DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
This Office Action is in response to the Amendment filed on 03/06/2026.
Claims 13-16 are canceled.
Claims 21-24 are new.
Claims 1, 5-12, 17, and 19-20 are currently amended.
Claims 1-12 and 17-24 are currently pending and examined below.
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 1-12 and 17-24 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 1-12 and 17-24 is/are directed towards a statutory category (i.e., a process, machine, manufacture, or composition of matter) (Step 1, Yes).
Step 2A Prong One:
Claim 1 recites (additional elements underlined):
A method comprising:
receiving, by a processing device, a content brief describing a goal to be achieved in controlling digital content output;
extracting, by the processing device, content brief data from the content brief;
generating, by the processing device, a content strategy automatically and without user intervention based on the content brief data, the content strategy including a journey having a plurality of stages usable to control output of items of digital content to a plurality of client devices, the generating including:
generating text descriptions of respective stages of the plurality of stages using a large language model (LLM);
generating digital images of respective stages of the plurality of stages using a diffusion model; and
outputting, by the processing device, the content strategy for display in a user interface; and
controlling the digital content output based on the journey as defined actions to be performed and responses to those actions through the plurality of stages.
Under the broadest reasonable interpretation, the limitations outlined above that describe or set forth the abstract idea, cover performance of the limitations in the mind but for the recitation of generic computer(s) and/or generic computer component(s). That is, other than reciting the additional elements identified below, nothing in the claim precludes the limitations from practically being performed in the mind. These limitations are considered a mental process because the limitations include an observation, evaluation, judgement, and/or opinion. These limitations are also similar to “collecting information, analyzing it, and displaying certain results of the collection and analysis” and/or “collecting and comparing known information” which were determined to be mental processes in MPEP 2106.04(a)(2)(III)(A). The Examiner notes that “[c]laims can recite a mental process even if they are claimed as being performed on a computer” (see MPEP 2106.04(a)(2)(III)(C)). The mere nominal recitation of the additional elements identified below do not take the claims out of the mental process grouping. Therefore, the claim recite a mental process (Step 2A Prong One, Yes).
The limitations outlined above also describe or set forth an advertising/marketing activity. Advertising/marketing falls within the certain method of organizing human activity enumerated grouping of abstract ideas. The limitations outlined above also describe or set forth a fundamental economic principle or practice because advertising/marketing is related to commerce and economy. The limitations outlined above also describe a commercial interaction (e.g., advertising, marketing or sales activities or behaviors, business relations), and managing personal behavior or relationships or interactions between people (e.g., social activities, teaching, and following rules or instructions). Therefore, the claim recites a certain method of organizing human activity (Step 2A Prong One, Yes).
Step 2A Prong Two:
In Step 2A Prong Two, the additional element(s) outlined above are recited at a high level of generality, and under the broadest reasonable interpretation, are generic computer(s) and/or generic computer component(s) that perform generic computer functions. The additional element(s) are merely used as tools, in their ordinary capacity, to perform the abstract idea. The additional element(s) amount adding the words “apply it” with the judicial exception. Merely implementing an abstract idea on generic computer(s) and/or generic computer component(s) does not integrate the judicial exception similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer. The Examiner notes that “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 pp 10-11 of FairWarning IP, LLC. v. Iatric Systems, Inc. (Fed. Cir. 2016)). The additional elements also amount to generally linking the use of the abstract idea to a particular technological environment or field of use (e.g., in a computer environment). The courts have found that simply limiting the use of the abstract idea to a particular environment does not integrate the judicial exception into a practical application. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. There is no indication that the combination of elements improves the functioning of a computer, improves any other technology or technical field, applies or uses the judicial exception to effect a particular treatment or prophylaxis for disease or medical condition, applies the judicial exception with, or by use of a particular machine, effects a transformation or reduction of a particular article to a different state or thing, or applies or uses 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 claims as a whole is more than a drafting effort designed to monopolize the exception. Their collective functions merely provide generic computer implementation (Step 2A Prong Two, No).
