time series data, which may include the average handle time and call volume; [0015] the call volume identifies the number of phone calls that are expected to be received during a specified time interval; [0023] for a given schedule of individual days/ points in time for which predictions are to be made, the system may select the best model by forecast group, prediction horizon, and model class for a particular day; predictions are then made with a set of models to generate a piecewise forecast using the best model for a particular day; [0032] the repository (150) is a computing system that may include multiple computing devices in accordance with the computing system (500) and the nodes (522) and (524); the data in the repository (150) include multiple variables, parameters, historical time series data (i.e., time series data comprising plurality of time intervals), and forecasts that are generated (i.e., interval value) and used by the components of the system - thus, comprising plurality of time intervals and each time interval associated with forecasts/ interval value; [0075] the nodes may include functionality to receive requests from the client device and transmit responses to the client device; the client device may be a computing system; [0076] the computing system or group of computing systems include functionality to perform a variety of operations disclosed);
receiving a plurality of forecasting algorithms by the computing device ([0002] obtaining, by a model discovery service, a plurality of models; [0037] the process generates predictions used to generate estimated headcounts from models with multiple horizons and classes; in Step 202, models are obtained corresponding to model horizons; [0043] multiple models may be trained having multiple model horizons and classes; a model discovery service may have a model space of different models that are trained; the model space may include time series models that have different classes, different lags, and be of different types; the different classes of models may include in-season models, out-of-season models, and all-season models; [0076] the computing system or group of computing systems include functionality to perform a variety of operations disclosed);
for each motif of the set of motifs, selecting a forecasting algorithm based on an associated forecast error when predicting the interval value for time intervals from the plurality of subsequences of the time series data associated with the motif by the computing device ([0020] a model class identifies, for a model, the class of the model; the classes may relate to seasonality, which is how the model performs with respect to a particular period of time; example classes may include “all-season”, “in-season”, and “out-of-season” (i.e., set of motifs); [0023] for a given schedule of individual days/ points in time for which predictions are to be made (i.e., predicting the interval value), the system may select the best model by forecast group, prediction horizon, and model class for a particular day (i.e., time intervals from the plurality of subsequences of the time series data/ period of time associated with the motif/ class); [0025] select the best scoring models according to the future time range to forecast; [0037] the models may include lags from target time series; a target time series is a time series for which a model is used to generate predictions; [0039] as a part of generating the values and for each day to be predicted, the model having the model horizon with a lowest value that is greater than or equal to an ordinal value of the day to be predicted may be selected for that day to generate a piecewise forecast for the time series variable being predicted - thus, each class/ motif associated with plurality of subsequences of the time series data/ period of time comprising plurality of time intervals/ individual days; [0046] cross validation metrics are generated; different classes of models may use different data for testing and generating cross validation metrics; in-season models may be tested against in-season data, out-of-season model may be tested against out-of-season data, and all-season models may be tested against all-season data; the cross validation metrics may include a mean absolute scaled error (MASE), a weighted mean absolute percentage error (wMAPE) for the models, mean absolute percentage error (MAPE), root mean square error (RMSE), explained variance (R2), and mean absolute error (MAE); [0047] models are selected based on cross validation metrics; the model discovery service may select the model with the lowest mean absolute scaled error or the lowest weighted mean absolute percentage error (i.e., minimum associated error); [0076] the computing system or group of computing systems include functionality to perform a variety of operations disclosed)
receiving a request to forecast the interval value at a future time interval by the computing device ([0025] select the best scoring models according to the future time range to forecast, and provide this selection of models to the demand prediction service; [0058] FIG. 4 illustrates how the piecewise model selection displayed on the graphical user interface (400); [0063] the row (410) displays the date for the predicted days; the dates range from January 5 to January 13 (i.e., future time interval); [0064] the row (412) displays a piecewise prediction forecast generated based on multiple models; [0037] the process generates predictions used to generate estimated headcounts (i.e., forecast interval value) from models with multiple horizons and classes; [0076] the computing system or group of computing systems include functionality to perform a variety of operations disclosed);
