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 .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on May 6, 2026 has been entered.
Status of Claims
• This action is in reply to the RCE filed on May 6, 2026.
• Claims 1, 4-8, 11, 14-15, and 17 have been amended and are hereby entered.
• Claim 21 has been added.
• Claims 3 has been canceled.
• Claims 1-2 and 4-21 are currently pending and have been examined.
• This action is made Non-FINAL.
Response to Arguments
Applicant’s arguments filed May 6, 2026 have been fully considered but they are not persuasive.
The Examiner is withdrawing the 35 USC § 103 rejections due to Applicant’s amendments.
Applicant’s arguments with respect to 35 USC § 101 have been fully considered and are not persuasive.
Regarding Applicant’s argument on pages 9-10, that the claims do not recite an abstract idea, the Examiner respectfully disagrees. Applicant further argues on pages 9-10 that the claims are rooted in technology. The argument is not persuasive. As indicated in the 35 USC § 101 rejection below, the claimed inventions allows for allowing an instructor user to create an education course for learner users. The Specification at [0013] states:
“Existing course structures are typically static, meaning they don't evolve based on learner feedback or performance data. This rigidity can result in content becoming outdated or less effective over time. Updating course content has traditionally been a manual and time-consuming process, often requiring significant effort from educators and instructional designers. This process can be inefficient, and slow or unable to respond to emerging educational needs. While some level of personalization is possible in modern educational tools, the tools often lack depth and real-time adaptability, limiting their effectiveness in addressing individual learning styles and needs. Many educational platforms collect vast amounts of data on student engagement and performance, but this data is often underutilized in informing course design and adaptation of content to individual learning styles, preferences, or approaches. Accordingly, some technical challenges in the field of course authoring software and systems include implementation of one-size-fits-all course design, static course structures, manual course update processes, limited personalization ability (if at all), ineffective utilization of data, and the like.”
The Specification and claims focus on an improvement to the process of instructors creating courses for students, which is managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions, which falls within the category of Certain Methods of Organizing Human Activity and therefore is an abstract idea.
Regarding Applicant’s arguments on pages 10-11, that the claims integrate a practical application, the Examiner respectfully disagrees. Under the Patent Subject Matter Eligibility analysis, Step 2A, prong two, integration into a practical application requires an additional element(s) or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. Limitations that are not indicative of integration into a practical application are those that generally link the use of the judicial exception into a particular technological environment or field of use-see MPEP 2106.05(h). Here the claims recite a system for implementing adaptive artificial intelligence-based course template generation, the system comprising: a processing system including one or more electronic processors, the processing system configured to perform claim functions; a processing system including one or more electronic processors; a non-transitory, computer-readable medium storing instructions that, when executed by a processing system including one or more electronic processors, perform a set of functions, the set of functions comprising claim functions; a retriever-augmented generation (RAG) model; an artificial intelligence (AI) engine; one or more databases; a communication network; a client device; display via a graphical user interface; fine-tune the Al engine by training at least one machine learning model of the Al engine using at least a second portion of the feedback data such that they amount to no more than generally linking the use of the judicial exception to a particular technological environment or field of use (e.g., a computer network) (see MPEP 2106.05(h)).
Furthermore, and in response to Applicant’s arguments on pages 10-11 that the claims improve technology, in determining whether a claim integrates a judicial exception into a practical application, a determination is made of whether the claimed invention pertains to an improvement in the functioning of the computer itself or any other technology or technical field (i.e., a technological solution to a technological problem). Here, the claims recite generic computer components, i.e., a generic processor, a memory storing a computer program executable by the processor to perform the claimed method steps and system functions. The processor, memory and system are recited at a high level of generality and are recited as performing generic computer functions customarily used in computer applications.
Furthermore, the Specification describes a problem and improvement to a business or commercial process at least at [0013], describing underutilizing data informing course design and adaptation to individual learning styles, and addressing challenges of one-size-fits-all course design, static course structures, manual course update processes, limited personalization ability (if at all), and ineffective utilization of data.