Step 2B:
In Step 2B, the additional elements also do not amount to significantly more for the same reasons set forth with respect to Step 2A Prong Two. The Examiner notes that revised Step 2A Prong Two overlaps with Step 2B, and thus, many of the considerations need not be reevaluated in Step 2B because the answer will be the same. Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. Their collective functions merely provide generic computer implementation (Step 2B, No).
Claims 2-12 recite further limitations that also fall within the same abstract ideas identified above with respect to claim 1 (i.e., certain methods of organizing human activities and/or mental processes).
Claim 2 does not recite any other additional elements. Therefore, for the same reasons explained above with respect to claim 1, claim 2 also does not integrate the judicial exception into a practical application or amount to significantly more.
Claim 3 recites the additional elements “using a machine-learning model” and “digital”. Claim 4 recites the additional elements “using a large language model (LLM),” “digital,” and “using a diffusion model.” Claim 5 recites the additional elements “digital” and “is performed by the diffusion model.” Claim 6 recites the additional elements “using the large language model (LLM),” “digital,” and “using the large language model (LLM).” Claim 7 recites the additional elements “using the large language model (LLM)” and “by the large language model (LLM).” Claim 8 recites the additional elements “using the large language model (LLM),” “digital,” and “using the diffusion model.” Claim 9 recites the additional element “using the large language model (LLM).” Claims 10-11 recites the additional elements “using the large language model (LLM)” and “digital.” Claim 12 recites the additional elements “based on the large language model (LLM)” and “using the large language model (LLM).” However, these additional elements also do not integrate the judicial exception into a practical application or amount to significantly more because they amount to adding the words “apply it” with the judicial exception, mere instructions to implement the idea on a computer, merely using a computer as a tool to perform an abstract idea, and generally linking the use of the judicial exception to a particular technological environment or field of use.
Claim 17 recites (additional elements underlined):
One or more computer-readable storage media storing instructions that, responsive to execution by a processing device, causes the processing device to perform operations comprising:
receiving a content brief describing a goal to be achieved in controlling digital content output;
extracting content brief data as text from the content brief;
generating a content strategy, automatically and without user intervention, based on the text, the content strategy including one or more metrics usable to track output of digital content towards achieving the goal, the generating including:
generating text descriptions of respective stages of a plurality of stages using a large language model (LLM); and
generating digital images of respective stages of the plurality of stages using a diffusion model;
outputting the content strategy for display in a user interface; and
controlling the digital content output based on the content strategy.
For the same reasons explained above with respect to claim 1, claim 17 also recites an abstract idea in Step 2A Prong One (i.e., mental process and certain methods of organizing human activities. For the same reasons explained above with respect to claim 1, claim 17 also does not integrate the judicial exception into a practical application or amount to significantly more.
Claims 18-20 recite further limitations that also fall within the same abstract ideas identified above with respect to claim 17 (i.e., certain methods of organizing human activities and/or mental processes).
Claim 18 recites the additional element “digital.” Claim 19 recites the additional elements “using the large language model (LLM)” and “digital.” Claim 20 recites the additional elements “using the large language model (LLM)” and “by the large language model (LLM).” However, these additional elements also do not integrate the judicial exception into a practical application or amount to significantly more because they amount to adding the words “apply it” with the judicial exception, mere instructions to implement the idea on a computer, merely using a computer as a tool to perform an abstract idea, and generally linking the use of the judicial exception to a particular technological environment or field of use.
Claim 21 recites (additional elements underlined):
A computing device comprising:
a processing device; and
a computer-readable storage medium storing instructions that, responsive to execution by the processing device, causes the processing device to perform operations including:
receiving a content brief describing a goal to be achieved in controlling digital content output;
extracting content brief data as text from the content brief;
generating a content strategy, automatically and without user intervention, based on the text, the content strategy including one or more metrics usable to track output of digital content towards achieving the goal, the generating including:
generating text descriptions of respective stages of a plurality of stages using a large language model (LLM); and
generating digital images of respective stages of the plurality of stages using a diffusion model;
outputting the content strategy for display in a user interface; and
controlling the digital content output based on the content strategy.
