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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 June 17, 2026 has been entered.
Response to Amendment
The amendment filed June 17, 2026 has been entered. Claims 1-20 remain pending in the application. Claims 1, 5-6, 8, 12-13, 15, and 19-20 are noted as amended.
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 without significantly more. Claims 1, 8, and 15 recite a computer system performing a process, the process, and a computer program product including the process, the process including the steps of generate a topic summary corresponding to the one or more lessons; summarize the plurality of topics in the one or more lessons; receive feedback on the one or more lessons including at least one quiz score value and one or more user ratings of the one or more lessons; determine one or more topics of the plurality of topics from the one or more lessons where student comprehension does not exceed a comprehension threshold based on the feedback; and update a course syllabus based on the feedback and the one or more topics on the one or more lessons. The recited steps, under their broadest reasonable interpretation, are generating a summary of one or more lessons based on topic data, summarizing the plurality of topics in the lessons, receiving feedback on the one or more lessons including at least one quiz score and one or more user ratings, determining one or more topics where student comprehension is does not exceed a comprehension threshold, and updating a course syllabus based on the feedback and topics. The recited steps, as drafted, are a process that is a method of applying an abstract idea, specifically mental processes (evaluation (summarize the topics; determine topics where student comprehension does not exceed a threshold); judgement (generating a topic summary; updating a course syllabus)) and/or certain methods of organizing human activity in the form of teaching/education (generating a topic summary, summarizing the topics, determining topics where comprehension does not exceed a threshold, receiving feedback, and updating a course syllabus). If claim limitations, under their broadest reasonable interpretation, include a mental process and/or certain methods of organizing human activity, the limitations fall under the abstract ideas judicial exception and therefore recite ineligible subject matter. Accordingly, claims 1, 8, and 15 recite abstract ideas.
The judicial exception is not integrated into a practical application because the claims do not recite additional elements that are significantly more than the judicial exception or meaningfully limit the practice of the judicial exception. The additional elements are at least one memory; at least one processer; at least one non-transitory computer-readable medium [claim 15]; receive, from a user, topic data corresponding to one or more lessons including a plurality of topics included in the one or more lessons; execute one or more trained machine learning models to generate a summary using the topic data as inputs into the one or more trained machine learning models, wherein the one or more trained machine learning models are trained to summarize; cause to be displayed, on a user interface of a user computing device, the topic summary corresponding to the one or more lessons; receiving the feedback via the user interface of the user computing device; and execute the one or more trained machine learning models to update a course syllabus. The additional elements are insignificant extra-solution activity and instructions for applying the judicial exception with a generic computing device as, under their broadest reasonable interpretation, the additional step(s) is/are merely data gathering the topic data (see MPEP 2106.05(g)) and displaying the results of the summarization (see MPEP 2106.05(a)). The other additional elements of applying the topic data to one or more trained machine learning models, executing the one or more trained machine learning models, a memory, a processor, a NTCRM, and a user computing device are generic computer components for performing the above method, per MPEP 2106.05(f). Under their broadest reasonable interpretation, the additional elements are generic components of a computing device used to apply the abstract idea. Further, paragraph 0038 of the specification states the user computing device may be “any device capable of accessing the internet” including a desktop or laptop. As such, these additional elements are interpreted as merely instructions to apply the judicial exception. With regard to the steps of applying the topic data to a machine learning model using topic data as inputs, training the models and executing the one or more trained models, the models are recited at a high level of generality amounting to computer code for applying the judicial exception and training a machine learning model is insignificant extra-solution activity and well-understood, routine, and conventional (see Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025)). Applicant’s recitations of a trained machine learning model and executing/using the models to update a course syllabus are merely applications of machine learning/generally linking the claimed invention with machine learning as the claims do not recite a technical improvement or specific implementation/particular machine with sufficient detail to amount to more than generally linking. Accordingly, the additional elements and steps do not integrate the abstract idea into a practical application because they do not impose any meaningful limitations on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, the additional step(s) of receiving topic data and causing the topic summary to be displayed is/are insignificant extra-solution activity performed during the abstract idea. The additional elements of applying the topic data to one or more trained machine learning models using topic data as inputs to train the models, executing the one or more models, a memory, a processor, and a user computing device used to perform the process are generic computing components/device used to apply the judicial exception and therefore fall under the “apply it” limitation of the judicial exception and do not amount to significantly more per MPEP 2106.05(f). Further, the limitations, taken in combination, add nothing that is not already present when looking at the elements taken individually. As such, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, under their broadest reasonable interpretation, the additional elements do not meaningfully limit the practice of the abstract idea and do not amount to significantly more than the judicial exceptions. Therefore, claims 1, 8, and 15 are not directed to eligible subject matter as they are abstract ideas without significantly more.
