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 .
Claim Objections
Claim 16 is objected to because of the following informality: Claim 16 depends upon itself. For purposes of examination, Examiner is interpreting Claim 16 was intended to instead depend upon claim 15. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea(s) without significantly more.
Regarding Claim 1, analyzed as the representative claim:
[Step 1] Claim 1 recites “A method…” which falls within the “process” statutory category of invention under 35 U.S.C. § 101.
[Step 2A – Prong 1] Claim 1 recites “A method, comprising: employing a processor to execute computer executable instructions stored on a computer readable storage medium to implement the following acts: initiating a generative artificial intelligence (AI) conversation with a learner, wherein the generative AI conversation is facilitated by an AI large language model (LLM) and configured to elicit learning data from the learner; creating a learner profile of the user based on the learning data elicited from the learner; and continuously adapting at least one of the generative AI conversation or the learner profile using the AI LLM based on subsequent learning data elicited from the learner.” The bolded limitations, under their broadest reasonable interpretation, encompass mental processes (including observation, evaluation, judgment, and opinion) or methods of organizing human activity (managing personal behavior or relationships or interactions between people – including social activities, teaching, and following rules or instructions). That is, other than reciting that the method is performed by “a processor execut[ing] computer executable instructions stored on a computer readable storage medium” and using an AI large language model (LLM),” nothing in the claim precludes the steps from practically being performed by a human and/or in the human mind. Specifically, the claim encompasses an educator assessing a student’s knowledge and needs, creating an educational plan for that student, and making adjustments as needed. Accordingly, the claim recites an abstract idea(s).
[Step 2A – Prong 2] The judicial exception is not integrated into a practical application. Specifically, the claim recites the additional elements of a program executing on a computing device for performing the method steps and the use of an AI large language model, wherein the computing device, executed computer program, and LLM are each recited at a high level of generality and merely automate the initiating, eliciting, creating, and adapting steps. Therefore, this additional element amounts to no more than mere instructions to apply the exception using a generic computing device, which does not impose any meaningful limits on practicing the abstract idea(s). Additionally and/or alternatively, the limitation of eliciting learning data from the learner is drawn to insignificant extra-solution activity (data gathering). See MPEP § 2106.05(g). Thus, the claim is directed to an abstract idea(s).
[Step 2B] The claim does 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(s) into a practical application, the additional elements of a computer program executing on a computing device for performing the method steps and the use of an LLM amount to no more than mere instructions to apply the exception using a generic computing device, which cannot provide an inventive concept. Accordingly, representative claim 1 is not patent eligible.
Claims 2-10 are dependent on representative claim 1 and include all of the limitations of claim 1. Therefore, the dependent claims recite the same abstract idea(s) as those recited in the independent claim or contain limitations drawn to generic computer components and/or reciting extra solution activities. While the dependent claims may have a narrower scope than the representative claim, no claim contains an additional element to integrate the abstract idea(s) into a practical application or to render an inventive concept that transforms the corresponding claim into a patent eligible application of the otherwise ineligible abstract idea(s). Thereby, claims 2-10 are also patent ineligible.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-10 and 17-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 2024/0379019 (hereinafter “Naufel”).
