DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed on November 20, 2025 cancelled no claims. Claims 1, 9, 11, and 19 were amended an no new claims were added. Thus, the currently pending claims addressed below are claims 1, 4, 6-11, 14, and 16-20.
Claim Interpretation
The following claim terms have been interpreted in light of the applicant’s specification:
Search lower funnel: historical weekly media spend data for advertisements associated with searches made by people close to making a purchase decision (Applicant’s specification - Paragraphs 22-23: lower funnel means users that are close to making a purchase; Paragraphs 55-60: search LF is historical weekly media spend data for advertisements associated with searches made by people in the lower funnel.);
Claim Rejections - 35 USC § 112
The amendment filed on November 20, 2025 has overcome the 35 USC 112(a) rejection of claims 1, 4, 6-11, 14, and 16-20. Thus, the rejection is hereby withdrawn.
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, 4, 6-11, 14, and 16-20 are directed to a method and a computer program product which would be classified under one of the listed statutory classifications (i.e., 2019 Revised Patent Subject Matter Eligibility Guidance (hereinafter “PEG”) “PEG” Step 1=Yes).
However, claims 1, 4, 6-11, 14, and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claim(s) 1 and 11 recite(s) the following abstract idea:
setting up input data comprising a search lower funnel, which is close to an action stage, and a predetermined period of lag, along with an original media spend driver;
creating an ensemble of forecasting models which comprise one or more time series regression models and one or more algorithmic models, wherein one of the one or more time series regression models comprises an autoregressive integrated moving average (ARIMA) errors model that includes a forecasting equation, and the one of the one or more time series regression models is built based on past media spends and changes in search engine optimization (SEO) driven consumer demand for a product or service;
ranking coefficients resulted from the ensemble of forecasting models, wherein a coefficient from one model identifies a relationship between a media spend associated with the one model, and the time to impact of the one media spend on SEO-driven consumer behavior;
generating, based on the ranking of the coefficients, a forecast that comprises recommended future media spends, and effects expected to be achieved by those future media spends, wherein the higher the coefficients are, the more lagging the future media spends are realized; and
implementing the recommended future media spends based on the forecast.
The limitations as detailed above, as drafted, falls within the “Certain Method of Organizing Human Activity” grouping of abstract ideas namely advertising, marketing, or sales activities or behaviors and/or the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea (i.e., “PEG” Revised Step 2A Prong One=Yes).
This judicial exception is not integrated into a practical application because the claim only recites the additional elements of:
one or more hardware processors and one or more machine learning model.
The additional technical elements above are recited at a high-level of generality (i.e., as a general-purpose computer with generic computer components and one or more generic machine learning model) such that it amounts to no more than mere instructions to apply the exception using a general-purpose computer with generic computer components and a machine learning model as tools. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional technical elements above do not integrate the abstract idea/judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea. More specifically, the additional elements fail to include (1) improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05(a)), (2) applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see Vanda memo), (3) applying the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), (4) effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), or (5) applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (see MPEP 2106.05(e) and Vanda memo).
Rather, the limitations merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), or generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).
Thus, the claim is “directed to” an abstract idea (i.e., “PEG” Revised Step 2A Prong Two=Yes)
When considering Step 2B of the Alice/Mayo test, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims do not amount to significantly more than the abstract idea.
More specifically, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using one or more hardware processors and machine learning models to perform the claimed functions amounts to no more than mere instructions to apply the exception using one or more general-purpose computers and/or one or more generic computer components.
“Generic computer implementation” is insufficient to transform a patent-ineligible abstract idea into a patent-eligible invention (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2352, 2357) and more generally, “simply appending conventional steps specified at a high level of generality” to an abstract idea does not make that idea patentable (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Mayo, 132 S. Ct. at 1300). Moreover, “the use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter (See FairWarning, 120 U.S.P.Q.2d. 1293, citing DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014)). As such, the additional elements of the claim do not add a meaningful limitation to the abstract idea because they would be generic computer functions in any computer implementation. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the computer or improves any other technology. Their collective functions merely provide generic computer implementation.
