DETAILED ACTION
Status of Claims
This Final Office Action is responsive to Applicant's reply filed 8/6/2026.
Claims 1, 7-9, 12, and 15-20 have been amended and claim 14 has been cancelled.
Claims 1-3, 7-13, and 15-20 are currently pending and have been examined.
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 .
Response to Amendments
The previously pending 35 USC 103 rejections for claims 7-10, 15-17, and 19-20 have been withdrawn in response to Applicant’s clam amendments. See below for reasoning.
Applicant’s amendments have been fully considered, but do not overcome the previously pending 35 USC 103 and 35 USC 101 rejections.
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive.
With regard to the limitations of claims 1-3, 7-13, and 15-20, Applicant argues that the claims are patent eligible under 35 USC 101 because the pending claims are not directed toward an abstract idea. The Examiner respectfully disagrees. The Examiner has already set forth a prima facie case under 35 USC 101. The Examiner has clearly pointed out the limitations directed towards the abstract idea, what the additional elements are and why they do not integrate the abstract idea into a practical application, and why the additional elements and remaining limitations do not amount to significantly more than the abstract idea. The Examiner asserts that stating that the beginning portion is the first 10% of data and the ending portion is the last 10% of data is just defining what data is being used in the training. The Examiner also specifically notes that assuming the training is continuous as in machine learning more and more data will become present which constantly changes what data is actually being used. Adding newer and relevant for further training is a basic concept of machine learning (please google if needed) and is recited at such a high level of generality that it merely adds the words apply it with the judicial exception. Applicant’s arguments are not persuasive.
The Examiner specifically notes that the claims do not appear to even recite a training of the model, but rather just generically recite selecting different time periods of data to use for specific models in the forecasting analysis. The claims are literally just analyzing data based on human input parameters, which is abstract. Applicant’s arguments are not persuasive.
The Examiner notes that the human has clearly defined what model to select based on being seasonal or not seasonal, which does not improve the machine learning itself, but rather merely defines what model to use in what instance for human purposes, which narrows the abstract idea. Applicant’s arguments are not persuasive.
Applicant argues the claims are eligible under step 2B. The Examiner respectfully disagrees. The Examiner points to Page 2 of the McRO-Bascom Memo from December 2016, "The McRO court indicated that it was the incorporation of the particular claimed rules in computer animation "that improved [the] existing technological process", unlike cases such as Alice where a computer was merely used as a tool to perform an existing process." The Applicants’ claims are geared toward analyzing content titles to make determinations a human will use to decide what to do with content (e.g. commercial interactions), where these techniques are merely being applied/calculated in a computing environment. Simply applying these known concepts to a specific technical environment (e.g. the computers/Internet) does not account for significantly more than the abstract idea because it does not solve a problem rooted in computer technology nor does it improve the functioning of the computer itself because it is merely making a determination based on rules and/or mathematical relationships to output to a user. The Applicant’s claimed limitations do not appear to bring about any improvement in the operation or functioning of a computer per se, or to improve computer-related technology by allowing computer performance of a function not previously performable by a computer (see page 2 of the McRo-Bascom memo). The solution appears to be more of a business-driven solution rather than a technical one. In addition, McRO had no evidence that the process previously used by animators is the same as the process required by the claims. The Applicant’s claimed limitations and originally filed specification provide no evidence that the claimed process/functions are any different than what would be done without a computer, where there are no adjustments to the mental process to accommodate implementation by computers. Applicant’s arguments are not persuasive.
The Examiner has clearly pointed out the limitations directed towards the abstract idea, what the additional elements are and why they do not integrate the abstract idea into a practical application, and why the additional elements and remaining limitations do not amount to significantly more than the abstract idea. Where Applicant has not properly identified the additional elements. Applicant’s arguments are not persuasive.
With regard to the limitations of claims 1-3, 11-13, and 18, Applicant argues that the claims are allowable over 35 USC 103 because the claim amendments overcome the current art rejection. The Examiner respectfully disagrees. Please see the updated rejection below since amendments by Applicant require additional reference to the Examiner’s art rejection.
The Examiner points to Paragraphs 0038-0039 and claim 2 of Bledsoe that specifically discloses determining of seasonality characteristics over different periods of time (e.g. a beginning/ending portion) and is specifically recited in the rejection below. The Examiner asserts that any time frame of data can be used as disclose by Bledsoe et al. Under BRI the first aggregation value corresponding to a beginning portion of the training data is data collected from a first time period and a second aggregation value corresponding to an ending portion of the training data is data collected from a second time period, where Bledsoe et al. teaches using data from different time periods and determining seasonal characteristics. Applicant’s arguments are not persuasive.