Regarding Applicant’s arguments on pages 11-13 regarding improvements of how machine learning models operate, the arguments have been considered and are not persuasive. Turning to the second prong of the “directed to” test, regarding the machine learning limitations, the claims only generically requires a retriever-augmented generation (RAG) model; an artificial intelligence (AI) engine and fine-tune the Al engine by training at least one machine learning model of the Al engine using at least a second portion of the feedback data. These components are described in the Specification at a high level of generality. Applicant has failed to demonstrate how the generic recitations of a basic computer implementation/components integrates the judicial exception as to “impose a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception.” Guidance, 84 Fed. Reg. at 53. Applicant has not directed to any indication that the operations recited in the claims invoke any assertedly inventive programming, require any specialized computer hardware or other inventive computer components, i.e., a particular machine, or that the claimed invention is implemented using other than generic computer components to perform generic computer functions. See DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014) (“After Alice, there can remain no doubt: recitation of generic computer limitations does not make an otherwise ineligible claim patent-eligible.”).
Regarding Applicant’s arguments on page 13, that the claims recite significantly more than the abstract idea, the Examiner respectfully disagrees. The limitations are directed to an abstract idea and when determining if the claims are directed to significantly more, the additional limitations of the claims in addition to the abstract idea are analyzed. In the instant application, the additional elements of the claim include a system for implementing adaptive artificial intelligence-based course template generation, the system comprising: a processing system including one or more electronic processors, the processing system configured to perform claim functions; a processing system including one or more electronic processors; a non-transitory, computer-readable medium storing instructions that, when executed by a processing system including one or more electronic processors, perform a set of functions, the set of functions comprising claim functions; a retriever-augmented generation (RAG) model; an artificial intelligence (AI) engine; one or more databases; a communication network; a client device; display via a graphical user interface; fine-tune the Al engine by training at least one machine learning model of the Al engine using at least a second portion of the feedback data. The additional limitations, when considered both individually and in combination, do not affect an improvement to another technology or technological field; the claims do not amount to an improvement to the functioning of the computer itself; and the claims do not move beyond a general link of use of an abstract idea to a particular technological environment. Therefore, the claims merely amount to merely generally linking the use of the abstract idea to a particular technological environment or field of use (e.g., a computer network), and is considered to amount to nothing more than requiring a generic computer network to carry out the abstract idea itself. The specifics about the abstract idea do not overcome the rejection
Regarding Applicant’s arguments on page 13 that the claims do not attempt to preempt all ways of performing the abstract idea, the argument has been considered and is not persuasive. In response to this argument, it is noted, “while preemption may signal patent ineligible subject matter, the absence of complete preemption does not demonstrate patent eligibility.” Ariosa Diagnostics, Inc. v. Sequenom, Inc., 788 F.3d 1371, 1379 (Fed. Cir. 2015). The instant application is reviewed within the framework of the Revised Guidance which specifies and particularizes the Mayo/Alice framework.
Regarding Applicant’s arguments on page 13 that a finding of significantly more is supported by the novelty and non-obviousness of the claims, the argument has been considered and is not persuasive. As an initial matter, the claims are not found to be non-obvious, in view of the 103 rejection below. Furthermore in response to this argument, it is noted that the inventiveness inquiry of § 101 should not be confused with the separate novelty inquiry of § 102 or obviousness inquiry of § 103. A novel and non-obvious claim directed to a purely abstract idea is, nonetheless, patent ineligible. See Mayo, 566 U.S. at 79. “Even assuming that is true, it does not avoid the problem of abstractness.” Affinity Labs, 838 F.3d at 1263; Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716 (Fed. Cir. 2014) (“That some of [these] steps were not previously employed in this art is not enough—standing alone—to confer patent eligibility upon the claims ”). Indeed, “a claim for a new abstract idea is still an abstract idea.” Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016) (explaining that the search for an inventive concept under § 101 is distinct from demonstrating novelty under § 102).
The claims are not patent eligible.
For the reasons above, Applicant’s arguments are not persuasive.
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 and 4-21 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea without significantly more. Independent claims 1, 14, and 17 are directed to a system (claim 1), a method (claim 14), and an apparatus (claim 17). Therefore, on its face, each independent claim 1, 14, and 17 are directed to a statutory category of invention under Step 1 of the Patent Subject Matter Eligibility analysis (see MPEP 2106.03).
Under Step 2A, Prong One of the Patent Subject Matter Eligibility analysis (see MPEP 2106.04), claims 1, 14, and 17 recite, in part, a system, a method, and an apparatus of organizing human activity. Claim 1 recites a system for implementing adaptive artificial intelligence-based course template generation; receive a request to generate a first course template for a course; retrieve, with a model, user data that is contextually relevant to the request, wherein the retrieved user data comprises one or more of instructor user profiles and one or more learner user profiles associated with one or more learner users of the course; synthesize the user data to determine a set of patterns for the user data; generate a set of recommendations based on the set of patterns; generate, based on the set of recommendations, a first course template for the course, wherein the first course template is personalized for the one or more learner users; generate a first set of learning course content that adheres to the first course template for the course; and transmit the first set of learning course content for display as a learning course content; receive feedback data associated with the first set of learning course content, wherein the feedback data comprises learner user feedback data and instructor user feedback data; update, based on at least a first portion of the feedback data, the one or more learner user profiles; generate a second course template for the course based on the model retrieving the one or more updated learner user profiles; generate a second set of learning course content that adheres to the second course template for the course; and transmit the second set of learning course content for display.