Regarding Claim 1, Naufel discloses employing a processor to execute computer executable instructions stored on a computer readable storage medium (par. 0050: “processing circuitry 199 including one or more processors 105, implements functionality and/or process instructions for execution within computing device 100. For example, one or more processors 105 may be capable of processing instructions stored in memory 104 and/or instructions stored on one or more storage devices 108”) to implement the following acts:
initiating a generative artificial intelligence (AI) conversation with a learner, wherein the generative AI conversation is facilitated by an AI large language model (LLM) and configured to elicit learning data from the learner (par. 0247: “conversational AI engagement architecture 205 and interactive AI tutor 270 enable learning platform 200 to deliver personalized guidance, feedback, and mentorship to individual learners throughout the learning process, by adapting responses and support provided by conversational AI engagement architecture 205 and interactive Al tutor 270 modules based on unique needs and goals of individual learners;” par. 0256: “Learning platform 200 provides conversational Al engagement architecture 205 to enable learning platform 200 to assess the current state of engagement with the user, generate relevant inquiries to achieve specific goals;” par. 0401: “Interactive Al tutor 270 emulates human-like mentorship by engaging with learners through conversation and interactive problem-solving exercises;” par. 0414: “conversational AI engagement architecture 205 may generate contextually relevant inquiries aimed at gathering information from the user”);
creating a learner profile of the user based on the learning data elicited from the learner (fig. 4B; pars. 0073-0074: “the example user 300, properties 406 are stored as a data representation within schema 401 depicting example data that LLM 210 is trained to detect and write queries for based on any input coming in from a user 300… user 300 ‘Mark’ has various properties 406 including age of 31, identifier ‘user_1,’ name ‘Mark’ and occupation ‘Professor of Practice.’ Within schema 401, details about user 300 are depicted including… goals, interests, and traits;” par. 0458: “AI model learnings about user 1320. In particular, the AI model has learned the specified elements about the user, including that user “Mark” is interested in the rise and fall of world powers, the user has a category of interest corresponding to “history,” the user has an interest in history which began on a specified date and was updated on a specified date;” par. 0585: “processing circuitry 199 is further configured to self-generate, by the processing circuitry using the large language model, graph database queries to load the new student learner data within the new nodes”); and
continuously adapting at least one of the generative AI conversation or the learner profile using the AI LLM based on subsequent learning data elicited from the learner (par. 0010: “scalable personalized learning solution, capable of dynamically adapting to the needs of individual learners;” par. 0247: “conversational AI engagement architecture 205 and interactive AI tutor 270 enable learning platform 200 to deliver personalized guidance, feedback, and mentorship to individual learners throughout the learning process, by adapting responses and support provided by conversational AI engagement architecture 205 and interactive Al tutor 270 modules based on unique needs and goals of individual learners;” par. 0622: “learning platform 200 provides real-time notifications and updates to learners… regarding progress, achievements, and relevant learning opportunities;” par. 0625: “graph database 230 of learning and AI components are designed to continuously update and refine content based on user feedback, improving the overall quality and relevance of learning materials over time;” par. 0460: “The system interface shows a sticky note indicating what Al model learnings about user 1320 were learned from the user input/engagement, and what has now been stored to graph database 230 for future use and contextualization to the adaptive AI learning platform 200”).
Regarding Claim 2, Naufel further discloses the generative AI conversation is configured to elicit learning data corresponding to an intellect profile of the learner (par. 0286: “Adaptive learning algorithm module 275 may analyze… interests, and prior knowledge of individual learners… may create personalized pathways by selecting relevant fields, subfields, topics, subtopics, and learning units from graph database 230, ensuring a targeted and efficient learning experience;” par. 0458: “the AI model has learned the specified elements about the user, including that user “Mark” is interested in the rise and fall of world powers, the user has a category of interest corresponding to “history,” the user has an interest in history which began on a specified date and was updated on a specified date;” par. 0321: “Learning analytics module 240 may gather and analyze data from assessment module 276, providing insights into performance by learners”).
Regarding Claim 3, Naufel further discloses the learning data corresponding to the intellect profile includes at least one of intellectual interests of the learner, knowledge aptitude of the learner, intellectual needs of the learner, or proximal development of the learner (par. 0286: “Adaptive learning algorithm module 275 may analyze… interests, and prior knowledge of individual learners… may create personalized pathways by selecting relevant fields, subfields, topics, subtopics, and learning units from graph database 230, ensuring a targeted and efficient learning experience;” par. 0458: “the AI model has learned the specified elements about the user, including that user “Mark” is interested in the rise and fall of world powers, the user has a category of interest corresponding to “history,” the user has an interest in history which began on a specified date and was updated on a specified date;” par. 0284: “Adaptive learning algorithm module 275 provides adaptive learning algorithms which may continuously analyze performance, preferences, and progress of individual learners through various data points, such as assessment scores, time spent on learning units, engagement patterns, and feedback. This data-driven approach allows learning platform 200 to adjust the learning experience in real-time, optimizing both the pace and content for each user”).
Regarding Claim 4, Naufel further discloses identifying resources based on the at least one of the intellectual interests of the learner, the knowledge aptitude of the learner, the intellectual needs of the learner, or the proximal development of the learner, wherein the continuously adapting comprises adapting the at least one of the generative AI conversation or the learner profile based on the resources (par. 0288: “Real-time… analysis enables learning platform 200 to identify areas where learners may need additional support or resources. Learning platform 200 may responsively adjust learning pathways accordingly; par. 0322: “learning analytics module 240 uses the collected data to generate personalized recommendations for learners. These recommendations may include additional resources, tailored learning activities, or modifications to the learning pathway, all aimed at enhancing the learning experience and promoting learner success”).