For the same reasons explained above with respect to claim 1, claim 21 also recites an abstract idea in Step 2A Prong One (i.e., mental process and certain methods of organizing human activities. For the same reasons explained above with respect to claim 1, claim 21 also does not integrate the judicial exception into a practical application or amount to significantly more.
Claims 22-24 recite further limitations that also fall within the same abstract ideas identified above with respect to claim 21 (i.e., certain methods of organizing human activities and/or mental processes).
Claim 22 recites the additional element “digital.” Claim 23 recites the additional elements “using the large language model (LLM)” and “digital.” Claim 24 recites the additional elements “using the large language model (LLM)” and “by the large language model (LLM).” However, these additional elements also do not integrate the judicial exception into a practical application or amount to significantly more because they amount to adding the words “apply it” with the judicial exception, mere instructions to implement the idea on a computer, merely using a computer as a tool to perform an abstract idea, and generally linking the use of the judicial exception to a particular technological environment or field of use.
Response to Arguments
Applicant's arguments filed 03/06/2026 have been fully considered but they are not persuasive. In the Remarks, Applicant argues:
Argument A: “The Examiner asserts that the claims recite a mental process because "nothing in the claim precludes the limitations from practically being performed in the mind." Office Action, p. 3. That conclusion is inconsistent with the claim language, the Specification, and controlling authority. The claims explicitly state generating the content strategy "automatically and without user intervention" (claims 1, 17, 21) and further state that the generation be performed using "generative artificial intelligence implemented using one or more machine-learning models" (claim 1) or "a large language model (LLM)" and "a diffusion model" (claims 1, 17, 21). These limitations cannot be performed in the human mind. A human cannot implement a large language model or diffusion model mentally, cannot automatically generate multi-modal content for multiple stages of a journey without intervention, cannot process content briefs through machine-learning models to extract features and generate comprehensive strategies automatically, cannot update a persona in real time using machine-learning inference, and cannot control digital content output to a plurality of client devices based on a multi-stage journey.”
In response, the Examiner respectfully disagrees. First, the limitations outlined above, which exclude the additional elements, can be practically performed in the human mind because the limitations include an observation, evaluation, judgement, and/or opinion. These limitations are also similar to “collecting information, analyzing it, and displaying certain results of the collection and analysis” and/or “collecting and comparing known information” which were determined to be mental processes in MPEP 2106.04(a)(2)(III)(A). The Examiner notes that “[c]laims can recite a mental process even if they are claimed as being performed on a computer” (see MPEP 2106.04(a)(2)(III)(C)). Second, the limitation “automatically and without user intervention” is addressed as an additional element in Step 2A Prong Two and Step 2B, and is not considered to be part of the abstract idea. Therefore, the limitations that describe or set forth the abstract idea in Step 2A Prong One do recite a mental process.
Argument B: “The Appeals Review Panel in In re Desjardins expressly rejected the type of overgeneralization applied by the Examiner. The Panel admonished that "Examiners and panels should not evaluate claims at such a high level of generality" and "The panel essentially equated any machine learning with an unpatentable 'algorithm'... without adequate explanation." In re Desjardins, p. 14-15. It is respectfully submitted that the Examiner's reasoning commits the same error by reducing concrete machine-learning architectures to "mental steps." The claims do not recite mental processes; the claims recite specific machine-learning models performing operations that no human mind can perform.”
In response, the Examiner respectfully disagrees. As can be seen from the rejection above, the Office Action clearly identifies the limitations that describe or set forth the abstract idea in Step 2A Prong One, and explains why the limitations fall within the enumerated groupings of abstract ideas. The Examiner notes that the machine-learning architectures are addressed in Step 2A Prong Two and in Step 2B.
Argument C: “The Examiner's citation to MPEP 2106.04(a)(2)(III)(A) regarding "collecting information, analyzing it, and displaying certain results" is misplaced. The claims do not merely collect and display information. Rather, the claims automatically generate new multi-modal content (coordinated text and images) using specific machine-learning models and then control digital content output based on that generated content.”
