DETAILED ACTION
Status of Claims
1. This is a Final office action in response to communication received 06/26/2026. Claims 1-7 and 11-23 are pending and examined herein.
Priority
2. The examiner acknowledges priority benefits being claimed by the Applicant for U.S. Provisional Application No. 63/519,968 filed on August 16, 2023.
Claim Rejections - 35 USC § 101
3. 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 1-7 and 11-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Next using the 2019 Revised Patent Subject Matter Eligibility Guidance (hereinafter 2019 PEG) the rejection as follows has been applied.
Under step 1, analysis is based on MPEP 2106.03, Claims 1-7, 11-14, and 21 are a method; claims 15-19 and 22 are a system; and claims 20 and 23 are a non-transitory processor readable medium. Thus, each claim 1-7 and 11-23, on its face, is directed to one of the statutory categories (i.e., useful process, machine, manufacture, or composition of matter) of 35 U.S.C. §101.
Under Step 2A Prong One, per MPEP 2106.04, prong one asks does the claim recite an abstract idea, law of nature, or natural phenomenon? In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. While the terms "set forth" and "described" are thus both equated with "recite", their different language is intended to indicate that there are two ways in which an exception can be recited in a claim. For instance, the claims in Diehr, 450 U.S. at 178 n. 2, 179 n.5, 191-92, 209 USPQ at 4-5 (1981), clearly stated a mathematical equation in the repetitively calculating step, and the claims in Mayo, 566 U.S. 66, 75-77, 101 USPQ2d 1961, 1967-68 (2012), clearly stated laws of nature in the wherein clause, such that the claims "set forth" an identifiable judicial exception. Alternatively, the claims in Alice Corp., 573 U.S. at 218, 110 USPQ2d at 1982, described the concept of intermediated settlement without ever explicitly using the words "intermediated" or "settlement."
Next, per 2019 PEG, to determine whether a claim recites an abstract idea in Prong One, examiners are now to: (I) Identify the specific limitation(s) in the claim under examination (individually or in combination) that the examiner believes recites an abstract idea; and (II) determine whether the identified limitation(s) falls within the subject matter groupings of abstract ideas enumerated in Section I of the 2019 PEG. If the identified limitation(s) falls within the subject matter groupings of abstract ideas enumerated in Section I, analysis should proceed to Prong Two in order to evaluate whether the claim integrates the abstract idea into a practical application.
(I) An abstract idea as recited per abstract recitation of claims 1-7 and 11-23 [i.e. recitation with the exception of additional elements, which are first considered under step 2A prong two when claim(s) is/are reconsidered as a whole and exclusively under step 2B inquiries below, i.e. under step 2A prong one the Examiner considered claim recitation other than the additional elements (which once again are expressly noted below) to be the abstract recitation] (II) is that of providing media plan recommendation based on evaluation of data which is certain methods of organizing human activity (but for its implementation in network based environment - which is considered further under prong two and step 2B analysis as set forth below).
The phrase "Certain methods of organizing human activity" applies to fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Further, see MPEP 2106.04(a)(2) II. A-C.
Therefore, the identified limitations fall within the subject matter groupings of abstract ideas enumerated in Section I of 2019 PEG, thus analysis now proceeds to Prong Two in order to evaluate whether the claim integrates the abstract idea into a practical application.
Under Step 2A Prong Two, per MPEP 2106.04, prong two asks does the claim recite additional elements that integrate the judicial exception into a practical application? In Prong Two, examiners evaluate whether the claim as a whole integrates the exception into a practical application of that exception. If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception (Step 2A: NO) and thus is eligible at Pathway B. This concludes the eligibility analysis. If, however, the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception (Step 2A: YES), and requires further analysis under Step 2B (where it may still be eligible if it amounts to an ‘‘inventive concept’’).
Next, per 2019 PEG, Prong Two represents a change from prior guidance. The analysis under Prong Two is the same for all claims reciting a judicial exception, whether the exception is an abstract idea, a law of nature, or a natural phenomenon. Examiners evaluate integration into a practical application by: (I) Identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (II) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application, using one or more of the considerations laid out by the Supreme Court and the Federal Circuit.
Accordingly, the examiner will evaluate whether the claims recite one or more additional element(s) that integrate the exception into a practical application of that exception by considering them both individually and as a whole.
The claim elements in addition to the abstract idea, i.e. additional elements, as recited in claims 1-7 and 11-23 at least are media platforms, a computing system, one or more data sources, electronic computation, a trained machine learning predictive recommendation model, machine readable implementation instructions, interaction data serving as iterative feedback mechanism, to retrain said machine learning predictive recommendation model (per claim 1); an electronic device comprising processor, memory, and program stored in the memory and executed by the processor (additionally per claim 15); a tangible, non-transitory readable medium storing instructions that, when executed by one or more processors (additionally per claim 20); (per claim 2 said recommendation media plan is displayed on an electronic device or electronically sent to a second electronic device via an electronic network); one or more electronic data locations or structures are third party databases (per claim 16); a second electronic device operatively connected to said system via an electronic network (per claim 19). Remaining claims either recite the same additional as already noted above or do not recite an additional element in which case note prong one.
As would be readily apparent to a person having ordinary skill in the art (hereinafter PHOSITA), the additional elements are described at a high level of generality, see at least as-filed Figs. 1-2 and their associated disclosure. The additional elements are simply utilized as generic tools to implement the abstract idea or plan as "apply it" instructions including the machine learning model (see MPEP 2106.05(f)). The processor executing the "apply it" instruction is further connected to one or more device(s) merely sending/receiving/transmitting data over a network, note receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014). Obtained data is considered insignificant extra solution activity (see MPEP 2106.05(g)). Further, the processor analyzes obtained data to recommend iteratively optimized media purchase plans. Thus, the process is like collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group) - certain result here is a tailored media plan based on evaluation of information or obtained datasets (Int. Ventures v. Cap One Bank ‘382 patent) and refining the plan iteratively based on comparison. The abstract idea is intended to be merely carried out in a technical environment such as collecting data via a network and analyzing data via a generic processor to provide personalized marketing plans to carry out a marketing campaign, however, fail to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment (see MPEP 2106.05(h)).
Accordingly, viewed as a whole, these additional claim element(s) do not provide any additional element that integrates the abstract idea (prong one), into a practical application (prong two) upon considering the additional elements both individually and as a combination or as a whole as they fail to provide: an additional element that reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field; or an additional element that implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim; or an additional element that effects a transformation or reduction of a particular article to a different state or thing; or an additional element that applies or uses the judicial exception, again, 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 as explained above.
Thus, the abstract idea of providing media plan recommendation based on evaluation of data which is certain methods of organizing human activity (prong one) is not integrated into a practical application upon consideration of the additional element(s) both individually and as a combination (prong two).
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.
Under step 2B, per MPEP 2106.05, as it applies to claims 1-7 and 11-20, the Examiner will evaluate whether the foregoing additional elements analyzed under prong two, when considered both individually and as a whole provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). The abstract idea of providing media plan recommendation based on evaluation of data which is certain methods of organizing human activity - has not been applied in an eligible manner. The claim elements in addition to the abstract idea are simply being utilized as generic tools to execute "apply it" instructions as they are described at a high level of generality. Additionally, the abstract idea is intended to be merely carried out in a technical environment, however, fail to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment (Id. or note step 2A prong two).
