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 .
Response to Arguments
The objections to the claims have been withdrawn in part in light of the amendments to the claims, filed 05/05/26. However, the objection to claim 19 has been maintained, as presented below. Moreover, new objections to claim 11-13 have been presented in light of the amendments to the claims, as shown below.
The rejections under 35 U.S.C. 112(b) have been withdrawn in light of the amendments to the claims, filed 05/05/26.
The rejection of the claims under 35 U.S.C. 101 has been withdrawn in light of the amendments to the claims, filed 05/05/26.
5. The previous rejections of claims 1-18 under 35 U.S.C. 102 and 35 U.S.C. 103 have been withdrawn in light of the amendments to the claims, filed 05/05/25. The rejections of claims 19-20 under 35 U.S.C. 102 have been maintained, as discussed below. Moreover, new grounds of rejection have been presented in light of the amendments to the claims, as discussed in detail below.
Claim Objections
Claims 11-13 and 19 are objected to because of the following informalities:
“to generate” recited in claim 11, ln. 2 and claim 12, ln. 2 should likely read “to synthetically generate”;
“wherein providing” recited in claim 13, ln. 1 should likely read “wherein the providing”; and
“interface, the generated” recited in claim 19, ln. 13 should likely read “interface[,,] the generated”.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Agley et al. (U.S. Pub. 2021/0074171 A1) (hereinafter “Agley”).
Regarding claim 19, Agley discloses a computing system comprising a processor and a non-transitory computer-readable medium having stored thereon program instructions (Fig. 7; [0107-0109]) that upon execution by the processor, cause performance of a set of acts comprising:
determining, by the computing system, an extent of a user’s understanding of one or more educational topics (Fig. 7; [0039]; [0043-0044]; [0049-0050]; [0107], wherein the system (computing device) assesses a user’s knowledge level about concepts in learning materials (e.g., a given topic or knowledge area));
using, by the computing system, at least the determined extent of the user’s understanding of the one or more educational topics to synthetically generate a personalized curriculum for the user ([0044]; [0104]; [0107], wherein the system recommends concepts (synthetic generation of a personalized curriculum) to a user based on the assessment of the user’s knowledge level);
using, by the computing system, at least the generated personalized curriculum and one or more trained machine learning (ML) models to generate personalized educational video content for the user ([0044]; [0051]; [0086]; [0104]; [0107], wherein the system further recommends learning materials (e.g., videos) only relevant to the recommended concepts (personalized educational video content), wherein machine learning is used to identify which area of learning materials/content (e.g., videos, slides, papers, presentations, images, questions, answers) is relevant to which concept); and
performing, by the computing system, a set of operations to facilitate outputting for presentation via a user interface, the generated personalized educational video content for the user (Figs. 1 & 5B; [0044]; [0049]; [0053]; [0091]; [0107]; [0110], wherein the recommended learning material/content about certain concepts are displayed to the user via a user interface).
Regarding claim 20, Agley discloses a non-transitory computer-readable medium having stored thereon program instructions (Fig. 7; [0107-0109]) that upon execution by a processor, cause performance of a set of acts comprising:
determining, by a computing system, an extent of a user’s understanding of one or more educational topics (Fig. 7; [0039]; [0043-0044]; [0049-0050]; [0107], wherein the system (computing device) assesses a user’s knowledge level about concepts in learning materials (e.g., a given topic or knowledge area));
using, by the computing system, at least the determined extent of the user’s understanding of the one or more educational topics to synthetically generate a personalized curriculum for the user ([0044]; [0104]; [0107], wherein the system recommends concepts (synthetic generation of a personalized curriculum) to a user based on the assessment of the user’s knowledge level);
using, by the computing system, at least the generated personalized curriculum and one or more trained machine learning (ML) models to generate personalized educational video content for the user ([0044]; [0051]; [0086]; [0104]; [0107], wherein the system further recommends learning materials (e.g., videos) only relevant to the recommended concepts (personalized educational video content), wherein machine learning is used to identify which area of learning materials/content (e.g., videos, slides, papers, presentations, images, questions, answers) is relevant to which concept); and
performing, by the computing system, a set of operations to facilitate outputting for presentation via a user interface the generated personalized educational video content for the user (Figs. 1 & 5B; [0044]; [0049]; [0053]; [0091]; [0107]; [0110], wherein the recommended learning material/content about certain concepts are displayed to the user via a user interface).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3-5, 7-8, 15, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Agley et al. (U.S. Pub. 2021/0074171 A1) (hereinafter “Agley”) in view of Holland et al. (U.S. Pub. 2025/0054212 A1) (hereinafter “Holland”).