determining a motif of the set of motifs that is associated with the future time interval by the computing device ([0020] a model class identifies, for a model, the class of the model; the classes may relate to seasonality, which is how the model performs with respect to a particular period of time; example classes may include “all-season”, “in-season”, and “out-of-season” (i.e., set of motifs); [0023] for a given schedule of individual days/ points in time for which predictions are to be made, the system may select the best model by forecast group, prediction horizon, and model class for a particular day (i.e., determining motif associated with the future time interval); [0025] select the best scoring models according to the future time range to forecast; [0051] for each of the identified dates, the class that applies to that date is identified; the classes may include in-season, out-of-season, and all-season classes, and one of the classes may be assigned to each date for which a prediction will be generated; [0058] FIG. 4 illustrates how the piecewise model selection displayed on the graphical user interface (400); [0060] the row (404) displays the classes of the models used to generate the predictions of the row (412); [0063] the row (410) displays the date for the predicted days; the dates range from January 5 to January 13 (i.e., future time interval) - thus, determining a class/ motif associated with the future time interval; [0076] the computing system or group of computing systems include functionality to perform a variety of operations disclosed);
using the forecasting algorithm selected for the motif to predict the interval value for the future time interval by the computing device ([0023] for a given schedule of individual days/ points in time for which predictions are to be made, the system may select the best model by forecast group, prediction horizon, and model class for a particular day; [0025] select the best scoring models according to the future time range to forecast; [0037] the process generates predictions used to generate estimated headcounts (i.e., forecast interval value) from models with multiple horizons and classes; [0058] FIG. 4 illustrates how the piecewise model selection displayed on the graphical user interface (400); [0060] the row (404) displays the classes of the models used to generate the predictions of the row (412); [0064] the row (412) displays a piecewise prediction forecast generated based on multiple models; the model “T(1,A)” is used to generate the predicted value of “8” for the predicted day “0” that corresponds to the date of January 5 using a horizon of 1 and a class of A; [0042] the estimated headcount generated using the Erlang-C traffic modeling formula with the values from the piecewise forecast and displayed on a client computing device; [0076] the computing system or group of computing systems include functionality to perform a variety of operations disclosed);
providing the predicted interval value for the future time interval by the computing device ([0037] the process generates predictions used to generate estimated headcounts (i.e., forecast interval value) from models with multiple horizons and classes; [0058] FIG. 4 illustrates how the piecewise model selection displayed on the graphical user interface (400); [0064] the row (412) displays a piecewise prediction forecast generated based on multiple models; the model “T(1,A)” is used to generate the predicted value of “8” for the predicted day “0” that corresponds to the date of January 5 using a horizon of 1 and a class of A; [0042] the estimated headcount generated using the Erlang-C traffic modeling formula with the values from the piecewise forecast and displayed on a client computing device); and
scheduling one or more workers to work during the future time interval based on the predicted interval value by the computing device ([0037] the process generates predictions used to generate estimated headcounts from models with multiple horizons and classes; [0001] call centers need to have an appropriate headcount of staff available to handle the amount of calls received; [0017] the headcount identifies how many representatives are needed to be able to handle the expected amount of calls; the headcount identifies the number of representatives needed over a period of time).
However, Yocum fails to expressly teach wherein receiving a window size by the computing device; calculating a matrix profile for the received time series data based on the received window size; determining a plurality of subsequences of the time series data using the calculated matrix profile; determining a set of motifs from the plurality of subsequences using the calculated matrix profile, wherein each motif of the set of motifs is associated with a plurality of the subsequences and each motif of the set of motifs has a size equal to the received window size.