In response, the Examiner respectfully disagrees. The limitations that describe or set forth the abstract idea in Step 2A Prong One clearly collect information (e.g., receiving content brief), analyze it (e.g., extract brief data and generate a content strategy based on the content brief data), and display certain results of the collection and analysis (e.g., outputting the content strategy for display and controlling the content output).
Argument D: “The Examiner asserts that the claims recite "advertising/marketing," which "falls within the certain method of organizing human activity enumerated grouping of abstract ideas." Office Action, p. 3-4. That conclusion is incorrect.”
In response, the Examiner respectfully disagrees. The claimed invention describes the concept of generating a content strategy based on content brief data that is used to control content output. This is clearly an advertising/marketing activity. At least ¶¶ 18, 47-51, 61, 66-67 of the specification and various drawings confirm that the claimed invention describes or sets forth an advertising/marketing activity.
Argument E: “The Specification explains that conventional techniques "involved in development of content strategies involve a manual and often laborious process in order to develop a plan in how to get a message across, what kind of stories are to be told to do so, how to guide potential consumer interaction, and so on." Application, [0017]. These conventional techniques "are difficult to understand without specialized knowledge" and "consume significant amounts of computational resources," making it "difficult to judge or even determine in how to judge progress towards a goal of the strategies." Application, [0017]. The claims address these technical challenges through a specific technical implementation involving multiple machine-learning models working in coordination. Application, [0041], [0043], [0057]-[0059], [0070]-[0071].”
In response, the Examiner respectfully disagrees. First, the mere automation of manual processes does not show an improvement in computer-functionality (see MPEP 2106.05(a)). "[O]ur precedent is clear that merely adding computer functionality to increase the speed or efficiency of the process does not confer patent eligibility on an otherwise abstract idea." (See p 12 of Intellectual Ventures I LLC v. Capital One Financial (Fed. Cir. 2015)).
Second, the alleged improvements are entirely in the realm of the abstract idea (i.e., improvement to advertising/marketing). Similar to the claimed invention in SAP, the advance here lies entirely in the realm of the abstract idea, with no plausibly alleged innovation in the non-abstract application realm.
Argument F: “This is analogous to MPEP 2106.04(a)(2), Example 6 (GPS Navigation), where claims that could be used for the business practice of navigation were nonetheless not directed to an abstract idea because the claimed features were directed to a specific technical implementation. In Example 6, the claims recited a particular way of generating and presenting route guidance using GPS and a database, which was found to be directed to a technical solution rather than to the business practice of navigation. Similarly, here the claims recite a particular way of generating and implementing a content strategy using specific machine-learning models and a defined multi-stage journey, rather than reciting generic marketing steps.”
In response, the Examiner respectfully disagrees. Applicant’s reliance on Example 6 (GPS Navigation) is misplaced. The claimed invention here is not related to GPS or location tracking at all.
Argument G: “The MPEP's other statutory examples further confirm that the present claims are directed to technological processes rather than methods of organizing human activity. Example 40 (Image Processing) describes claims directed to a specific image-processing technique that improves the functioning of a computer by using a particular algorithm to enhance images. The present claims likewise recite a specific image-generation technique using a diffusion model to generate digital images based on text generated by an LLM. Application, [0022], [0025]. Example 42 (Neural Network for Facial Detection) describes claims directed to training and using a neural network for facial detection, which are not abstract because the claims recite a particular machine-learning architecture performing operations that cannot be carried out by a human. The present claims are analogous because the claims recite particular machine-learning architectures (LLMs, diffusion models, CNN- and LSTM-based captioning models) performing specific generative transformations of text and images. Application, [0021]-[0022], [0049]-[0055].”
