DETAILED ACTION
Status of Claims
1. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Accordingly, Applicant's filed response has been entered.
This is a Non-final office action in response to communication received 03/02/2026. Claims 1-7 and 11-20 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 11-20 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 Guidances (hereinafter 2019 PEG) the rejection as follows has been applied.
Under step 1, analysis is based on MPEP 2106.03, Claims 1-7 and 11-14 are a method; claims 15-19 are a system; and claim 20 is a non-transitory processor readable medium. Thus, each claim 1-7 and 11-20, 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 11-20 [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 11-20 at least are a computing device, one or more databases, real time, AI engine, one or more media channels, one or more media platforms; an electronic device comprising processor, memory, and program stored in the memory and executed by the processor, or electronic network, a machine learning model or AI model, displaying said media plan on the electronic device or sent to s second electronic device via said electronic network, one or more platforms which could be digital such as social media, TV, radio, one or more internal, external databases which contain datasets and one or more databases to store media plans, a tangible, non-transitory readable medium storing instructions that, when executed by one or more processors, and outputting real-time plan as an electronic report.
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 (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 a media purchase plan using AI engine. Thus, the process is similar to 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 content such as ads, 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 promotions 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.
Claim Rejections - 35 USC § 102
4. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless—
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention; or
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-7 and 11-20 are 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.
As per claims 1, 15, and 20, Simmons discloses
- as per claim 1, a method for providing media plans across one or more media types comprising (see [0147]; [0230]; [0327]):
- as per claim 15, a system for providing artificial intelligence (AI)-based media plans across one or more media types comprising (see [0147]; [0230]; [0327]): an electronic device comprising at least one processor, memory, and one or more programs stored in said memory and configured to be executed by said one or more processors; and a machine learning system that trains a machine learning model that outputs a media plan recommendation based on sored or obtained information relative to media plans, wherein, said one or more programs stored in said memory preform the method of developing a media buy and/or media plan recommendation by (see [0241]; [0245] note “targeting the placement of advertising within an available channel based at least in part on contextual information, the system comprising: a computer having a processor and software which is operable on the processor. The software may include an analytics platform facility that includes at least a learning machine and a valuation algorithms facility. The software may be adapted to … ”):
- as per claim 20, a tangible, non-transitory readable medium storing instructions’ that, when executed by one or more processors, cause the development of a media plan recommendation by performing instructions for (see [0135]; [0144]; [0149]):
(a) receiving or obtaining, by a computing system, information from a user related to a commercial product to be advertised to a targeted audience (see [0326]; [0329]; [0332]);
(b) receiving or obtaining data from one or more databases, said data comprising information for developing a real time or predictive media plan, based on audience data, product data, market conditions data, media platforms data, or combinations thereof (see Figs. 2, 4, and their associated disclosure; [0091]; [0142]; [0173]-[0174]; [0252]; [0255]; [0326]);
(c) dynamically generating, using an artificial intelligence engine, a real time or predictive media plan based on information received or stored within said one or more databases or obtained from said user input wherein, (see [0103]-[0105]; [0134]-[0143]; [0241]; [0244]; [0245]; [0252]-[0254]; [0258]; [0309]).