Regarding, insignificant solution activity such as data gathering or post solution activity such as displaying on interface, the Examiner relies on court cases and publications that demonstrate that such a way to gather data and display information is indeed well-understood, routine, or conventional in the industry or art, at least note as follows:
(i) receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network) [similarly here user's data is received/sent/transmitted over a network]; and
(ii) Affinity v DirecTV - "The court rejected the argument that the computer components recited in the claims constituted an “inventive concept.” It held that the claims added “only generic computer components such as an ‘interface,’ ‘network,’ and ‘database,’” and that “recitation of generic computer limitations does not make an otherwise ineligible claim patent-eligible.” Id. at 1324-25 (citations omitted). The court noted that nothing in the asserted claims purported to improve the functioning of the computer itself or “effect an improvement in any other technology or technical field.” Mortgage Grader, 811 F.3d at 1325 (quoting Alice, 134 S. Ct. at 2359)." [similarly here as a post solution media plans are communicated or displayed to user on an interface].
Next, in view of compact prosecution only further analysis per the Berkheimer Memo dated April 19, 2018 is being conducted as the following additional elements would be readily apparent as generic to a person having ordinary skill in the art (hereinafter PHOSITA), in other words analysis is similar to Berkheimer claim 1 and not claims 4-7 where there was "a genuine issue of material fact in light of the specification," nevertheless the Examiner finds the additional elements when considered both individually and as a combination to be well-understood, routine or conventional and expressly supports in writing as follows:
2. The Examiner provides citation to one or more publications as noting the well-understood, routine, conventional nature of machine learning or AI models as follows:
i) Chandramouli, Patent: US 8,442,683 note para. [0005]-[0007] and [0029]-[0033]; (ii) Lee, Pub. No.: US 2002/0107926 note para. [0020]; (iii) Kwok, Pub. No.: US 2002/0150295 note para. [0015]; (iv) Teller, Pub. No.: US 2004/0133081 [0236]-[0238]; (v) Agrawal and Srikant Patent No.: US 6546389 note "As recognized herein, the primary task of data mining is the development of models about aggregated data. Accordingly, the present invention understands that it is possible to develop accurate models without access to precise information in individual data records."; (vi) Deshpande et al., Pub. No.: US 2015/0134413 [0046] Using the target and input features, in step F3 of FIG. 1, a plurality of forecasting models are built for a product or a product category, a location, and a time window. A plurality of forecasting models can be built using existing machine learning based methods and/or time-series forecasting methods, and using the standard training-testing-validation methods. In an exemplary embodiment, only the highest quality models with high quality (high accuracy, precision, recall, etc.) are retained.; [0078] The processing system forecasting engine 202 can also include a forecasting model building engine 224 and a forecast calculation engine 226. In the model building stage, target and input features based on a customer or a customer segment's past data are used to train, test, and validate different types of forecasting models using machine learning and/or time series forecasting based approaches. Individual models are retained depending on the performance. The output of plurality of these retained models can then be fused into a single model 228. The fusion can be based on a rule-based approach or by assigning weights to individual model and combining those using ranking or combination techniques." (vii) Wei et al., Pub. No.: US 2015/0235260 [0080] Then, analysis module 532 may determine one or more predefined model(s) 546 based on event data 538 and the one or more targeting criteria. For example, analysis module 532 may use training and testing subsets of this information to generate one or more machine-learning models. The one or more predefined model(s) 546 may allow estimates of the number of future events to be determined for terms 544 in the one or more targeting criteria 542.; (viii) Beatty, Pub. No.: US 2012/0166267 see [0177] note "the prediction of conversion rate is performed by a machine-learning system that is trained using historical purchase data available to the ad system. The training set contains instances of purchase/no purchase decisions and many data points about the (user, context, offer). For example, the training examples might contain the following data points about the offer that was made to a user: price of offer, % discount of offer, popularity of merchant, time of day, gender of user, income of user, interests of user, websites visited by user, categories of websites visited by user, search queries by user, category of business, number of friends that had purchased the offer, "closeness" of friends that had purchased the offer, physical distance between the user's home and the business, physical distance between the user's workplace and the business, the "cluster id" of the user (generated by a clustering algorithm that placed, and users into clusters based on similar attributes of preferences)."
Therefore the claims here fail to contain any additional element(s) or combination of additional elements that can be considered as significantly more and the claims are rejected under 35 U.S.C. 101 for lacking eligible subject matter.
Reason(s) For Non-applicability Of Prior Art
4. Claims 1-7 and 11-20 were previously rejected under 35 U.S.C. 102 (a)(1) and (a)(2) as being clearly anticipated by Simmons et al. (Pub. No.: US2012/0323674) referred to hereinafter as Simmons.
The Examiner also discovered the following while updating the search in view of claim amendments filed June 26, 2026, note:
- US2023/0123322 see Abstract " method for prioritizing predictive model data streams includes receiving, by a first device, a plurality of predictive model data streams. Each predictive model data stream includes a set of model parameters for a corresponding predictive model. Each predictive model is trained to predict future data values of a data source. The method includes prioritizing, by the first device, priorities to each of the plurality of predictive model data streams. The method includes selecting at least one of the predictive model data streams based on a corresponding priority. The method includes parameterizing, by the first device, a predictive model using the set of model parameters included in the selected predictive model data stream. The method includes predicting, by the first device, future data values of the data source using the parameterized predictive model.";
[0199] In embodiments, providing coordinated intelligence for the set of demand management applications 824 may include configuring at least one of the adaptive intelligence systems 614 (e.g., through the user interface 3020 and the like) for at least one or more demand management applications selected from a list of demand management applications including a demand planning application, a demand prediction application, a sales application, a future demand aggregation application, a marketing application, an advertising application, an e-commerce application, a marketing analytics application, a customer relationship management application, a search engine optimization application, a sales management application, an advertising network application, a behavioral tracking application, a marketing analytics application, a location-based product or service-targeting application, a collaborative filtering application, a recommendation engine for a product or service, and the like.
[0207] Providing coordinated intelligence may include employing a neural network to process at least one of the inputs and outputs of the sets of demand management and supply chain applications. Neural networks may be used with demand applications, such as a demand planning application, a demand prediction application, a sales application, a future demand aggregation application, a marketing application, an advertising application, an e-commerce application, a marketing analytics application, a customer relationship management application, a search engine optimization application, a sales management application, an advertising network application, a behavioral tracking application, a marketing analytics application, a location-based product or service-targeting application, a collaborative filtering application, a recommendation engine for a product or service, and the like. Neural networks may also be used with supply chain applications such as a goods timing management application, a goods quantity management application, a logistics management application, a shipping application, a delivery application, an order for goods management application, an order for components management application, and the like. Neural networks may provide coordinated intelligence by processing data that is available in any of a plurality of value chain data sources for the category of goods including without limitation processes, bill of materials, weather, traffic, design specification, customer complaint logs, customer reviews, Enterprise Resource Planning (ERP) System, Customer Relationship Management (CRM) System, Customer Experience Management (CEM) System, Service Lifecycle Management (SLM) System, Product Lifecycle Management (PLM) System, and the like.
