DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claim Rejections - 35 USC § 101
3. Non-Statutory (Directed to a Judicial Exception without an Inventive Concept/Significantly More)
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 the claimed invention is directed to an abstract idea without significantly more.
Step 1
The current claims fall within one of the four statutory categories of invention (MPEP 2106.03).
Step 2A [Wingdings font/0xE0] Prong One:
The current claims recite a judicial exception, namely an abstract idea, as shown below:
— Considering each of claims 1, 16 and 19 as representative claims, the following claimed limitations recite an abstract idea:
define one or more training objectives for a user, wherein the one or more training objectives include skill-development goals, compliance targets, or knowledge-assessment metrics;
refine the one or more training objectives by proposing recommended training topics or identifying common pitfalls;
generate one or more scenarios and test cases aligned with the refined training objectives, each scenario or test case reflecting a real-world situation relevant to a role or experience of the user;
personalize the one or more scenarios and test cases based on user data, wherein the personalizing is based on the role, the experience, proficiency, or past interactions to tailor each scenario; and
render the personalized content in a test environment that provides interactive elements selected from text-based [materials], quizzes components, thereby enabling the user to engage with and respond to the personalized content.
Thus, the limitations identified above recite an abstract idea since the limitations correspond to certain methods of organizing human activity, and/or mental processes, which are part of the enumerated groupings of abstract ideas identified according to the current eligibility standard (see MPEP 2106.04(a)). For instance, the current claims correspond to managing personal behavior, wherein a training objective that includes one or more attributes (e.g., skill-development goals, compliance targets, or knowledge assessment metrics) is defined for a user; the training objective is also refined based on recommended training topics or identified common pitfalls; and thereby, a training scenario(s) and test cases aligned with the above refined training objective is generated, including personalizing or tailoring the training scenario(s) and test cases based on user data (e.g., the user’s role, experience, proficiency or past interaction); and accordingly, the personalized content generated above is presented to the user in an environment that provides one more interactive elements—such as, text-based materials, quizzes, etc., so that the user engages and responds to the personalized content, etc.
Similarly, given the limitations that recites the process of: defining for a user a training objective that includes skill-development goals, compliance targets, or knowledge-assessment metrics; refining the training objective by querying a model that proposes recommended training topics or identifies common pitfalls; generating a scenario(s) and test cases aligned with the training objective, etc., the current claims also correspond to mental processes—i.e., limitations that can practically be performed in the human mind (and/or using a pen and paper).
Step 2A [Wingdings font/0xE0] Prong Two:
The claims recite additional element(s), wherein a computer system, which executes a generative artificial intelligence, is utilized to—as a tool—to facilitate the recited functions/steps with respect to: defining and refining a training objective for a user based on the analysis of collected information (e.g., “defining one or more training objectives for a user, wherein the one or more training objectives include skill-development goals, compliance targets, or knowledge-assessment metrics; refining the one or more training objectives by querying a large language model (LLM), wherein the LLM proposes recommended training topics or identifies common pitfalls based on its trained parameters”); generating a scenario(s) and test cases based on the refined training objective above (e.g., “generating one or more scenarios and test cases aligned with the refined training objectives, each scenario or test case reflecting a real-world situation relevant to a role or experience of the user”); personalizing the scenario(s) and test cases to generate personalized content for the user (e.g., “personalizing the one or more scenarios and test cases based on user data, wherein the personalizing is based on the role, the experience, proficiency, or past interactions to tailor each scenario”); and presenting the personalized content in an environment that involves interactive elements (e.g., “rendering the personalized content in a test environment that provides interactive elements selected from text-based modules, video simulations, quizzes, or extended reality components, thereby enabling the user to engage with and respond to the personalized content”), etc.
However, the claimed additional element(s) fail to integrate the abstract idea into a patent-eligible practical application since the additional element(s) are utilized merely as a tool to facilitate the abstract idea. Accordingly, when each of the claims is considered as a whole, the additional element(s) fail to impose meaningful limits on practicing the abstract idea. For instance, when each of the claims is considered as a whole, none of the claims provides an improvement over the relevant existing technology.
The observations above confirm that the claims are indeed directed to an abstract idea.