In response, the Examiner respectfully disagrees. First, the Examiner notes that “the examples should not be used as a basis for a subject matter eligibility rejection” (see May 2016 Memorandum: Formulating a Subject Matter Eligibility Rejection and Evaluating Applicant’s Response to a Subject Matter Eligibility Rejection). Second, the facts of the application here do not uniquely match the facts at issue in Examples 39 or 40. Example 39 was patent-eligible because it did not recite an abstract idea in Step 2A Prong One. Here, the claims clearly recite a certain method of organizing human activity and a mental process. Example 40 was patent-eligible because the claimed invention avoided excess traffic volume on the network and hinderance of network performance which resulted in an improved network monitoring system. Here, alleged improvement is entirely in the realm of the abstract idea.
Argument H: “The Appeals Review Panel in In re Desjardins emphasized that AI-related claims are not to be dismissed as abstract merely because the claims arise in a commercial context: "Categorically excluding Al innovations from patent protection... jeopardizes America's leadership... Examiners should not evaluate claims at such a high level of generality." In re Desjardins, p. 14-15. It is respectfully submitted that the Examiner's analysis commits the same error by reducing the claimed technical process to "marketing."
In response, the Examiner respectfully disagrees. As explained above, the limitations that exclude the additional elements are limitations that fall within the certain methods of organizing human activities enumerated grouping of abstract ideas.
Argument I: “The Examiner states that "there is no indication that the combination of elements improves the functioning of a computer." Office Action, p. 4. That statement overlooks the specific improvements described in the Specification and recited in the claims. The Specification identifies multiple technical problems with conventional techniques, including their manual and laborious nature, their reliance on specialized knowledge, their consumption of significant computational resources, and their inability to judge progress toward a goal. Application, [0001], [0017]. The claims address these technical problems by providing specific improvements to computer functionality.”
In response, the Examiner respectfully disagrees. Unlike in Enfish in which the claimed invention achieved other benefits over conventional databases such as increased flexibility, faster search times, and smaller memory requirements that provided improvements to the functioning of the computer itself, here 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 a computer or improve any other technology. Their collective functions merely provide generic computer implementation.
Argument J: “The Specification explains that the strategy generation service "is configurable to receive the content brief in a portable document format (PDF) and from this generate a comprehensive content strategy, automatically and without user intervention." Application, [0043]. By "streamlining the content strategy generation process, the strategy generation service increases accessibility and reduces computational resource consumption with increased time efficiency, especially for individuals with limited experience." Application, [0043]. The system automatically extracts content-brief data, generates text descriptions using LLMs, generates digital images using diffusion models, generates personas, identifies metrics, and controls digital content output-all without user intervention. Application, [0025], [0048]-[0059], [0070]-[0071].”
In response, the Examiner respectfully disagrees. As explained above, the alleged improvements are entirely in the realm of the abstract idea (i.e., improvement to advertising/marketing). The advance here lies entirely in the realm of the abstract idea, with no plausibly alleged innovation in the non-abstract application realm.
Argument K: “These improvements are directly analogous to MPEP 2106.04(d)(1), Example 21 (Improved User Interface), where claims directed to a specific improvement in the way computers operate were found to integrate an abstract idea into a practical application. In Example 21, the claims improved the way a computer displayed and managed information, thereby improving computer functionality. Here, the claims improve the way computers generate and control digital content output by automating the generation of a comprehensive, multi-modal content strategy and enabling real-time tracking and control of digital content distribution.”
In response, the Examiner respectfully disagrees. Unlike Example 21 in which the claimed invention addressed the Internet-centric challenge of alerting a subscriber with time sensitive information when the subscriber’s computer is offline, here the additional elements are merely used as a tool, in their ordinary capacity, to perform the abstract idea. The Examiner notes that “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 pp 10-11 of FairWarning IP, LLC. v. Iatric Systems, Inc. (Fed. Cir. 2016)).
Argument L: “The Appeals Review Panel in In re Desjardins held that claims directed to improving the operation of a machine-learning model constitute a practical application: "We are persuaded that [the claim] constitutes an improvement to how the machine learning model itself operates." In re Desjardins, p. 13. The present claims likewise recite improvements to machine-learning-based content-strategy generation and to the way computer systems generate and control digital content output.”