Regarding Claim 5, Naufel further discloses identifying learning experiences based on the proximal development of the learner, wherein the continuously adapting comprises adapting the at least one of the generative AI conversation or the learner profile based on the learning experiences (par. 0284: “Adaptive learning algorithm module 275 provides adaptive learning algorithms which may continuously analyze performance, preferences, and progress of individual learners through various data points, such as assessment scores, time spent on learning units, engagement patterns, and feedback. This data-driven approach allows learning platform 200 to adjust the learning experience in real-time, optimizing both the pace and content for each user”).
Regarding Claim 6, Naufel further discloses the generative AI conversation is configured to elicit learning data corresponding to at least one of a social-emotional skills profile of the learner or an executive function skills profile of the learner (par. 0255: “Learning platform 200 provides content and activities that encourage the development of ethical reasoning and moral values, integrating ethical and moral teaching alongside academic subjects;” par. 0291: “learning platform 200 supports development of essential teamwork, communication;” par. 0296: “social learning features encourage networking, collaboration, and the exchange of ideas, providing an engaging and supportive learning environment;” par. 0298: “By incorporating interactive, collaborative learning features, learning platform 200 fosters a supportive learning environment that promotes the development of essential skills;” par. 0441: “Ethical and moral framework module 290 helps provide a well-rounded and values-based educational experience. By incorporating engaging content, activities, and personalized learning pathways;” par. 0436: “the module helps them develop their ethical reasoning skills, empathy, and understanding of the nuances of moral values”).
Regarding Claim 7, Naufel further discloses generating a strategy to strengthen the at least one of the social-emotional skills profile of the learner or the executive function skills profile of the learner, wherein the continuously adapting comprises adapting the at least one of the generative AI conversation or the learner profile based on the strategy (par. 0623: “creation of customized learning plans;” par. 0438: “Ethical and moral framework module 290 works in conjunction with adaptive learning algorithm module 275 to create personalized ethical and moral learning pathways. These pathways consider interests, background, and prior knowledge of individual learners, tailoring the ethical and moral learning experience to their unique needs and preferences;” par. 0436: “the module helps them develop their ethical reasoning skills, empathy, and understanding of the nuances of moral values;” par. 0441: “Ethical and moral framework module 290 helps provide a well-rounded and values-based educational experience. By incorporating engaging content, activities, and personalized learning pathways;”).
Regarding Claim 8, Naufel further discloses monitoring a learning progress of the learner, wherein the continuously adapting comprises adapting the at least one of the generative AI conversation or the learner profile based on the learning progress (par. 0247: “Learning platform 200 may incorporate adaptive learning algorithms that continuously analyze the performance, progress… of individual learners and responsively adjust the learning experience delivered by learning platform 200 to such individual learners in real-time;” par. 0284: “adaptive learning algorithms which may continuously analyze performance… and progress of individual learners through various data points, such as assessment scores, time spent on learning units, engagement patterns, and feedback. This data-driven approach allows learning platform 200 to adjust the learning experience in real-time, optimizing both the pace and content for each user”).
Regarding Claim 9, Naufel further discloses the continuously adapting comprises generating an evolving learning strategy based on the learning progress, and wherein the evolving learning strategy includes at least one of a learning data elicitation strategy, a resource curation strategy, or a learning experience curation strategy (par. 0289: “learning platform 200 remains responsive to evolving needs and preferences of users, maintaining its effectiveness as a personalized learning solution;” par. 0277: “Instructional design subsystem 215 may curate relevant and high-quality reading materials, including articles, textbooks, and research papers, that support and enhance the learning experience by providing additional context and information;” par. 0658: “content generation and curation processes are designed to be adaptive and responsive to changes in industry trends, educational standards, and user preferences, ensuring that the learning experience remains relevant and up-to-date;” par. 0414: “Based on an engagement state assessment of individual learners, conversational AI engagement architecture 205 may generate contextually relevant inquiries aimed at gathering information from the user, driving goal-oriented behavior, or improving the overall learning experience”).
