DETAILED ACTION
This action is responsive to papers filed on 12/22/2026 and 1/20/2026.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because, while the claims herein are directed to a method and/or system, which could be classified under one of the listed statutory classifications (i.e., 2019 Revised Patent Subject Matter Eligibility Guidance (hereinafter “PEG”) “PEG” Step 1=Yes), 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.
Regarding claims 1, 11, the claims recite, in part, receiving a first campaign comprising campaign text content; executing a clustering algorithm to identify target groups within a reader audience based on features of the first campaign; providing a description of each identified target group; predicting a probability of a reader clicking on a given campaign using a Click-Through Rate (CTR) prediction model; generating revised campaign candidates for each target group based on the first campaign and the target group description using a campaign generator comprising a model receiving a prompt that includes the first campaign text content and the target group description, wherein the revised campaign candidates comprise rephrased versions of the first campaign text tailored to each target group; and selecting a revised campaign that maximizes the predicted CTR, deploy the selected revised campaign to readers, and creating a human-feedback dataset based on a performance of the revised campaign and fine-tuning the campaign generator, wherein adjusting model parameters in response to rewards and penalties determined based on the collected click-through data to produce an update for use in future campaign generation tasks; and collecting the click-through data from the deployed revised campaign and facilitating creation of the human-feedback dataset for adjusting the reinforcement learning.
The limitations, as drafted and detailed above, recites revising advertising candidates based on collected data about the campaign, target groups, and predicted CTR, and collecting campaign performance, which falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, and more specifically commercial interactions including advertising, marketing or sales activities or behaviors. Accordingly, the claim recites an abstract idea (i.e. “PEG” Revised Step 2A Prong One=Yes).
This judicial exception is not integrated into a practical application. In particular, the claims only recite the additional elements of campaign management server (claims 1, 11), clustering algorithm (claims 1, 11, merely programming), characterization module (claims 1, 11, merely programming), prediction module (claims 1, 11, merely programming), campaign generator (claims 1, 11, merely programming), selection module (claims 1, 11, merely programming), fine-tuning module with reinforcement learning (claims 1, 11, merely programming), performance server (claims 1, 11), Large Language Model (claims 1, 11, merely used at an “apply it” level to apply the abstract idea), and a network (claims 1, 11). The additional technical elements above are recited at a high-level of generality (i.e. as a generic processor performing a generic computer function of receiving, executing, providing, predicting, generating, selecting, deploying, fine-tuning, and collecting) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. There are no additional functional limitations to be considered under prong two.
Accordingly, the additional technical elements above do not integrate the abstract idea/judicial exception into a practical application because it does not impose any meaningful limits on practicing the abstract idea. More specifically, the additional elements fail to include (1) improvements to the functioning of a computer or to any other technology or technical field (see MPEP 2106.05(a)), (2) applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition (see Vanda memo), (3) applying the judicial exception with, or by use of, a particular machine (see MPEP 2106.05(b)), (4) effecting a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), or (5) applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (see MPEP 2106.05(e) and Vanda memo).
Rather, the limitations merely add the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)), or generally link the use of the
judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Thus, the claim is “directed to” an abstract idea (i.e. “PEG” Revised Step 2A Prong Two=Yes).
When considering Step 2B of the Alice/Mayo test, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims do not amount to significantly more than the abstract idea.
More specifically, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of campaign management server (claims 1, 11), clustering algorithm (claims 1, 11, merely programming), characterization module (claims 1, 11, merely programming), prediction module (claims 1, 11, merely programming), campaign generator (claims 1, 11, merely programming), selection module (claims 1, 11, merely programming), fine-tuning module with reinforcement learning (claims 1, 11, merely programming), performance server (claims 1, 11), Large Language Model (claims 1, 11, merely used at an “apply it” level to apply the abstract idea), and a network (claims 1, 11) to perform the claimed functions amounts to no more than mere instructions to apply the exception using a generic computer component.
“Generic computer implementation” is insufficient to transform a patent-ineligible abstract idea into a patent-eligible invention (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2352, 2357) and more generally, “simply appending conventional steps specified at a high level of generality” to an abstract idea does not make that idea patentable (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Mayo, 132 S. Ct. at 1300). Moreover, “the use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter (See FairWarning, 120 U.S.P.Q.2d. 1293, citing DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014)). As such, the additional elements of the claim do not add a meaningful limitation to the abstract idea because they would be generic computer functions in any computer implementation. Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the computer or improves any other technology. Their collective functions merely provide generic computer implementation.
