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 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.
Claim 7 is 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.
Claim 7 contains the trademark/trade name PDF, iFrame. Where a trademark or trade name is used in a claim as a limitation to identify or describe a particular material or product, the claim does not comply with the requirements of 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph. See Ex parte Simpson, 218 USPQ 1020 (Bd. App. 1982). The claim scope is uncertain since the trademark or trade name cannot be used properly to identify any particular material or product. A trademark or trade name is used to identify a source of goods, and not the goods themselves. Thus, a trademark or trade name does not identify or describe the goods associated with the trademark or trade name. In the present case, the trademark/trade name is used to identify/describe at least one learner experience type and, accordingly, the identification/description is indefinite.
Claim 7 includes a plurality of acronyms such as PDF, HTML 5, LMSes, SCORM/AICC/xAPI, iFramed, LTI, APIs, … that are not clearly explained in the claims.
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 - 12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: Is the claimed invention a statutory category of invention?
Claims 1, 8, 9 and 10 are directed to a method / system for generating adaptive e-learning
experiences (Step 1, Yes).
Step 2A, Prong 1: Does the claim recite an abstract idea?
The limitation of steps: Claims 1, 8 and 9 … accessing a graph database storing a learner knowledge graph comprising an ontology for a plurality of learning topics; developing content comprising at least one course material associated with one of the plurality of learning topics, wherein the content comprises a learning section; and wherein the content is associated with at least one learner experience type; distributing the at least one course material in accordance with a selected e-learning experience, wherein the content within each learning section is tagged with one of the plurality of learning topics suited for the e-learning experience; tracking learning events generated by the plurality of learner, wherein the learning events comprise at least one of viewing and interaction activities related to content consumption and learner validation activities; generating learner event data from the learning events; anonymizing the learner event data; sharing the learner event data with another at least one content producer; based on the learner event data, determining an effectiveness quotient of the content for teaching the course material. Claim 10 requires … associating each of the e-learning experiences with a learning topic comprising at least one adaptive learning variable; executing a first set of instructions to train an adaptive learning model on a schedule or on-demand; inputting anonymized learner event data, scoring data and learner knowledge graph from the at least one content producer as training data; training the adaptive learning model with the training data to optimize the at least one adaptive learning variable; and updating the learner knowledge graph with the outputted optimized at least one adaptive learning variable as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The claimed method akin to mental process of a human author of observations learning style, evaluations learning model, and updating learning content. The mere nominal recitation of a processor performing these steps does not take the claim limitation outside of the mental processes grouping. Thus, the claim recites a mental process (Step 2A, Prong 1: yes).
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
Per the 2019 Revised Patent Subject Matter Eligibility Guidance, if a claim as a whole integrates the recited judicial exception into a practical application of that exception, a claim is not "directed to" a judicial exception. Alternatively, a claim that does not integrate a recited judicial exception into a practical application is directed to the exception. Evaluating whether a claim integrates an abstract idea into a practical application is performed by a) identifying whether there are any additional elements recited in the claim beyond the abstract idea, and b) evaluating those additional elements individual and in combination to determine whether they integrate the abstract idea into a practical application, using one or more of the considerations laid out by the Supreme Court and the Federal Circuit. Exemplary considerations indicative that an additional element (or combination of elements) may have or has not been integrated into a practical application are set forth in the 2019 PEG
With respect to the instant claims, Claims 1, 8, 9 and 10 recite the additional elements of: at least one memory device, at least one processor and a graph database. It is particularly noted that the use of processor "as a tool" to perform an abstract method and steps for developing learning content that only amount to extra solution activity are indicated in the 2019 PEG as examples that an additional element has not been integrated into a practical application. Even in combination, the recited additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits, such as an improvement to a computing system, on practicing the abstract idea (STEP 2A, Prong 2: NO).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Claims 1, 8, 9 and 10 recite the additional elements of: at least one memory device, at least one processor and a graph database set forth above for Step 2A, Prong 2. Regarding these limitations: Applicant's specification describes these features in generic manner "… Machine 600 (e.g., computer system) may include a hardware processor 601 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory 602” in the Applicant’s published application, para. [0125]). There is no indication in the Specification that Applicants have achieved an advancement or improvement in computer for generating learning content based on feedback. Dependent claims 2 – 7 and 11 – 12 inherit the deficiencies of their respective parent claims through their dependencies and do not recite additional limitations sufficient to direct the claims to more than the claimed abstract idea, and are thus rejected for the same reasons.