In the same field of endeavor, Law teaches wherein receiving a window size by the computing device ([0172] computing the matrix profile allows quickly finding motifs and identifies the nearest neighbor for subsequences within the time series; [0173] the stump function receives two parameters: a time series and a window size, m; [0109] a UI element allows a user to specify a pattern length); calculating a matrix profile for the received time series data based on the received window size ([0172] computing the matrix profile allows quickly finding motifs and identifies the nearest neighbor for subsequences within the time series; [0173] the stump function receives two parameters: a time series and a window size, m; [0200] calculates the matrix profile with a window size of m=20; [0109] based on that pattern length, a matrix profile and a matrix profile index are calculated); determining a plurality of subsequences of the time series data using the calculated matrix profile ([0173] the output of stump is an array that contains all of the matrix profile values, such as, z-normalized Euclidean distance to the nearest neighbor and matrix profile indices; the following code can be used to plot the matrix profile below the raw data, as shown in FIG. 15; [0174] the global minima (vertical dashed lines) of the matrix profile correspond to the locations of the two subsequences (i.e., plurality of subsequences of the time series data using the matrix profile) that make up the motif pair; [0069] the matrix profile 320 at line 324 is a measure of the similarity between the subsequence 332 and the most similar subsequence from among all the other possible subsequences of length m in the time-series data 300); determining a set of motifs from the plurality of subsequences using the calculated matrix profile, wherein each motif of the set of motifs is associated with a plurality of the subsequences and each motif of the set of motifs has a size equal to the received window size ([0173] the output of stump is an array that contains all of the matrix profile values, such as, z-normalized Euclidean distance to the nearest neighbor and matrix profile indices; the following code can be used to plot the matrix profile below the raw data, as shown in FIG. 15; [0174] the global minima (vertical dashed lines) of the matrix profile correspond to the locations of the two subsequences that make up the motif pair; [0066] an indication 304 of length m demonstrates the subsequence length (also called pattern length) that will be studied in the time-series data; [0173] the stump function receives two parameters: a time series and a window size, m - thus, the pattern length/ size of motifs equal to the received window size, m).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated wherein receiving a window size by the computing device; calculating a matrix profile for the received time series data based on the received window size; determining a plurality of subsequences of the time series data using the calculated matrix profile; determining a set of motifs from the plurality of subsequences using the calculated matrix profile, wherein each motif of the set of motifs is associated with a plurality of the subsequences and each motif of the set of motifs has a size equal to the received window size, as taught by Law into Yocum. Doing so would be desirable because it would allow for identifying favorable trends and making favorable predictions (Law [0004]), thereby enhancing user experience.
As to dependent Claim 3, Yocum and Law teach all the limitations of Claim 1. Yocum further teaches wherein the interval value is one of a communication volume, an average handling time, or a shrinkage ([0037] the process generates predictions used to generate estimated headcounts (i.e., interval value) from models with multiple horizons and classes; [0017] the headcount identifies how many representatives are needed to be able to handle the expected amount of calls).
As to dependent Claim 5, Yocum and Law teach all the limitations of Claim 1. Yocum further teaches wherein selecting the forecasting algorithm based on the associated forecast error comprises selecting the forecasting algorithm with a minimum forecast error ([0045] the training data used to train the models may include historical time series data for at least two years; [0046] cross validation metrics are generated; different classes of models may use different data for testing and generating cross validation metrics; in-season models may be tested against in-season data, out-of-season model may be tested against out-of-season data, and all-season models may be tested against all-season data; the cross validation metrics may include a mean absolute scaled error (MASE), a weighted mean absolute percentage error (wMAPE) for the models, mean absolute percentage error (MAPE), root mean square error (RMSE), explained variance (R2), and mean absolute error (MAE); [0047] models are selected based on cross validation metrics; the model discovery service may select the model with the lowest mean absolute scaled error or the lowest weighted mean absolute percentage error (i.e., minimum forecast error)).
As to dependent Claim 21, Yocum and Law teach all the limitations of Claim 1. Yocum further teaches wherein selecting the forecasting algorithm for a motif comprises calculating a forecast error using absolute differences between predicted interval values and observed interval values averaged across subsequences associated with the motif ([0045] the training data used to train the models may include historical time series data for at least two years; [0046] cross validation metrics are generated; different classes of models may use different data for testing and generating cross validation metrics; in-season models may be tested against in-season data, out-of-season model may be tested against out-of-season data, and all-season models may be tested against all-season data (i.e., models based on the subsequences of the time series data associated with the class/ days associated with the season/ motif); the cross validation metrics may include a mean absolute scaled error (MASE), a weighted mean absolute percentage error (wMAPE) for the models, mean absolute percentage error (MAPE), root mean square error (RMSE), explained variance (R2), and mean absolute error (MAE) - these averages absolute differences between predicted and observed values across the data associated with each class model/ subsequences associated with the motif; [0047] models are selected based on cross validation metrics; the model discovery service may select the model with the lowest mean absolute scaled error or the lowest weighted mean absolute percentage error).