Step 2B:
Accordingly, when the claim(s) is considered as a whole (i.e., considering all claim elements both individually and in combination), the claimed additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to “significantly more” than the abstract idea itself (also see MPEP 2106). The claimed additional elements are directed to conventional computer elements, which are serving merely to perform conventional computer functions.
Accordingly, when each of the current claims is considered as a whole (e.g., see the discussion under Prong Two above regarding such consideration of the claim as a whole), none of the claims recites an element—or a combination of elements—directed to an inventive concept.
It is also worth noting—per the original disclosure—that the claimed invention is directed to a conventional and generic arrangement of the additional elements. For instance, the specification describes a system that comprises one or more commercially available computing devices (e.g., a laptop computer, a desktop computer, a smartphone, etc.), wherein the device(s) communicates—via the conventional network—with at least one online server ([0021] to [0029]); and thereby, the system allows a user(s) to access and interact with content items—e.g., educational content items—that an online service provider is generating based on objectives and/or goals of an entity—i.e., a company, a government agency, an educational institution, etc. (see [0030] to [0038]).
Although the specification describes the use of generative AI and/or a Large Language Model (LLM), it does not provide any description regarding any new or advanced AI or LLM implementation. Instead, the description as a whole is directed to the use of such already developed models to facilitate the process of customizing and presenting pertinent content items to a user(s), based on the analysis of data gathered regarding the user(s), etc.
The above observation confirms that the current claimed invention fails to amount to “significantly more” than an abstract idea.
It is worth noting that the above analysis already encompasses each of the current dependent claims (i.e., claims 2-15, 17, 18 and 20). Particularly, each of the dependent claims also fails to amount to “significantly more” than the abstract idea since each dependent claim is directed to a further abstract idea, and/or a further conventional computer element(s) utilized to facilitate the abstract idea.
Accordingly, the findings above demonstrate that none of the claims implements an element—or a combination of elements—directed to an inventive concept (e.g., none of the current claims is reciting an element—or a combination of elements—that provides a technological improvement over the existing/conventional technology).
Claim Rejections - 35 USC § 112
4. The following is a quotation of 35 U.S.C.112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C.112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
● Claims 1-20 are rejected under 35 U.S.C.112(b), or second paragraph (pre-AIA ), as being indefinite for failing to particularly point out and distinctly claim the subject matter which applicant regards as the invention.
Each of claims 1, 16 and 19 recites, “rendering the personalized content in a test environment” (see the last paragraph per each of claims 1, 16 and 19; emphasis added).
However, given the lack of proper antecedent basis, it is unclear what is encompassed per the term “the personalized content”. Although the preamble of each claim appears to recite an alternative term, “personalized educational or training content”, it does not appear to provide sufficient support given the alternative terms used in the expression. It is further worth noting that the claims do not necessarily signify whether “the personalized content” is content generated based on the personalized “one or more scenarios and test cases”.
Thus, at least for the reason above, the current claims (claims 1-20) are ambiguous.
Applicant is also recommended to evaluate each of the current claims and make appropriate corrections if additional discrepancies are discovered.
Claim Rejections - 35 USC § 103
5. 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 of this title, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating
obviousness or nonobviousness.
Note that the one or more citations (paragraphs or columns) presented in this office action regarding the teaching of a cited reference(s) are exemplary only. Accordingly, such citation(s) are not intended to limit/restrict the teaching of the reference(s) to the cited portion(s) only. Applicant is required to evaluate the entire disclosure of each reference; such as additional portions that teach or suggest the claimed limitations.
● Claims 1-10 and 12-20 are rejected under 35 U.S.C.103 as being unpatentable over Fu 2025/0182639 in view of Wolochow 2024/0370804.
