DETAILED ACTION
This is in reference communication received 25 November 2025. Cancellation of claims 3, 10 and 17 is acknowledged. Claims 1 – 2, 4 – 9, 11 – 16 and 18 – 20 are pending for examination. 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 – 2, 4 – 9, 11 – 16 and 18 – 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.
Independent 8 and representative claims 1 and 15, in part is directed toward a statutory category of invention, the claim appears to be directed toward a judicial exception namely an abstract idea. Claim 8 recites invention directed to providing to a requested user, a journey definition to a campaign (e.g., a workflow for a campaign) for execution. After receiving input data (e.g., text defining a journey that the user wants (e.g., objective of the user). Context data of other users are extracted and considered in conjunction with the input-data received from the user to creates a prompt that is used to query transformer-based generative language model (a deep-learning query) to produce plurality of nodes (e.g., software code provided as a result of the query made to the deep-learning) and the received software code are modified (e.g., mapped) to a desired schema and provided as an output for campaign execution, which, pursuant to MPEP 2106.04, is aptly categorized as a method of organizing human activity (i.e. get answers to solve a problem from a deep-learning system). Therefore, under Step 2A, Prong One, the claims recite a judicial exception.
The aforementioned claims also recite additional technical elements including: “one or more computer processors” for executing computer-executable instructions (claim 1), “non-transitory computer storage media” for storing computer-executable instructions (software) (claim 15); “user device” used by users for providing their desired user journey using the prompts. These limitations are recited at a high level of generality, and appear to be nothing more than generic computer components. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 134 S. Ct. at 2358, 110 USPQ2d at 1983. See also 134 S. Ct. at 2389, 110 USPQ2d at 1984.
Representative claims 1 and 15, which do recite statutory categories (machine, product of manufacture, for example), the same analysis as above applies to these claims since the method steps are the same. However, the judicial exception is not integrated into a practical application. These claims add the generic computer components (additional elements) of a system comprising one or more hardware processors and a memory (claim 1), and a non-transitory machine-readable medium comprising instructions that when executed by a processor of a machine cause the machine to perform the method addressed above (claim 15).
The processor, memory, and non-transitory machine-readable medium are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of the processor, memory, and non-transitory machine-readable medium amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
When taken as an ordered combination, nothing is added that is not already present when the elements are taken individually. When viewed as a whole, the marketing activities amount to instructions applied using generic computer components.
As for dependent claims 2, 4 – 6, 9, 11 – 14, 16 and 18 – 20 these claims recite limitations that further define the same abstract idea with details regarding descriptions of various data, what decisions may be made by the merchant based upon the received fraudulent score, what algorithm and what data elements will be used to generate fraudulent score. Thus, the dependent claims merely provide additional non-structural (and predominantly non-functional) details that fail to meaningfully limit the claims or the abstract idea(s).
Therefore, claims 1 – 2, 4 – 9, 11 – 16 and 18 – 20 are not drawn to eligible subject matter, as they are directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 – 2, 8 – 9 and 15 – 16 are rejected under 35 U.S.C. 103 as being unpatentable over Sikand et al. US Publication 2025/0055028 in view of AI-Watch-Tower published YouTube Video “ChatGPT – How To Use To Create A Marketing Strategy”
Regarding claim 8 and representative claims 1 and 15, Sikand teaches system and method wherein an artificial intelligence code generator that uses natural language prompts as inputs to provide outputs of computer code used to generate various components. As the project progresses, particular natural language prompts that are used and stored in a prompt database or other datastore [Sikand, 0007] comprising:
one or more computer processors [Sikand, 0082];
one or more computer memories [Sikand, 0082];
a set of instructions stored in the one or more computer memories, the set of instructions configuring the one or more computer processors to perform operations, the operations [Sikand, 0082] comprising:
receiving user input data prompt describing a desired user journey objective for an automated customer engagement workflow (Sikand, an artificial intelligence code generator that uses natural language prompts as inputs to provide outputs of computer code used to generate various components. As the project progresses, particular natural language prompts that are used and stored in a prompt database or other datastore) [Sikand, 0007];
Sikand does not specifically teach extracting of context data. However, Sikand teaches to receive design requirements of the design requirement project [Sikand, 0020] and generate a dependency graph of components of the software development project based on the design requirements [Sikand, 0022]. AI-Watch-Tower teaches ChatGPT enables users to provided their desired journey as a text (AI-Watch-Tower, user enter as a text prompt into ChatGPT [AI-Watch-Tower, page 4 – 6]. AI-Watch-Tower further teaches user enter as a text prompt into ChatGPT their desired request for an answer or a solution, and in response ChatGPT provides user guidance for the requested task.
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Sikand by adopting teachings of AI-Watch-Tower to analyze user input to determine their desired request to generate and fulfill user requested task.
Sikand in view of AI-Watch-Tower teaches system and method further comprising:
extracting context data for a plurality of users from a customer data platform (as responded to above) [AI-Watch-Tower, page 4 – 6].
constructing a journey generation prompt comprising the received user input data (Sikand, determine from attributes of the first component a code intent;) [Sikand, 0024] and the extracted context data for the plurality of users from the customer data platform (Sikand, determine from the dependency graph a subset of components of the software development project having one or more dependencies related to a first component of the software development project;) [Sikand, 0023, 0025];
inputting the constructed journey generation prompt into a transformer-based generative languate model trained on domain-specific customer journey data to produce a user journey definition (Sikand, using a pre-trained language model, generate for the first component a natural language summary text based on the code intent of the first component, the identified characteristics of the objects, and the natural language text retrieved from the first database;) [Sikand, 0028] comprising a plurality of nodes representing one or more journey steps for automated customer engagement workflow (Sikand, retrieve from the first database natural language text describing one or more components of the software development project having one or more dependencies related to the first component that have been used to generate computer code stored in the second database using the artificial intelligence code generator;) [Sikand, 0068; 0065-0067];
modifying a set of the plurality of nodes in the produced user journey definition to conform with a set of predefined schema rules (Sikand, The final textual prompt may be generated following further processing. This may include generating a rough draft of the prompt intent by incorporating information from the dependency sub-graph (subsets of components), component name and/or related high level functional requirements.) [Sikand, 0157]; and
outputting the user journey definition to a campaign orchestration system for execution
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[Sikand, Fig. 3 and associated disclosure].
