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
Claims 1-4, 6-13, 15, and 18-20 are objected to because of the following informalities:
“one or more educational topics” recited in claim 1, ln. 5, claim 2, ln. 2, claim 3, ln. 2, 3, & 5-6, claim 4, ln. 2 & 5-6, claim 6, ln. 2 & 5, claim 7, ln. 2 & 5, claim 8, ln. 2 & 5, claim 9, ln. 2 & 4-5, claim 10, ln. 4-5 & 6, claim 13, ln. 2 & 3, claim 19, ln. 7, and claim 20, ln. 6 should likely read “the one or more educational topics” to avoid claim ambiguity;
“models, to generate” recited in claim 1, ln. 7, claim 11, ln. 2, claim 12, ln. 2, claim 19, ln. 9, and claim 20, ln. 8 should likely read “models[[,]] to generate”;
“interface, the generated” recited in claim 1, ln. 10, claim 15, ln. 2, claim 18, ln. 2, claim 19, ln. 12, and claim 20, ln. 11 should likely read “interface[[,]] the generated”;
“wherein determining” recited in claim 2, ln. 1, claim 3, ln. 1, claim 4, ln. 1, claim 6, ln. 1, claim 7, ln. 1, and claim 8, ln. 1 should likely read “wherein the determining” to avoid claim ambiguity;
“wherein using” recited in claim 9, ln. 1, claim 11, ln. 1, and claim 12, ln. 1 should likely read “wherein the using” to avoid claim ambiguity;
“from trained model” recited in claim 9, ln. 6 should likely read “from the trained ML model” for consistency purposes and to avoid claim ambiguity;
“wherein providing” recited in claim 10, ln. 4 should likely read “wherein the providing” to avoid claim ambiguity; and
“wherein performing” recited in claim 15, ln. 1 and claim 18, ln. 1 should likely read “wherein the performing” to avoid claim ambiguity.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 10 and 13-14 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “current event topic” in claim 10 is a relative term which renders the claim indefinite. The term “current event topic” is not defined by the claim, the Specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claim 13 recites in part “The method of claim 1, wherein providing at least the determined extent of the user’s understanding of one or more educational topics to a trained ML model comprises […]”. However, claim 1 does not previously recite wherein at least the determined extent of the user’s understanding of one or more educational topics is provided to a trained ML model. Accordingly, this limitation has insufficient antecedent basis in claim 1, rendering claim 13 indefinite.
Claim 14 is rejected by virtue of its dependency on claim 13.
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 abstract ideas without significantly more.
Regarding claim 1, analyzed as representative claim:
[Step 1] Claim 1 recites in part “A method”, which falls within the “process” statutory category of invention.
[Step 2A – Prong 1] The claim recites a series of steps which can practically be performed by one or more humans through mental process (i.e., observation, evaluation, judgment, and/or opinion) (see MPEP 2106.04(a)(2)(III)) and certain methods of organizing human activity (i.e., managing personal behavior or relationships or interactions between people – including social activities, teaching, and following rules or instructions) (see MPEP 2106.04(a)(2)(II)).
Claim 1 recites: A method comprising:
determining, by a computing system, an extent of a user’s understanding of one or more educational topics (mental process: observation/evaluation/judgment);
using, by the computing system, at least the determined extent of the user’s understanding of one or more educational topics to generate a personalized curriculum for the user (mental process: evaluation/judgment);
using, by the computing system, at least the generated personalized curriculum and one or more trained machine learning (ML) models, to generate personalized educational media content for the user (mental process: evaluation/judgment); and
performing, by the computing system, a set of operations to facilitate outputting for presentation via a user interface, the generated personalized educational media content for the user (human activity: interactions between two individuals – teaching; and/or insignificant extra-solution activity of data transmission/output).
The limitations, under their broadest reasonable interpretation, encompass mental processes and certain methods of organizing human activity, as indicated above, but for the recitation of generic computing components (italicized above). Thus, the claim recites abstract ideas.