[0452] FIG. 38 illustrates example embodiments of a system for controlling and/or making decisions, predictions, and/or classification on behalf of a value chain system 2030. In embodiments, an artificial intelligence system 2010 leverages one or more machine-learned models 2004 to perform value chain-related tasks on behalf of the value chain system 2030 and/or to make decisions, classifications, and/or predictions on behalf of the value chain system 2030. In some embodiments, a machine learning system 2002 trains the machine learned models 2004 based on training data 2062, outcome data 2060, and/or simulation data 2022. As used herein, the term machine-learned model may refer to any suitable type of model that is learned in a supervised, unsupervised, or hybrid manner. Examples of machine-learned models include neural networks (e.g., deep neural networks, convolution neural networks, and many others), regression based models, decision trees, hidden forests, Hidden Markov models, Bayesian models, and the like. In embodiments, the artificial intelligence system 2010 and/or the value chain system 2030 may provide outcome data 2060 to the machine-learning system 2002 that relates to a determination (e.g., decision, classification, prediction) made by the artificial intelligence system 2010 based in part on the one or more machine-learned models and the input to those models. The machine learning system may in-turn reinforce/retrain the machine-learned models 2004 based on the feedback. Furthermore, in embodiments, the machine-learning system 2002 may train the machine-learning models based on simulation data 2022 generated by the digital twin simulation system 2020. In these embodiments, the digital twin simulation system 2020 may be instructed to run specific simulations using one or more digital twins that represent objects and/or environments that are managed, maintained, and/or monitored by the value chain system. In this way, the digital twin simulation system 2020 may provide richer data sets that the machine-learning system 2002 may use to train/reinforce the machine-learned models. Additionally or alternatively, the digital twin simulation system 2020 may be leveraged by the artificial intelligence system 2010 to test a decision made by the artificial intelligence system 2010 before providing the decision to the value chain entity.
[0472] In embodiments, the set of project management facilities are configured to manage a wide variety of types of projects, such as procurement projects, logistics projects, reverse logistics projects, fulfillment projects, distribution projects, warehousing projects, inventory management projects, product design projects, product management projects, shipping projects, maritime projects, loading or unloading projects, packing projects, purchasing projects, marketing projects, sales projects, analytics projects, demand management projects, demand planning projects, resource planning projects and many others.
[0628] In embodiments, models may then be updated or reinforced based on the model outcomes 5360. For example, the artificial intelligence system may receive a set of circumstances that led to a prediction of failure and the outcome and may update the model based on the feedback.
[0655] In example embodiments, a model 5750 is trained to select advertisement features to optimize one or more outcomes (e.g., maximize product sales for a product 1510 in the value chain network 668). The machine-learning system 5720 may train the models 5750 using n-tuples that include the features pertaining to advertisements and one or more outcomes associated with the advertisements. In this example, features for an advertisement may include, but are not limited to, product and/or service category advertised, advertised product features (price, product vendor, and the like), advertised service features, advertisement type (television, radio, podcast, social media, e-mail or the like), advertisement length (10 seconds, 30 seconds, or the like), advertisement timing (in the morning, before a holiday, and the like), advertisement tone (comedic, informational, emotional, or the like), and/or other relevant advertisement features. In this example, outcomes relating to the advertisement may include product sales, total cost of the advertisement, advertisement interaction measures, and the like. In this example, one or more digital twins 1700 may be used to simulate the different arrangements (e.g., digital twins of advertisements, customers, customer profiles, and environments), whereby one or more properties of the digital twins are varied for different simulations and the outcomes of each simulation may be recorded in a tuple with the proprieties. Other examples of training advertising models may include a model that is trained to generate advertisements for value chain products 650, a model that is trained to manage an advertising campaign for value chain products 650, and the like. In operation, the artificial intelligence system 1160 may use such models 5750 to make advertisement decisions on behalf of an advertising application 5602 given one or more features relating to an advertising-related task or event. For example, the artificial intelligence system 1160 may select a type of advertisement (e.g., social media, podcast, and the like) to use for a value chain product 1510. In this example, the advertising application 5602 may provide the features of the product to artificial intelligence system 1160. These features may include product vendor, the price of the product, and the like. In embodiments, the artificial intelligence system 1160 may insert these features into one or more of the models 5750 to obtain one or more decisions, which may include which type of advertisement to use.
[0871] In embodiments, a Chief Marketing officer (CMO) digital twin 8308 may be a digital twin configured for a CMO of an enterprise, or an analogous executive tasked with overseeing the marketing tasks of the enterprise. A CMO digital twin 8308 may provide functionality including, but not limited to, management of personnel and partners, development and oversight of marketing budgets and resources, management of marketing and advertising platforms, development and management of marketing content, strategies and campaigns, reporting, competitor analysis, regulatory analysis, and management of data privacy and security.
[0877] In embodiments, a CMO digital twin 8308 may be configured to research, create, track and report on a marketing department budget including, but not limited to, an overall department budget, a budget for a single or group of marketing or advertising campaigns, a budget for a third-party vendor, or some other type of budget. The CMO digital twin 8308 may interact with and share such budget data and reporting with other executive twins, as described herein, including, but not limited to, a digital twin related to the finance department, accounts payable, executive staff such as the CEO and CFO, or others. The CMO digital twin 8308 may include intelligence, based at least in part on the data analytics, machine learning and A.I. processes, as described herein, to read marketing budgets and related summaries and data in order to identify key departments, personnel, third-party or others that are, for example, listed in, or subject to, the budget line item and who therefore may have an interest in such material. Budget material pertaining to a given party may be abstracted and summarized for presentation independent from the entirety of the budget, and formatted and presented automatically, or at the direction of a user, to the party that is the subject of the budget item. In a simplified example, a CMO may create a new marketing campaign, “Airline—Airfare coupon texting campaign—January,” which includes the following line items: Third-party advertising firm content creation $15,000; Social media platform placement $50,000; analytics department $25,000, and so forth. The entirety of the budget may be shared (at the election of the user or automatically) with parties that must approve the full budget, such as a CFO. As described herein this sharing may be accomplished by the CMO digital twin 8308 communicating directly with a CFO digital twin, so that the information is presented to the CFO without requiring the CFO to have knowledge of the budget or requesting the budget. Subparts of the budget, for example, the analytics department line item, may be automatically sent to the head of the analytics department by the CMO digital twin 8308 to inform that department of the total amount of authorized spending that is approved for that department for the specific marketing campaign.