As to dependent Claim 22, Yocum and Law teach all the limitations of Claim 1. Yocum further teaches wherein the forecast error comprises a mean absolute percentage error ([0045] the training data used to train the models may include historical time series data for at least two years; [0046] cross validation metrics are generated; different classes of models may use different data for testing and generating cross validation metrics; in-season models may be tested against in-season data, out-of-season model may be tested against out-of-season data, and all-season models may be tested against all-season data; the cross validation metrics may include a mean absolute scaled error (MASE), a weighted mean absolute percentage error (wMAPE) for the models, mean absolute percentage error (MAPE), root mean square error (RMSE), explained variance (R2), and mean absolute error (MAE); [0047] models are selected based on cross validation metrics; the model discovery service may select the model with the lowest mean absolute scaled error or the lowest weighted mean absolute percentage error).
As to dependent Claim 23, Yocum and Law teach all the limitations of Claim 1. Yocum further teaches wherein generating a mapping that associates each motif of the set of motifs with a corresponding selected forecasting algorithm ([0023] for a given schedule of individual days/ points in time for which predictions are to be made, the system may select the best model by forecast group, prediction horizon, and model class for a particular day; [0025] select the best scoring models according to the future time range to forecast; [0058] FIG. 4 illustrates how the piecewise model selection displayed on the graphical user interface (400); [0060] the row (404) displays the classes of the models used to generate the predictions of the row (412); [0064] the row (412) displays a piecewise prediction forecast generated based on multiple models; the model “T(1,A)” is used to generate the predicted value of “8” for the predicted day “0” that corresponds to the date of January 5 using a horizon of 1 and a class of A - thus, generating a model schedule/ list of models that maps each segment of the series (by date/ horizon/ class) to its selected model).
Claims 8, 10, and 12 are system claims that are corresponding to the method claims 1, 3, 5 respectively and therefore, rejected for the same reasons. Yocum further teaches a system ([0067] computing system) comprising: one or more processors ([0067] computing system include one or more computer processors); and a memory storing instructions that when executed by the one or more processors cause the system to perform steps ([0067] computing system include one or more computer processors, non-persistent storage (e.g., volatile memory, such as random access memory (RAM), cache memory), persistent storage (e.g., a hard disk, an optical drive such as a compact disk (CD) drive or digital versatile disk (DVD) drive, a flash memory, etc.); [0071] software instructions in the form of computer readable program code to perform embodiments of the invention may be stored on a non-transitory computer readable medium; the software instructions may correspond to computer readable program code that, when executed by a processor(s), is configured to perform one or more embodiments of the invention).
Claims 15 and 20 are medium claims that are corresponding to the method claims 1 and 5 respectively and therefore, rejected for the same reasons. Yocum further teaches a non-transitory computer-readable medium storing instructions that when executed by one or more processors of a system cause the system to perform steps ([0067] computing system include one or more computer processors, non-persistent storage (e.g., volatile memory, such as random access memory (RAM), cache memory), persistent storage (e.g., a hard disk, an optical drive such as a compact disk (CD) drive or digital versatile disk (DVD) drive, a flash memory, etc.); [0071] software instructions in the form of computer readable program code to perform embodiments of the invention may be stored on a non-transitory computer readable medium; the software instructions may correspond to computer readable program code that, when executed by a processor(s), is configured to perform one or more embodiments of the invention).
Claims 2, 7, 9, 14, 16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Yocum in view of Law, further in view of Mitra et al. (US 2023/0075453 A1 hereinafter Mitra).