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.
Claims 1- 12 are rejected under 35 U.S.C. 103 as being unpatentable over Sharma (US 2020/0357296 A1) in view of Holiday et al. (US 2008/0014569 A1)
Re claims 1, 8, 9, 10:
Sharma teaches 1. A system configured for at least one content producer to generate adaptive e-learning experiences for a plurality of learners (Sharma, Abstract, “personalized learning platform to users”; [0007], “The method includes creating learning curriculum map and learning course resources for the learners based on the learning outcomes”), the system comprising:
at least one memory device configured for storing instructions; and at least one processor coupled to the at least one memory device and configured to execute the instructions (Sharma, [01196]) to at least:
access a graph database storing a learner knowledge graph comprising an ontology for a plurality of learning topics (Sharma, fig. 5A, 506a – “Create a learning plan for your learner”; figs. 12A – 12B, “Assign Initial Learning Plans for Each Sub Topics”; figs. 13A – 13B; [0062], “prepare individual learning plans that helps in addressing the individual student problems, by the organization and sequencing of course content”);
develop content comprising at least one course material associated with one of the plurality of learning topics, wherein the content comprises a learning section (Sharma, [0007] – [0009], “creating learning curriculum map and learning course resources for the learners based on the learning outcomes … The method further includes providing personalized learning plans to the learners based on the recommendations”; [0451], “After adding Unit/Topic, academic user will create multiple Lesson plans under Unit/Topic by using multiple goals objective, which was mapped in Unit/Topics”); and wherein the content is associated with at least one learner experience type (Sharma, [0284], “provides a personalized learning experience to the students”; [0452], ‘Student/faculty/other officers will store the data in different formats like audio, video, ppt, doc, xlsx, swf, image”; [01412], “a student's personalized learning style and capabilities”);
distribute the at least one course material in accordance with a selected e-learning experience, wherein the content within each learning section is tagged with one of the plurality of learning topics suited for the e-learning experience (Sharma, [0096], “The student is provided a custom topic map, which maps all previously studied pages (the path taken)”; [01037], “they can be interpolated into the list of topics in their appropriate order”; [1454], “There may be different models for linguistic skills, logical skills, and expertise in the course topics”);
track learning events generated by the plurality of learner (Sharma, figs. 1 – 3 show a plurality of nodes (learning events)), wherein the learning events comprise at least one of viewing and interaction activities related to content consumption (Sharma, [0205], “completing the course material”; [0337], “first complete a task such as reading a text, viewing an image or video, listening to an audio recording, or responding to a test question in the node”; [0380]) and learner validation activities (Sharma, [0258], “The author creates the test node 10/, and prepares appropriate skill test material, such as test questions”; [0260], “the user is simply presented with the test. If the user passes the test, then he continues on the advanced path”);
generate learner event data from the learning events (Sharma, [0049], “a graphical status indicator on the custom map, wherein the graphical status indicator indicates a current location of a user on the custom map, the current location being a current node; and content of a current page of the electronic textbook corresponding to the current node”);
8. A method for generating adaptive e-learning experiences for a plurality of learners by at least one content producer, with a processor coupled to at least one memory device storing instructions executable by the processor to at least perform the operations (Sharma, Abstract, “personalized learning platform to users”; [0007], “The method includes creating learning curriculum map and learning course resources for the learners based on the learning outcomes”; [1196]) of:
accessing a graph database storing a learner knowledge graph comprising an ontology for a plurality of learning topics (Sharma, fig. 5A, 506a – “Create a learning plan for your learner”; figs. 12A – 12B, “Assign Initial Learning Plans for Each Sub Topics”; figs. 13A – 13B; [0062], “prepare individual learning plans that helps in addressing the individual student problems, by the organization and sequencing of course content”);