Claims 2-7, 9-14, and 16-20 are dependent from claims 1, 8, and 15, respectively, and include all the limitations of the independent claims. Therefore, the dependent claims recite the same abstract idea. The limitations of the dependent claims fail to amount to significantly more than the judicial exception. For example:
The limitations of claims 2, 9, and 16 recite specific topic data that is manipulated and the additional step of converting speech detected by a microphone. The additional elements are insignificant extra-solution activity and well-known within the art. Per Ghulman (US PGPub 20120078628, paragraphs 0018-0019), speech-to-text software is well-known in the art. The limitations fail to provide any teaching that integrates the judicial exceptions into a practical application or amounts to significantly more than the judicial exceptions. For this reason, the analysis performed on the independent claims is also applicable on these claims.
The limitations of claims 3, 10, and 17 recite clarification of the type of data used/comprising the feedback. The limitations, under their broadest reasonable interpretation, are merely defining/selecting a type of data to be manipulated which, per MPEP 2106.05(g), is insignificant extra-solution activity. The limitations fail to provide any teaching that integrates the judicial exceptions into a practical application or amounts to significantly more than the judicial exceptions. For this reason, the analysis performed on the independent claims is also applicable on these claims.
The limitations of claims 4-7, 11-14, and 18-20 recite further abstract ideas including generating a quiz (judgement MP; CMOHA), receiving one or more answers to the quiz (CMOHA), determining at least one score (evaluation MP; CMOHA), generating a plurality of questions for the quiz based on the lessons (judgement MP; CMOHA), and generating the quiz based on a difficulty level (judgement MP; CMOHA). As the limitations are further abstract ideas, the limitations cannot meaningfully limit or amount to significantly more than the abstract ideas of the independent claims. The limitations fail to provide any teaching that integrates the judicial exceptions into a practical application or amounts to significantly more than the judicial exceptions. For this reason, the analysis performed on the independent claims is also applicable on these claims.
Accordingly, claims 2-7, 9-14, and 16-20 recite abstract ideas without significantly more and are not drawn to eligible subject matter.
Claim Rejections - 35 USC § 103
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 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.
Claim(s) 1, 3-4, 8, 10-11, 15, and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Morris et al. (US PGPub 20180261118), hereinafter referred to as Morris, in view of Karlberg et al. (US PGPub 20230034911), hereinafter referred to as Karlberg, and further in view of Capps et al. (US PGPub 20190114937), hereinafter referred to as Capps.
With regard to claims 1, 8, and 15, Morris teaches a learning management (LM) system comprising at least one memory and at least one processor in communication with the at least one memory [claim 1] (Paragraph 0272 teaches the system includes a computer system including one or more memory and processors), a computer-implemented method for automatically generating learning materials (Abstract; Paragraph 0010; method for automatically generating a curriculum), the method implemented using a computing system including a processor communicatively coupled to a memory device [claim 8] (Paragraph 0272 teaches the system includes a computer system including one or more memory and processors), and at least one non-transitory computer-readable medium comprising instructions stored thereon, the instructions executable by at least one processor to cause the at least one processor to perform steps [claim 15] (Paragraphs 0272 teaches storage devices and memory for storing programs to be executed by processor wherein the storage can be a non-transitory computer readable storage medium), comprising:
receive, from a user, topic data corresponding to one or more lessons (Paragraphs 0009, 0045, 0047, 0066, 0144-0145 teach the system can source content from various sources including community and user inputs and can receive learner inputs related to a topic/subject) including a plurality of topics included in the one or more lessons (Paragraphs 0076, 0094, 0099 teach the curriculum and courses/modules (lessons) include topics and/or learning objects wherein a module/course can contain a plurality of topics);
receive, via the user interface of the user computing device, feedback on the one or more lessons including one or more user ratings of the one or more lessons (Paragraphs 0077, 0218, 0223 teach the system can receiving ratings (feedback) from users on the maps and learning objects/content (lessons)); and
executing the one or more trained machine learning models to update a course syllabus based on the feedback and the one or more topics on the one or more lessons (Paragraphs 0065, 0073-0074, 0077, 0251 teach the system continuously updates the curriculum and maps based on the algorithm and user and community inputs including topics and user behavior and ratings, wherein the algorithm uses a combination of machine learning techniques and models trained with training data).