As to dependent Claim 2, Yocum and Law teach all the limitations of Claim 1. Yocum further teaches wherein selecting the forecasting algorithm based on the associated forecast error when predicting the interval value for time intervals from the plurality of subsequences of the time series data associated with the motif ([0020] a model class identifies, for a model, the class of the model; the classes may relate to seasonality, which is how the model performs with respect to a particular period of time; example classes may include “all-season”, “in-season”, and “out-of-season” (i.e., set of motifs); [0023] for a given schedule of individual days/ points in time for which predictions are to be made (i.e., predicting the interval value), the system may select the best model by forecast group, prediction horizon, and model class for a particular day (i.e., time intervals from the plurality of subsequences of the time series data/ period of time associated with the motif/ class); [0025] select the best scoring models according to the future time range to forecast; [0037] the models may include lags from target time series; a target time series is a time series for which a model is used to generate predictions; [0039] as a part of generating the values and for each day to be predicted, the model having the model horizon with a lowest value that is greater than or equal to an ordinal value of the day to be predicted may be selected for that day to generate a piecewise forecast for the time series variable being predicted - thus, each class/ motif associated with plurality of subsequences of the time series data/ period of time comprising plurality of time intervals/ individual days; [0046] cross validation metrics are generated; different classes of models may use different data for testing and generating cross validation metrics; in-season models may be tested against in-season data, out-of-season model may be tested against out-of-season data, and all-season models may be tested against all-season data; the cross validation metrics may include a mean absolute scaled error (MASE), a weighted mean absolute percentage error (wMAPE) for the models, mean absolute percentage error (MAPE), root mean square error (RMSE), explained variance (R2), and mean absolute error (MAE); [0047] models are selected based on cross validation metrics; the model discovery service may select the model with the lowest mean absolute scaled error or the lowest weighted mean absolute percentage error (i.e., minimum associated error)) comprises: for each forecasting algorithm of the plurality of forecasting algorithms: training the forecasting algorithm to predict the interval value using a portion of the time series data ([0036] training models and predicting call center volume (i.e., interval value); [0038] the time series variable values are predictions generated with trained models; the values may correspond to the days to be predicted; [0045] the training data used to train the models may include historical time series data for at least two years; additional data beyond two years may be included and more or less data may be used; the initial training of the models may use a subset of data (i.e., portion of time series data), such as the trailing two years of data; then after the models are selected, the selected models may be trained with the additional training data that was not used prior to model selection); and selecting the forecasting algorithm for the motif based on the associated forecast error for the forecasting algorithm ([0020] a model class identifies, for a model, the class of the model; the classes may relate to seasonality, which is how the model performs with respect to a particular period of time; example classes may include “all-season”, “in-season”, and “out-of-season” (i.e., set of motifs); [0023] for a given schedule of individual days/ points in time for which predictions are to be made (i.e., predicting the interval value), the system may select the best model by forecast group, prediction horizon, and model class for a particular day (i.e., time intervals from the plurality of subsequences of the time series data/ period of time associated with the motif/ class); [0025] select the best scoring models according to the future time range to forecast; [0037] the models may include lags from target time series; a target time series is a time series for which a model is used to generate predictions; [0039] as a part of generating the values and for each day to be predicted, the model having the model horizon with a lowest value that is greater than or equal to an ordinal value of the day to be predicted may be selected for that day to generate a piecewise forecast for the time series variable being predicted - thus, each class/ motif associated with plurality of subsequences of the time series data/ period of time comprising plurality of time intervals/ individual days; [0046] cross validation metrics are generated; different classes of models may use different data for testing and generating cross validation metrics; in-season models may be tested against in-season data, out-of-season model may be tested against out-of-season data, and all-season models may be tested against all-season data; the cross validation metrics may include a mean absolute scaled error (MASE), a weighted mean absolute percentage error (wMAPE) for the models, mean absolute percentage error (MAPE), root mean square error (RMSE), explained variance (R2), and mean absolute error (MAE); [0047] models are selected based on cross validation metrics; the model discovery service may select the model with the lowest mean absolute scaled error or the lowest weighted mean absolute percentage error (i.e., minimum associated error)).
However, Yocum and Law fail to expressly teach wherein for each time interval in the plurality of subsequences of the time series data that is not in the portion: using the forecasting algorithm to predict the interval value for the time interval; determining a difference between the interval value predicted for the time interval and the interval value associated with the time interval in the time series data; and updating the associated forecast error for the forecasting algorithm using the determined difference.