In response, the Examiner respectfully disagrees. The claimed machine learning models here are being used in their ordinary capacity. “[P]atents that do not more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101” (p. 18 of Recentive Analytics Inc. v. Fox Corp. (Fed. Cir. 2025)).
Argument M: “The Claims Apply the Judicial Exception With a Particular Machine The Examiner states that the additional elements "are recited at a high level of generality, and under the broadest reasonable interpretation, are generic computer(s) and/or generic computer component(s)." Office Action, p. 4. That assertion overlooks the specific machine-learning architectures recited in the claims and disclosed in the Specification. Claims 1, 17, and 21 specifically recite "generating text descriptions of respective stages of the plurality of stages using a large language model (LLM)" and "generating digital images of respective stages of the plurality of stages using a diffusion model." These are not generic computers or generic components. The Specification explains that an LLM "is a type of machine-learning model that is designed to understand, generate, and interact with human language inputs at a large scale," trained on "vast amounts of text data using deep learning techniques" and involving "billions or even trillions of parameters." Application, [0021]. A diffusion-based model "is trained to learn to remove noise added to training digital images as part of an iterative process" and, once trained, "is configured to generate digital images based on a text input." Application, [0022].”
In response, the Examiner respectfully disagrees. First, “while the application of a judicial exception by or with a particular machine is an important clue, it is not a stand-alone test for eligibility” Second, as explained above, the claimed machine learning models are recited at a high level of generality, and amount to adding the words “apply it”.
Argument N: “This coordinated use of LLMs and diffusion models represents a specific technical implementation, not a generic computer performing generic functions. It is analogous to MPEP 2106.05(b), Example 39 (Genetic Sequencing), where the use of a particular machine (a DNA sequencer) to perform a process was found to integrate the abstract idea into a practical application. Here, the specific use of LLMs for text generation and diffusion models for image generation, working in coordination to automatically generate multi-stage journeys with synchronized text and images for controlling digital content output, similarly integrates any abstract idea into a practical application.”
In response, the Examiner respectfully disagrees. Applicant’s reliance on Example 31 is misplaced. Example 31 is directed towards screening for gene alterations. The claimed invention here is directed towards generating content strategies to control content output.
Argument O: “The Claims Effect a Transformation. The claims further recite "controlling the digital content output" based on the generated content strategy and journey (claims 1, 17, 21). The Specification explains that the content strategy "is usable to control which items of digital content are provided to which entities (e.g., segments of a user population) to achieve the goal." Application, [0019]. The journey "is illustrative of actions to be performed and responses to those actions as part of digital content control." Application, [0058]. Once a desired strategy is obtained, a digital service export module "exports the content strategy to respective digital services," and those digital services output digital content via channels such as email, instant messaging, and social media. Application, [0070]. This control function transforms the operation of the computer system from a state where digital content output is uncontrolled or ad hoc to a state where it is controlled according to a multi-stage journey with defined actions and responses. This is not merely data manipulation but a transformation in how the computer system operates to distribute digital content.”
In response, the Examiner respectfully disagrees. First, “while the transformation of an article is an important clue, it is not a stand-alone test for eligibility” (MPEP 2106.05(c)). Second, “mental processes in which thoughts or human based actions are ‘changed’ are not considered an eligible transformation.” (MPEP 2106.05(c)). “For data, mere manipulation of data is not a transformation” (see slide 33 of 2019 PEG – Advanced Module).
Argument P: “The Claims Do Not Merely Link the Use of the Judicial Exception to a Particular Technological Environment. The Examiner states that "the additional elements also amount to generally linking the use of the abstract idea to a particular technological environment or field of use." Office Action, p. 4. The claims, however, do not merely limit an abstract idea to a particular field. The claims, rather, recite specific technical operations that define how the technical solution operates: extracting content-brief data from the content brief; generating a multi-stage journey using LLMs; generating text descriptions for each stage using generative AI; generating digital images for each stage using a diffusion model based on the text descriptions; and controlling digital content output to client devices based on the journey. These are not field-of-use limitations but integral steps of the claimed process that define the technical implementation of the solution.”