Morris may not explicitly teach executing one or more trained machine learning models to generate a topic summary corresponding to the one or more lessons using the topic data as inputs into the one or more trained machine learning models, wherein the one or more trained machine learning models are trained to summarize the plurality of topics in the one or more lessons; cause to be displayed, on a user interface of a user computing device, the topic summary corresponding to the one or more lessons. However, Karlberg teaches a system and method for creating a learning graph/map wherein the system generates brief descriptions/summaries of topics based on information associated with the topic, metadata, and documents by using trained machine learning models, wherein the models are continuously trained using inputted data to summarize the content including the plurality of topics in a graph, and displaying the summaries/brief descriptions via a user interface (Paragraphs 0029-0031, 0050, 0054-0055, 0058).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Morris to incorporate the teachings of Karlberg by applying the technique of generating a brief description and/or summary of learning content of Karlberg to the content and data of Morris, as both references and the claimed invention are directed to learning management systems that include generating learning maps/curriculums. One of ordinary skill in the art would modify Morris by coding the system to include metadata for the learning content and generating summaries using trained machine learning based on the metadata and user inputs about the topics and learning content to summarize the topics and curricula of Morris. Upon such modification, the method and system of Morris would include executing one or more trained machine learning models to generate a topic summary corresponding to the one or more lessons using the topic data as inputs into the one or more trained machine learning models, wherein the one or more trained machine learning models are trained to summarize the plurality of topics in the one or more lessons; cause to be displayed, on a user interface of a user computing device, the topic summary corresponding to the one or more lessons. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Karlberg with Morris’s system and method in order to provide an adaptive and comprehensive learning experience (Karlberg Paragraph 0050).
Morris further teaches using assessments to evaluate learner mastery and retention of the curricula (Paragraphs 0228-0234), but Morris in view of Karlberg may not explicitly teach receive feedback including at least one quiz score value; determine one or more topics of the plurality of topics from the one or more lessons where student comprehension does not exceed a comprehension threshold based on the feedback. However, Capps teaches a system and method for recommended activities including educational activities/content based on assessment scores for a user wherein the assessments are at the topic/subject level wherein the system identifies learning objectives as problematic when the learner’s score is below a threshold score for each specific learning objective and wherein the system automatically calculates a score for the user based on their inputs and can generate a personalized course curriculum based in part on historical activity data of the user (Paragraphs 0083, 0091-0092, 0108-0111, 0136, 0154).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Morris in view of Karlberg to incorporate the teachings of Capps by including scoring user inputs/responses to questions/an assessment and determining user mastery of each learning objective/topic based on a threshold score of Capps to the curricula and assessments of Morris, as the references and the claimed invention are directed to learning management systems. One of ordinary skill in the art would modify Morris in view of Karlberg by coding the system to use the assessment data of Morris to determine user scores for each learning objective/topic by automatically scoring user inputs/responses, using the quiz/assessment score as feedback for the content/learning objective, and determining if one or more learning objectives/topics are problematic for a user or users if the score does not exceed a threshold score based on the user assessment (feedback) wherein the assessment/quiz score can be used in addition to the user ratings and other feedback to update the curriculum/syllabus. Upon such modification, the method and system of Morris in view of Karlberg would include receive feedback including at least one quiz score value; and determine one or more topics of the plurality of topics from the one or more lessons where student comprehension does not exceed a comprehension threshold based on the feedback. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Capps with Morris in view of Karlberg’s system and method in order to further assess user performance and content mastery, identify problematic topics/objectives, identify patterns, and make appropriate recommendations (Capps Paragraphs 0110-0111).
With regard to claims 3, 10, and 17, Morris further teaches wherein the feedback comprises upvotes and downvotes input by the user via the user interface of the user computing device (Paragraphs 0077, 0150, 0218, 0221, 0273 teach uses can rate the various content using upvotes and downvotes which re input via the computer system and input device including a display (user interface)).
With regard to claims 4, 11, and 18, Morris further teaches wherein the at least one processor is further configured to generate a quiz corresponding to the one or more lessons (Paragraph 0231 teaches the system can generate a quiz related to the learning object/lesson to test mastery).
Claim(s) 2, 9, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Morris in view of Karlberg and Capps as applied to claims 1, 8, and 15 above, and further in view of Reyes Ramirez et al. (US PGPub 20200233925), hereinafter referred to as Reyes.