Regarding claim 1, Agley discloses a method ([0013]; [0039]; [0114]) comprising:
determining, by a computing system, an extent of a user’s understanding of one or more educational topics (Fig. 7; [0039]; [0043-0044]; [0049-0050]; [0107], wherein the system (computing device) assesses a user’s knowledge level about concepts in learning materials (e.g., a given topic or knowledge area));
using, by the computing system, at least the determined extent of the user’s understanding of the one or more educational topics to generate a personalized curriculum for the user ([0044]; [0104]; [0107], wherein the system recommends concepts (a personalized curriculum) to a user based on the assessment of the user’s knowledge level); and
performing, by the computing system, a set of operations to facilitate outputting for presentation via a user interface the generated personalized educational video content for the user (Figs. 1 & 5B; [0044]; [0049]; [0053]; [0091]; [0107]; [0110], wherein recommended learning material/content about certain concepts are displayed to the user via a user interface).
Agley further discloses where the system recommends learning materials (e.g., video(s)) relevant to the recommended concepts, wherein machine learning is used to identify which area of learning materials/content (e.g., videos, slides, papers, presentations, images, questions, answers) is relevant to which concept ([0044]; [0051]; [0086]; [0104]; [0107]). However, Agley may not explicitly disclose using, by the computing system, at least the generated personalized curriculum and one or more trained machine learning (ML) models to synthetically generate personalized educational video content for the user. Nevertheless, Holland, directed to content adaptation ([0001]; [0047], wherein content can be generated to inform, educate, and/or entertain users), teaches wherein a machine learning model can generate content (e.g., text, images, audio, video, or the like) based on a user query ([0043]; [0047]; [0050], the query indicative of a lack of/low understanding of the query topic). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to synthetically generate the personalized educational video content, as taught by Holland, in the invention of Agley as an alternative method of generating video content for the user (Holland, [0043]; [0047], wherein the generated and presented content is created in response to the query and can be used for educational purposes; Agley, [0051], “In some examples, the recommender 110 may generate recommended content based on the user’s knowledge level (or ability around a concept).”).
Regarding claim 3, Agley further discloses wherein the determining the extent of the user’s understanding of the one or more educational topics comprises: receiving user input indicating the extent of the user’s understanding of the one or more educational topics; and using the received user input to determine the extent of the user’s understanding of one or more educational topics ([0049-0050], wherein user data received from user engagement with the user interface may provide inputs to the system regarding the user’s knowledge level about concepts/topics).
Regarding claim 4, Agley further discloses wherein the determining the extent of the user’s understanding of the one or more educational topics comprises: providing the user with a questionnaire and receiving corresponding user input indicating answers to the questionnaire; and using the received user input to determine the extent of the user’s understanding of the one or more educational topics ([0049-0050], wherein the system may be a testing system which displays questions for users to answer to determine a user’s understanding of a topic).
Regarding claim 5, Agley further discloses wherein the questionnaire is an adaptive test or a diagnostic test ([0049-0050], wherein the system may be a testing system which displays questions (diagnostic test) for users to answer to determine a user’s understanding of a topic).
Regarding claim 7, Agley further discloses wherein the determining the extent of the user’s understanding of the one or more educational topics comprises: determining a content interaction history of the user; and using the determined content interaction history of the user to determine the extent of the user’s understanding of the one or more educational topics ([0049-0050], wherein user assessment or feedback variables may be analyzed to generate a user model representative of the user’s level of proficiency or ability with respect to a topic in order to predict a user’s knowledge level around a concept, wherein variables may include for example a user’s response time and/or confidence in answering questions for users to answer to determine a user’s understanding of a topic).
Regarding claim 8, Agley further discloses wherein the determining the extent of the user’s understanding of the one or more educational topics comprises: determining a content engagement history of the user; and using the determined content engagement history of the user to determine the extent of the user’s understanding of the one or more educational topics ([0049-0050], wherein user assessment or feedback variables may be analyzed to generate a user model representative of the user’s level of proficiency or ability with respect to a topic in order to predict a user’s knowledge level around a concept, wherein variables may include user engagement with content (e.g., eye contact, eye tracking, facial expressions, note taking, head motion, or the like) or attention-level).