Regarding Claim 10, Naufel further discloses predicting at least one of a college placement of the learner or a career placement of the learner based on the learning data elicited from the learner (fig. 10; par. 0446: “AI learning interface 1001 depicts a user responding to the ‘find your path’ prompt. Specifically, users enter information about their enjoyment of playing chess and video games, their favorite subject being mat[h], and the fact that they build websites for fun. The user may then click the submit button 1010 and AI learning interface 1002 responsively generates as output, suggested career development paths;” par. 0332: “Career module 281 may generate personalized career recommendations based on interests, strengths, and educational progress of individual learners. These recommendations may include specific job roles, industries, or further education opportunities that align with goals and professional aspirations of the learners”).
Regarding Claim 17, Naufel discloses employing a processor to execute computer executable instructions stored on a computer readable storage medium (par. 0050: “processing circuitry 199 including one or more processors 105, implements functionality and/or process instructions for execution within computing device 100. For example, one or more processors 105 may be capable of processing instructions stored in memory 104 and/or instructions stored on one or more storage devices 108”) to implement the following acts:
initiating a generative artificial intelligence (AI) conversation with a learner, wherein the generative AI conversation is facilitated by an AI large language model (LLM) and configured to elicit learning data from the learner corresponding to at least one of a social-emotional skills profile of the learner or an executive function skills profile of the learner (par. 0438: “Ethical and moral framework module 290 works in conjunction with adaptive learning algorithm module 275 to create personalized ethical and moral learning pathways. These pathways consider interests, background, and prior knowledge of individual learners, tailoring the ethical and moral learning experience to their unique needs and preferences;” par. 0255: “Learning platform 200 provides content and activities that encourage the development of ethical reasoning and moral values, integrating ethical and moral teaching alongside academic subjects;” par. 0291: “learning platform 200 supports development of essential teamwork, communication;” par. 0296: “social learning features encourage networking, collaboration, and the exchange of ideas, providing an engaging and supportive learning environment;” par. 0298: “By incorporating interactive, collaborative learning features, learning platform 200 fosters a supportive learning environment that promotes the development of essential skills;” par. 0247: “conversational AI engagement architecture 205 and interactive AI tutor 270 enable learning platform 200 to deliver personalized guidance, feedback, and mentorship to individual learners throughout the learning process, by adapting responses and support provided by conversational AI engagement architecture 205 and interactive Al tutor 270 modules based on unique needs and goals of individual learners;” par. 0256: “Learning platform 200 provides conversational Al engagement architecture 205 to enable learning platform 200 to assess the current state of engagement with the user, generate relevant inquiries to achieve specific goals;” par. 0401: “Interactive Al tutor 270 emulates human-like mentorship by engaging with learners through conversation and interactive problem-solving exercises;” par. 0414: “conversational AI engagement architecture 205 may generate contextually relevant inquiries aimed at gathering information from the user”);
creating a learner profile of the user based on the learning data elicited from the learner (fig. 4B; pars. 0073-0074: “the example user 300, properties 406 are stored as a data representation within schema 401 depicting example data that LLM 210 is trained to detect and write queries for based on any input coming in from a user 300… user 300 ‘Mark’ has various properties 406 including age of 31, identifier ‘user_1,’ name ‘Mark’ and occupation ‘Professor of Practice.’ Within schema 401, details about user 300 are depicted including… goals, interests, and traits;” par. 0458: “AI model learnings about user 1320. In particular, the AI model has learned the specified elements about the user, including that user “Mark” is interested in the rise and fall of world powers, the user has a category of interest corresponding to “history,” the user has an interest in history which began on a specified date and was updated on a specified date;” par. 0585: “processing circuitry 199 is further configured to self-generate, by the processing circuitry using the large language model, graph database queries to load the new student learner data within the new nodes”);
continuously adapting at least one of the generative AI conversation or the learner profile using the AI LLM based on subsequent learning data elicited from the learner (par. 0010: “scalable personalized learning solution, capable of dynamically adapting to the needs of individual learners;” par. 0247: “conversational AI engagement architecture 205 and interactive AI tutor 270 enable learning platform 200 to deliver personalized guidance, feedback, and mentorship to individual learners throughout the learning process, by adapting responses and support provided by conversational AI engagement architecture 205 and interactive Al tutor 270 modules based on unique needs and goals of individual learners;” par. 0622: “learning platform 200 provides real-time notifications and updates to learners… regarding progress, achievements, and relevant learning opportunities;” par. 0625: “graph database 230 of learning and AI components are designed to continuously update and refine content based on user feedback, improving the overall quality and relevance of learning materials over time”); and
generating a strategy to strengthen the at least one of the social-emotional skills profile of the learner or the executive function skills profile of the learner (par. 0623: “creation of customized learning plans;” par. 0438: “Ethical and moral framework module 290 works in conjunction with adaptive learning algorithm module 275 to create personalized ethical and moral learning pathways. These pathways consider interests, background, and prior knowledge of individual learners, tailoring the ethical and moral learning experience to their unique needs and preferences;” par. 0436: “the module helps them develop their ethical reasoning skills, empathy, and understanding of the nuances of moral values;” par. 0441: “Ethical and moral framework module 290 helps provide a well-rounded and values-based educational experience. By incorporating engaging content, activities, and personalized learning pathways”).