In the same field of endeavor, Mitra teaches wherein for each time interval in the plurality of subsequences of the time series data that is not in the portion: using the forecasting algorithm to predict the interval value for the time interval ([0008] the system performs forecasting using the machine learning based model by determining a training data set based on the time series data; the training dataset includes a training subset and a test subset; the system trains the machine learning based model using the training subset of the training dataset and evaluates the machine learning based model using the test subset of the training dataset; [0047] the training dataset D includes a training subset T1 and a test subset T2; the model generation module 130 trains 420 the machine learning based model using the training subset T1; the model generation module 130 evaluates 430 the machine learning based model using the test subset T2; [0057] the system trains all machine learning models on S11 and evaluates their performance on S1); determining a difference between the interval value predicted for the time interval and the interval value associated with the time interval in the time series data ([0036] a model metric represents a criterion for evaluating machine learning based models; a model metric represents a function or an expression used for determining a difference between data forecasted using a model (i.e., interval value predicted for the time interval) with observed data (or labelled data) (i.e., interval value associated with the time interval in the time series data)); and updating the associated forecast error for the forecasting algorithm using the determined difference ([0036] a model metric represents a criterion for evaluating machine learning based models; a model metric represents a function or an expression used for determining a difference between data forecasted using a model with observed data (or labelled data); model metrics include MAPE (mean absolute percentage error), RMSE (root mean square error), MAE (mean absolute error), MSE (mean squared error), symmetric mean absolute percentage error (sMAPE), etc. (i.e., forecast error based on difference between data forecasted using a model with observed data); [0058] the model generation module 130 trains 720 each machine learning based model from the pool P1 of machine learning based models using training subset S11; the model generation module 130 evaluates 730 each machine learning based model from the pool 610 of machine learning based models using the given model metric and using the test subset S12; [0060] the model generation module 130 evaluates 760 each machine learning based model from the pool P2 of machine learning based models using the given model metric and using training subset S22 - thus, calculating the model metric (updating forecast error) to evaluate the models).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated wherein for each time interval in the plurality of subsequences of the time series data that is not in the portion: using the forecasting algorithm to predict the interval value for the time interval; determining a difference between the interval value predicted for the time interval and the interval value associated with the time interval in the time series data; and updating the associated forecast error for the forecasting algorithm using the determined difference, as taught by Mitra into Yocum and Law. Doing so would be desirable because it would enable the system to automatically select a model from different models based on previously known applications (Mitra [0070]).
As to dependent Claim 7, Yocum and Law teach all the limitations of Claim 1. Yocum further teaches wherein selecting the forecasting algorithm based on the associated forecast error when predicting the interval value for time intervals from the plurality of subsequences of the time series data associated with the motif ([0020] a model class identifies, for a model, the class of the model; the classes may relate to seasonality, which is how the model performs with respect to a particular period of time; example classes may include “all-season”, “in-season”, and “out-of-season” (i.e., set of motifs); [0023] for a given schedule of individual days/ points in time for which predictions are to be made (i.e., predicting the interval value), the system may select the best model by forecast group, prediction horizon, and model class for a particular day (i.e., time intervals from the plurality of subsequences of the time series data/ period of time associated with the motif/ class); [0025] select the best scoring models according to the future time range to forecast; [0037] the models may include lags from target time series; a target time series is a time series for which a model is used to generate predictions; [0039] as a part of generating the values and for each day to be predicted, the model having the model horizon with a lowest value that is greater than or equal to an ordinal value of the day to be predicted may be selected for that day to generate a piecewise forecast for the time series variable being predicted - thus, each class/ motif associated with plurality of subsequences of the time series data/ period of time comprising plurality of time intervals/ individual days; [0046] cross validation metrics are generated; different classes of models may use different data for testing and generating cross validation metrics; in-season models may be tested against in-season data, out-of-season model may be tested against out-of-season data, and all-season models may be tested against all-season data; the cross validation metrics may include a mean absolute scaled error (MASE), a weighted mean absolute percentage error (wMAPE) for the models, mean absolute percentage error (MAPE), root mean square error (RMSE), explained variance (R2), and mean absolute error (MAE); [0047] models are selected based on cross validation metrics; the model discovery service may select the model with the lowest mean absolute scaled error or the lowest weighted mean absolute percentage error (i.e., minimum associated error)) comprises: for each forecasting algorithm of the plurality of forecasting algorithms: for each subsequence of the plurality of subsequences of the time series data associated with the motif: training the forecasting algorithm to predict the interval value using a portion of the time series data that is before the subsequence in the time series data ([0036] training models and predicting call center volume (i.e., interval value); [0038] the time series variable values are predictions generated with trained models; the values may correspond to the days to be predicted; [0045] the training data used to train the models may