In response, the Examiner respectfully disagrees. Unlike in DDR in which the claimed invention solved the business challenge of retaining website visitors that is particular to the Internet, here the claimed invention amounts to merely reciting the performance of a business practice (e.g., generating content strategies to control content output) along with the requirement to perform it on the Internet. The claimed invention here is not necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computer networks. The advance lies here entirely in the realm of the abstract idea.
Argument Q: “Under Step 2B, the additional elements amount to significantly more because the claimed features provide specific machine-learning model architectures and a non-conventional ordered combination of operations. The claims recite LLMs for text generation and diffusion models for image generation working in coordination, as well as machine-learning-based extraction of content-brief data, persona generation, and metric identification. Application, [0021]-[0022], [0048]-[0059], [0070]-[0071]. The claims also recite automatic operation "without user intervention" (claims 1, 17, 21), reflecting a specific technical achievement in automating complex tasks that previously involved specialized knowledge and manual intervention. Application, [0043]. The ordered combination of elements yields an inventive concept. The claims recite a multi-model generative AI pipeline that produces a unified, multi-modal content strategy and then uses that strategy to control digital content output. The Examiner's analysis improperly treats each machine-learning component in isolation. Step 2B requires evaluating the ordered combination of elements, not each element in isolation.”
In response, the Examiner respectfully disagrees. First, the Office Action does not take the position that any of the additional elements amount to adding insignificant extra-solution activity in Step 2A Prong Two that would warrant an analysis in Step 2B to determine that the additional elements also amount to significantly more. The Examiner notes that revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be reevaluated in Step 2B because the answer will be the same. However, unless an Examiner had previously concluded under revised Step 2A that an additional element was insignificant extra-solution activity, they should reevaluate that conclusion in Step 2B (see 2019 Revised Patent Subject Matter Eligibility Guidance, now in MPEP 2106).
Argument R: “The Appeals Review Panel in In re Desjardins confirmed that machine-learning-based improvements satisfy Step 2B "Independent claim 1, when considered as a whole, integrates an abstract idea into a practical application." In re Desjardins, p. 15. The present claims likewise integrate machine-learning-based generation into a practical application: automated control of digital content output based on a multi-stage, multi-modal content strategy. This is analogous to MPEP 2106.05(a), Example 24 (Automation of Financial Record Matching), where claims that automated a process in a specific way were found to amount to significantly more because the claims provided a technical solution to a technical problem. Here, the claims automate the generation and implementation of content strategies in a specific technical manner that reduces computational resource consumption and provides real-time control capabilities. Application, [0043], [0070]-[0071].”
Viewing the limitations as an ordered combination does not add anything further than looking at the limitations individually. The additional elements amount no more than a mere instruction to apply the abstract idea using generic computer(s) and/or generic computer component(s).
With regard to the argument that the claimed invention is analogous to Example 24 (Automation of Financial Record Matching), the Examiner notes that Example 24 was directed towards updating alarm limits and was determined to be ineligible.
Argument S: “The Examiner asserts that "looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually." Office Action, p. 4. That conclusion is incorrect. In McRO, the Federal Circuit found the claims patent-eligible as reciting a specific improvement to computer animation that automated tasks previously performed by human animators. The court emphasized that "the claimed process uses a combined order of specific rules that renders information into a specific format that is then used and applied to create desired results." McRO, Inc. v. Bandai Namco Games America Inc., 837 F.3d 1299, 1315 (Fed. Cir. 2016). The present claims are directly analogous. The claims recite a specific ordered combination in which content-brief data is extracted, an LLM generates text descriptions for multiple stages of a journey, a diffusion model generates digital images based on those text descriptions, personas are generated and updated using machine learning, metrics are identified using machine learning, and the resulting content strategy is used to control digital content output to client devices. Application, [0025], [0048]-[0059], [0070]-[0071]. This ordered combination creates a specific technical result: automated generation and implementation of a multi-modal content strategy that controls digital content distribution. The combination, in the specific order claimed, enables automation of a process that previously involved specialized knowledge and manual intervention. The Specification explains that the strategy generation service "is usable to define a strategy to control output of digital content through a variety of stages, define those stages, a persona representative of a population segment that is to receive the digital content, and identify metrics usable to track progress towards achieved a goal of the strategy." Application, [0071]. As a result, the strategy generation service "addresses conventional technical challenges in development of content strategies involving a manual inputs and specialized knowledge, the results of which are difficult to judge progress towards a goal of the strategies." Application, [0071]. This is directly analogous to McRO, where the court found that the claimed rules-based automation of animation was a specific technological improvement.”