Regarding claim 1, Fu teaches the following claimed limitations: a computer-implemented method for delivering personalized educational or training content using a generative artificial intelligence (GenAI) education platform ([0024]; [0032]; [0042]: e.g., a computer-based system/method that generates educational materials for learners; wherein the system incorporates various modules, including (i) a dynamic course generation module that implements a generative AI for generating educational content based on data gathered from multiple sources, and (ii) a personalization module that personalizes the educational content to the learner based on data analyzed regarding the learner), the method comprising steps of: defining one or more training objectives for a user, wherein the one or more training objectives include skill-development goals, compliance targets, or knowledge-assessment metrics ([0049]; [0050]: e.g., the instructor defines training objectives; wherein the instructor provides course syllabus, learning objectives, etc. In this regard, the course syllabus or learning objectives already encompass skill-development goals since they specify one or more courses that the learner is required to complete); generating one or more scenarios and test cases aligned with refined training objectives, each scenario or test case reflecting a real-world situation relevant to a role or experience of the user; personalizing the one or more scenarios and test cases based on user data, wherein the personalizing is based on the role, the experience, proficiency, or past interactions to tailor each scenario ([0052] to [0055]; [0065]: e.g., the system already incorporates at least one AI model, which the personalized learning and adaptive simulation engine executes; wherein the AI model is tuned/trained based on datasets acquired from the data acquisition module; and the data acquisition module further refines the data—namely, the course parameters generated above, per [0049], which indicates the refining of training objectives; and furthermore, the personalized learning and adaptive simulation engine, via at least one generative AI model it is executing, generates course materials based on the course specification, objectives and model training/tuning above. Thus, the course materials generated above correspond to the one or more scenarios aligned with refined training objectives. In this regard, the course materials, which correspond to the scenarios, already reflect real-world situation relevant to a role or experience of the user; the role being a student; and accordingly, the personalization of the scenarios is accomplished based on at least user data, which indicates the role of the user as a student. Note also that the course materials being generated already include one or more test cases since the course specification/objectives already include instructor fix metrics); and rendering the personalized content in a test environment that provides interactive elements selected from text-based modules, video simulations, quizzes, or extended reality components, thereby enabling the user to engage with and respond to the personalized content ([0055]; [0089]; [0090]; [0133]: e.g., the personalized educational material/content generated above is presented to the user according to one or more modalities, including: text, audio, video, etc., and accordingly, it is understood that the content is presented to the learner via a computer-based environment; and wherein the learner interacts with the personalized content using one or more input/output devices. Note that the term “test environment” is merely indicating the name or label assigned to the environment).
Fu does not expressly teach that the model above is a large language model (LLM), which the system queries to refine the one or more training; wherein the LLM proposes the recommended training topics or identifies common pitfalls based on its trained parameters.
However, Wolochow teaches a system/method for building educational courses (see [0002]); wherein the system implements a Large Language Model (LLM) that can be instructed to perform various tasks, including (i) generating an outline for a course based on parameters that the author specified ([0048]), generating search criteria for finding courses in online repositories ([0054]); and accordingly, once the course author evaluated one or more appropriate course sources (e.g., one or more online databases, etc.), the LLM is prompted to generate—from the sources—a recommendation regarding one or more course content items; thereby, the LLM provides the generated recommendation ([0062] to [0070]).
Accordingly, given the above teaching, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Fu in view of Wolochow; by upgrading the system’s algorithm, wherein a Large Language Model (LLM), which is trained to recommend content, is incorporated as one of the system’s models; and the instructor further provides—to the LLM—the course relevant data; such as, the course syllabus, learning objectives, etc., and thereby, the LLM generates one or more recommended course or training topics based on the course relevant data; so that, besides verifying the consistency of the course materials being recommended, the modified system helps the learner to engage with course materials that are more pertinent to the learner.
Fu in view of Wolochow teaches the claimed limitations as discussed above. Fu further teaches:
Regarding claim 2, storing the user data, including personally identifiable information (PII) and historical performance logs, in a secure local data store that prevents unauthorized access or exfiltration of the user data ([0041]; [0060]; [0078]; [0103]: e.g., the system already gathers and stores various attributes regarding the user, including: historical performance data regarding the learner, the learner’s behavior, the learner’s preferences, the habit of the individual learner, etc., and thus, it is understood that the system already acquires at least one personally identifiable information—such as, the name of the learner, which the system uses to associate the attributes above with the specific learner. Furthermore, the system implements one of more data safeguarding mechanisms—including data encrytion and access control—in order to secure the leaner’s data; and this indeed prevents unauthorized access or exfiltration of the user data);
Regarding claim 3, prior to the storing, collecting user data by gathering information from at least one of: user activity logs within the GenAI education platform; external enterprise or academic directories; authorized monitoring of user browsing history; or previous training records, such that the user data is analyzed to identify risk profiles, learning preferences, or knowledge gaps ([0041]; [0060]: e.g., the system gathers various data related to the learner—such as, historic learner performance or completion data, learner’s preference, learner’s strengths and areas that require improvement, etc. Thus, the system collects user data by gathering at least previous training records, such that the user data is analyzed to identify risk profiles, learning preferences, or knowledge gaps, etc.).