Regarding Claim 18, Naufel further discloses the continuously adapting comprises adapting the at least one of the generative AI conversation or the learner profile based on the strategy (par. 0438: “Ethical and moral framework module 290 works in conjunction with adaptive learning algorithm module 275 to create personalized ethical and moral learning pathways… tailoring the ethical and moral learning experience to their unique needs and preferences;” par. 0284: “Adaptive learning algorithm module 275 provides adaptive learning algorithms which may continuously analyze performance, preferences, and progress of individual learners through various data points, such as assessment scores, time spent on learning units, engagement patterns, and feedback. This data-driven approach allows learning platform 200 to adjust the learning experience in real-time, optimizing both the pace and content for each user;” par. 0460: “The system interface shows a sticky note indicating what Al model learnings about user 1320 were learned from the user input/engagement, and what has now been stored to graph database 230 for future use and contextualization to the adaptive AI learning platform 200”).
Regarding Claim 19, Naufel further discloses generating an assessment of the learner based on the at least one of the generative AI conversation or the learner profile (par. 0275: “instructional design subsystem 215 creates assessments, quizzes, and tests that align with the learning objectives and accurately measure learners' progress and understanding of the course material. These assessments are designed to challenge learners while providing valuable feedback for improvement”).
Regarding Claim 20, Naufel further discloses the assessment is at least one of a knowledge assessment of the learner, a social-emotional assessment of the learner, an intellectual needs assessment of the learner, or an executive skills function assessment of the learner (par. 0375: “For instance… a student learner has selected assess 610 as one of the available learning modalities 615 is therefore presented with questions to test the knowledge of the learner on the topic of ‘buoyancy and stability;’” par. 0440: “Ethical and moral framework module 290 may integrate with assessment module 276 to evaluate learners for understanding of ethical concepts and the development of moral values”).
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.
The factual inquiries 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.
Claims 11-12 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Naufel in view of CN 116993544 (hereinafter “Wang”).