The Examiner notes simply implementing an abstract concept on a computer, without meaningful limitations to that concept, does not transform a patent-ineligible claim into a patent- eligible one (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bancorp, 687 F.3d at 1280), limiting the application of an abstract idea to one field of use does not necessarily guard against preempting all uses of the abstract idea (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Bilski, 130 S. Ct. at 3231), and further the prohibition against patenting an abstract principle “cannot be circumvented by attempting to limit the use of the [principle] to a particular technological environment” (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Flook, 437 U.S. at 584), and finally merely limiting the field of use of the abstract idea to a particular existing technological environment does not render the claims any less abstract (See Affinity Labs, _F.3d_, 120 U.S.P.Q.2d 1201 (Fed. Cir. 2016), citing Alice, 134 S. Ct. at 2358; Mayo, 132 S. Ct. at 1294; Bilski v. Kappos, 561 U.S. 593, 612 (2010); Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat' l Ass' n, 776 F.3d 1343, 1348 (Fed. Cir. 2014); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014).
Applicant herein only requires a general purpose computer (see Applicant specification Paragraphs 0029, 0030, 0088-0093, and 0095); therefore, there does not appear to be any alteration or modification to the generic activities indicated, and they are also therefore recognized as insignificant activity with respect to eligibility.
The dependent claims 2-10 and 12-20 appear to merely limit a specific clustering algorithm (K-modes), using a greedy approach for the characterization module, a specific CTR prediction model (deep factorization), a specific campaign generator (LLM model), use of a feedback loop for fine-tuning, comparing CTR using a statistical test, use of the reinforcement learning to fine-tune the campaign generator, generating a dataset based on performance, and increasing of click-through rates using previous engagements and attributes, and therefore only limit the application of the idea, and not add significantly more than the idea (i.e. “PEG” Step 2B=No).
The campaign management server (claims 1, 11), clustering algorithm (claims 1, 11, merely programming), characterization module (claims 1, 11, merely programming), prediction module (claims 1, 11, merely programming), campaign generator (claims 1, 11, merely programming), selection module (claims 1, 11, merely programming), fine-tuning module with reinforcement learning (claims 1, 11, merely programming), performance server (claims 1, 11), Large Language Model (claims 1, 11, merely used at an “apply it” level to apply the abstract idea), and a network (claims 1, 11) are each functional generic computer components that perform the generic functions of receiving, executing, providing, predicting, generating, selecting, deploying, fine-tuning, and collecting, all common to electronics and computer systems.
Applicant's specification does not provide any indication that the campaign management server (claims 1, 11), clustering algorithm (claims 1, 11, merely programming), characterization module (claims 1, 11, merely programming), prediction module (claims 1, 11, merely programming), campaign generator (claims 1, 11, merely programming), selection module (claims 1, 11, merely programming), fine-tuning module with reinforcement learning (claims 1, 11, merely programming), performance server (claims 1, 11), Large Language Model (claims 1, 11, merely used at an “apply it” level to apply the abstract idea), and a network (claims 1, 11) are anything other than generic, off-the-shelf computer components. Therefore, the claims do not amount to significantly more than the abstract idea (i.e. “PEG” Step 2B=No).
Thus, based on the detailed analysis above, claims 1-20 are not patent eligible.
Novel/Non-Obvious Subject Matter
Claims 1-20 as currently written are novel/non-obvious over prior art. However, the rejection under 35 U.S.C. 101 is currently pending and represents a barrier to allowability. Examiner notes that any amendments made to the claims in an attempt to correct pending rejections could drastically alter the claim scope and could open up the possibility of prior art being applied in a future action.
Baird (U.S. Pub No. 2021/0049628) teaches receiving a first campaign at a campaign management server; executing a clustering algorithm by the campaign management server to identify target groups within a reader audience based on features of the first campaign; providing a description of each identified target group via a characterization module of the campaign management server; generating revised campaign candidates for each target group based on the first campaign and the target group description using a campaign generator of the campaign management server; selecting a revised campaign that maximizes effectiveness with a selection module of the campaign management server, and creating a human-feedback dataset based on a performance of the revised campaign and fine-tuning the campaign generator using a fine-tuning module with reinforcement learning of the campaign management server; and collecting campaign performance data at a performance server in communication with the campaign management server and facilitating creation of the human-feedback dataset for adjusting the reinforcement learning. Baird, however, does not teach each and every limitation recited in the independent claim language.