developing content comprising at least one course material associated with one of the plurality of learning topics (Sharma, [0007] – [0009], “creating learning curriculum map and learning course resources for the learners based on the learning outcomes … The method further includes providing personalized learning plans to the learners based on the recommendations”; [0451], “After adding Unit/Topic, academic user will create multiple Lesson plans under Unit/Topic by using multiple goals objective, which was mapped in Unit/Topics”), wherein the content comprises a learning section (Sharma, [0007] – [0009], “creating learning curriculum map and learning course resources for the learners based on the learning outcomes … The method further includes providing personalized learning plans to the learners based on the recommendations”; [0451], “After adding Unit/Topic, academic user will create multiple Lesson plans under Unit/Topic by using multiple goals objective, which was mapped in Unit/Topics”); and wherein the content is associated with at least one learner experience type (Sharma, [0284], “provides a personalized learning experience to the students”; [0452], ‘Student/faculty/other officers will store the data in different formats like audio, video, ppt, doc, xlsx, swf, image”; [01412], “a student's personalized learning style and capabilities”);
distributing the at least one course material in accordance with a selected e-learning experience, wherein the content within each learning section is tagged with one of the plurality of learning topics suited for the e-learning experience (Sharma, [0096], “The student is provided a custom topic map, which maps all previously studied pages (the path taken)”; [01037], “they can be interpolated into the list of topics in their appropriate order”; [1454], “There may be different models for linguistic skills, logical skills, and expertise in the course topics”);
tracking learning events generated by the plurality of learner (Sharma, figs. 1 – 3 show a plurality of nodes (learning events)), wherein the learning events comprise at least one of viewing and interaction activities related to content consumption (Sharma, [0205], “completing the course material”; [0337], “first complete a task such as reading a text, viewing an image or video, listening to an audio recording, or responding to a test question in the node”; [0380]) and learner validation activities (Sharma, [0258], “The author creates the test node, and prepares appropriate skill test material, such as test questions”; [0260], “the user is simply presented with the test. If the user passes the test, then he continues on the advanced path”);
generating learner event data from the learning events (Sharma, [0049], “a graphical status indicator on the custom map, wherein the graphical status indicator indicates a current location of a user on the custom map, the current location being a current node; and content of a current page of the electronic textbook corresponding to the current node”);
9. At a content producer, a computer readable medium storing instructions executable by a processor to generate adaptive e-learning experiences for a plurality of learners, wherein the instructions carry out the operations comprising:
accessing a graph database storing a learner knowledge graph comprising an ontology for a plurality of learning topics (Sharma, fig. 5A, 506a – “Create a learning plan for your learner”; figs. 12A – 12B, “Assign Initial Learning Plans for Each Sub Topics”; figs. 13A – 13B; [0062], “prepare individual learning plans that helps in addressing the individual student problems, by the organization and sequencing of course content”);
developing content comprising at least one course material associated with one of the plurality of learning topics (Sharma, [0007] – [0009], “creating learning curriculum map and learning course resources for the learners based on the learning outcomes … The method further includes providing personalized learning plans to the learners based on the recommendations”; [0451], “After adding Unit/Topic, academic user will create multiple Lesson plans under Unit/Topic by using multiple goals objective, which was mapped in Unit/Topics”), wherein the content comprises a learning section; and wherein the content is associated with at least one learner experience type (Sharma, [0007] – [0009], “creating learning curriculum map and learning course resources for the learners based on the learning outcomes … The method further includes providing personalized learning plans to the learners based on the recommendations”; [0451], “After adding Unit/Topic, academic user will create multiple Lesson plans under Unit/Topic by using multiple goals objective, which was mapped in Unit/Topics”); and wherein the content is associated with at least one learner experience type (Sharma, [0284], “provides a personalized learning experience to the students”; [0452], ‘Student/faculty/other officers will store the data in different formats like audio, video, ppt, doc, xlsx, swf, image”; [01412], “a student's personalized learning style and capabilities”);
distributing the at least one course material in accordance with a selected e-learning experience, wherein the content within each learning section is tagged with one of the plurality of learning topics suited for the e-learning experience (Sharma, [0096], “The student is provided a custom topic map, which maps all previously studied pages (the path taken)”; [01037], “they can be interpolated into the list of topics in their appropriate order”; [1454], “There may be different models for linguistic skills, logical skills, and expertise in the course topics”);