Regarding Claim 11, Naufel discloses employing a processor to execute computer executable instructions stored on a computer readable storage medium (par. 0050: “processing circuitry 199 including one or more processors 105, implements functionality and/or process instructions for execution within computing device 100. For example, one or more processors 105 may be capable of processing instructions stored in memory 104 and/or instructions stored on one or more storage devices 108”) to implement the following acts:
initiating a first generative artificial intelligence (AI) conversation, wherein the first generative AI conversation is with a learner, and wherein the first generative AI conversation is facilitated by an AI large language model (LLM) and configured to elicit learning data from the learner (par. 0247: “conversational AI engagement architecture 205 and interactive AI tutor 270 enable learning platform 200 to deliver personalized guidance, feedback, and mentorship to individual learners throughout the learning process, by adapting responses and support provided by conversational AI engagement architecture 205 and interactive Al tutor 270 modules based on unique needs and goals of individual learners;” par. 0256: “Learning platform 200 provides conversational Al engagement architecture 205 to enable learning platform 200 to assess the current state of engagement with the user, generate relevant inquiries to achieve specific goals;” par. 0401: “Interactive Al tutor 270 emulates human-like mentorship by engaging with learners through conversation and interactive problem-solving exercises;” par. 0414: “conversational AI engagement architecture 205 may generate contextually relevant inquiries aimed at gathering information from the user”);
creating a learner profile of the user based on the learning data elicited from the learner (fig. 4B; pars. 0073-0074: “the example user 300, properties 406 are stored as a data representation within schema 401 depicting example data that LLM 210 is trained to detect and write queries for based on any input coming in from a user 300… user 300 ‘Mark’ has various properties 406 including age of 31, identifier ‘user_1,’ name ‘Mark’ and occupation ‘Professor of Practice.’ Within schema 401, details about user 300 are depicted including… goals, interests, and traits;” par. 0458: “AI model learnings about user 1320. In particular, the AI model has learned the specified elements about the user, including that user “Mark” is interested in the rise and fall of world powers, the user has a category of interest corresponding to “history,” the user has an interest in history which began on a specified date and was updated on a specified date;” par. 0585: “processing circuitry 199 is further configured to self-generate, by the processing circuitry using the large language model, graph database queries to load the new student learner data within the new nodes”); and
continuously adapting the first generative AI conversation using the AI LLM based on the learning data elicited from the learner (par. 0010: “scalable personalized learning solution, capable of dynamically adapting to the needs of individual learners;” par. 0247: “conversational AI engagement architecture 205 and interactive AI tutor 270 enable learning platform 200 to deliver personalized guidance, feedback, and mentorship to individual learners throughout the learning process, by adapting responses and support provided by conversational AI engagement architecture 205 and interactive Al tutor 270 modules based on unique needs and goals of individual learners;” par. 0622: “learning platform 200 provides real-time notifications and updates to learners… regarding progress, achievements, and relevant learning opportunities;” par. 0625: “graph database 230 of learning and AI components are designed to continuously update and refine content based on user feedback, improving the overall quality and relevance of learning materials over time;” par. 0460: “The system interface shows a sticky note indicating what Al model learnings about user 1320 were learned from the user input/engagement, and what has now been stored to graph database 230 for future use and contextualization to the adaptive AI learning platform 200”).
Naufel implies but does not explicitly disclose a generative AI conversation with a learner associate. However, Wang discloses initiating a second generative AI conversation, wherein the second generative AI conversation is with a learner associate, and wherein the second generative AI conversation is configured to elicit supplemental learning data from the learner associate corresponding to the learner profile (par. 0049: “Teachers input students' learning needs and subject knowledge points into the teaching support platform,” par. n0004: “an instructional design method based on LLM to improve teaching effectiveness”); and
continuously adapting the first generative AI conversation using the AI LLM based on a combination of the learning data elicited from the learner and the supplemental learning data elicited from the learner associate (par. 0050: “Teachers interact with the teaching support platform, which sends instructions to the teaching support model via prompts to generate course content and teaching materials based on teaching objectives, knowledge structure diagrams, and course maps;” par. 0029: “instructional design method based on large-scale language models can tailor instruction to students' individual needs, thereby improving teaching quality; at the same time, it can automatically generate teaching content and questions… can design more personalized teaching strategies based on students' learning needs… Through automatic grading and evaluation functions;” par. n0026: “module can also obtain explanations and suggestions from the teaching support model based on students' answers and error types;” par. n0033: “The teaching support model automatically adjusts the teaching content and difficulty based on students' learning progress and level, as well as the course content and teaching materials;” Examiner notes the adapting occurs due to some combination of the learning data elicited at least from learners’ assessments and their teacher’s input).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the LLM-based adaptive learning platform of Naufel with the learner associate and second generative AI conversation of Wang in order to gather even more information about the learner and from a third party rather than only from the learner (Wang, pars. 0049-0051, n0029, n0035). Furthermore, it is well known within the art that supplying additional information to an LLM will yield better results.
Regarding Claim 12, Naufel modified by Wang further discloses the second generative AI conversation is configured to elicit supplemental learning data corresponding to an intellect profile of the learner (Wang, par. 0049: “Teachers input students'… subject knowledge points into the teaching support platform, and use the teaching support platform to send instructions to the teaching support model via prompt to generate targeted teaching objectives”).
The combination of the LLM-based adaptive learning platform of Naufel with the learner associate and second generative AI conversation of Wang described above for Claim 11 would have included this elicitation of the learner’s intellect profile.