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, 7-13, and 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter;
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. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. 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 amounts to significantly more than the abstract idea itself.
In the instant case (Step 1), claims 12-13 and 15-17 are directed toward a process and claims 1-3, 7-11, and 18-20 are directed toward a system; which are statutory categories of invention.
Additionally (Step 2A Prong One), the independent claims are directed toward a computing system, comprising: a processor; and memory comprising computer-readable instructions that, when executed by the processor, cause the computer system to: receive training data for a forecasting model, the training data comprising a time-series of a content title; identify, based upon characteristics of the training data, whether or not the content title is associated with a seasonal trend; identifying a beginning portion of the time-series comprising a prescribed amount of the time-series extending from the start of the time-series; identifying an ending portion of the time-series comprising a prescribed amount of the time-series extending from the end of the time-series comparison between a first aggregation of values of the beginning portion of the time series and a second aggregation of values of the ending portion of the time series; and determining whether the content title is associated with a seasonal trend based upon the comparison; in response to identifying whether or not the content title is associated with a seasonal trend, dynamically select a particular forecasting model for the content title from a plurality of forecasting models, by: when the content title is associated with a seasonal trend, selecting a first forecasting model of the plurality of forecasting models, the first forecasting model comprising a Gradient Boosting Machine (GBM) based model; and when the content title is not associated with a seasonal trend, selecting a second forecasting model of the plurality of forecasting models that is different than the first forecasting model, the second forecasting model comprising an Exponential curve-fitting (Exp) based model; forecast a metric associated with the content title based on the selected particular forecasting model; and provide electronic data indicating the forecasted metric to a content provision platform, a content provider, or both (Organizing Human Activity), which are considered to be abstract ideas (See MPEP 2106.05). The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Organizing Human Activity because the claimed limitations are analyzing a content titles using forecasting models to determine if a title is associated with a seasonal trend based on time series data and then forecasting a metric associated with the title, which is analyzing titles of commercial project for commercial purposes.
Dependent claims 2-3, 7-11, 13, 15-17, and 19-20 further narrow the abstract idea identified in the independent claims, where any additional elements introduced are discussed below.
Step 2A Prong Two: In this application, even if not directed toward the abstract idea, the independent claims additionally recite “a computing system, comprising: a processor; and memory comprising computer-readable instructions that, when executed by the processor, cause the computer system to: receive training data for a forecasting model, comprising a Gradient Boosting Machine (GBM) based model; comprising an Exponential curve-fitting (Exp) based model; to a content provision platform, a content provider, or both (claim 1)”; “a computer; comprising a Gradient Boosting Machine (GBM) based model; comprising an Exponential curve-fitting (Exp) based model; to a content provision platform, a content provider, or both (claim 12)”; “a content provision metric forecasting system, comprising a computer processor and memory, configured to; to a content provision platform, a content provider, or both (claim 18)”, which are additional elements that do not integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106.05) and are recited at such a high level of generality. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computer or other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology.
In addition, dependent claims 2-3, 7-11, 13, 15-17, and 19-20 further narrow the abstract and recite no additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106.05). The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed.
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106.05). Further, method; and System Independent claims 1, 12, and 18 recite “a computing system, comprising: a processor; and memory comprising computer-readable instructions that, when executed by the processor, cause the computer system to: receive training data for a forecasting model, comprising a Gradient Boosting Machine (GBM) based model; comprising an Exponential curve-fitting (Exp) based model; to a content provision platform, a content provider, or both (claim 1)”; “a computer; comprising a Gradient Boosting Machine (GBM) based model; comprising an Exponential curve-fitting (Exp) based model; to a content provision platform, a content provider, or both (claim 12)”; “a content provision metric forecasting system, comprising a computer processor and memory, configured to; to a content provision platform, a content provider, or both (claim 18)”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0008-0009 and 0022 and Figures 1. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
In addition, claims 2-3, 7-11, 13, 15-17, and 19-20further narrow the abstract idea identified in the independent claims and present no additional elements that provide significantly more. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. The claimed structure merely amounts to the application or instructions to apply the abstract idea on a computer and does not move beyond a general link of the use of an abstract idea to a particular technological environment (See MPEP 2106.05). The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1-3, 11-13, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bledsoe et al. (US 2018/0300737 A1) in view of Schnoor et al. (US 2019/0066170 A1) and further in view of Leach et al. (US 11,526,261 B1).