Regarding claim 4, Fu in view of Wolochow teaches the claimed limitations as discussed above per claim 2.
Fu further teaches, the secure local data store is configured to encrypt all stored PII, thereby ensuring that sensitive user information remains protected in compliance with data protection regulations and is not inadvertently exposed to external servers or third parties ([0103]: e.g., the system already safeguards/secures the learner’s data by implementing a comprehensive data encryption; and furthermore, the usage of the learner’s data is controlled based on established permissions);
Although Fu does not expressly indicate that the system also partitions the stored PII, the Office takes an Official Notice that it is an old and well-known practice in the art to partition at least a portion of a storage device to isolate some critical data from the general content items; so that encrypted critical data (e.g., bank information, medical records, etc.) is separately stored in the partitioned portion.
Accordingly, given such old and well-known practice in the art, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to further modify Fu’s system; for example, by incorporating at least one additional scheme—namely, partitioning one or more portions of the storage device to separately store the encrypted learner’s data; wherein one or more access rights are also established with respect to accessing the partitioned portion of the database; so that the learner’s data would have enhanced safeguards.
Regarding claim 5, Fu in view of Wolochow teaches the claimed limitations as discussed above per claim 1.
Wolochow further teaches, supplying additional contextual materials comprising at least one of recent news articles, internal documents, or domain-specific data to the LLM to enhance realism, relevance, or accuracy of the one or more scenarios and test cases ([0046]; [0047]: e.g., the course author provides one or more existing content items; such as, text files, PDF files, graphical files etc., and wherein the LLM is prompted to generate a preliminary outline based on the information the author provided above. Accordingly, the above indicates the process of supplying additional contextual materials comprising at least one of internal documents to the LLM to enhance realism, relevance, or accuracy of the one or more scenarios and test cases).
Accordingly, given the above teaching, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to further modify the invention of Fu in view of Wolochow; for example, by allowing one or more authorized users (e.g., one or more teachers, etc.) to provide one or mor relevant content items (e.g., one or more notes that the teachers prepared when teaching students various subjects, etc.); wherein the content items gathered from the authorized users is presented as dataset that the LLM accesses; so that the LLM would use this additional data when customizing one or more educational content items for the learner; and this helps the system to generate a more refined course content items to the learner.
Fu in view of Wolochow teaches the claimed limitations as discussed above per claim 1. Fu further teaches:
Regarding claim 6, steps of claim 1 further include one of: providing real-time feedback to the user as the user engages with the personalized content; and storing the feedback for future iterations of the personalizing, wherein the feedback identifies correct or incorrect decisions, highlights missed indicators, and offers recommended follow-up resources ([0061]; [0063]; [0066]: e.g., the system already actively interacts with the learner; such as, the AI instructor provides—in real-time—a response or answer to a question that the learner is asking; including a scenario where the learner is presented with supplemental educational materials when learner incorrectly answers a question);
Regarding claim 7, the real-time feedback comprises contextual guides, example solutions, or best-practice references displayed upon detecting user errors, prompting the user to review and correct identified deficiencies ([0061]; [0063]; [0066]: e.g., as already pointed out with respect to claim 6 above that the type of feedback that the system provides to the leaner includes—for example, presenting the learner with supplemental educational materials when learner incorrectly answers a question. The above indicates that the real-time feedback comprises contextual guides, example solutions, or best-practice references, which is displayed upon detecting user errors, prompting the user to review and correct identified deficiencies. It is also worth noting that the content—such as the theme—of the feedback does not patentably distinguish the claim from the prior art since the content is merely nonfunctional descriptive matter);
Regarding claim 8, iteratively updating subsequent scenarios or test cases based on at least one of performance metrics for the user, behavioral data, or feedback logs, to continuously address emerging gaps or vulnerabilities ([0040] to [0043]; [0060]; [0062]; [0064]: e.g., the system adapts the generation of the educational materials based on various attributes of the learner, including: the learner’s performance data/score, the learner’s history in completing a given course, the leaner’s evolving preference, etc. Thus, the system indeed iteratively updates subsequent scenarios or test cases based on at least one of performance metrics for the user, behavioral data, or feedback logs, to continuously address emerging gaps or vulnerabilities);