Regarding claim 15, Agley further discloses wherein the performing the set of operations to facilitate outputting for presentation via the user interface the generated personalized educational video content for the user comprises transmitting the generated personalized educational video content to a content-presentation device ([0053-0056]; [0081]; [0091]; [0107]; [0110], wherein the recommended content is received and presented to the user (e.g., via user interface of a display/media player) based on the determined user knowledge for the user to improve upon certain topics/concepts).
Regarding claim 18, Agley further discloses wherein performing the set of operations to facilitate outputting for presentation via the user interface the generated personalized educational video content for the user comprises displaying the generated personalized educational video content (Figs. 1 & 5B; [0044]; [0049]; [0053]; [0091]; [0107]; [0110], wherein the recommended learning material/content about certain concepts are displayed to the user via the user interface).
Regarding claim 19, Agley discloses a computing system comprising a processor and a non-transitory computer-readable medium having stored thereon program instructions (Fig. 7; [0107-0109]) that upon execution by the processor, cause performance of a set of acts comprising:
determining, by the computing system, an extent of a user’s understanding of one or more educational topics (Fig. 7; [0039]; [0043-0044]; [0049-0050]; [0107], wherein the system (computing device) assesses a user’s knowledge level about concepts in learning materials (e.g., a given topic or knowledge area));
using, by the computing system, at least the determined extent of the user’s understanding of the one or more educational topics to synthetically generate a personalized curriculum for the user ([0044]; [0104]; [0107], wherein the system recommends concepts (synthetic generation of a personalized curriculum) to a user based on the assessment of the user’s knowledge level);
using, by the computing system, at least the generated personalized curriculum and one or more trained machine learning (ML) models to generate personalized educational video content for the user ([0044]; [0051]; [0086]; [0104]; [0107], wherein the system further recommends learning materials (e.g., videos) only relevant to the recommended concepts (personalized educational video content), wherein machine learning is used to identify which area of learning materials/content (e.g., videos, slides, papers, presentations, images, questions, answers) is relevant to which concept); and
performing, by the computing system, a set of operations to facilitate outputting for presentation via a user interface, the generated personalized educational video content for the user (Figs. 1 & 5B; [0044]; [0049]; [0053]; [0091]; [0107]; [0110], wherein the recommended learning material/content about certain concepts are displayed to the user via a user interface).
To the extent that a person of ordinary skill in the art would find that Agley does not disclose or teach using, by the computing system, at least the generated personalized curriculum and one or more trained machine learning (ML) models to generate personalized educational video content for the user (though the examiner would respectfully disagree), Holland, directed to content adaptation ([0001]; [0047], wherein content can be generated to inform, educate, and/or entertain users), teaches wherein a machine learning model can generate content (e.g., text, images, audio, video, or the like) based on a user query ([0043]; [0047]; [0050], the query indicative of a lack of/low understanding of the query topic). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to generate the personalized educational video content, as taught by Holland, in the invention of Agley as an alternative method of generating video content for the user (Holland, [0043]; [0047], wherein the generated and presented content is created in response to the query and can be used for educational purposes; Agley, [0051], “In some examples, the recommender 110 may generate recommended content based on the user’s knowledge level (or ability around a concept).”).
Regarding claim 20, Agley discloses a non-transitory computer-readable medium having stored thereon program instructions (Fig. 7; [0107-0109]) that upon execution by a processor, cause performance of a set of acts comprising:
determining, by a computing system, an extent of a user’s understanding of one or more educational topics (Fig. 7; [0039]; [0043-0044]; [0049-0050]; [0107], wherein the system (computing device) assesses a user’s knowledge level about concepts in learning materials (e.g., a given topic or knowledge area));
using, by the computing system, at least the determined extent of the user’s understanding of the one or more educational topics to synthetically generate a personalized curriculum for the user ([0044]; [0104]; [0107], wherein the system recommends concepts (synthetic generation of a personalized curriculum) to a user based on the assessment of the user’s knowledge level);
using, by the computing system, at least the generated personalized curriculum and one or more trained machine learning (ML) models to generate personalized educational video content for the user ([0044]; [0051]; [0086]; [0104]; [0107], wherein the system further recommends learning materials (e.g., videos) only relevant to the recommended concepts (personalized educational video content), wherein machine learning is used to identify which area of learning materials/content (e.g., videos, slides, papers, presentations, images, questions, answers) is relevant to which concept); and
performing, by the computing system, a set of operations to facilitate outputting for presentation via a user interface the generated personalized educational video content for the user (Figs. 1 & 5B; [0044]; [0049]; [0053]; [0091]; [0107]; [0110], wherein the recommended learning material/content about certain concepts are displayed to the user via a user interface).