[0879] In embodiments, a CMO digital twin 8308 may be configured to depict marketing campaign twins. In these embodiments, the CMO digital twin 8308 may depict various states and/or items relating to a marking campaign such as marketing content associated with a marketing campaign, market research performed with respect to a marketing campaign, tracking data of marketing content associated with marketing campaigns (e.g., geographic reach of marketing campaigns, demographic data associated with campaigns, etc.), analyses of marketing campaigns (e.g., outcomes related to marketing campaigns on various platforms), and the like. In some embodiments, a CMO digital twin may be configured to automatically report on marketing campaign-related activity via a user interface associated with the CMO digital twin 8308. Such activities may be determined using marketing department metadata that indicates state changes, such as an alteration to a website content, a change to a product photograph in an advertisement, a change in wording of a mailing, and the like. The CMO digital twin 8308 may also depict activity among a class of entities that are monitored or that are specified for monitoring in the CMO digital twin 8308, such as a new press release regarding a discounted advertising opportunity available from an ad exchange. In embodiments, a CMO digital twin 8308 may be configured to provide research, tracking, monitoring, and analyses of media content performance across various marketing related platforms, and automatically report on such activity to a user interface associated with the CMO digital twin 8308. Such platforms may include, but are not limited to, customer relationship platforms (CRMs), organization website(s), social media, blogs, press releases, mailings, in-store or other promotions, or some other type of marketing platform-related material or activity.
[0880] In some of these embodiments, the CMO digital twin 8308 may be configured to simulate marketing campaigns, such that the simulations of the marketing campaign may vary parameters such as vehicles (e.g., social media, television, billboards, print, etc.), budget, targeting parameters (e.g., geographic, demographic, or the like), and/or other suitable marketing campaign parameters. In these embodiments, the digital twin simulation system 8116 may receive a request to perform the simulation CMO digital twin, where the request indicates campaign features and the parameters that are to be varied. In response, the digital twin simulation 8116 may return the simulation results to the CMO digital twin 8308, which in turn outputs the results to the user via the client device display. In this way, the user is provided with various outcomes corresponding to different parameter configurations. In some embodiments, the user may select a parameter set based on the various outcomes. In some embodiments, an executive agent trained by the user may select the parameter sets based on the various outcomes.
[0881] In embodiments, a CMO digital twin 8308 may be configured to store, aggregate, merge, analyze, prepare, report and distribute material relating to a marketing strategy, plan, campaign or initiative. For example, the CMO digital twin 8308 may be associated with a plurality of databases or other repositories of marketing presentation materials, summaries and reports and analytics, including such presentation materials, summaries and reports and analytics related to prior marketing campaigns, each of which may be further associated with financial and performance metrics pertaining to the campaign and which are also accessible to the CMO digital twin 8308. Such historical marketing campaign material may consist of advertising, marketing or other content that may be categorized based in part on the financial and performance metrics with which it is associated. For example, there may be a first category called “Market Tested Content,” which consists of content that has been field deployed in a marketing campaign within a customer population, the actual performance of which is therefore fully known based on actual market testing. Because the marketing content from this category has been field tested, the content may be scored based at least in part on the financial, performance or other data with which it is associated. A second category may be “New Content—Simulation Tested,” which consists of content that has not been deployed in the field, but which has been subject to analytic testing such as simulated customer segmentation analysis, simulated A/B testing, simulated attribution modeling, simulated market mix modeling, machine learning, A.I. techniques including, but not limited to, classification, probabilistic modeling, learning techniques, and the like. Because the marketing content from this category has been simulation tested, the content may be scored based at least in part on the simulated performance data or other data with which it is associated. Continuing the example, a third category of content may be “New Content—Panel Tested,” which consists of content that has not been deployed in the field, nor simulation tested, but which has been subject to testing among a human panel for their views, opinions and impressions. Because the marketing content from this category has been human panel tested, the content may be scored based at least in part on the performance data, as reported by the human panel, or other data with which it is associated. A final, fourth category of content may be “New—Untested,” which is newly developed or other content that has not been tested in the field, in simulation, or by a human panel. The CMO digital twin 8308 may utilize the machine learning, A.I. and other analytic capabilities, as described herein, to analyze the content of the four categories of content and classify and score the content characteristics that are probabilistically associated with improved financial or other performance for stated types of marketing campaigns or marketing subject matter. Statistical weights may be applied to such characteristics, where the weight is indicative of a greater degree of financial or some performance metric of interest. Similarly, the characteristics of the market may be analyzed vis-a-vis the marketing content to determine the consumer characteristics that are probabilistically associated with improved financial or other performance for given marketing content. The CMO digital twin 8308 may provide a user interface within which access to this repository of stored data on content category, consumer and performance is available. When planning a marketing campaign, the CMO, or other marketing personnel, may use the CMO digital twin 8308 to select from this repository of content, that content which probabilistically will perform better with the intended consumer targets of the new campaign. For example, from historical marketing field tests from actual prior marketing campaigns, the data may show that marketing content having images of large dogs outperformed (based on, for example, ad conversion rates) content picturing small dogs, and this effect was positively correlated with age (i.e., older persons have an even greater preference for larger dogs). The performance data from the simulation-tested content may show a similar, but smaller effect based on the size of the dog images in the content, and the panel-tested data may show a similar effect for large dog imagery in content, but also have performance data indicating that the effect appears, based on the panel data, to be muted for persons 15 years or younger (i.e., young persons are more attracted to smaller dog breeds than older persons). For the CMO using the CMO digital twin 8308 this data, and the characteristics of the more successful content, may be used to select from the fourth category of content (“New—Untested”) that content that is most appropriate for a new marketing campaign intended to sell a soft drink. In embodiments, the artificial intelligence services system 8010 of the EMP 8000 may select the content and segment its presentation based at least in part on the prior performance data, so that the ads that are presented on platforms that tend to have persons over 15 will use content having a predominance of large breed dogs, and those platforms with younger audiences will offer a greater mix of dog breeds and possibly a preference for small breed dogs in marketing images. As the marketing campaign deployed to the field, the CMO digital twin 8308 may monitor, track and report on the marketing campaign's performance so that the CMO can review and intervene as necessary. Once the new content has been field tested it may be stored and classified in the first category of content, “Market Tested Content,” along with the related financial and performance metrics. In another example, similar stored content, content categories, characteristics and financial and performance metrics may be used by the CMO digital twin 8308 to recommend, for example, search engine optimization (SEO), or other marketing strategies and techniques.
[0883] In embodiments, a CMO digital twin 8308 may be configured to assist in the development of a new marketing campaign. For example, the CMO digital twin 8308 may identify an internal and external partner team for a marketing campaign. For example, individuals who are ideal candidates to assist with a marketing campaign may be identified based at least in part on experience and expertise data that is stored within or in association with the CMO digital twin 8308. In another example, the CMO digital twin 8308 may identify marketing campaign goals and record, monitor and track the campaign's performance relative to those goals and present, in real-time, the tracking of the campaign to the CMO within a user interface that is associated with the CMO digital twin 8308. Examples of marketing targets include, but are not limited to, unit distribution, customer acquisition customer retention, customer churn, customer loyalty (e.g., repeat purchases), customer acquisition costs, duration of average sales cycle, ad conversion rate, sales growth, geographic expansion of sales, demographic expansion of sales, market penetration, percentage of market control, marketing campaign ROI, regional comparison of performance, channel analysis, sales partner analysis, marketing partner analysis, or some other marketing target.