Regarding claim 9, the LLM dynamically adjusts complexity or difficulty of each scenario or test case in response to real-time performance, so that a user demonstrating improved proficiency is presented with more advanced challenges, while a user requiring additional support receives more fundamental materials ([0041]; [0043]; [0060]; [0066]: e.g., the system dynamically adjusts complexity/difficulty of each scenario/test case in response to real-time performance since the system adapts the educational content, which it presents to the leaner, based on the learner’s performance—such as: “suggesting the next level of a subject when the learner has demonstrated competence in the current level of the subject”; generating supplemental instructional material to the learner when the learner incorrectly answers the current question, etc. It is worth noting that the system already incorporates LLM given the modification applied to the system based on the teaching gleaned from Wolochow, as discussed with respect to claim 1);
Regarding claim 10, the refining the one or more training objectives via the LLM further comprises incorporating stakeholder feedback from at least one of human resources managers, security teams, academic faculty, or subject matter experts (see [0049]; [0050]; [0091]; [0119]: e.g., the system already utilizes data presented from one or more users, including one or more instructors—i.e., subject matter experts, to adapt the educational materials it is generating to the learner. Of course, here also the system already incorporates LLM given the modification applied to the system based on the teaching gleaned from Wolochow, as discussed with respect to claim 1. Accordingly, the system does refine the one or more training objectives via the LLM further comprises incorporating stakeholder feedback from at least one of human resources managers, security teams, academic faculty, or subject matter experts).
Regarding claim 12, Fu in view of Wolochow teaches the claimed limitations as discussed above per claim 1.
The limitation, “the personalizing the one or more scenarios includes simulating at least one context including: a phishing or social-engineering attempt reflecting a communication style of the user; a high-stakes examination environment replicating a professional certification; or a compliance scenario targeted to a regulatory jurisdiction or departmental function for the user”, is referring merely to the content (e.g., the topic) of the simulation/scenario presented to the user. However, such content does not patentably distinguish the claim from the prior art since the content is directed merely to nonfunctional descriptive matter.
Nevertheless, Fu’s system already provides the learner with one or more simulation scenarios, which allow the learner to “engage in realistic, practice-based scenarios designed to build and assess specific skills in real-time”, including a virtual/augmented reality simulation that allows the learner to participate in one or more realistic activities ([0023]; [0061]; [0102]).
Accordingly, given the above teaching, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to further modify Fu’s system; for example, by addition additional simulation scenarios that relate to the particular job or task (e.g., job/task related to painting, job/task related to a given department, etc.) that the learner is expected to perform once completing the training, etc., so that the leaner would have additional opportunity to further expand his/her specific skills.
Fu in view of Wolochow teaches the claimed limitations as discussed above per claim 1. Fu further teaches:
Regarding claim 13, the rendering the personalized content further comprises generating on-demand multimedia assets, including at least one of: generative Al-based video segments replicating familiar voices or likenesses; branching simulations prompting user decisions or role-play; or gamified exercises awarding digital badges or achievement points ([0061]; [0063]: e.g., as part of adapting the educational content being presented to the learner, the system simulates an AI instructor that mimics the style of the instructor; wherein the AI instructor not only presents training material to the learner, but also engages the learner in one or more interactive activities, including answering questions that the learner is providing, etc. Thus, the process of rendering the personalized content further comprises generating on-demand multimedia assets, including at least generative Al-based video segments replicating familiar voices or likenesses);
Regarding claim 14, the automatically detecting a user's vulnerabilities or knowledge gaps by analyzing historical user data; and providing supplementary learning modules or corrective feedback tailored to address the user's vulnerabilities or knowledge gaps ([0041]; [0043]; [0066]: e.g., based on analyzing the learner’s historical data, the system dynamically adapts the lesson materials it presents to the learner; such as, anticipating—based on the user’s historical data—a question(s) that the learner is likely to ask; and thereby, generating relevant content material to address the question(s). In addition, the system also provides, in response to the question that the learner answered incorrectly, pertinent lesson materials to question that the learner missed, etc. Thus, the system already automatically detects a user's vulnerabilities or knowledge gaps by analyzing historical user data; and provided supplementary learning modules or corrective feedback tailored to address the user's vulnerabilities or knowledge gaps);
Regarding claim 15, the personalizing is for one or more groups of individuals, entire organizations, or subsets of users that share particular attributes or commonalities (e.g., the system is generating personalized educational material to one or more learners; and thus, the personalization is done at least for one or more groups of individuals, or subsets of users that share particular attributes, etc.).
