DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 1/21/2026 has been entered.
Status of Claims
This is in reply to the claim amendments and remarks of the RCE filed 1/21/2026.
Claims 1, 12, and 18 have been amended and claims 4-5 have been cancelled.
Claims 1-3 and 7-20 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 Examiner questions why claim 18 is not being amended similarly to claims 1 and 12.
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 and 7-20, 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.
The Examiner further notes these arguments are not relevant for claim 18 because these details are not even recited in claim 18. Applicant’s arguments are not persuasive.
Applicant argues the claims are eligible in view of McRO. The Examiner respectfully disagrees. The Examiner asserts that the McRO case is related to “Automatically Animating Lip Synchronization and Facial Expression of Animated Characters”, which is unrelated to the Applicant’s claimed limitations involving analyzing content titles to predict if they are related to seasonal trends. 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.
Applicant further argues the claims are eligible because they provide an inventive concept. The Examiner respectfully disagrees. The Examiner points to MPEP 2106.05 which states “the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973 (rejecting "the Government’s invitation to substitute §§ 102, 103, and 112 inquiries for the better established inquiry under § 101 "). As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter”, where a narrow abstract idea is still an abstract idea. Applicant’s arguments are not persuasive.
Applicant argues the claims are eligible in view of Bascom. The Examiner respectfully disagrees. The Examiner notes the McRO response above. The Examiner further notes that Applicant merely claims a general purpose computer for implementing the abstract idea, which does not make the claims eligible (See MPEP 2106). Bascom involved specific placement of hardware within the series of steps that made the claims eligible, where Applicant’s claims merely recite a general purpose computer for implementing the analysis, which is unrelated. Applicant’s arguments are not persuasive.
With regard to the limitations of claims 1-3 and 7-20, 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 and 7-20 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 12-17 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 being associated with 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 training data; identifying an ending portion of the training data comparing a first aggregation of values of the beginning portion of the training data to a second aggregation of values of the ending portion of the training data; and determining whether or not the content title is associated with a seasonal trend based upon the comparison; in response to receiving the training data, 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 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, 13-17, 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, 13-17, 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, 13-17, 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 and 7-20 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 being associated with “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 training data (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 training data (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)”;
comparing a first aggregation of values of the beginning portion of the training data to a second aggregation of values of the ending portion of the training data; and determining whether or not 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 receiving the training data, 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 7: Bledsoe et al. in view of Schnoor et al. and further in view of Leach et al. teach the limitations of claim 6. Bledsoe et al. further teach wherein the memory comprises computer-readable instructions that, when executed by the processor, cause the computer system to: compare the beginning portion of the training data to the ending portion of the training data, by: identifying a mean of the beginning portion of the training data as the first aggregation of values of the beginning portion; identifying a mean of the ending portion of the training data as the second aggregation of values of the ending portion; and determining whether a ratio of the mean of the beginning portion of the training data to the mean of the ending portion of the training data 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 (See Paragraph 0032 – “the number of features (e.g., exogenous features) associated with a time series, variability of recent observations, standard deviation of recent observations, compute time to produce forecasted data points, mean absolute error (MAE), mean absolute percent error (MAPE), mean absolute scaled error (MASE), root-mean-square error (RMSE), Akaike information criterion corrected (AICc), mean of the test set, standard deviation of the test set, normalized root mean square error, coefficient of variation, and other suitable values”, Paragraph 0042 – “determine time series seasonality characteristics to filter entrant forecasting models”, Paragraph 0074 – “Mean Absolute Percentage Error (MAPE)”, Paragraph 0078 – “weekly seasonality, where the days of the week are related by the same customer ratios week after week”, Paragraph 0093 – “calculating a Root Mean Squared Error (RMSE)”, Paragraph 0095 – “forecast accuracy techniques include computing mean absolute percentage errors (MAPE), mean absolute scaled errors (MASE), normalized root mean square error (NRMSE), coefficient of valuation (CV), mean of forecasted values (MFV), standard deviation of forecasted values (SFV), and other suitable measures for the assessment of accuracy of forecasted data points”, and Paragraph 0109 – “predetermined period of time”).
Regarding Claim 8: Bledsoe et al. in view of Schnoor et al. and further in view of Leach et al. teach the limitations of claim 7. Bledsoe et al. do not specifically disclose the following. However, Schnoor et al. further teach wherein the metric comprises an inflow of the 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 9: Bledsoe et al. in view of Schnoor et al. and further in view of Leach et al. teach the limitations of claim 8. Bledsoe et al. further teach wherein: the beginning portion of the training data comprises a temporal first 10% of the training data; the ending portion of the training data comprises a temporal ending 10% of the training data; and the criterion threshold comprises 4 (See Paragraph 0032 – “the number of features (e.g., exogenous features) associated with a time series, variability of recent observations, standard deviation of recent observations, compute time to produce forecasted data points, mean absolute error (MAE), mean absolute percent error (MAPE), mean absolute scaled error (MASE), root-mean-square error (RMSE), Akaike information criterion corrected (AICc), mean of the test set, standard deviation of the test set, normalized root mean square error, coefficient of variation, and other suitable values”, Paragraph 0042 – “determine time series seasonality characteristics to filter entrant forecasting models”, Paragraph 0074 – “Mean Absolute Percentage Error (MAPE)”, Paragraph 0078 – “weekly seasonality, where the days of the week are related by the same customer ratios week after week”, Paragraph 0093 – “calculating a Root Mean Squared Error (RMSE)”, Paragraph 0095 – “forecast accuracy techniques include computing mean absolute percentage errors (MAPE), mean absolute scaled errors (MASE), normalized root mean square error (NRMSE), coefficient of valuation (CV), mean of forecasted values (MFV), standard deviation of forecasted values (SFV), and other suitable measures for the assessment of accuracy of forecasted data points”, and Paragraph 0109 – “predetermined period of time”).
Regarding Claim 10: Bledsoe et al. in view of Schnoor et al. and further in view of Leach et al. teach the limitations of claim 8. Bledsoe et al. do not specifically disclose the following. However, Schnoor et al. further teach wherein the inflow is specific to paid subscribers of a content provision platform of the content title, a particular tier of paid subscribers, or both (See Paragraph 0024 – “subscription-based services”, Paragraph 0026, Paragraph 0029, 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 15-20: Claims 12-13 and 15-20 recite limitations already addressed by the rejections of claims 1-3 and 7-11 above; therefore the same rejections apply.
Conclusion
The prior art made of record, but not relied upon is considered pertinent to Applicant's disclosure is listed on the attached PTO-892 and should be taken into account / considered by the Applicant upon reviewing this office action.
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.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, BRIAN EPSTEIN can be reached on (571)-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW D HENRY/Primary Examiner, Art Unit 3625
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