[Step 2A – Prong 2] The claim fails to recite additional limitations to integrate the abstract ideas into a practical application. That is, while the claim recites that the steps are performed by a computing system, data (content) is generated at least in part based on one or more trained machine learning (ML) models, and the generated data is presented via a user interface, these limitations are recited at a high level of generality and amount to no more than mere instructions to apply generic computing components (e.g., a computer) as tools to perform the abstract ideas, and/or implement the abstract ideas in a particular environment (field of use) (see MPEP 2106.05(f) & (h)). A human (e.g., teacher/tutor/etc.) can perform the recited functions of the computing system and one or more ML models mentally (i.e., mentally determine (e.g., through visual observation or evaluation of user/student performance) a user’s understanding of educational topic(s), mentally determine/create a curriculum and corresponding educational media content for the user based on the determined understanding/knowledge level of the user regarding certain topic(s), and provide the personalized educational media content accordingly (e.g., handouts, verbal lectures, videos, etc.)).
Additionally, the outputting for presentation via a user interface the generated personalized educational media content for a user is directed to the insignificant extra-solution activity of data transmission/output, which does not integrate the abstract ideas into a practical application (see MPEP 2106.05(g)).
There is no indication that the combination of elements improves the functioning of a computer or other technology (see MPEP 2106.05(a)), recites a “particular machine” to apply or use the abstract ideas (see MPEP 2106.05(b)), recites a particular transformation of an article to a different thing or state (see MPEP 2106.05(c)), or recites any other meaningful limitation (see MPEP 2106.05(e)).
Accordingly, the claim is directed to the abstract ideas.
[Step 2B] As discussed above with respect to integration of the abstract ideas into a practical application, the claim does not further include additional elements that are sufficient to amount to significantly more than the judicial exceptions. The additional limitations amount to no more than mere instructions to use generic computing components as tools to perform the abstract ideas, generally link the abstract ideas to a particular technological environment (i.e., machine learning), and/or insignificant extra-solution activity. The claim utilizes a generic computing system, user interface, and off-the-shelf ML model(s) as tools in executing the claimed process. Taking the claim elements separately, the functions performed by the computing system, user interface, and ML model(s) are devoid of technical/technological implementation details. Further, the limitations, when taken in combination, add nothing that is not already present when looking at the elements taken individually.
Furthermore, the Specification further demonstrates that the additional elements are recited for their well-understood, routine, and conventional functionality, wherein the Specification refers to the elements in a manner that indicates that they are sufficiently well-known that the Specification does not need to describe the particulars of such additional elements to satisfy enablement (see Figs. 1-2, depicting boxes indicating components of the invention (e.g., content generator, content-distribution system, computing system, etc.); [0029-0030], “The computing system 200 can be configured to perform and/or can perform one or more operations, such as the operations described in this disclosure. The computing system 200 can include various components, such as a processor 202, a data-storage unit 204, a communication interface 206, and/or a user interface 208. The processor 202 can be or include a general-purpose processor (e.g., a microprocessor) and/or a special-purpose processor (e.g., a digital signal processor).”; [0034], “The user interface 208 can allow for interaction between the computing system 200 and a user of the computing system 200. As such, the user interface 208 can be or include an input component such as a keyboard, a mouse, a remote controller, a microphone, and/or a touch-sensitive panel. The user interface 208 can also be or include an output component such as a display device (which, for example, can be combined with a touch-sensitive panel) and/or a sound speaker.”; [0036-0037], “The computing system 200 can include one or more of the above-described components and can be configured or arranged in various ways. For example, the computing system 200 can be configured as a server and/or a client”; [0052], [0058], “In some examples, the content generator 102 can employ a machine learning technique, such as the one that uses a deep neural network (DNN) to train an ML model”). Additionally, employing well-known computer functions (e.g., transmitting/outputting data) to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not add significantly more (i.e., an inventive concept).
Therefore, claim 1 is not patent eligible.