Regarding each of claims 16 and 19, Fu teaches the following claimed limitations: a generative artificial intelligence (GenAI) education platform (or a non-transitory computer-readable medium, per claim 19) for delivering personalized educational or training content, the GenAI education platform comprising: one or more processors; and memory (or the non-transitory computer-readable medium, per claim 19) storing instructions ([0024]; [0032]; [0042]: e.g., a computer-based system that generates educational materials for learners; wherein the system incorporates various modules, including (i) a dynamic course generation module that implements a generative AI for generating educational content based on data gathered from multiple sources, and (ii) a personalization module that personalizes the educational content to the learner based on data analyzed regarding the learner. It is understood that the computer-based system already comprises basic computer components—such as, a processor, a memory or a non-transitory computer readable medium, etc.) that, when executed, cause the one or more processors to define one or more training objectives for a user, wherein the one or more training objectives include skill-development goals, compliance targets, or knowledge-assessment metrics ([0049]; [0050]: e.g., the instructor defines training objectives—such as, providing course syllabus, learning objectives, etc. In this regard, the course syllabus or learning objectives already encompass skill-development goals since they specify one or more courses that the learner is required to complete); generate one or more scenarios and test cases aligned with refined training objectives, each scenario or test case reflecting a real-world situation relevant to a role or experience of the user; personalize the one or more scenarios and test cases based on user data, wherein the personalizing is based on the role, the experience, proficiency, or past interactions to tailor each scenario ([0052] to [0055]; [0065]: e.g., the system already incorporates at least one AI model, which the personalized learning and adaptive simulation engine executes; wherein the AI model is tuned/trained based on datasets acquired from the data acquisition module; and the data acquisition module further refines the data—namely, the course parameters generated above, per [0049], which indicates the refining of training objectives; and furthermore, the personalized learning and adaptive simulation engine, via at least one generative AI model it is executing, generates course materials based on the course specification, objectives and model training/tuning above. Thus, the course materials generated above correspond to the one or more scenarios aligned with refined training objectives. In this regard, the course materials, which correspond to the scenarios, already reflect real-world situation relevant to a role or experience of the user; the role being a student; and accordingly, the personalization of the scenarios is accomplished based on at least user data, which indicates the role of the user as a student. Note also that the course materials being generated already include one or more test cases since the course specification/objectives already include instructor fix metrics); and render the personalized content in a test environment that provides interactive elements selected from text-based modules, video simulations, quizzes, or extended reality components, thereby enabling the user to engage with and respond to the personalized content ([0055]; [0089]; [0090]; [0133]: e.g., the personalized educational material/content generated above is presented to the user according to one or more modalities, including: text, audio, video, etc., and accordingly, it is understood that the content is presented to the learner via a computer-based environment; and wherein the learner interacts with the personalized content using one or more input/output devices. Note that the term “test environment” is merely indicating the name or label assigned to the environment).
Fu does not expressly teach that the model above is a large language model (LLM), which the system queries to refine the one or more training; wherein the LLM proposes the recommended training topics or identifies common pitfalls based on its trained parameters.
However, Wolochow teaches a system/method for building educational courses (see [0002]); wherein the system implements a Large Language Model (LLM) that can be instructed to perform various tasks, including (i) generating an outline for a course based on parameters that the author specified ([0048]), generating search criteria for finding courses in online repositories ([0054]); and accordingly, once the course author evaluated one or more appropriate course sources (e.g., one or more online databases, etc.), the LLM is prompted to generate—from the sources—a recommendation regarding one or more course content items; thereby, the LLM provides the generated recommendation ([0062] to [0070]).