With regard to claims 2, 9, and 16, Morris in view of Karlberg and Capps may not explicitly teach wherein the topic data comprises text data converted from speech detected by a microphone worn by the user, though Karlberg teaches the system including microphones (Paragraph 0078). However, Reyes teaches a system and method for summarizing a piece of information including using speech-to-text to analyze and use audio information about the information/topic (Paragraphs 0029, 0045, 0068).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Morris in view of Karlberg and Capps to incorporate the teachings of Reyes by including speech-to-text to gather audio data related to the topic/information of Reyes to the content of Morris and the summarization of Karlberg, as the references and the claimed invention are directed to learning management systems. Further, one of ordinary skill in the art would have found it obvious to apply the technique of speech-to-text to summarizing a topic/piece of information as speech to text is well-known in the art (see Ghulman discussed above) and in order to achieve the expected result of summarizing relevant content to more efficiently deliver the knowledge/information. One of ordinary skill in the art would modify Morris in view of Karlberg and Capps by coding the system to receive and use audio such as speech captured by a microphone as input data about the topic to generate the summary. Upon such modification, the method and system of Morris in view of Karlberg and Capps would include wherein the topic data comprises text data converted from speech detected by a microphone worn by the user. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Reyes with Morris in view of Karlberg and Capps’s system and method as speech-to-text is well-known in the art in order to convert audio data into text data for analysis and processing (Ghulman Paragraphs 0018-0019).
Claim(s) 5-7, 12-14, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Morris in view of Karlberg and Capps as applied to claims 4, 11, and 18 above, and further in view of Nealon et al. (US PGPub 20140024009), hereinafter referred to as Nealon.
With regard to claims 5, 12, and 19, Morris further teaches wherein the at least one processor is further configured to: receive, via the user interface of the user computer device, one or more answers to the quiz (Paragraphs 0231, 0233, 0235 teach the system can receive learner responses/answers to assessments), but Morris in view of Karlberg and Capps may not explicitly teach in response to receiving the one or more answers to the quiz, determine the at least one quiz score value for the quiz. However, Nealon teaches a system and method for customized assessments and educational content based on learner needs and learning styles including scoring user responses/answers to assessments (Paragraphs 0060, 0067).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Morris in view of Karlberg and Capps to incorporate the teachings of Nealon by including the scoring of user answers/responses to assessment questions of Nealon to the quiz content of Morris, as the references and the claimed invention are directed to learning management systems. One of ordinary skill in the art would modify Morris in view of Karlberg and Capps by coding the system to score the user responses/answers to the assessments. Upon such modification, the method and system of Morris in view of Karlberg and Capps would include in response to receiving the one or more answers to the quiz, determine the at least one quiz score value for the quiz. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Nealon with Morris in view of Karlberg and Capps’s system and method as scoring assessments is well-known in the art and in order to assess learner mastery and performance more efficiently.
With regard to claims 6, 13, and 20, Morris, as discussed above, teaches generating quizzes including questions generated by community members (Paragraph 0231), but Morris in view of Karlberg and Capps may not explicitly teach wherein the at least one processor is further configured to generate a plurality of questions for the quiz based on the one or more lessons. However, Nealon further teaches the system can generate assessment questions wherein an assessment can be created/generated using multiple/a plurality of the generated questions wherein the questions are based in part on the topics, lessons, and content items of the content and system (Fig. 13; Paragraphs 0068, 0104, 0113).
As discussed above, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Morris in view of Karlberg and Capps to incorporate the teachings of Nealon by including generating questions (plurality) for an assessment of Nealon to the quizzes of Morris, as the references and the claimed invention are directed to learning management systems. One of ordinary skill in the art would modify Morris in view of Karlberg and Capps by coding the system to generate questions based on the topics, lessons, and content items wherein quizzes can be composed of the plurality of generated questions. Upon such modification, the method and system of Morris in view of Karlberg and Capps would include wherein the at least one processor is further configured to generate a plurality of questions for the quiz based on the one or more lessons. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Nealon with Morris in view of Karlberg and Capps’s system and method as generating assessments/quizzes having multiple questions is well-known in the art and in order to further evaluate users by presenting new/generated questions based on the content.
With regard to claims 7 and 14, Morris in view of Karlberg and Capps may not explicitly teach wherein the quiz is generated based on a difficulty level associated with the user. However, Nealon further teaches the assessments are generated based on a difficulty level of the learner/user (Paragraphs 0048, 0067).