Claim 14 recites a method of implementing adaptive artificial intelligence-based course template generation, the method comprising: retrieving, using a model, user data that is contextually relevant to a course, wherein the retrieved user data comprises one or more instructor user profiles and one or more learner user profiles associated with one or more learner users of the course; receiving feedback data associated with a first set of learning course content for the course, the first set of learning course content adhering to the first course template for the course, wherein the feedback data comprises learner user feedback data and instructor user feedback data; updating, based on at least a first portion of the feedback data, the one or more learner user profiles; generating a second course template for the course based on the model retrieving the one or more updated learner user profiles; generating a second set of learning course content that adheres to the second course template for the course; and transmitting the second set of learning course content for display as a learning course content.
Claim 17 recites receiving a request to generate a first course template for a course; retrieving, with a model, user data that is contextually relevant to the request, wherein the retrieved user data comprises one or more instructor profiles and one or more learner user profiles associated with one or more learner users of the course; generating, the first course template for the course, the first course template identifying a first set of learning course content that adheres to the first course template for the course, wherein the first course template is personalized for the one or more learner users; transmitting the first set of learning course content for display as a learning course content; receiving feedback data associated with the first set of learning course content, wherein the feedback data comprises learner user feedback data and instructor user feedback data; updating, based on at least a first portion of the feedback data, the one or more learner user profiles; generating, a second course template for the course based on the model retrieving one or more updated learner user profiles, the second course template identifying a second set of learning course content that adheres to the second course template for the course; and transmitting the second set of learning course content for display.
The limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers managing personal behavior or relationships of interactions between people (certain methods of organizing human activity), but for the recitation of generic computer components. The claims as a whole recite a method of organizing human activity. The claimed inventions allows for allowing an instructor user to create an education course for learner users, which is managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions. The mere nominal recitation of an AI engine does not take the claim out of the methods of organizing human activity grouping. Thus, the claims recite an abstract idea.
Under Step 2A, Prong Two of the Patent Subject Matter Eligibility analysis (see MPEP 2106.04), the judicial exception is not integrated into a practical application. In particular, the additional elements of a system for implementing adaptive artificial intelligence-based course template generation, the system comprising: a processing system including one or more electronic processors, the processing system configured to perform claim functions; a processing system including one or more electronic processors; a non-transitory, computer-readable medium storing instructions that, when executed by a processing system including one or more electronic processors, perform a set of functions, the set of functions comprising claim functions; a retriever-augmented generation (RAG) model; an artificial intelligence (AI) engine; one or more databases; a communication network; a client device; display via a graphical user interface; fine-tune the Al engine by training at least one machine learning model of the Al engine using at least a second portion of the feedback data are recited at a high-level of generality (i.e., as a generic computer components performing generic computer functions receiving a request to generate a course and generate recommendation and a course template based on the request user data and provide the course to a user) such that it amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use (e.g., a computer network).-see MPEP 2106.05(h).
Accordingly, the combination of the 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.
Under Step 2B of the Patent Subject Matter Eligibility analysis (see MPEP 2106.05), the claim(s) does/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 elements in the claims amount to no more than generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Generally linking the use of the judicial exception to a particular technological environment or field of use using generic computer components cannot provide an inventive concept.
The claims are not patent eligible.
The dependent claims have been given the full two part analysis including analyzing the additional limitations both individually and in combination. The dependent claim(s) when analyzed both individually and in combination are also held to be patent ineligible under 35 U.S.C. 101 because for the same reasoning as above and the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea Dependent 2, 4-13, 15-16, and 18-21 simply help to define the abstract idea. The additional limitations of the dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Viewing the claim limitations as an ordered combination does not add anything further than looking at the claim limitations individually. When viewed either individually, or as an ordered combination, the additional limitations do not amount to a claim as a whole that is significantly more than the abstract idea. Accordingly, claims 1-2 and 4-21 are ineligible.
Conclusion
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/RAVEN E YONO/Primary Examiner, Art Unit 3694
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