Regarding Claim 1: Bledsoe et al. teach a computing system, comprising: a processor; and memory comprising computer-readable instructions that, when executed by the processor, cause the computer system to (See Paragraphs 0117-0118 and claim 1):
receive training data for a forecasting model, the training data comprising a time-series of “data” (See Figure 7, Figure 8, Paragraph 0030 – “TSF server 101 trains each of the selected entrant forecasting models with received and/or collected time series data points”, Paragraph 0044 – “entrant forecasting models that are constrained to handle only one seasonality can be trained and tested multiple times, once for each of the determined seasonality characteristics”, Paragraph 0045, Paragraph 0048 – “receive training data sets”, and claim 1);
identify, based upon characteristics of the training data, whether or not the “data” is associated with a seasonal trend (See Paragraphs 0038-0039 – “data analyzer and filtering engine 217 determines seasonality characteristics of a time series”, Paragraph 0041, Paragraph 0042 – “determine time series seasonality characteristics to filter entrant forecasting models”, Paragraph 0044, Table 3, and claim 2);
identifying a beginning portion of the time-series comprising a prescribed amount of the time-series extending from the start of the time-series (See Paragraph 0021 - “information computed from observed or historical data points”, Paragraph 0062 – “analyze past observations”, and Paragraph 0105 – “training datasets are defined with time series data points sampled during training period A (starting at time labeled as 801 and ending at time labeled as 803), training period B (starting at time labeled as 801 and ending at time labeled as 805), and training period C (starting at time labeled as 801 and ending at time labeled as 807)”);
identifying an ending portion of the time-series comprising a prescribed amount of the time-series extending from the end of the time-series (See Figure 5, Figure 6, Paragraph 0096, and Paragraph 0105 – “training datasets are defined with time series data points sampled during training period A (starting at time labeled as 801 and ending at time labeled as 803), training period B (starting at time labeled as 801 and ending at time labeled as 805), and training period C (starting at time labeled as 801 and ending at time labeled as 807)”;
performing a comparison between first aggregation of values of the beginning portion of the time series and a second aggregation of values of the ending portion of the time series; and determining whether the content title is associated with a seasonal trend based upon the comparison (See Paragraphs 0038-0039, Paragraph 0042, Paragraph 0045 – “Model training engine 219 trains a selected set of entrant forecasting models using fitness and training datasets selected from sampled datasets 227”, Paragraph 0050, Paragraph 0080, Paragraph 0084, claim 1 – “select at least one forecasting model from the set of trained entrant forecasting models based on an accuracy evaluation of each forecast value from the set of forecasted values”, and claim 2 – “determine at least one seasonality on the time series by the identification of at least one data set from the data content satisfying a predetermined statistically significant autocorrelation condition, the at least one seasonality used, at least in part, to select the set of entrant forecasting models”);
in response to identifying whether or not the content title is associated with a seasonal trend, dynamically select a particular forecasting model for the “data” from a plurality of forecasting models, by: when the “data” is associated with a seasonal trend, selecting a first forecasting model of the plurality of forecasting models (See Paragraph 0042, Paragraph 0045 – “Model training engine 219 trains a selected set of entrant forecasting models using fitness and training datasets selected from sampled datasets 227”, Paragraph 0050, Paragraph 0080, Paragraph 0084, claim 1 – “select at least one forecasting model from the set of trained entrant forecasting models based on an accuracy evaluation of each forecast value from the set of forecasted values”, and claim 2 – “determine at least one seasonality on the time series by the identification of at least one data set from the data content satisfying a predetermined statistically significant autocorrelation condition, the at least one seasonality used, at least in part, to select the set of entrant forecasting models”);
and when the “data” is not associated with a seasonal trend, selecting a second forecasting model of the plurality of forecasting models that is different than the first forecasting model (See Paragraph 0042, Paragraph 0045 – “Model training engine 219 trains a selected set of entrant forecasting models using fitness and training datasets selected from sampled datasets 227”, Paragraph 0050, Paragraph 0080, Paragraph 0084, claim 1 – “select at least one forecasting model from the set of trained entrant forecasting models based on an accuracy evaluation of each forecast value from the set of forecasted values”, and claim 2 – “determine at least one seasonality on the time series by the identification of at least one data set from the data content satisfying a predetermined statistically significant autocorrelation condition, the at least one seasonality used, at least in part, to select the set of entrant forecasting models”);
and forecast a metric associated with the “data” based on the selected particular forecasting model (See Paragraph 0030 – “TSF server 101 executes each of the trained entrant forecasting models to produce a set of forecasted values. The forecasted values indicate forecasted estimations of future data points of the time series”, Paragraph 0051, Paragraph 0067, Paragraph 0082 – “produce estimates or forecast of time series data points for a given testing period(s) of time”, and claim 1).