(d) said artificial intelligence engine is continually trained by evaluating historical media planning data, evaluating historical performance data, evaluating previous media planning proposals (see [0144]; [0152]; [0162]), analyzing audience analytics, measurements or ratings data, analyzing consumer behavior indicators, analyzing cost metrics, and applying budget related and timing constraints (see [0091]; [0123]; [0229]-[0231]; [0311]; [0331]; [0325]);
(e) using real-time and dynamic analysis across multiple media channels, said artificial intelligence engine incorporating an iterative feedback mechanism to continuously learn and improve based on success or failure of said real time or predictive media plan, wherein after execution of said real time or predictive media plan, actual performance metrics are compared to projected outcomes of said real time or predictive media plan , said continuously learning and improving process comprising (see [0152]; [0229]; [0243]; [0252]-[0254])
(f) weighing said information for developing said real time or predictive media plan (see [0298]; [0310]-[0317]; [0331]),
(g) identifying patterns and similarities between said real time or predictive media plan and said actual performance metrics (see [0091]; [0165]; [0225]-[0226]; [0254]; [0336]-[0337]),
(h) comparing said real time or predictive media plan outcome with an actual outcome(s) wherein data related to actual performance metrics compared to projected outcomes of said real time or predictive media plan form post analysis data (see [0124]; [0185]; [0196]-[0197]; [0202]), and continually reweighing one or more said data sets based on differences found between said media plan outcome and said actual outcome post analysis data, storing said post analysis data differences in said one or more data banks or incorporating said post analysis data into said real-time or predictive media plan (see [0082]-[0083]; [0124]; [0134]; [0165]; [0185]; [0190]; [0200]; [0331]),
(i) said artificial intelligence engine generating predictive assessment of media platforms in local and national markets using ratings data, consumer behavior, and historical performance data (see [0092]-[0093]; [0102]; [0149]; [0196]; [0205]; [0327]-[0330]); and
(j) outputting said media plan as an electronic report identifying user-specific steps to undertake across one or more media platforms (see Fig. 4, 5B, and their associated disclosure; [0022]; [0098]-[0099]; [0152]; [0162]; [0226]-[0229]; [0271]-[0272]; [0283]; [0308]-[0310]; [0335]).
As per claim 2, Simmons discloses the claim limitations of claim 1. Simmons teaches wherein said generated media plan recommendation is displayed on said electronic device or sent to a second electronic device via said electronic network (see [0270]-[0279]).
As per claim 3, Simmons discloses the claim limitations of claim 1. Simmons teaches wherein said artificial intelligence model is configured to optimize media plan decisions by analyzing data sets to identify patterns or trends (see [0091]).
As per claim 4, Simmons discloses the claim limitations of claim 1. Simmons teaches wherein said artificial intelligence model is configured to provide individual buys per media type (see [0284]).
As per claim 5, Simmons discloses the claim limitations of claim 1. Simmons teaches wherein said media buys or plans includes a recommendation to purchase advertising space on one or more platforms (see [0286]-[0287]).
As per claim 6, Simmons discloses the claim limitations of claim 5. Simmons teaches wherein said on one or more platforms are digital platforms, non-digital platforms, or combinations thereof (see Fig. 43, 51; [0117]; [0262]).
As per claim 7, Simmons discloses the claim limitations of claim 6. Simmons teaches wherein said on one or more digital platforms or non-digital platforms include websites, social media, television, radio, or print (see [0104]; [0117]; [0262]).
As per claim 11, Simmons discloses the claim limitations of claim 1. Simmons teaches wherein said media buys or plans includes a recommendation to purchase advertising time or purchase advertising space on one or more platforms (see [0300]-[0309]).
As per claim 12, Simmons discloses the claim limitations of claim 11. Simmons teaches wherein said on one or more platforms are digital platforms, non-digital platforms, or combinations thereof (see Fig. 43, 51; [0117]; [0262]).
As per claim 13, Simmons discloses the claim limitations of claim 12. Simmons teaches wherein said on one or more digital platforms or non-digital platforms include websites, social media, television, radio, or print (see Fig. 43, 51; [0117]; [0262]).
As per claim 14, Simmons discloses the claim limitations of claim 1. Simmons teaches wherein said artificial intelligence model is configured to provide predictive assessments of media platforms in local markets, national markets, or combinations thereof (see Figs. 21, 22, 53, and their associated disclosure; [0100]; [0327]).
As per claim 16, Simmons discloses the claim limitations of claim 15. Simmons teaches further including one or more databases for receiving or storing said information for use in developing said media plans (see Figs. 2, 4, and their associated disclosure; [0082]-[0089]; [0173]-[0174]).
As per claim 17, Simmons discloses the claim limitations of claim 16. Simmons teaches wherein said one or more databases may include external data sets (see [0085]-[0089]).
As per claim 18, Simmons discloses the claim limitations of claim 16. Simmons teaches wherein said one or more databases may include internal data sets (see [0085]-[0089]).