Independent claim 19 is a computing system comprising a processor and a non-transitory computer-readable medium having stored thereon program instructions that upon execution by the processor, cause performance of the limitations recited above, while independent claim 20 is a non-transitory computer-readable medium having stored thereon program instructions that upon execution by a processor, cause performance of the limitations recited above. These additional limitations are recited at a high level of generality such that they do not amount to a particular machine or technical improvement thereof, nor do they represent an improvement in other technology. Rather, the generic manner in which the additional elements are claimed amount to mere instructions to implement the abstract ideas in a computer environment and/or to utilize generic computing components as tools to perform the abstract ideas. Thus, the additional limitations do not integrate the abstract ideas into a practical application or provide significantly more (i.e., an inventive concept). Thereby, claims 19 and 20 are also not patent eligible.
Claims 2-18 are dependent from claim 1 and therefore recite the same abstract ideas noted above. While dependent claims 2-18 may have a narrower scope than the representative independent claim, the claims fail to recite additional limitations that would integrate the abstract ideas into a practical application or provide significantly more. Therefore, claims 2-18 are also not patent eligible. For example:
The limitations of claims 2-8 further define the mental process of determining the extent of the user’s understanding of one or more educational topics, and fail to provide any teaching that integrates the judicial exceptions into a practical application or amounts to significantly more than the judicial exceptions. In particular, claim 2 recites determining a respective score indicating the extent of the user’s understanding for each of the one or more educational topics (mental process: evaluation/judgment), claim 3 recites receiving user input indicating the extent of the user’s understanding (human activity: interactions between individuals, e.g., teaching; and/or insignificant extra-solution activity of data gathering) and using the received user input to determine the extent of the user’s understanding (mental process: evaluation/judgment), claims 4-5 recite providing a questionnaire (e.g., an adaptive or diagnostic test) to the user and receiving corresponding user input (answers) (human activity: interactions between individuals, e.g., teaching; and/or insignificant extra-solution activity of data gathering) to determine the extent of the user’s understanding (mental process: evaluation/judgment), and claims 6-8 recite determining a content consumption, a content interaction history, and a content engagement history of the user, respectively, in order to determine the extent of the user’s understanding (mental process: observation/evaluation/judgment). Accordingly, the analysis performed on the representative independent claim is also applicable on the recited dependent claims, and the recited claims are thereby also not patent eligible.
The limitations of claims 9-14 further define the mental process of the generation of data (the personalized curriculum and corresponding personalized educational media content) to be performed by trained ML models by providing the gathered data (e.g., the determined extent of the user’s understanding of one or more educational topics, a mentally determined current event topic, generated personalized curriculum, and/or user profile (i.e., preference) data) as inputs to the trained ML models. However, the trained ML models are recited at a high level of generality and merely amount to instructions to implement the abstract ideas on a generic computing component (off-the-shelf machine learning model) and/or generally link the abstract ideas to a particular environment (machine learning environment). Thus, as noted above for claim 1, these additional limitations likewise do not integrate the abstract ideas into a practical application or provide significantly more (i.e., an inventive concept). The analysis performed on the representative independent claim is also applicable on these recited dependent claims, and the recited claims are thereby also not patent eligible.
The limitations of claims 15-18 further define the outputting/presentation of the generated data (personalized educational media content). However, the additional limitations amount to no more than insignificant extra-solution activity (data transmission/display), which does not integrate the abstract ideas into a particular application or provide significantly more (i.e., an inventive concept). There is no indication that the combination of elements improves the functioning of a computer or other technology, recites a “particular machine” to apply or use the abstract ideas (i.e., wherein a content-presentation device (e.g., television/set-top box) is recited for its routine functionality of outputting/presenting data), recites a particular transformation of an article to a different thing or state, or recites any other meaningful limitation. For these reasons, the analysis performed on the representative independent claim is also applicable on these recited dependent claims, and the recited claims are thereby also not patent eligible.