include historical time series data for at least two years; additional data beyond two years may be included and more or less data may be used; the initial training of the models may use a subset of data (i.e., training using portion of time series data before subsequence in the time series data), such as the trailing two years of data; then after the models are selected, the selected models may be trained with the additional training data (i.e., subsequence in the time series data) that was not used prior to model selection); and selecting the forecasting algorithm for the motif based on the associated forecast error ([0020] a model class identifies, for a model, the class of the model; the classes may relate to seasonality, which is how the model performs with respect to a particular period of time; example classes may include “all-season”, “in-season”, and “out-of-season” (i.e., set of motifs); [0023] for a given schedule of individual days/ points in time for which predictions are to be made (i.e., predicting the interval value), the system may select the best model by forecast group, prediction horizon, and model class for a particular day (i.e., time intervals from the plurality of subsequences of the time series data/ period of time associated with the motif/ class); [0025] select the best scoring models according to the future time range to forecast; [0037] the models may include lags from target time series; a target time series is a time series for which a model is used to generate predictions; [0039] as a part of generating the values and for each day to be predicted, the model having the model horizon with a lowest value that is greater than or equal to an ordinal value of the day to be predicted may be selected for that day to generate a piecewise forecast for the time series variable being predicted - thus, each class/ motif associated with plurality of subsequences of the time series data/ period of time comprising plurality of time intervals/ individual days; [0046] cross validation metrics are generated; different classes of models may use different data for testing and generating cross validation metrics; in-season models may be tested against in-season data, out-of-season model may be tested against out-of-season data, and all-season models may be tested against all-season data; the cross validation metrics may include a mean absolute scaled error (MASE), a weighted mean absolute percentage error (wMAPE) for the models, mean absolute percentage error (MAPE), root mean square error (RMSE), explained variance (R2), and mean absolute error (MAE); [0047] models are selected based on cross validation metrics; the model discovery service may select the model with the lowest mean absolute scaled error or the lowest weighted mean absolute percentage error (i.e., minimum associated error)).
However, Yocum and Law fail to expressly teach wherein for each time interval in the subsequence: using the forecasting algorithm to predict the interval value for the time interval; determining a difference between the interval value predicted for the time interval and the interval value associated with the time interval in the time series data; and updating the associated forecast error for the forecasting algorithm using the determined difference.
In the same field of endeavor, Mitra teaches wherein for each time interval in the subsequence: using the forecasting algorithm to predict the interval value for the time interval ([0008] the system performs forecasting using the machine learning based model by determining a training data set based on the time series data; the training dataset includes a training subset and a test subset; the system trains the machine learning based model using the training subset of the training dataset and evaluates the machine learning based model using the test subset of the training dataset; [0047] the training dataset D includes a training subset T1 and a test subset T2; the model generation module 130 trains 420 the machine learning based model using the training subset T1; the model generation module 130 evaluates 430 the machine learning based model using the test subset T2; [0057] the system trains all machine learning models on S11 and evaluates their performance on S1); determining a difference between the interval value predicted for the time interval and the interval value associated with the time interval in the time series data ([0036] a model metric represents a criterion for evaluating machine learning based models; a model metric represents a function or an expression used for determining a difference between data forecasted using a model (i.e., interval value predicted for the time interval) with observed data (or labelled data) (i.e., interval value associated with the time interval in the time series data)); and updating the associated forecast error for the forecasting algorithm using the determined difference ([0036] a model metric represents a criterion for evaluating machine learning based models; a model metric represents a function or an expression used for determining a difference between data forecasted using a model with observed data (or labelled data); model metrics include MAPE (mean absolute percentage error), RMSE (root mean square error), MAE (mean absolute error), MSE (mean squared error), symmetric mean absolute percentage error (sMAPE), etc. (i.e., forecast error based on difference between data forecasted using a model with observed data); [0058] the model generation module 130 trains 720 each machine learning based model from the pool P1 of machine learning based models using training subset S11; the model generation module 130 evaluates 730 each machine learning based model from the pool 610 of machine learning based models using the given model metric and using the test subset S12; [0060] the model generation module 130 evaluates 760 each machine learning based model from the pool P2 of machine learning based models using the given model metric and using training subset S22 - thus, calculating the model metric (updating forecast error) to evaluate the models).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated wherein for each time interval in the subsequence: using the forecasting algorithm to predict the interval value for the time interval; determining a difference between the interval value predicted for the time interval and the interval value associated with the time interval in the time series data; and updating the associated forecast error for the forecasting algorithm using the determined difference, as taught by Mitra into Yocum and Law. Doing so would be desirable because it would enable the system to automatically select a model from different models based on previously known applications (Mitra [0070]).