tracking learning events generated by the plurality of learner (Sharma, figs. 1 – 3 show a plurality of nodes (learning events)), wherein the learning events comprise at least one of viewing and interaction activities related to content consumption (Sharma, [0205], “completing the course material”; [0337], “first complete a task such as reading a text, viewing an image or video, listening to an audio recording, or responding to a test question in the node”; [0380]) and learner validation activities (Sharma, [0258], “The author creates the test node, and prepares appropriate skill test material, such as test questions”; [0260], “the user is simply presented with the test. If the user passes the test, then he continues on the advanced path”);
generating learner event data from the learning events (Sharma, [0049], “a graphical status indicator on the custom map, wherein the graphical status indicator indicates a current location of a user on the custom map, the current location being a current node; and content of a current page of the electronic textbook corresponding to the current node”);
10. A method for generating adaptive e-learning experiences for a plurality of learners by at least one content producer, comprising with a processor coupled to at least one memory device storing instructions executable by the processor to at least perform the operations (Sharma, Abstract, “personalized learning platform to users”; [0007], “The method includes creating learning curriculum map and learning course resources for the learners based on the learning outcomes”; [1196]) of:
associating each of the e-learning experiences with a learning topic comprising at least one adaptive learning variable (Sharma, [0007] – [0009], “creating learning curriculum map and learning course resources for the learners based on the learning outcomes … The method further includes providing personalized learning plans to the learners based on the recommendations”; [0451], “After adding Unit/Topic, academic user will create multiple Lesson plans under Unit/Topic by using multiple goals objective, which was mapped in Unit/Topics”;
executing a first set of instructions to train an adaptive learning model on a schedule or on-demand (Sharma, [0055], “custom map is updated in real-time”; [1334], “user's interactions and/or progress may be monitored in real-time. The custom maps may be updated in real-time. Alternatively, the monitoring and/or updating may be performed at a predetermined schedule, such as periodically. Alternatively, or additionally, the monitoring and/or updating may be triggered manually”; [1343]; [1345], ““Real-time” may also refer to the simultaneous or substantially simultaneous occurrence of a first event (e.g., updating of user profile) with respect to a second event (e.g., updating of a custom map). In other instances, the assessment module may update the custom map based on a predetermined periodic schedule (e.g., every second, every minute, every hour, every day, every week, every month, etc.). Alternatively or in addition, the assessment module may update the custom map upon manual instructions (e.g., such as by a user issuing a ‘refresh’ command). Alternatively or in addition, the assessment module may be triggered to update the custom map upon any change in the user profile. The interactive user interface may dynamically update the custom map for the user in real-time”; the custom map (adaptive learning model) can be updated manually (on-demand) or on a periodic schedule);
scoring data and learner knowledge graph from the at least one content producer as training data (Sharma, [0367], “threshold test scores … the lower threshold for the average path 12 was 80”; [0365], “the electronic textbook 5 can adjust the paths based on other metrics, such as average test scores for users taking a test at any given node, or combination of nodes”; [0096], “a custom topic map”; [0061], “each navigation path corresponds to a level of instruction, such as average, advanced or remedial”);
training the adaptive learning model with the training data to optimize the at least one adaptive learning variable (Sharma, [0258], “Path Transitions Based on Performance: It is also advantageous for the user to be permitted to change paths, as appropriate for the user's skill level and understanding of the material”; [0365], “the electronic textbook 5 can adjust the paths based on other metrics, such as average test scores for users taking a test at any given node, or combination of nodes”; [0809], “exposition, and that inform the ET's calibration of the user's performance and learning preferences. The record of the student's performance and learning choices as a whole can be analyzed for learning patterns, which may lead to adjustments of the student user's path through the material”; Sharma’s learning pattern / paths / custom map are adjusted based on the users’ skill level, performance and/or metrics); and
updating the learner knowledge graph with the outputted optimized at least one adaptive learning variable (Sharma, [0055], “the custom map is updated in real-time”; [0365], “the electronic textbook 5 can adjust the paths based on other metrics, such as average test scores for users”).
Sharma does not explicitly disclose anonymize the learner event data; share the learner event data with another at least one content producer; based on the learner event data, determine an effectiveness quotient of the content for teaching the course material.
Holiday et al. (US 2008/0014569 A1) teaches a learning system wherein students are provided with course content via the Internet and perform learning activities online (Holiday, Abstract).