To the extent that a person of ordinary skill in the art would find that Agley does not disclose or teach using, by the computing system, at least the generated personalized curriculum and one or more trained machine learning (ML) models to generate personalized educational video content for the user (though the examiner would respectfully disagree), Holland, directed to content adaptation ([0001]; [0047], wherein content can be generated to inform, educate, and/or entertain users), teaches wherein a machine learning model can generate content (e.g., text, images, audio, video, or the like) based on a user query ([0043]; [0047]; [0050], the query indicative of a lack of/low understanding of the query topic). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to generate the personalized educational video content, as taught by Holland, in the invention of Agley as an alternative method of generating video content for the user (Holland, [0043]; [0047], wherein the generated and presented content is created in response to the query and can be used for educational purposes; Agley, [0051], “In some examples, the recommender 110 may generate recommended content based on the user’s knowledge level (or ability around a concept).”).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Agley in view of Holland, as applied to claim 1, and in further view of Karna et al. (U.S. 11,620,918 B2) (hereinafter “Karna”).
Regarding claim 2, Agley may not further explicitly disclose wherein determining the extent of the user’s understanding of one or more educational topics comprises, for each of the one or more educational topics, determining a respective score indicating the extent of the user’s understanding of that educational topic. However, Karna, directed to delivering personalized learning material based on a student’s comprehension level (Col. 1, ln. 6-9), teaches this limitation (Col. 2, ln. 43-Col. 4, ln. 5; Col. 10, ln. 22-39; Col. 13, ln. 18-43, wherein a student comprehension score for a student based on monitoring reading performance and the complexity of advance learning material is determined, and wherein learning material for the student is based on the student comprehension score). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to determine a respective score indicating the user’s understanding of a topic, as taught by Karna, in the invention of Agley in order to more easily quantify user understanding (Karna, Col. 1, ln. 6-9; Col. 13, ln. 18-43, where the invention relates to improving learning efficiency by providing tailored learning material based on each student’s comprehension level, wherein a high student comprehension score results in a larger or more complex learning material provided to the student; Agley, [0053], wherein recommended content is displayed to the user for the user to learn or improve upon certain topics/concepts).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Agley in view of Holland, as applied to claim 1, and in further view of Levy et al. (U.S. Pub. 2015/0363795 A1) (hereinafter “Levy”).
Regarding claim 6, Agley may not further discloses wherein determining the extent of the user’s understanding of one or more educational topics comprises: determining a content consumption history of the user; and using the determined content consumption history of the user to determine the extent of the user’s understanding of one or more educational topics. However, Levy, directed to generating personalized study plans targeted to reinforce students in specific subjects/topics ([0019-0021]), teaches collecting student usage data as the student performs learning assignments for the purpose of assessing their competencies ([0017]; [0051]). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to determine and use a content consumption history of the user, as taught by Levy, in the invention of Agley as an alternative user data/assessment variable to predict a user’s knowledge level around a concept (Levy, [0051], wherein students’ usage data is acquired for the purpose of assessing competencies in order to provide students-related information and services based on this knowledge, such as personalized study aids, preparation plans for exams, etc.; Agley, [0049-0050]).
Claims 9-14 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Agley in view of Holland, as applied to claim 1, and in further view of Guttman et al. (U.S. Pub. 2023/0419849 A1) (hereinafter “Guttman”).
Regarding claim 9, Agley discloses the use of one or more trained machine learning (ML) models to aid in the generation of personalized educational video content for the user, as described above ([0086], wherein machine learning is used to identify area(s) of a learning material relevant to a concept). Agley may not further explicitly disclose wherein the using at least the determined extent of the user’s understanding of the one or more educational topics to generate the personalized curriculum for the user comprises: providing at least the determined extent of the user’s understanding of the one or more educational topics to a trained ML model; and responsive to the providing, receiving from the trained ML model, the generated personalized curriculum for the user. However, Guttman, directed to educational content recommendation based on measured user comprehension ([0002]; [0020]; [0024-0025]), teaches applying a trained machine-learned model to identified characteristics of portion(s) of target educational content to recommend supplemental educational content accordingly based on determined user comprehension ([0020]; [0040]; [0050]; [0056-0059], wherein characteristics of the educational content may refer to the content (i.e., concepts)). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to provide the determined extent of the user’s understanding of the one or more educational topics (user comprehension) to a trained machine learning model in order to receive generated personalized curriculum/corresponding educational content thereof, as taught by Guttman, in the invention of Agley as an alternative, accurate technique for identifying and recommending concepts (e.g., portion(s) (content/concepts) of educational content) to the user based on the assessment of the user’s knowledge level (Guttman, [0020]; [0050-0051]; [0091]).