[0884] In embodiments, a CMO digital twin 8308 may be configured to monitor customer feedback loops, customer opinions, customer satisfaction, complaints, product returns and the like based at least in part on use of the monitoring agent of the client application 8052, as described herein, that is associated with the CMO digital twin 8308. Such feedback data may include, but is not limited to, data that derives from call center activity, chatbot activity, email (e.g., complaints), product returns, Better Business Bureau submissions, or some other type of customer feedback or manifestation of customer opinion. The client application 8052 may include a monitoring agent that monitors the manner by which customers or others respond to a marketing campaign. The monitoring agent may report the customer's response to such campaigns to the EMP 8000 for presentation in a user interface that is associated with the CMO digital twin 8308. In response, the EMP 8000 may train an executive agent (which may include one or more machine-learned models) to handle and process such notifications when they next arrive, and escalate and/or alert the CMO when such notifications are of an urgent nature, for example, an announcement of a class action lawsuit related to a product that is the subject of a marketing campaign. In embodiments, the CMO digital twin 8308 may generate performance alerts based on performance trends. This may allow a CMO to optimize marketing campaigns in real-time without having to manually request such real-time performance data; the CMO digital twin 8308 may automatically present such information and related/necessary alerts as configured by the organization, CMO, or some other interested party.
[0885] In embodiments, a CMO digital twin 8308 may be configured to report on the performance of the marketing department, personnel of the marketing department, marketing campaigns, marketing content, marketing platforms, marketing partners, or some other aspect of management within a CMO's purview. Reporting may be to the CMO, the marketing department, to other executives of an organization (e.g., the CEO), or to outside third parties (e.g., marketing partners, press releases, and the like). As described herein, reporting may include sales summaries, customer data, marketing campaign performance metrics, cost-per-sale data, cost-per-conversion data, customer analysis, such as predicted customer lifetime value for newly acquired customers, or some other type of reporting data. Reporting and the content of reporting may be shared by the CMO digital twin 8308 with other executive digital twins, for example, data related to new customers having a particularly high predicted customer lifetime value may be shared with a sales staff for the purpose of exploring cross-selling opportunities. The reporting functionality of the CMO digital twin 8308 may also be used for populating required data for formal reporting requirements such as shareholder statements, annual reports, SEC filings, and the like. Templets of common reporting formats may be stored and associated with the CMO digital twin 8308 to automate the presentation of data and analytics according to pre-defined formats, styles and system requirements
[0887] In embodiments, a CMO digital twin 8308 may be configured to monitor, store, aggregate, merge, analyze, prepare, report and distribute material relating to regulatory activity, such as government regulations, industry best practices or some other requirement or standard. For example, the marketing industry is subject to data privacy and security laws in many jurisdictions, and it is an area of law and regulation that is experiencing rapid change. In embodiments, the CMO digital twin 8308 may be in communication with another enterprise digital twin, such as a General Counsel digital twin 8314, through which the legal team can keep the CMO apprised of new regulation or regulation changes as they occur. Similarly, as a CMO develops new market campaigns and selects the jurisdictions (e.g., United States vs Europe) and populations that will be a part of the campaigns (e.g., minors vs. adults), the CMO digital twin 8308 may automatically send a synopsis of the aspects of the campaigns that are relevant for privacy law review so that the campaign may be vetted for legal and regulatory compliance prior to launch. In an example, such a marketing campaign synopsis might include a summary of the jurisdictions of the campaign, intended audience, means of obtaining consent, the type of consent to be obtained (e.g., opt-in, opt-out, passive), and so forth. Once approved and launched, as customer consents and other data privacy-related information is received by an organization, the CMO digital twin 8308 may facilitate the CMO tracking metrics, for example the percentage of customers choosing to opt-in to receive future marketing material (e.g., email solicitations). As the organization receives privacy related material it may store such information for future retrieval, summary, deletion or other activity, for example, in response to a data subject request from an EU citizen who has requested their data be deleted (i.e., exercising their “right to be forgotten”). In embodiments, the CMO digital twin 8308 may monitor, store, aggregate, merge, analyze, prepare, report and distribute material relating to what customer data is collected, the party responsible for its collection and storage, the location and duration of storage, and so forth. This data may be called forth by the CMO digital twin 8308, for example, in the event of a data breach. The CMO digital twin 8308 may be able to summarize, for example, a list of persons affected by the breach and the type of data that was breached and share this information with a Chief Privacy Officer (CPO), including sharing with the CPO digital twin.
[0895] In embodiments, the CTO digital twin 8310 may be configured to allow a user to research, create, track and report on a technology, development, and/or technology or engineering department initiative including, but not limited to, a new product development, update, enhancement, replacement, upgrade, or the like. In embodiments, the CTO digital twin 8310 may be associated and/or in communication with databases, including databases storing analytic and/or product data and product performance data, and present information to an interface associated with the CTO digital twin 8310, as described herein. As product development advances, real time operations and other technical information may be used to continuously update the product development summary that is available for the CTO or other technical personnel to review. The CTO digital twin 8310 may also be associated and/or in communication with databases, including databases storing analytic and/or competitive product data and product performance data, and present this information to an interface associated with the CTO digital twin 8310, as described herein. As the CTO's company's products change, and competitor products change, their current state and specifications may be presented by the CTO digital twin 8310 for the CTO or other technical personnel to review direct product comparisons. Such comparisons may be used, in part, to produce analytics, scores, reports and the like indicating the relative advantages and/or disadvantages that a company's product(s) has relative to competitor product(s). In an example, a report may be automatically provided to the marketing department to emphasize the relative advantages that a company product has over a competitor product (e.g., speed of processing) that should be used in a new marketing campaign. Sharing with the marketing department may be accomplished, in part, by the CTO digital twin 8310 communicating with the CMO digital twin 8308 to present reports or other information to the CMO or marketing staff.
[0984] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a radial basis function (RBF) neural network, which may be preferred in some situations involving interpolation in a multi-dimensional space (such as where interpolation is helpful in optimizing a multi-dimensional function, such as for optimizing a data marketplace as described here, optimizing the efficiency or output of a power generation system, a factory system, or the like, or other situation involving multiple dimensions. In embodiments, each neuron in the RBF neural network stores an example from a training set as a “prototype.” Linearity involved in the functioning of this neural network offers RBF the advantage of not typically suffering from problems with local minima or maxima.
[1023] In embodiments, methods and systems described herein that involve an expert system or self-organization capability may use a radial basis function (RBF) neural network, which may be preferred in some situations involving interpolation in a multi-dimensional space (such as where interpolation is helpful in optimizing a multi-dimensional function, such as for optimizing a data marketplace as described here, optimizing the efficiency or output of a power generation system, a factory system, or the like, or other situation involving multiple dimensions). In embodiments, each neuron in the RBF neural network stores an example from a training set as a “prototype.” Linearity involved in the functioning of this neural network offers RBF the advantage of not typically suffering from problems with local minima or maxima.