As per claim 19, Simmons discloses the claim limitations of claim 15. Simmons teaches further including a second electronic device operatively connected to said electronic device via an electronic network (see Fig. 1A; [0082]; [0347]).
Response to Applicant’s Remarks
5. Regarding 35 USC 101, the Applicant argues that the claims now recite “looking at the invention as a whole, Applicant's system and methods provide an electronic media plan carried using a plurality steps for optimized outcomes that would not be achievable by human activity. Such tasks not practically performable by a human includes reweighting in real time of input variables across media types, integrations of vast data sets, continuous integration of campaign attribution data and adaptive allocation decisions made at a scale and frequency only achievable by machine. These capabilities exceed human cognitive limits and are only realizable through specifically structured system/methods in accordance with Applicant's claims as currently pending.” However, prong one analysis is based on abstract recitation, not additional elements. The Applicant appears to argue in view of machine learning which is an additional element first considered in claim as a whole in prong two. 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 as certain methods of organizing human activity. Therefore the Examiner respectfully maintains that prong one analysis is proper.
Next, the Applicant argues “under Prong two of Step 2A (claim is not directed to abstract idea if that claims integrates the abstract idea into a practical application). Applicant's invention relates to systems and methods which provide unique solutions for media planning, including media plan buy recommendations and media plan sell recommendations, that solves known issues in the industry, such as media inefficiency and suboptimal allocation across channels. While Applicant's systems and methods employ artificial intelligence and data processing, such steps are integrated into the system and methods in using unique steps, such as continuously integrating conversion feedback, reweighting variables in real time, and autonomously optimizing media planning across formats. Such systems are not recited at high levels, such as implementing a generic AI system, but rather specific steps which are integrated into a practical application of creating media comprising various steps to undertake to solve industry recognized problems.” However, once again, upon applying BRI in light of the as-filed specification and considering AI and Machine learning as claimed, 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. AI machine learning is merely being applied as “apply it” to an otherwise abstract idea. Therefore the Examiner respectfully disagrees with the broad assertions and maintains the rejection.
Regarding 102, it appears that the full scope of Simmons is being underappreciated. The Applicant generally alleges that Simmons lacks autonomous decision making, for instance note Simmons [0081] “The real-time bidding facility may further enable the collection of data regarding ad performance and use this data to provide ongoing feedback to parties wanting to place ads, and automatically adjust and target the ad delivery channels used to present sponsored content. The real-time bidding system l00A may facilitate the selection of a particular ad type to show in each placement opportunity, and the associated costs of the ad placements overtime (and, for example, adjusted by time of placement). The real-time facility may facilitate valuation of ads, using valuation algorithms, and may further optimize return on investment for an advertiser 104.”; using AI algorithms, see at least [0189]; also at least see [0261], [0309], [0335], and [0339]-[0340] which pertain to autonomous decision making.
The Applicant notes “Specifically, claims 1, 15, and 20 now include artificial intelligence engine trained by evaluating historical media planning data, evaluating historical performance data, evaluating previous media planning measurements, or proposals, analyzing audience analytics, ratings data, analyzing consumer behavior indicators, analyzing cost metrics, and applying budget related and timing constraints. Simmons fails to teach such feature. Claims 1,15, and 20 have been amended to further include a system/method which incorporates an iterative feedback mechanism to continuously learn and improve based on success or failure of previous recommendations compared to past user data, wherein after execution of said real time or predictive media plan, actual performance metrics are compared to projected outcomes. Simmons fails to teach such feature. Claims 1,15, and 20 have been amended to further include a system/method which generates predictive assessment of media platforms in local and national markets using ratings data, consumer behavior, and historical performance data. Simmons fails to teach such feature.” However, the rejection has been updated in view of filed claim amendments, as such, the Applicant’s broad arguments are unpersuasive.
Therefore, the Examiner respectfully disagrees and maintains the rejection.
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:
*Being noted initially
- 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”
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/DIPEN M PATEL/Primary Examiner, Art Unit 3621