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 1, 3-5, 7-8, 15, and 18-20 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 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 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);
using, by the computing system, at least the generated personalized curriculum and one or more trained machine learning (ML) models, to generate personalized educational media content for the user ([0044]; [0051]; [0086]; [0104]; [0107], wherein the system further recommends learning materials only relevant to the recommended concepts (personalized educational media 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 media 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 3, Agley further discloses wherein determining the extent of the user’s understanding of one or more educational topics comprises: receiving user input indicating the extent of the user’s understanding of 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 determining the extent of the user’s understanding of 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 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 determining the extent of the user’s understanding of 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 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 determining the extent of the user’s understanding of 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 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 performing the set of operations to facilitate outputting for presentation via the user interface, the generated personalized educational media content for the user comprises transmitting the generated personalized educational media 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 media content for the user comprises displaying the generated personalized educational media 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, claim 19 is a computing system of claim 1 and is thereby rejected for like reasoning (see Agley, Fig. 7; [0107-0109]).
Regarding claim 20, claim 20 is a non-transitory computer-readable medium of claim 1 and is thereby rejected for like reasoning (see Agley, Fig. 7; [0107-0109]).
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.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Agley in 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 to aid in the recommendation of content to the user for learning or improving upon certain topics (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 the 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 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 in order to generate recommended content (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 9-12 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Agley in 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 media 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 using at least the determined extent of the user’s understanding of 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 one or more educational topics to a trained ML model; and responsive to the providing, receiving from trained 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 one or more educational topics (user comprehension) 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]).
Regarding claim 10, Agley may not further explicitly disclose, however, Guttman teaches – as best understood in light of the rejection under 35 U.S.C. 112(b) presented above – using at least one of the one or more educational topics to identify a corresponding [] 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 providing at least the determined extent of the user’s understanding of one or more educational topics to a trained ML model comprises providing at least the determined extent of the user’s understanding of one or more educational topics and the identified [] 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 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 a current 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 a current 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 media 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 using at least the generated personalized curriculum and one or more trained ML models, to generate personalized educational media 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 media content related to the personalized curriculum; and using the received program instructions to generate the interactive media 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 (interactive media 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 media content (supplemental educational content used to engage users), as taught by Guttman, in the invention of Agley an alternative, accurate technique for generating the personalized, interactive educational media 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 media 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 using at least the generated personalized curriculum and one or more trained ML models to generate personalized educational media 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 an alternative, accurate technique for generating the personalized educational media content (e.g., video content) for the user (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 media 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 media content to the user.
Claims 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Agley in view of Sherman et al. (U.S. Pub. 2022/0044583 A1) (hereinafter “Sherman”).
Regarding claim 13, Agley may not explicitly disclose wherein providing at least the determined extent of the user’s understanding of one or more educational topics to a trained ML model comprises providing at least the determined extent of the user’s understanding of one or more educational topics and user profile data associated with the user to the trained ML model. However, Sherman, directed to personalized education generation, evaluation, and delivery ([0024]), teaches where presentation of personalized educational materials is presented based on alignment with a student’s prior knowledge (understanding of one or more educational topics) and learning profile (user profile data), wherein machine learning is implemented to identify the appropriate sequence of concepts of the presented materials ([0024]; [0045-0046]). 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, as well as user profile data, to a trained ML model, as taught by Sherman, in the invention of Agley as an alternative, accurate technique for generating the personalized curriculum/corresponding educational content thereof in alignment with the user data such that the curriculum and content is further tailored to the user (Sherman, [0024]; [0041]; [0045-0046]).
Regarding claim 14, Agley may not further explicitly disclose, however, Sherman teaches wherein the user profile data indicates user media content preference data ([0024]; [0046], wherein learning profiles for students include favorite style of learning). It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to further utilize user profile data which indicates user media content preference data (favorite style of learning), as taught by Sherman, in the invention of Agley in order to identify more effective educational materials personalized for the user (Sherman, [0041], wherein educational materials can be more effective based on teaching styles being matched to a learning profile).
Conclusion
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/ALYSSA N BRANDLEY/Examiner, Art Unit 3715