Accordingly, given the above teaching, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Fu in view of Wolochow; by upgrading the system’s algorithm, wherein a Large Language Model (LLM), which is trained to recommend content, is incorporated as one of the system’s models; and the instructor further provides—to the LLM—the course relevant data; such as, the course syllabus, learning objectives, etc., and thereby, the LLM generates one or more recommended course or training topics based on the course relevant data; so that, besides verifying the consistency of the course materials being recommended, the modified system helps the learner to engage with course materials that are more pertinent to the learner.
Regarding each of claims 17 and 20, Fu in view of Wolochow teaches the claimed limitations as discussed above per claims 16 and 19 respectively.
Wolochow further teaches, supply additional contextual materials comprising at least one of recent news articles, internal documents, or domain-specific data to the LLM to enhance realism, relevance, or accuracy of the one or more scenarios and test cases ([0046]; [0047]: e.g., the course author provides one or more existing content items; such as, text files, PDF files, graphical files etc., and wherein the LLM is prompted to generate a preliminary outline based on the information the author provided above. Accordingly, the above indicates the process of supplying additional contextual materials comprising at least one of internal documents to the LLM to enhance realism, relevance, or accuracy of the one or more scenarios and test cases).
Accordingly, given the above teaching, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to further modify the invention of Fu in view of Wolochow; for example, by allowing one or more authorized users (e.g., one or more teachers, etc.) to provide one or mor relevant content items (e.g., one or more notes that the teachers prepared when teaching students various subjects, etc.); wherein the content items gathered from the authorized users is presented as dataset that the LLM accesses; so that the LLM would use this additional data when customizing one or more educational content items for the learner; and this helps the system to generate a more refined course content items to the learner.
Regarding claim 18, Fu in view of Wolochow teaches the claimed limitations as discussed above per claim 16.
Fu further teaches, iteratively update subsequent scenarios or test cases based on at least one of performance metrics for the user, behavioral data, or feedback logs, to continuously address emerging gaps or vulnerabilities ([0040] to [0043]; [0060]; [0062]; [0064]: e.g., the system adapts the generation of the educational materials based on various attributes of the learner, including: the learner’s performance data/score, the learner’s history in completing a given course, the leaner’s evolving preference, etc. Thus, the system indeed iteratively updates subsequent scenarios or test cases based on at least one of performance metrics for the user, behavioral data, or feedback logs, to continuously address emerging gaps or vulnerabilities).
● Claim 11 is rejected under 35 U.S.C.103 as being unpatentable over Fu 2025/0182639 in view of Wolochow 2024/0370804 and Moreno 2017/0046971.
Regarding claim 11, Fu in view of Wolochow teaches the claimed limitations as discussed above per claim 1.
Fu does not expressly teach, leveraging role-based access controls to restrict or unlock particular scenarios or test cases for users holding specific roles, thereby ensuring alignment with organizational policies and confidentiality requirements.
However, Moreno teaches system/method that provides training materials to a user—such as a student ([0002]; [0011]); wherein the system provides different users with different permission levels, including (i) an administrator permission level that allows an administrator to manage training materials—such as, adjusting training/lesson content materials, (ii) a user permission level that allows a user (e.g., a student) with limited access control ([0167]).
Accordingly, given the above teaching, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to further modify Fu in view of Moreno; for example, by setting one or more permission levels to each of the one or more users based on the specific role of each user—such as, providing: (i) the instructor with a high permission level that allows the instructor to make one or more changes (e.g., modifications, etc.) to one or more of the lesson materials being presented, (ii) the learner with a low permission level that allows the learner only to access and interact with one or more of the lesson materials, etc., so that the system would have additional option to safeguard the data from an unauthorized user who may accidentally—or intentionally—attempt to modify the lesson materials, etc.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRUK A GEBREMICHAEL whose telephone number is (571) 270-3079. The examiner can normally be reached from 7:00 AM - 3:00 PM.
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, PETER VASAT can be reached on (571) 270-7625. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/BRUK A GEBREMICHAEL/Primary Examiner, Art Unit 3715
Prosecution Insights