Regarding claim 10, Agley may not further explicitly disclose, however, Guttman teaches using at least one of the one or more educational topics to identify a corresponding event topic ([0038]; [0057], wherein, for example, characteristics, such as content (i.e., concept/topic) of a portion(s) of educational content (one or more educational topics) may be identified); wherein the providing at least the determined extent of the user’s understanding of the one or more educational topics to a trained ML model comprises providing at least the determined extent of the user’s understanding of the one or more educational topics and the identified event topic to the trained ML model ([0020]; [0040]; [0050]; [0056-0059], wherein the measured user comprehension and identified characteristics of the portions of content (topic(s)) are provided to the trained machine-learned model). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to provide the determined extent of the user’s understanding of the one or more educational topics (user comprehension) and identified characteristics of the portion(s) of educational content (topics) to a trained machine learning model in order to receive the generated personalized curriculum/corresponding educational content thereof, as taught by Guttman, in the invention of Agley as an alternative, accurate technique for identifying and recommending concepts (e.g., portion(s) (content/concepts) of educational content) to the user based on the assessment of the user’s knowledge level (Guttman, [0020]; [0050-0051]; [0091]).
While Guttman may not explicitly teach wherein the identified characteristics (content/topic) comprises an event topic, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention for the characteristics of the educational content to comprise an event topic depending on the subject educational content.
Regarding claim 11, Agley discloses the use of one or more trained machine learning (ML) models to aid in the generation of personalized educational video content for the user, as described above ([0086], wherein machine learning is used to identify area(s) of a learning material relevant to a concept). Holland teaches wherein a machine learning model can generate content (e.g., text, images, audio, video, or the like) based on a user query ([0043]; [0047]; [0050], the query indicative of a lack of/low understanding of the query topic). However, Agley in view of Holland may not further teach wherein the using at least the generated personalized curriculum and one or more trained ML models to generate personalized educational video content for the user comprises: providing the generated personalized curriculum to a trained ML model; responsive to the providing, receiving from the trained ML model, program instructions for interactive video content related to the personalized curriculum; and using the received program instructions to generate the interactive video content. However, Guttman, directed to educational content recommendation based on measured user comprehension ([0002]; [0020]; [0024-0025]), teaches providing identified characteristics (e.g., content/concepts) of portion(s) of target educational content to a trained machined-learned model to identify supplemental educational content related to the portion(s) of the target educational content to be provided to a user for review (interaction) ([0020]; [0025]; [0029]; [0039-0040]; [0050]; [0056-0062]). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to provide the identified characteristics of the portion(s) of target educational content (recommended concepts/topics) to a trained ML model in order to generate interactive video content (supplemental educational content used to engage users), as taught by Guttman, in the invention of Agley in view of Holland as additional and/or alternative input for generating the personalized, interactive educational video content for the user (Guttman, [0020]; [0050-0051]; [0091]).
Regarding claim 12, Agley discloses the use of one or more trained machine learning (ML) models to aid in the generation of personalized educational video content for the user, as described above ([0086], wherein machine learning is used to identify area(s) of a learning material relevant to a concept). Holland teaches wherein a machine learning model can generate content (e.g., text, images, audio, video, or the like) based on a user query ([0043]; [0047]; [0050], the query indicative of a lack of/low understanding of the query topic). However, Agley in view of Holland may not further teach wherein the using at least the generated personalized curriculum and one or more trained ML models to generate personalized educational video content for the user comprises: providing the generated personalized curriculum to a trained ML model; and responsive to the providing, receiving from the trained ML model, generated video content related to the personalized curriculum. However, Guttman, directed to educational content recommendation based on measured user comprehension ([0002]; [0020]; [0024-0025]), teaches providing identified characteristics (e.g., content/concepts) of portion(s) of target educational content to a trained machined-learned model to identify supplemental educational content related to the target educational content to be provided to a user for review (interaction) ([0020]; [0025]; [0029]; [0038-0040]; [0050]; [0056-0062], wherein the supplemental educational content may include video clips, streaming video, etc.). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to provide the identified characteristics of the portion(s) of target educational content (recommended concepts/topics) to a trained ML model in order to generate corresponding video content (supplemental educational content), as taught by Guttman, in the invention of Agley in view of Holland as additional and/or alternative input for generating the personalized educational video content for the user (Guttman, [0020]; [0050-0051]; [0091]).