[1289] The value chain entities 10126 include various entities involved in production, supply, demand, distribution or supply chain environments including any of the wide variety of assets, systems, devices, machines, components, equipment, facilities, individuals or other entities mentioned throughout this disclosure or in the documents incorporated herein by reference, such as, without limitation: machines and their components (e.g., delivery vehicles, forklifts, conveyors, loading machines, cranes, lifts, haulers, trucks, loading machines, unloading machines, packing machines, picking machines, and many others, including robotic systems, e.g., physical robots, collaborative robots (e.g., “cobots”), drones, autonomous vehicles, software bots and many others); workers (such as designers, engineers, process supervisors, supply chain managers, floor managers, demand managers, delivery workers, shipping workers, barge workers, port workers, dock workers, train workers, ship workers, distribution of fulfillment center workers, warehouse workers, vehicle drivers, business managers, marketing managers, inventory managers, cargo handling workers, inspectors, delivery personnel, environmental control managers, financial asset managers, security personnel, safety personnel and many others); suppliers (such as suppliers of goods and related services of all types, component suppliers, ingredient suppliers, materials suppliers, manufacturers, and many others); customers (including consumers, licensees, businesses, enterprises, value added and other resellers, retailers, end users, distributors, and others who may purchase, license, or otherwise use a category of goods and/or related services); retailers (including online retailers and others such as in the form of eCommerce sites, conventional bricks and mortar retailers, pop-up shops and the like); value chain processes (such as shipping processes, hauling processes, maritime processes, inspection processes, hauling processes, loading/unloading processes, packing/unpacking processes, configuration processes, assembly processes, installation processes, quality control processes, environmental control processes (e.g., temperature control, humidity control, pressure control, vibration control, and others), border control processes, port-related processes, software processes (including applications, programs, services, and others), packing and loading processes, financial processes (e.g., insurance processes, reporting processes, transactional processes, and many others), testing and diagnostic processes, security processes, safety processes, reporting processes, asset tracking processes, and many others); wearable and portable devices (such as mobile phones, tablets, dedicated portable devices for value chain applications and processes, data collectors (including mobile data collectors), sensor-based devices, watches, glasses, hearables, head-worn devices, clothing-integrated devices, arm bands, bracelets, neck-worn devices, AR/VR devices, headphones, and many others); a wide range of operating facilities (such as loading and unloading docks, storage and warehousing facilities, vaults, distribution facilities and fulfillment centers, air travel facilities (including aircraft, airports, hangars, runways, refueling depots, and the like), maritime facilities (such as port infrastructure facilities (such as docks, yards, cranes, roll-on/roll-off facilities, ramps, containers, container handling systems, waterways, locks, and many others), shipyard facilities, floating assets (such as ships, barges, boats and others), facilities and other items at points of origin and/or points of destination, hauling facilities (such as container ships, barges, and other floating assets, as well as land-based vehicles and other delivery systems used for conveying goods, such as trucks, trains, and the like); items or elements factoring in demand (i.e., demand factors) (including market factors, events, and many others); items or elements factoring in supply (i.e., supply factors)(including market factors, weather, availability of components and materials, and many others); logistics factors (such as availability of travel routes, weather, fuel prices, regulatory factors, availability of space (such as on a vehicle, in a container, in a package, in a warehouse, in a fulfillment center, on a shelf, or the like), and many others); pathways for conveyance (such as waterways, roadways, air travel routes, railways and the like); robotic systems (including mobile robots, cobots, robotic systems for assisting human workers, robotic delivery systems, and others); drones (including for package delivery, site mapping, monitoring or inspection, and the like); autonomous vehicles (such as for package delivery); software platforms (such as enterprise resource planning platforms, customer relationship management platforms, sales and marketing platforms, asset management platforms, Internet of Things platforms, supply chain management platforms, platform as a service platforms, infrastructure as a service platforms, software-based data storage platforms, analytic platforms, artificial intelligence platforms, and others); and many others.
[1391] In embodiments, the models trained by machine learning system 10210 may be utilized by the artificial intelligence system 10212 to execute simulations on part twins, product twins, printer twins and manufacturing node twins for optimizing the production sequencing of parts based on quoted price, delivery, sale margin, order size, or similar characteristics. In embodiments, optimization may include optimization based on public data, such as market data, website data, manufacturer-provided data (such as by APIs) and/or terms and conditions of a set of smart contracts that relate to such characteristics.
[1423] In embodiments, the Enterprise resource planning (ERP) system 10644 helps streamline and integrate business processes across finance, sales, marketing, service, engineering, product management, accounting, procurement, distribution, resources, project management, risk management and compliance, among other functions, both within a manufacturing node and across multiple manufacturing nodes in the distributed manufacturing network 10130. ERP System 10644 may tie together various production and value chain processes in the distributed manufacturing network 10130 and enable the flow of data between them.
[1436] The metal additive manufacturing platform 10110 described herein may help in automating and optimizing a very wide range of manufacturing and value chain functions. Some examples of such functions include process and material selection, feedback formulation, design optimization, risk prediction and management, sales and marketing, coordination with supply chain and logistics workflows (including reverse logistics and returns) for manufactured products and/or related items or services (such as parts, accessories or the like, among others), maintenance workflows, recycling workflows and customer service. FIG. 119 is a schematic illustrating an example implementation of the platform 10110 for automating and managing manufacturing functions and sub-processes including process and material selection, hybrid part workflow, feedstock formulation, part design optimization, risk prediction and management, marketing and customer service according to some embodiments of the present disclosure.
[1475] In example embodiments, the model 10213 may be trained to predict behavior and purchase patterns of one or more customers to provide personalized sales, marketing, advertising, promotion and/or customer service. In embodiments, the machine learning system 10210 may train the model using customer data and one or more outcomes associated with customer response to a personalized campaign, such as using various data sources that provide insight into consumer sentiment, behavior, or the like, including search engines, news sites, websites, behavioral analytic systems and algorithms, consumer sentiment measures, microeconomic measures, macroeconomic measures, and many others. A model may be seeded with various economic, behavioral, and other models, including demographic, psychological, economic, game theoretic, cognitive, and other models. Customer data may include any of the types described throughout this disclosure and the documents incorporated by reference herein, such as identity data, transactional and payment data, location data, demographic data, psychographic data, location data, wealth data, income data, sentiment data, affinity data, loyalty program data, clickstream data (including interactions with social media, applications, websites, mobile devices, AR/VR systems, video games, entertainment content and other digital content), point-of-sale data, in-store behavioral data (such as path tracing data within stores, dwell times associated with particular types of products, and the like), brand loyalty data, shopping data, search engine data (such as search topics involving shopping), social media footprint, purchase history, loyalty program data and many others. The customer twin 10718 may capture a set of customer responses to a marketing or advertising campaign or one or more product recommendations, offers, advertisements or other communications by tracking outcomes like customer attention or actions (including mouse movements, mouse clicks, cursor movements, navigation actions, menu selections, and many others) measured through a software interaction observation system, or purchase of a product by a customer. In this example, one or more parameters of the marketing or advertising campaign may be varied for different simulations of a customer twin and the outcomes of each simulation may be recorded.