Regarding Claim 14, Naufel modified by Wang further discloses the second generative AI conversation is configured to elicit supplemental learning data corresponding to learning progress of the learner (Wang, par. 0052: “Teachers input students' learning progress… the teaching support platform”).
The combination of the LLM-based adaptive learning platform of Naufel with the learner associate and second generative AI conversation of Wang described above for Claim 11 would have included this elicitation of the learner’s learning progress.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Naufel in view of Wang as applied to claim 11 above, and further in view of US 2017/0039876 (hereinafter “Alyuz”).
Regarding Claim 13, Naufel modified by Wang discloses the second generative AI conversation is configured to elicit supplemental learning data (Wang, par. 0052: “Teachers input students' learning progress… the teaching support platform”), but does not explicitly specify the supplemental learning data corresponds to a socio-emotional skills profile or executive function skills profile of the learner.
However, Alyuz discloses configured to elicit supplemental learning data corresponding to at least one of a socio-emotional skills profile of the learner or an executive function skills profile of the learner (par. 0031: “human labeling of engagement states may be requested… of the labeler 120, for example a teacher… these current learning engagement state labels may include one or more behavioral states, for example body language indicating whether the learner is on or off task, and/or one or more emotional states, for example whether the learner appears excited, motivated or bored. In addition, cognitive elements may be included, for example, from results of the learner 102 interaction with the educational device 104”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the LLM-based adaptive learning platform and second generative AI conversation of modified Naufel with the learning data related to socio-emotional skills as disclosed by Alyuz in order to provide even more information about the learner (Alyuz, pars. 0031-0032, 0003, 0024) as it is well known within the art that supplying additional information to an LLM will yield better results. Additionally, this information could be used to gauge effectiveness of a given lesson (Alyuz, par. 0003).
Claims 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Naufel in view of Wang as applied to claim 11 above, and further in view of US 2024/0274025 (hereinafter “Vleugels”).
Regarding Claim 15, modified Naufel does not explicitly disclose a second learner associate or third generative AI conversation. However, Vleugels discloses initiating a third generative AI conversation, wherein the third generative AI conversation is with a second learner associate, and wherein the third generative AI conversation is configured to elicit additional supplemental learning data from the second learner associate corresponding to the learner profile (par. 0054: “One or more attribute values from the first and/or second set of attributes may also be generated using the input from an authority, such as… a parent;” par. 0114: “create request generator (1070) is tasked with sending create requests (1069), including the selected…attributes derived from… user inputs to the content generator (1065) to initiate the generation of assessment content… Content generator 1065 may, for example, use Large Language Models to create content”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the LLM-based adaptive learning platform and second generative AI conversation of modified Naufel with the second learner associate of Vleugels in order to provide even more information about the learner (Vleugels, par. 0054) as it is well known within the art that supplying additional information to an LLM will yield better results.
Regarding Claim 16, Naufel modified by Vleugels further discloses the continuously adapting comprises adapting the at least one of the generative AI conversation or the learner profile based on the additional supplemental learning data elicited from the second learner associate (Vleugels, par. 0075: “generate one or more content elements according to at least one attribute value that is based on one or more previous attribute values, one or more user inputs, and/or one or more user profile information element;” par. 0055: “one or more previously created sets of attributes, one or more user inputs, information stored in a user profile, historical data stored from previous interactions with the online educational platform;” par. 0023: “provide automated and adaptive generation of customized educational assessments leveraging vectorized representations (embeddings) to deeply understand both user (eg educator, content creator) input criteria and educational content. The system dynamically adjusts content embeddings based on aggregated performance data and real-time feedback, enhancing personalized learning experiences by tailoring difficulty levels, content relevance, and format relevance to improve learning outcomes”).
The combination of the LLM-based adaptive learning platform of modified Naufel with the second learner associate of Vleugels described above for Claim 11 would have included this elicitation of the learner’s learning progress.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 2025/0356774 (Forrest) teaches an LLM-based classroom workflow dashboard which tracks students’ progress.
US 2025/0299065 (Whelan) teaches a system which utilizes an LLM to track data related to a student’s academic performance, mental state, progress, etc. and makes personalized recommendations.
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/J.G.D./Examiner, Art Unit 3715
/DMITRY SUHOL/Supervisory Patent Examiner, Art Unit 3715