Unlike in McRO in which the claimed invention allowed computers to produce accurate and realistic lip synchronization and facial expression in animated characters that previously could only be performed by human animators which provided an improvement to an existing technological process, here 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 a computer or improve any other technology. Their collective functions merely provide generic computer implementation. “It is the incorporation of the claimed rules, not the use of the computer, that ‘improved [the] existing technological process’ by allowing the automation of further tasks” (see p. 24 of McRO, Inc. v. Bandai Namco Games America (Fed. Cir. 2016)). The specification fails to provide a teaching about how the claimed invention improves a computer or other technology, nor do the claims recite a particular solution to a problem or a particular way to achieve a desired outcome defined by the claimed invention. The claims merely use the computer as a tool instead of an improved computer capability.
Argument T: “The Claims Do Not Preempt Any Abstract Idea.”
In response, the Examiner respectfully disagrees. “While preemption is the concern underlying the judicial exceptions, it is not a standalone test for determining eligibility. […] [W]hile a preemptive claim may be ineligible, the absence of complete preemption does not demonstrate that a claim is eligible” (MPEP 2106.03).
Prior Art
The Examiner notes that the claims as currently amended overcome the prior art of record because the independent claims have incorporated the objected to subject matter. While the prior art teach some of the elements of the claimed invention, one of ordinary skill in the art would not have arrived at Applicant’s claimed invention unless one was using Applicant’s claims and specification as a roadmap, thus using impermissible hindsight. The closest prior art found to date are the following:
Pitkin et al. (US 2025/0086403 A1) discloses a systems, apparatus, methods, and computer program products for campaign brief generation. Pitkin also discloses content strategy being generated automatically and without user intervention; campaign recommendations being predicted by a machine learning model; editing a content strategy, and controlling a digital content output based on the editing of the content strategy. However, Pitkin does not appear to explicitly disclose or render obvious the claims as currently amended.
Sisson et al. (US 2020/0134675 A1) disclose a defined journey of actions to be performed and responses to those actions as part of a digital content control. However, Sisson also does not appear to explicitly disclose or render obvious the claims as currently amended.
Zachariah et al. (US 20210350202 A1) discloses the generation of a persona using generative artificial intelligence. However, Zacharaiah also does not appear to explicitly disclose or render obvious the claims as currently amended.
LaMarche et al. (US 2020/0160359 A1) discloses the concept of updating the persona in real-time. However, LaMarche also does not appear to explicitly disclose or render obvious the claims as currently amended.
Zachariah et al. (US 2021/0350202 A1) discloses the concept of generating a persona using generative artificial intelligence (see Figures 1-3 and 8, ¶¶ 21, 28-31, and 66-69). Zachariah et al. was used to reject claims 7-9 as originally filed.
Mathur (US 2025/0139229 A1) discloses the concept of extracting text on a digital image and using image understanding and language modeling (see ¶ 86).
Raviv et al. (US 2025/0104117 A1) discloses a generative artificial intelligence model for creating advertisements.
Siebel et al. (US 2024/0202221 A1) pertains to generative artificial intelligence-based systems to transform information access and content creation for enterprise information systems.
Kulkarni et al. (US 2018/0189843 A1) discloses the concept of providing a preview of a content item that is composed of individual components to a content provider.
Conclusion
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.
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/SAM REFAI/Primary Examiner, Art Unit 3621
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