Bledsoe et al. do not specifically disclose a content title; the first forecasting model comprising a Gradient Boosting Machine (GBM) based model; the second forecasting model comprising an Exponential curve-fitting (Exp) based model. However, Schnoor et al. further teach a content title (See Paragraph 0024, Paragraph 0026, Paragraph 0033 – “statistical modeling algorithms generate a viewership forecast … such attribution data for a specific instance of media content, such as a particular episode of a TV series, can include one or more of genre, type of show, whether the media content is historically a higher or lower performer, what day and time the specific media content previously aired, whether the media content was aired as a back-to-back episode, what media content was the lead-in and/or lead-out, etc”, and claim 1).
The teachings of Bledsoe et al. and Schnoor et al. are related because both are analyzing data using machine learning techniques to make determinations. Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the machine learning forecasting system of Bledsoe et al. to incorporate the content of Schnoor et al. in order to forecast how many viewers tv content will likely receive.
Bledsoe et al. in view of Schnoor et al. do not specifically disclose the first forecasting model comprising a Gradient Boosting Machine (GBM) based model; the second forecasting model comprising an Exponential curve-fitting (Exp) based model. However, Leach et al. further teach:
the first forecasting model comprising a Gradient Boosting Machine (GBM) based model (See Abstract, columns 39-40 lines 50-67 and 1-42 – “an exponential smoothing (ETS) forecasting model”, and columns 41-42 lines 39-67 and 1-36 - “a GBM (Gradient Boosting Machine) method”);
the second forecasting model comprising an Exponential curve-fitting (Exp) based model (See Abstract, columns 39-40 lines 50-67 and 1-42 – “an exponential smoothing (ETS) forecasting model”, and columns 41-42 lines 39-67 and 1-36 - “a GBM (Gradient Boosting Machine) method”).
The teachings of Bledsoe et al., Schnoor et al., and Leach et al. are related because all are analyzing data using machine learning techniques to make determinations. Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the machine learning forecasting system of Bledsoe et al. in view of Schnoor et al. to incorporate the specific techniques of Leach et al. in order to more accurately forecast the data needed to be forecasted.
Regarding Claim 2: Bledsoe et al. in view of Schnoor et al. and further in view of Leach et al. teach the limitations of claim 1. Bledsoe et al. further teach wherein the memory comprises computer-readable instructions that, when executed by the processor, cause the computer system to train the selected particular forecasting model using the training data (See Figure 7, Figure 8, Paragraph 0030 – “TSF server 101 trains each of the selected entrant forecasting models with received and/or collected time series data points”, Paragraph 0044 – “entrant forecasting models that are constrained to handle only one seasonality can be trained and tested multiple times, once for each of the determined seasonality characteristics”, Paragraph 0045, Paragraph 0048 – “receive training data sets”, and claim 1).
Regarding Claim 3: Bledsoe et al. in view of Schnoor et al. and further in view of Leach et al. teach the limitations of claim 2. Bledsoe et al. further teach wherein the memory comprises computer-readable instructions that, when executed by the processor, cause the computer system to train the selected particular forecasting model using the training data in parallel with training of a second particular forecasting model using second training data of a second “data” (See Figure 7, Figure 8, Paragraph 0030 – “TSF server 101 trains each of the selected entrant forecasting models with received and/or collected time series data points”, Paragraph 0044 – “entrant forecasting models that are constrained to handle only one seasonality can be trained and tested multiple times, once for each of the determined seasonality characteristics”, Paragraph 0045, Paragraph 0048 – “receive training data sets”, Paragraph 0116 – “a parallel process”, and claim 1 – “a plurality of forecasting models”).
Bledsoe et al. do not specifically disclose a content title. However, Schnoor et al. further teach a content title (See Paragraph 0024, Paragraph 0026, Paragraph 0033 – “statistical modeling algorithms generate a viewership forecast”, and claim 1)
The teachings of Bledsoe et al. and Schnoor et al. are related because both are analyzing data using machine learning techniques to make determinations. Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the machine learning forecasting system of Bledsoe et al. to incorporate the content of Schnoor et al. in order to forecast how many viewers tv content will likely receive.
Regarding Claim 11: Bledsoe et al. in view of Schnoor et al. and further in view of Leach et al. teach the limitations of claim 1. Bledsoe et al. do not specifically disclose the following. However, Schnoor et al. further teach wherein the content title comprises a collection of digital content, the collection of digital content comprising a current season of a content series, an aggregation of previous seasons of the content series, or both (See Paragraph 0024, Paragraph 0026, Paragraph 0033 – “statistical modeling algorithms generate a viewership forecast”, and claim 1).