Regarding claim 9 and represented claims 2 and 16, as combined and under the same rationale as above, Sikand in view of AI-Watch-Tower teaches system and method, wherein the modifying of the set of the plurality of nodes comprises:
parsing the produced user journey definition into a data structure having a predefined format [see at least AI-Watch-Tower, page 4 – 6 and associated transcript];
traversing the data structure to identify a set of nodes violating the set of predefined schema rules [Sikand, The high level/functional requirements may be aligned with the dependency graph entities (components). To align high level requirements to various graph parts, sentence embedding (e.g., using ROBERTa) may be generated for the requirements and a similarity metric may be calculated (e.g., using cosine similarity) to match to the closest entities or components.) [Sikand, 0151]; and
regenerating the set of nodes violating the set of predefined schema rules [Sikand, The final prompt is generated by paraphrasing this rough summary text, while including all the necessary information. This may be achieved using a natural language processor.) [Sikand, 0148].
Claims 11 – 13, 4 – 6 and 18 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Sikand et al. US Publication 2025/0055028 in view of AI-Watch-Tower published YouTube Video “ChatGPT – How To Use To Create A Marketing Strategy” and Archived web pages of “www.Twilio.com” labeled as “Information on Twilio.com hereinafter referred to as Twilio.
Regarding claim 11 and representative claims 4 and 18, as combined and under the same rationale as above, Sikand in view of AI-Watch-Tower does not teach monitoring execution of the outputted journey. However, Twilio teaches monitoring of workflow by verifying that SMS was sent 2-minutes after the email was sent to a recipient) [see at least, Twilio page 21 – 23 and associated transcript].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Sikand in view of AI-Watch-Tower by adopting teachings of Twilio to verify the the generated code is functioning as desired.
Regarding claim 12 and representative claims 5 and 19, as combined and under the same rationale as above, Sikand in view of AI-Watch-Tower teaches system and method, wherein the context data comprises at least one of customer profile attributes, behavioral event data, transaction event data, engagement event data, predicted next purchase data, or audience membership data (Twilio, customer has an appointment (e.g., an event data)) [see at least, Twilio page 21 – 23 and associated transcript].
Regarding claim 13 and represented claims 6 and 20, as combined and under the same rationale as above, Sikand in view of AI-Watch-Tower and Twilio teaches system and method, wherein the generating of the user journey definition comprises:
generating one or more audience criteria steps personalized based on extracted context data [see at least AI-Watch-Tower, page 4 – 6 and associated transcript];
generating one or more message steps comprising selected message templates for different channels [see at least, Twilio page 21 – 23 and associated transcript]; and
generating one or more wait steps between the one or more message steps (Twilio, send SMS 2-minutes after email is sent to the recipient) [see at least, Twilio page 21 – 23 and associated transcript].
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Sikand et al. US Publication 2025/0055028 in view of AI-Watch-Tower published YouTube Video “ChatGPT – How To Use To Create A Marketing Strategy” and Akinwande Komalfe published article “Retraining Model During Deployment: continuous Training and Continuous Testing”.
Regarding claim 14 and representative claim 7, as responded to above, Sikand in view of AI-Watch-Tower does not explicitly teach retraining of machine learning model based on collected user engagement data. However, Komalfe teaches periodic training is the most intuitive and straightforward approach to retraining your model. By choosing an interval for retraining your model, you have an idea of when your retraining pipeline will be triggered. It depends on how frequently your training data gets updated. Retraining your model based on an interval only makes sense if it aligns with your business use case. Otherwise, the selection of a random period will lead to complexities and might even give you a worse model than the previous model [Komalfe, page 10]. Komalfe further recites “Continual learning is also called lifelong learning. This type of learning algorithm tries to mimic human learning. A machine learning algorithm is applied to a dataset to produce a model without considering any previously learned knowledge and as new data is made available, continual learning algorithm makes small consistent updates to the machine learning model over time.” [Komalfe, page 14].
Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to modify Sikand in view of AI-Watch-Tower by adopting teachings of Komalfe to ensure that the quality of your model in production is up to data.
as combined and under the same rationale as above, Sikand in view of Sikand in view of AI-Watch-Tower and Komalfe teaches system and method further comprising:
collecting user engagement data during execution of the outputted journey; and retraining the transformer-based generative language model based on the collected user engagement data [Komalfe, page 10, 14, 3].
Response to Arguments
Applicant's argument are directed to amended claimed invention. While performing an updated search considering amended claimed invention, additional prior art was found that is cited in this office action.
Therefore, applicant’s arguments are moot under new grounds of rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wikipedia published article “Generative pre-trained transformer” that OpenAI GPT fodels are artificial neural networks that are based on the transformer architecture.
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 Naresh Vig whose telephone number is (571)272-6810. The examiner can normally be reached Mon-Fri 06:30a - 04:00p.
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/NARESH VIG/Primary Examiner, Art Unit 3622
February 25, 2026