As discussed above, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Morris in view of Karlberg and Capps to incorporate the teachings of Nealon by including generating assessments based on a learner’s difficulty level of Nealon to the quiz content of Morris, as the references and the claimed invention are directed to learning management systems. One of ordinary skill in the art would modify Morris in view of Karlberg and Capps by coding the system to generate the assessments based on the difficulty level of the learner. Upon such modification, the method and system of Morris in view of Karlberg and Capps would include wherein the quiz is generated based on a difficulty level associated with the user. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Nealon with Morris in view of Karlberg and Capps’s system and method in order to increase the learner’s/user’s mastery of the subject matter (Nealon Paragraph 0048).
Response to Arguments
Applicant's arguments, see Remarks, filed June 17, 2026, with respect to the rejection(s) of claim(s) 1-20 under 35 U.S.C. 101 have been fully considered but they are not persuasive. Applicant’s arguments in general are that the claimed invention is directed to a practical application and/or significantly more by reciting a specific technical improvement. Examiner notes that pages 7-12 of Applicant’s arguments are either a summary of cited case law (Enfish, Carmody, Desjardins) and Office Guidance and contain no arguments for the examiner to rebut. Applicant’s substantiative arguments are as follows: A) the claimed invention is similar to Desjardins in that the claimed invention describes a specific process of training and using a machine-learning model; B) the claimed invention is similar to Enfish in that it provides adaptive learning paths personalized to each user’s feedback and performance thereby resulting in a technical improvement of increased accuracy and usability of teaching plans; C) the claims do not recite mental processes; D) the claimed invention recites a specific technical improvement integrating the judicial exception into a practical application; and E) the claims recite an inventive concept amount to significantly more than the judicial exceptions.
With regard to Applicant’s arguments A, B, and D, the claimed invention is not equivalent to the claims of Desjardins and Enfish. Specifically, Desjardins is explicitly and specifically directed to training a machine learning model and amounts to a technical improvement by improving the performance of the machine learning models. The instant application is using machine learning and training the machine learning model as a generic tool, as a computer algorithm, for performing/applying the judicial exceptions discussed above. This is more in line with the teachings and analysis of Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025). Examiner further notes that Desjardins, and Ex Parte Carmody as cited by Applicant, in their analysis and discussion focus on how the machine-learning system/model works and specific technical steps rather than applying or using the model to perform steps or make decision and what is decided/analyzed by the model. The instant application recites using the model for analysis and is more focused on what the models do (generating a topic summary and updating a course syllabus) rather than how the models function and perform the analysis in specific technical ways. The claimed limitations, considered individually and in combination, are not evidence of a technical improvement as Applicant’s claimed technical improvement is conclusory and, regardless, is an improvement experience by the user of a more accurate learning plan and adaptive learning paths. Further, the claimed accuracy improvement is not evidenced by the claim limitations and is a conclusory statement. For these reasons, the claim limitations are also not similar to Enfish as, discussed above, the additional elements and application by machine learning of the judicial exceptions is insignificant extra-solution activity, not evidence of a practical application or technical improvement, and well-understood, routine, and conventional. This is also why Applicant’s argument D is not persuasive.
With regard to Applicant’s arguments C and E, Applicant’s arguments are conclusory and are not commensurate with the claim language and previous discussion (specifically, Examiner has expressly pointed out in the previous 101 rejections/discussion which limitations and elements are mental processes which is once again present in the discussion above). Therefore, there is nothing substantive for the examiner to rebut with regard to arguments C and E. As discussed above, the claims not directed to eligible subject matter as they are directed to abstract ideas without significantly more. Therefore, the claims stand rejected under 35 U.S.C. 101.
Applicant's arguments, see Remarks, filed June 17, 2026, with respect to the rejection(s) of claim(s) 1-20 under 35 U.S.C. 103 have been fully considered but they are not persuasive. Specifically, Applicant argues Morris in view of Karlberg and Capps does not teach the amended limitations. However, as discussed above, Morris does teach receiving one or more user ratings and executing the algorithm/machine learning models, and Capps teaches receiving quiz/assessment scores. See the rejection above for the exact teachings and discussion of the rejection. Claim(s) 1-20 stand rejected under 35 U.S.C. 103 in view of the previously cited combination of prior art.
Conclusion
Accordingly, claims 1-20 are rejected.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CORRELL T FRENCH whose telephone number is (571)272-8162. The examiner can normally be reached M-Th 7:30am-5pm; Alt Fri 7:30am-4pm EST.
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/CORRELL T FRENCH/Examiner, Art Unit 3715
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