The Examiner notes simply implementing an abstract concept on a computer, without meaningful limitations to that concept, does not transform a patent-ineligible claim into a patent-eligible one (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bancorp, 687 F.3d at 1280), limiting the application of an abstract idea to one field of use does not necessarily guard against preempting all uses of the abstract idea (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bilski, 130 S. Ct. at 3231), and further the prohibition against patenting an abstract principle “cannot be circumvented by attempting to limit the use of the [principle] to a particular technological environment” (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Flook, 437 U.S. at 584), and finally merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2358; Mayo, 132 S. Ct. at 1294; Bilski v. Kappos, 561 U.S. 593, 612 (2010); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat’l Ass’n, 776 F.3d 1343, 1348 (Fed. Cir. 2014); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014).
Applicant herein only requires one or more general-purpose computers and/or one or more generic computer components (as evidenced from Figure 8 and paragraph 87 of the applicant’s specification which discloses that the processor is a general-purpose processor; at least page 7, lines 23-29 of Langley et al., Approaches to Machine Learning, Journal of the American Society for Information Science, February 16, 1984, pgs. 1-28 that discloses that machine learning processes were old and well known by at least 1984; and Mohammed et al., A comprehensive review on ensemble deep learning: Opportunities and challenges, February 2023, Journal of King Saud University – Computer and Information Sciences, Volume 35, Issue 2, Pages 757-774 which discloses that ensembles of machine learning models were well-known by at least February of 2023); therefore, there does not appear to be any alteration or modification to the generic activities indicated, and they are also therefore recognized as insignificant activity with respect to eligibility.
Thus, taken individually and in combination, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea) (i.e., “PEG” Step 2B=No).
The dependent claims 4, 6-10, 14, and 16-20 appear to merely further limit the abstract idea by further limiting the effects which considered part of the abstract idea (Claims 4 and 14); adding an additional step of generating a forecast and/or further limiting the generating of the forecast which are both considered part of the abstract idea (Claims 6-9 and 16-19); adding an additional step of recommendation for one or more future media spends which is considered part of the abstract idea (Claims 10 and 20), and therefore only further limit the abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes), does/do not include any new additional elements that are sufficient to amount to significantly more than the judicial exception, and as such are “directed to” said abstract idea (i.e. “PEG” Step 2A Prong Two=Yes); and do not add significantly more than the idea (i.e. “PEG” Step 2B=No).
Thus, based on the detailed analysis above, claims 1, 4, 6-11, 14, and 16-20 are not patent eligible.
Possible Allowable Subject Matter
Claims 1, 4, 6-11, 14, and 16-20 would be allowable if the applicant were to be able to overcome the 35 USC 101 rejections above.
The following is a statement of reasons for the indication of allowable subject matter: The examiner has found prior art (see Paulsen et al. – US2014/0195339 and Zhao – WO2024/136847 that discloses a method and a non-transitory storage medium executable by a processor, comprising:
using an ensemble comprising machine learning (ML) forecasting models, determining respective relationships between past media spends and one or more marketing channels based on historical attributions for a product or service, wherein in the one or more marketing channels include search engine optimization;
ranking the relationships according to a criterion;
generating, based on the ranking, a forecast that comprises recommended future media spends, and effects expected to be achieved by those future media spends; and
implementing the recommended future media spends.
The examiner has also found prior art (see timeseriesreasoning, Introduction to Regression with ARIMA Errors Model, May 20, 2022, https://web.archive.org/web/ 20220520095428/https://timeseriesreasoning.com/contents/regression-with-arima-errors-model/, pages 1-20) that setting up input data comprising a predetermined period of lag and original drivers to be analyzed and building a time series regression model with an autoregressive integrated moving average (ARIMA) errors model.
However, the examiner has been unable to find prior art that discloses “input data comprising search lower funnel (i.e., historical weekly media spend data for advertisements associated with searches made by people close to making a purchase decision) along with an original media spend driver and “a time-series regression model built based on past media spends and changes in search engine optimization (SEO) driven consumer demand for a product or service”.
As such, claims 1, 4, 6-11, 14, and 16-20 include subject matter that would be allowable over the prior art if the applicant were to be able to overcome the 35 USC 101 rejections above.
Response to Arguments
Applicant's arguments filed November 20, 2025 have been fully considered but they are not persuasive.