[1476] In embodiments, the marketing and customer service system 10716 may interface with the artificial intelligence system 10212 to provide personalized sales, marketing, advertising, promotions and/or customer service, including providing personalized marketing and advertising campaigns and providing product recommendations. In embodiments, the artificial intelligence system 10212 may utilize one or more of the machine-learned models 10213 to determine a product recommendation. In embodiments, the simulations run by the customer twin 10718 may be used to train the product recommendation machine-learning models. In each of these examples, a campaign communication, recommendation, or the like may involve a product or other item that can be manufactured by the additive manufacturing unit 10102 with a set of attributes that are tailored to the customer and that can be delivered to a designated site of the customer within a designated time frame at a proposed price. Customization of the offer/recommendation may include providing a design of a product or part to include attributes favored by the customer, including functional attributes, preferred materials (such as to match materials of products already owned by the customer), preferred colors, preferred shapes, and many others. In embodiments, customization may reference an understanding of products already owned by the customer, such as based on purchase history information, such as where a recommended product can be configured to work as part of a family of products, such as by recommending a product that has compatible color, shape, size, material type, connectivity (e.g., to work as part of a connected set of products), communication protocol, logo, or the like.
- NPL: Xie, Y., Ye, H.-Q., & Zhu, W. (2025). Prediction and Optimization for Multi-Product Marketing Resource Allocation in Cross-Border E-Commerce. Journal of Theoretical and Applied Electronic Commerce Research, 20(2), 124. https://doi.org/10.3390/jtaer20020124,
see Abstract note "In cross-border e-commerce, effective marketing resource allocation is crucial due to the complexity introduced by diverse product categories, regional differences, and competition among category managers. Current methods either overlook these constraints or fail to enforce them efficiently due to computational challenges. We propose a two-stage optimization framework that integrates predictive models with constrained optimization. In the first stage, predictive models estimate user purchase probabilities and determine upper bounds on product-specific sending volumes. In the second stage, the resource allocation problem is formulated as a large-scale integer programming model, which is then transformed into a minimum-cost flow problem to ensure computational efficiency while preserving solution optimality. Experiments on real-world data show that our framework significantly outperforms baseline strategies, achieving a 14.48% increase in order volume and revenue improvements ranging from 0.19% to 43.91%. The minimum-cost flow algorithm consistently outperforms the greedy approach, especially in large-scale instances. The proposed framework enables scalable and constraint-compliant marketing resource allocation in cross-border e-commerce. It not only improves sales performance but also ensures strict adherence to operational constraints, making it well-suited for large-scale commercial deployment.
Keywords: cross-border e-commerce; product push notification; machine learning; predictive model; integer programming; minimum-cost flow"
- NPL: Reduanul Hasan. (2025). ENHANCING MARKET COMPETITIVENESS THROUGH AI-POWERED SEO AND DIGITAL MARKETING STRATEGIES IN E-COMMERCE. ASRC Procedia: Global Perspectives in Science and Scholarship, 1(01), 465-500. https://doi.org/10.63125/31tpjc54,
see Abstract note "This study systematically investigates how artificial intelligence (AI) enhances market competitiveness through its application in search engine optimization (SEO) and digital marketing strategies within e-commerce environments. In an increasingly saturated and algorithm-driven digital marketplace, firms are under continuous pressure to improve visibility, personalization, and customer engagement. The research followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a transparent and rigorous review process. A total of 112 peer-reviewed articles, published between 2012 and 2025, were selected and analyzed across five major academic databases and relevant grey literature. The findings reveal that AI technologies—such as machine learning, natural language processing, robotic process automation, and predictive analytics—are instrumental in transforming traditional marketing workflows across the entire digital funnel. AI-powered SEO tools significantly improve organic reach, technical site health, and semantic keyword alignment. Concurrently, predictive personalization and lifecycle- based automation enhance customer retention, conversion, and lifetime value. The review also highlights AI’s impact on social media intelligence, influencer marketing optimization, and attribution modeling, all of which contribute to improved ROI and operational efficiency. Longitudinal evidence from multiple industries—specially fashion, electronics, and healthcare— demonstrates that sustained AI adoption leads to compounding strategic advantages, including innovation capacity, brand loyalty, and agility in response to market shifts. By synthesizing empirical findings and industry case applications, this study positions AI not merely as a technological enhancement but as a core strategic capability that redefines digital competitiveness in e-commerce. The results offer both scholars and practitioners a comprehensive understanding of how AI can be systematically leveraged to achieve differentiation, efficiency, and sustainable growth in the evolving digital economy. Keywords: Artificial Intelligence, SEO, E-Commerce, Digital Marketing, Market Competitiveness"
However, the above noted references fail to, when considered both singularly and/or in combination, fail to teach the amended claim limitations as filed on June 26, 2026 such as generating a structured media plan, dynamically generating, generating feedback data, and selectively training as claimed, in the claim as a whole, without relying upon impermissible hindsight - which is improper. Thus, a prima facie case of obviousness could not be established using the above noted references. Therefore, prior art based rejection is not applicable based on the one or more foregoing references.
Response to Applicant’s Remarks
5. Regarding 35 USC 101, the Applicant argues against prong one on pages 19-21 of the response filed June 26, 2026. The Applicant is reminded that (i) the claims must be given their broadest reasonable interpretation in light of the as-filed disclosure, and (2) the analysis is based on 2019 PEG framework at the USPTO. Accordingly, based on the 2019 PEG framework, prong one analysis is based on abstract recitation, not additional elements. The Applicant appears to argue in view of trained machine learning which is an additional element first considered in the claim as a whole in prong two. Indeed and squarely what the Applicant describes “optimizing a media plan associated with a product to be advertised across one or more media platforms” invokes certain methods of organizing human activity based on a proper evaluation of the claim recitation. Furthermore, the Examiner has not invoked mental processes grouping, rather certain methods of organizing human activity. Indeed a PHOSITA when applying BRI to abstract recitation of the claims, would consider media planning being claimed as advertising or marketing strategy or planning, which is certain methods of organizing human activity. Therefore the Examiner respectfully maintains that prong one analysis is proper.
Next, the Applicant argues against prong two, note “The Examiner alleges that claims fail to describe an improvement in the functioning of a computer or other technology or technical field. Applicant disagrees and submits that its invention provides for technical system which improves computer-based optimization and feedback control. Applicant's invention improves the operation of computing systems designed to evaluate and generate media plans across diverse advertising platforms. Applicant's invention improves computer optimization by using the various feedback systems to drive selective training, based on performance deviation data representing differences between post-execution analysis data and stored recommendation data. Such systems and method enable generation of subsequent media plans without full model retraining, thereby reducing system overhead, improving system processing, and improving system efficiency, and producing more accurate and relevant media plans. Accordingly, Applicant requests that the rejection under Step 2A, Prong 2, of the Alice/Mayo test be reversed.” However, once again, upon applying BRI in light of the as-filed specification and considering machine learning as claimed with the other additional elements in the claim as a whole, indeed a PHOSITA would consider these additional elements as being utilized as generic tools to evaluate data to as they are claimed at a high level generality, i.e. machine learning is merely being applied as “apply it” to an otherwise abstract idea. Thus, there is a clear distinction between utilizing computing device(s) and machine learning as tools to evaluate data and actually set forth an improvement, i.e. “viewed as a whole, these additional claim element(s) do not provide any additional element that integrates the abstract idea (prong one), into a practical application (prong two) upon considering the additional elements both individually and as a combination or as a whole as they fail to provide: an additional element that reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field; or an additional element that implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim; or an additional element that effects a transformation or reduction of a particular article to a different state or thing; or an additional element that applies or uses the judicial exception, again, 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 as explained above.