Regarding claim 13, Guttman further teaches wherein providing at least the determined extent of the user’s understanding of the one or more educational topics to a trained ML model comprises providing at least the determined extent of the user’s understanding of the one or more educational topics and user profile data associated with the user to the trained ML model ([0020-0021]; [0025]; [0029]; [0036-0040]; [0043]; [0050]; [0056-0062], providing historical consumption information (i.e., user profile data) and historical comprehension information to identify supplemental educational content related to the target educational content to be provided to a user for review (interaction), wherein the supplemental educational content may include video clips, streaming video, etc.). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to provide the determined extent of the user’s understanding of one or more educational topics (user comprehension) as well as user profile data (i.e., user consumption) to a trained ML model in order to receive generated personalized curriculum/corresponding educational content thereof, as taught by Guttman, in the invention of Agley as an alternative, accurate technique for identifying and recommending concepts (e.g., portion(s) (content/concepts) of educational content) to the user based on the assessment of the user’s knowledge level and additional user data (Guttman, [0020]; [0050-0051]; [0091]).
Regarding claim 14, Guttman further teaches wherein the user profile data indicates user video content preference data ([0020-0021]; [0025]; [0029]; [0036-0040]; [0042], wherein the consumption data and corresponding historical measures of comprehension of educational content for the user may indicate a format that suits (is preferred by) the user (e.g., listening to lectures)). While Guttman may not explicitly disclose the user profile data indicative of user video content preference data, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to allow for any educational content format to be suited to the user, wherein the discussed content formats may include text, PDF, E-book, video clip, streaming video, audio clip, streaming audio, etc. ([0025]; [0042]). Moreover, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to provide the determined extent of the user’s understanding of one or more educational topics (user comprehension), as well as user profile data (i.e., user consumption) which may be indicative of video content preference data, to a trained ML model in order to receive generated personalized curriculum/corresponding educational content thereof, as taught by Guttman, in the invention of Agley as an alternative, accurate technique for identifying and recommending concepts (e.g., portion(s) (content/concepts) of educational content) to the user based on the assessment of the user’s knowledge level and additional user data (Guttman, [0020]; [0050-0051]; [0091]).
Regarding claim 16, Agley may not further explicitly disclose wherein the content-presentation device is a television. However, Guttman, directed to educational content recommendation based on measured user comprehension ([0002]; [0020]; [0024-0025]), teaches wherein client devices, such as televisions, television boxes, or receivers, present information (i.e., educational content (e.g., to improve user comprehension)) to a user in the form of user interfaces ([0024-0026]). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to utilize any device for presenting information to a user in the form of user interfaces, such as a television, as taught by Guttman, in the invention of Agley in order to present the personalized educational video content to the user.
Regarding claim 17, Agley may not further explicitly disclose wherein the content-presentation device is a set-top box. However, Guttman, directed to educational content recommendation based on measured user comprehension ([0002]; [0020]; [0024-0025]), teaches wherein client devices, such as televisions, television boxes, or receivers, present information (i.e., educational content (e.g., to improve user comprehension)) to a user in the form of user interfaces ([0024-0026]). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to utilize any device for presenting information to a user in the form of user interfaces, such as a set-top box, as taught by Guttman, in the invention of Agley in order to present the personalized educational video content to the user.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. 10,789,602 B2 – This reference teaches monitoring a student’s performance and activities to assess proficiencies and generate personalized content (e.g., study plans).
U.S. Pub. 2022/0051581 A1 – This reference teaches a personalized user education system, wherein after completion of one or more educational content modules, the user’s interest preferences may be updated to include more complicated topics.
U.S. Pub. 2021/0133598 A1 – This reference teaches estimating learning efficiency of a user and using machine learning to recommend educational content based on the learning efficiency.
U.S. Pub. 2015/0325133 A1 – This reference teaches a software platform that uses machine learning algorithms to generate recommendations of educational material items (e.g., videos) based on a user’s specific interests and needs.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ALYSSA N BIANCAMANO/Examiner, Art Unit 3715
/DMITRY SUHOL/Supervisory Patent Examiner, Art Unit 3715
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