Claims 9 and 14 are system claims that are corresponding to the method claims 2 and 7 respectively and therefore, rejected for the same reasons.
Claims 16 and 18 are medium claims that are corresponding to the method claims 2 and 7 respectively and therefore, rejected for the same reasons.
Response to Arguments
35 U.S.C. §101: In the remarks, Applicant argues that:
(a) the claims are not directed to mental processes because matrix profile computation, subsequence extraction, motif determination, and quantitative forecast error calculation across multiple subsequences cannot practically be performed in the human mind.
(b) the claims integrate any abstract idea into a practical application; the claims apply motif-based forecasting to generate predicted interval values and use those values to schedule workers; this constitutes an application for the workforce management and operational planning.
(c) the claimed invention improves forecasting accuracy by selecting forecasting algorithms based on motifs determined through matrix profile.
Examiner respectfully disagrees with Applicant’s arguments.
As to point (a), the steps of calculating a matrix profile; determining a plurality of subsequences of the time series data using the calculated matrix profile; determining a set of motifs from the plurality of subsequences using the calculated matrix profile; selecting a forecasting algorithm based on an associated forecast error when predicting the interval value for time intervals from the plurality of subsequences of the time series data associated with the motif, etc. encompasses a person to perform these steps mentally, such as a person making an evaluation/ judgement to calculate matrix profile, identify patterns/ motifs using the calculated matrix profile (mathematical calculations), select a forecasting algorithm for a motif based on forecasting error (mathematical relationship), decide a motif associated with a future time interval and use the forecasting algorithm (mathematical calculations) associated with the motif to predict/ calculate interval value. Accordingly, the claims recite an abstract idea that falls under the “mental process” and/ or “mathematical concepts” grouping. See the detailed analysis under 35 U.S.C. 101 rejections above.
As to point (b), the limitation of using forecasting algorithm associated with the motif to calculate interval value recite mathematical calculations/ judicial exception. The limitation of “scheduling one or more workers to work during the future time interval based on the predicted interval value” is recited at a high level of generality such that it amounts to no more than merely indicating the field of use/ workforce planning in which to apply the judicial exception. The additional elements do not integrate the judicial exception into a practical application because they do not impose any meaningful limits on practicing the abstract idea. See the detailed analysis under 35 U.S.C. 101 rejections above.
As to point (c), any purported improvement is based on identify patterns/ motifs having specific size/ duration from time series data using mathematical calculations, select a forecasting algorithm for a motif based on forecasting error (mathematical relationship), decide a motif associated with a future time interval and use the forecasting algorithm (mathematical calculations) associated with the motif to predict/ calculate interval value, which are abstract ideas (mental process and mathematical concepts) as analyzed in detail under Step 2A, Prong 1 portion of the 35 U.S.C. 101 rejections above. Examiner notes that “It is important to note, the judicial exception alone cannot provide the improvement” (see MPEP § 2106.05 (a)). Further, the recited claim merely involves, at most, an improvement to the abstract idea itself with the aid of a generic computer components. Examiner notes that “It is important to keep in mind that an improvement in the abstract idea itself is not an improvement in technology” (see MPEP 2106.05(a)(II)).
Accordingly, Applicant’s arguments concerning the §101 rejections are not persuasive.
35 U.S.C. §103: In the remarks, applicant argues that the cited references do not teach the features recited in amended independent claims 1, 8, and 15.
Applicant's arguments with respect to the 103 rejections have been considered, but are moot in view of new ground of rejection made under 35 U.S.C. § 103.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 CFR § 1.111(c) to consider these references fully when responding to this action.
Kotorov et al. (US 2019/0188201 A1) teaches: displaying the time-series data on an interactive line chart component, selecting a time sequence subset from the time-series data displayed on the interactive line chart, converting data points from the selected time sequence subset into query parameters, generating a search query against the time-series data to retrieve a set of time sequences matching the query parameters, generating a similarity score for each member of the set of time sequences to the time sequence subset, and displaying a motif on the interactive line chart formed by the time sequences with a similarity score satisfying a threshold condition (see Abstract).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REJI KARTHOLY whose telephone number is (571)272-3432. The examiner can normally be reached on Monday - Thursday from 7:30 am to 3:30 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Welch, can be reached at telephone number 571-272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/REJI KARTHOLY/Primary Examiner, Art Unit 2143
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