Thus, the abstract idea of providing media plan recommendation based on evaluation of data which is certain methods of organizing human activity (prong one) is not integrated into a practical application upon consideration of the additional element(s) both individually and as a combination (prong two).
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.”. Therefore the Examiner respectfully disagrees with the broad assertions and maintains the rejection.
Next, the Applicant broadly argues against step 2B, note “Even if the claims fail Sep 2A, Prong 2, which Applicant does not concede, the pending claims, when viewed as a whole, amount to significantly more than the alleged judicial exception, thus satisfying Step 2B of the Alice/Mayo Test. Applicant's invention provides, inter alia, systems and methods which utilize specific, structured feedback-to-optimization components. Such features are not (nor has the Examiner provided reasoning otherwise) "well-understood, routine, conventional" in the field; or appends well-understood, routine, conventional activities previously known to the advertising industry, as required, see Mayo, Alice, and MPEP 2106.” The Applicant is reminded that evaluation is limited to additional elements considered both singularly and in-combination. As already explained under prong two, merely utilizing computing devices and machine learning to evaluate data to implement an abstract idea is considered “apply it” as there is no improvement to the additional element(s) utilized as tools. Furthermore, it was already explained operating the abstract idea in a technical environment is considered general linking to a technical environment without actually setting forth an improvement to said environment.
For instance, note Recentive Analytics v. Fox Corp see Page 2, lines 15-18: We affirm because the patents are directed to the abstract idea of using a generic machine learning technique in a particular environment, with no inventive concept.
Page 10, lines 16-19: claims that do no more than apply established methods of machine learning to a new data environment are not patent eligible.
Page 13, lines 1-26: claims that do not delineate steps through which machine learning technology achieves an improvement are not patent eligible.
Page 14, lines 13-25: an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment.
Page 14, line 26 through Page 15, line 13: disclosure of an "already available [technology] with [its] already available basic functions, to use as [a] tool[] in executing the claimed process" is still an abstract idea.
Page 15, line 14 through Page 16, line 3: the use of existing machine learning technology to perform a task previously undertaken by humans with greater speed and efficiency than could be previously achieved does not render a claim eligible.
Therefore, the Examiner finds the Applicant’s unpersuasive and respectfully maintains the rejection.
Regarding 102 or prior art based rejection, it has been withdrawn in view of filed claim amendments for the reason(s) as set forth above.
Conclusion
6. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and all the references on PTO-892 Notice of Reference Cited should be duly noted by the Applicant as they can be subsequently used during prosecution, at least note the following:
*Previously noted
- US12443979 see Abstract "Media organizations are riding the wave of technology to multiple distribution platforms, sales channels and business models, and into a world of cross-platform advertising. The interrelated functions of ad sales are managed as a single system if an organization is to maximize advertising revenue. How does an organization manage, value, and optimize ad sales inventory in an ecosystem that has multiple sales channels competing for the same overlapping inventory segments in a multi-platform distribution model? Many organizations have tried to solve this problem with teams of analysts and consultants. However, conflicting goals and siloed analysis lead these teams to failure. Artificial intelligence is better equipped to cope with the overwhelming complexities of these mixed business models. By making decisions with a holistic view of the business, artificial intelligence can drive increased revenues through optimized allocation, placement, and pricing strategies across sales channels. The analytics platform supports and maximizes the revenues that can be achieved through cross-media advertising by integrating disparate advertising ecosystems using artificial intelligence."
- US11783379 see Abstract "Method and computing device for performing dynamic digital signage campaign optimization. Screen data associated to screens controlled by the computing device and requirements of active campaigns are stored at the computing device. The screen data comprise characteristics of the screens and screen activity data defining the activity the screens for the active campaigns. The computing device receives requirements of a candidate campaign and generates a mathematical model based on the requirements of the candidate campaign, the requirements of the active campaigns, and at least some of the screen data. The mathematical model is transmitted to a mathematical solver and a mathematical solution generated by the mathematical solver is received. The computing devices generates configuration data for the candidate campaign based on the mathematical solution. The configuration data define a configuration for displaying a content of the candidate campaign on selected screens among the screens controlled by the computing device."
*Provided previously
- Pub. No.: US2020/0364755 see Abstract “Media organizations are riding the wave of technology to multiple distribution platforms, sales channels and business models, and into a world of cross-platform advertising. The interrelated functions of ad sales are managed as a single system if an organization is to maximize advertising revenue. How does an organization manage, value, and optimize ad sales inventory in an ecosystem that has multiple sales channels competing for the same overlapping inventory segments in a multi-platform distribution model? Many organizations have tried to solve this problem with teams of analysts and consultants. However, conflicting goals and siloed analysis lead these teams to failure. Artificial intelligence is better equipped to cope with the overwhelming complexities of these mixed business models. By making decisions with a holistic view of the business, artificial intelligence can drive increased revenues through optimized allocation, placement, and pricing strategies across sales channels. The analytics platform supports and maximizes the revenues that can be achieved through cross-media advertising by integrating disparate advertising ecosystems using artificial intelligence.”
- Pub. No.: US2024/0273569 note “digital advertisement optimization system for digital advertisement optimization is described. The system includes a budget pacing module for selecting a budget pacing model from a group of budget pacing models, and a budget distributor module for selecting a budget distributor model from a group of budget distributor models. The system further includes a single platform budget optimization module for allocating a budget, within each advertisement platform of a group of advertisement platforms for the digital advertisement optimization, to advertisement sets based at least in part on the selected budget pacing model, the selected budget distributor model, and one or more single platform budget optimization models”
- Patent No.: US11,288,598 “providing third - party analytics via a virtual assistant interface are disclosed . A third – party analytics service trains a machine learning model , based at least on interaction histories of users of a consumer - facing application. The interaction histories include sales data associated with the users . The third - party analytics service receives , via a virtual assistant interface , a request for a recommended marketing strategy to be targeted at one or more users of the consumer - facing application. The third - party analytics service applies the request to the machine learning model, to obtain the recommended marketing strategy responsive to the request. The recommended marketing strategy is based at least on a predicted effectiveness of the recommended marketing strategy targeted at the one or more users of the consumer - facing application . The third - party analytics service presents , via the virtual assistant interface , the recommended marketing strategy responsive to the request”
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DIPEN M PATEL whose telephone number is (571)272-6519. The examiner can normally be reached Monday-Friday, 08:30-17:00 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Waseem Ashraf can be reached on (571)270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DIPEN M PATEL/Primary Examiner, Art Unit 3621
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