Somekh (U.S. Pub No. 2023/0214882) teaches predicting a probability of a reader clicking on a given campaign using a Click-Through Rate (CTR) prediction model of the campaign management server; and selecting a revised campaign that maximizes the predicted CTR with a selection module of the campaign. Somekh, however, does not cure all the deficiencies of Baird, and the combination of Baird and Somekh do not teach each and every limitation recited in the independent claim language.
None of the prior art of record, alone or in combination, teaches each and every limitation of the claimed invention. Specifically, none of the applied references teaches “a Large Language Model (LLM) receiving a prompt that includes the first campaign text content and the target group description, wherein the revised campaign candidates comprise rephrased versions of the first campaign text content tailored to each target group…collecting click-through data from the deployed revised campaign…wherein the fine-tuning module adjusts model parameters of the LLM in response to rewards and penalties determined based on the collected click- through data to produce an updated campaign generator for use in future campaign generation tasks”. While such features on their own would not normally be an allowable feature (large language models have been old and well known before the filing of Applicant’s invention, and using click-through result data to gauge campaign performance is also well known), it would simply not be obvious to apply another prior art reference to the other references already applied to arrive at the currently claimed invention and the order of steps currently taken by the currently claimed invention. There is no prior art that teaches each and every limitation of the invention as a whole in combination with one another. Therefore Examiner finds the independent claims to be allowable over the prior art of record.
Response to Arguments
Applicant cites to some of the amended claim language and argues “These specific technical components and network-based operations cannot be performed as a mental process or as a method of organizing human activity. The claims are eligible under Step 2A Prong 1 because these elements require physical network infrastructure and involve systematic technical operations for deploying content and collecting real-world user engagement data”. However, just because a claim requires physical network infrastructure and involves systematic technical operations does not mean that the claims does not recite an abstract idea under step 2A prong 2. Rather, any physical or technical limitations that aren’t part of the abstract idea are removed as additional elements. Once the additional elements are removed, everything else does indeed recite a Method of Organizing Human Activity, as outlined by the rejection above.
Applicant argues “The pending claims solve a real-world technical problem: efficiently generating and deploying personalized campaign content while continuously improving the campaign generator based on actual user engagement data collected over a network” and “This comprehensive technical architecture represents a specific practical application in network-based content delivery and machine learning optimization that provides automated campaign refinement-a technical capability that cannot be achieved through mental processes regardless of skill or resources. The system transforms campaign optimization from a manual process into an automated, network-based feedback system by creating persistent technical relationships between deployed content and collected performance data”. However, mere automation is not enough to integrate an abstract idea into a practical application. General purpose computer functionality can be applied to an abstract idea to introduce automation. In the instant claim language, this is also the case, in which the general purpose computer components are merely applied to implement the abstract idea.
Applicant argues “The automated deployment and data collection process represents a technological improvement in content delivery systems that goes far beyond simple automation of marketing activities. The system creates new technical capabilities for iterative content refinement using real-world engagement data collected over a network, enabling efficient campaign optimization through comprehensive network-based feedback management. The closed-loop system of deploying revised campaigns and collecting click-through data to fine-tune the campaign generator provides a solution that adds significantly more than any recited judicial exception by transforming campaign optimization from a manual process into an automated, self-improving technical system”. However, there is no improvement made to the actual computer or technical components claimed. Rather, the only improvement appears to be to the abstract idea. In the SAP decision (See SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163, 127 USPQ2d 1597, 1599 (Fed. Cir. 2018)), the courts found that an improvement made to the abstract idea is not patent eligible. SAP v. Investpic: Page 2, line 22 through Page 3, line 13 - Even assuming that the algorithms claimed are groundbreaking, innovative or even brilliant, the claims are ineligible because their innovation is an innovation in ineligible subject matter because there are nothing but a series of mathematical algorithms based on selected information and the presentation of the results of those algorithms. Thus, the advance lies entirely in the realm of abstract ideas, with no plausible alleged innovation in the non-abstract application realm. An advance of this nature is ineligible for patenting; and Page 10, lines 18-24 - Even if a process of collecting and analyzing information is limited to particular content, or a particular source, that limitations does not make the collection and analysis other than abstract.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
The following references have been cited to further show the state of the art with respect to campaign content generation using AI tools:
U.S. Pub No. 2024/0312087 to Agarwal
U.S. Pub No. 2025/0200114 to Parasnis
U.S. Pub No. 2024/0320714 to West
WO 2024/233741 to Boyd
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL BEKERMAN whose telephone number is (571)272-3256. The examiner can normally be reached 9PM-3PM EST M, T, TH, F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, WASEEM ASHRAF can be reached at (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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/MICHAEL BEKERMAN/ Primary Examiner, Art Unit 3621
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