The applicant argues that the claims overcome the 35 USC 101 rejection because the claimed ensemble of forecasting models provides an improvement over traditional mixed media modeling by taking into account recent marketing activities for any recommendation because traditional MMM analyzes only historical quarters or other time periods and do not provide any insight on how much time it takes for a given marketing vehicle, or mix of marketing vehicles, to start impacting SEO demand and therefore revenue from Organic channels which, even if not they would not integrate the abstract idea into a practical application under Step 2a, Prong 2, would be significantly more than an abstract idea under Step 2b. The examiner disagrees. In order to overcome a 35 USC 101 rejection under Step 2a, Prong 2, the purported improvement must be rooted in the “additional elements” of a claim in a manner other than just applying the abstract idea using the additional elements as merely a tool. In order to overcome a 35 USC 101 rejection under Step 2b, it is the “additional elements” of a claim that must be considered significantly more. “Additional elements” are defined as those elements outside the identified abstract idea. In the instant case the only additional elements of the claim are a general-purpose computer and generic computer components (e.g., machine learning model, memory, and processor) upon which the abstract idea is merely being applied which is insufficient to transform an abstract idea into a practical application under Step 2a, Prong 2 and insufficient to be considered “significantly more” under Step 2B. The claimed improvement is rooted solely in the abstract idea itself which is then merely applied using the “additional elements” as a tool. The claimed ensemble of forecasting models are part of the abstract idea itself comprising a statistical model and another model. An ARIMA model is a traditional statistical model for performing time series regression and, as such, is merely an algorithm for analyzing data and determining results. The claim requires the “other model” to be a machine learning model but imposes no restriction on how this machine learning model operates. Additionally, there is no indication in the applicant’s disclose to indicate that they have invented a new type of machine learning model that operates in a manner different from traditional machine learning models. As such, the claim merely recites an abstract idea that applies a generic machine learning model as a tool. As such, the purported improvement is rooted solely in the abstract idea itself. Improvements of this nature are improvements to an abstract idea which is an improvement in ineligible subject matter(see SAP v. Investpic: Page 2, line 22 through Page 3, line 13 - Even assuming that the algorithms claimed are groundbreaking, innovative or even brilliant, the claims are ineligible because their innovation is an innovation in ineligible subject matter because they are nothing but a series of mathematical algorithms based on selected information and the presentation of the results of those algorithms. Thus, the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non-abstract application realm. An advance of this nature is ineligible for patenting; and Page 10, lines 18-24 - Even if a process of collecting and analyzing information is limited to particular content, or a particular source, that limitations does not make the collection and analysis other than abstract.). Thus, the claim is incapable of overcoming the 35 USC 101 rejection under Step 2a, Prong 2. Likewise, the only additional elements in the claim, whether considered individual or as a whole, are merely a general-purpose computer with generic components executing software such as a generic machine learning model. The applicant has neither invented the general-purpose computer nor the generic machine-learning model that are being used as a tool to apply the abstract idea and, as such, the “additional elements” of the claim, individually and as a whole, cannot be considered “significantly more” than the abstract idea under Step 2b. As made clear in the Recentive Analytics v. Fox Corp decision, as well as Abstract Idea examples 47-49, make it clear that merely applying an abstract idea using known machine learning models is insufficient to transform and abstract idea into a practical application under Step 2a, Prong 2 and insufficient to be considered significantly more under Step 2b. Thus, the rejection has been maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Chittilappilly et al. (US2017/0337505) which discloses a predictive model that estimates the performance of a media spend plan taking into account a plurality dynamic variables in relating the stimuli and responses associated with a marketing campaign, wherein the predictive model uses historical stimulus and response data to predict the response to various stimuli mix scenarios including media spend levels and certain performance metrics using historical ad pricing (e.g., cost per impression).
McGovern et al. (US2017/0091810) which discloses a media plan analyzer and simulator to determine a media spend allocation scenarios based in part on historical engagement performance metrics (e.g., user reach, engaged users, conversions, etc.) across all channels (e.g., TV, search, display, social, email, etc.) of a given engagement campaign, wherein the media plan analyzer and simulator generates a set of predicted media spend allocation performance parameters corresponding to a predicted performance (e.g., engagement level, conversions, ROI, other performance metrics, etc.) of the media spend allocation scenario.
Huber et al. (PGPUB: 2020/0257943) which discloses obtaining times series-based analytics about the frequency of searches using specific search terms and building a time-series regression model with an ARIMA model resulting in an ARIMA errors model.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN W VAN BRAMER whose telephone number is (571)272-8198. The examiner can normally be reached Monday-Thursday 5:30 am - 4 pm EST.
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/John Van Bramer/Primary Examiner, Art Unit 3622