The teachings of Bledsoe et al. and Schnoor et al. are related because both are analyzing data using machine learning techniques to make determinations. Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the machine learning forecasting system of Bledsoe et al. to incorporate the content of Schnoor et al. in order to forecast how many viewers tv content will likely receive.
Regarding Claims 12-13 and 18: Claims 12-13 and 18 recite limitations already addressed by the rejections of claims 1-3 and 11 above; therefore the same rejections apply.
Allowable over 35 USC 103
Claims 7-10, 15-17, and 19-20 are allowable over the prior art, but remain rejected under §101 for the reasons set forth above. Dependent claims 7-10, 15-17, and 19-20 disclose a system and method for dynamic model selection by forecasting metrics of particular content selected by training a model with seasonal trends and time series data of a beginning portion and an ending portion to determine whether the content is associated with seasonal trends to decide whether to use a GBM model or an EXP model to actually forecast the metric while taking a mean of the beginning portion of the time series and a mean of the ending portion of the time series and determining a ration of the mean to see if it meets a threshold.
Regarding a possible 103 rejection: The closest prior art of record is:
Bledsoe et al. (US 2018/0300737 A1) – which discloses a self adaptive time series forecasting engine (See previous OA’s citations).
Schnoor et al. (US 2019/0066170 A1) – which discloses predictive modeling for generating ratings forecasts (See previous OA’s citations).
Leach et al. (US 11,526,261 B1) – which discloses aggregating and enriching data using models (See previous OA’s citations).
The prior art of record neither teaches nor suggests all particulars of the limitations as recited in claims 7-10, 15-17, and 19-20, such as a system and method for dynamic model selection by forecasting metrics of particular content selected by training a model with seasonal trends and time series data of a beginning portion and an ending portion to determine whether the content is associated with seasonal trends to decide whether to use a GBM model or an EXP model to actually forecast the metric while taking a mean of the beginning portion of the time series and a mean of the ending portion of the time series and determining a ration of the mean to see if it meets a threshold. While individual features may be known per se, there is no teaching or suggestion absent applicants’ own disclosure to combine these features other than with impermissible hindsight and the combination/arrangement of features are not found in analogous art. Specifically the claimed “a computing system, comprising: a processor; and memory comprising computer-readable instructions that, when executed by the processor, cause the computer system to: receive training data for a forecasting model, the training data comprising a time-series of a content title; identify, based upon characteristics of the training data, whether or not the content title is associated with a seasonal trend; identifying a beginning portion of the time-series comprising a prescribed amount of the time-series extending from the start of the time-series; identifying an ending portion of the time-series comprising a prescribed amount of the time-series extending from the end of the time-series comparison between a first aggregation of values of the beginning portion of the time series and a second aggregation of values of the ending portion of the time series; and determining whether the content title is associated with a seasonal trend based upon the comparison; in response to identifying whether or not the content title is associated with a seasonal trend, dynamically select a particular forecasting model for the content title from a plurality of forecasting models, by: when the content title is associated with a seasonal trend, selecting a first forecasting model of the plurality of forecasting models, the first forecasting model comprising a Gradient Boosting Machine (GBM) based model; and when the content title is not associated with a seasonal trend, selecting a second forecasting model of the plurality of forecasting models that is different than the first forecasting model, the second forecasting model comprising an Exponential curve-fitting (Exp) based model; forecast a metric associated with the content title based on the selected particular forecasting model; and provide electronic data indicating the forecasted metric to a content provision platform, a content provider, or both … wherein the memory comprises computer-readable instructions that, when executed by the processor, cause the computer system to: compare the beginning portion of the time-series to the ending portion of the time-series, by: identifying a mean of the beginning portion of the time-series as the first aggregation of values of the beginning portion; identifying a mean of the ending portion of the time-series as the second aggregation of values of the ending portion; and determining whether a ratio of the mean of the beginning portion of the time-series to the mean of the ending portion of the time-series meets or breaches a criterion threshold; and determine whether the content title is associated with a seasonal trend based upon whether the criterion threshold is met or breached by the ratio (as required by claims 7-10, 15-17, and 19-20)”, thus rendering claims 7-10, 15-17, and 19-20 as allowable over the prior art.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record, but not relied upon is considered pertinent to applicant's disclosure is listed on the attached PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW D HENRY whose telephone number is (571)270-0504. The examiner can normally be reached on Monday-Thursday 9AM-5PM.
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/MATTHEW D HENRY/Primary Examiner, Art Unit 3625