Holiday further teaches
anonymize the learner event data (Holiday, [0017], “The system also monitors teacher effectiveness at teaching specific lessons or skills based on student progress, time used, feedback and review scores”; [0022], “The system records feedback and uses it to rate the effectiveness of the material, which is used to suggest material most effective at solving a particular problem”; [0051], “Teacher Rating--Cumulative rating of content by teachers (higher weightings for those teachers who are most effective)”; [0148], “materials may be ranked according to their effectiveness”; [0149], “examples or illustrations of the course materials may be marked as to their effectiveness. The examples and materials may be ranked according to their effectiveness, and the most effective materials may be presented to students.”; [0153], “Students may also be able to rate and comment on the teacher effectiveness and the effectiveness of examples and other content provided by the teacher”; the ratings, scores, rankings, comments and feedbacks are used to determine the effectiveness of the content);
share the learner event data with another at least one content producer (Holiday, [0004], “provides monitoring and ranking of student abilities and needs, teacher abilities and weakness, and content effectiveness, and can match teachers and students based on student needs and teacher abilities to meet those needs”; [0117] – [0118]; the ratings, scores, rankings, comments and feedbacks are used to determine the effectiveness of the content);
based on the learner event data, determine an effectiveness quotient of the content for teaching the course material (Holiday, [0110], “Selecting a piece of content with the highest effectiveness for teaching their students their needs”; [0149], “the course materials may be marked as to their effectiveness. The examples and materials may be ranked according to their effectiveness, and the most effective materials may be presented to students”; [0155], “Tracking the effectiveness of the content for each student allows the system to determine the overall effectiveness of the content in teaching, and allows the system to rate content according to effectiveness and present the most effective content to a student”).
Claim 10, inputting anonymized learner event data (Holiday, [0017], “The system also monitors teacher effectiveness at teaching specific lessons or skills based on student progress, time used, feedback and review scores”; [0022], “The system records feedback and uses it to rate the effectiveness of the material, which is used to suggest material most effective at solving a particular problem”; [0051], “Teacher Rating--Cumulative rating of content by teachers (higher weightings for those teachers who are most effective)”; [0148], “materials may be ranked according to their effectiveness”; [0149], “examples or illustrations of the course materials may be marked as to their effectiveness. The examples and materials may be ranked according to their effectiveness, and the most effective materials may be presented to students.”; [0153], “Students may also be able to rate and comment on the teacher effectiveness and the effectiveness of examples and other content provided by the teacher”; the ratings, scores, rankings, comments and feedbacks are used to determine the effectiveness of the content),
Therefore, in view of Holiday, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the system and method described in Sharma, by providing the effectiveness rating as taught by Holiday, in order to select a piece of content with the highest effectiveness for teaching their students their needs. Sort the content by the parameters and allow the teacher to pick content they're comfortable using from the top several contents (Holiday, [0103] – [0114]).
Re claim 2:
2. The system of claim 1, wherein the graph database is a centrally hosted solution (Sharma, fig. 18, 1850, 1860), and ontology for a plurality of learning topics is shared among each of the at least one content producer (Holiday, [0158], “effectiveness for a particular topic”).
Re claim 3:
3. The system of claim 2, wherein the instructions comprise a set of instructions executable by the processor to determine at least one of the plurality of learners’ progress based on the learner event data (Sharma, [0096]; [0204]; [0206]).
Re claim 4:
4. The system of claim 3, wherein the set of instructions comprises adaptive learning algorithms (Sharma, [0258], “Path Transitions Based on Performance: It is also advantageous for the user to be permitted to change paths, as appropriate for the user's skill level and understanding of the material”; [0365], “the electronic textbook 5 can adjust the paths based on other metrics, such as average test scores for users taking a test at any given node, or combination of nodes”; [0809], “exposition, and that inform the ET's calibration of the user's performance and learning preferences. The record of the student's performance and learning choices as a whole can be analyzed for learning patterns, which may lead to adjustments of the student user's path through the material”; Sharma’s learning pattern / paths / custom map are adjusted based on the users’ skill level, performance and/or metrics).
Re claim 5:
5. The system of claim 4, wherein an output feedback of the adaptive learning algorithms is received by the at least one service provider, and the at least one service provider uses the output feedback to refine the content (Sharma, [0181], “many author or user notes and comments naturally attach to connections 7, not to nodes 10. Such comments include explanatory comments clarifying why a particular connection was traversed”; [0289], “The user is offered recommendations for paths, nodes or connections to visit, optionally supported by comments (from the author or other users), based upon the user's experiences so far”; [1515], “Such comments can offer feedback to the author and information for other students in the form of ratings on electronic textbook features and responses to questions asked by the author in an accompanying comment. Student comments can also contribute to a systematic effort to link locations in the textbook traversal with relevant information on the web that is appropriate for the student population”).
Re claim 6:
6. The system of claim 1, wherein the at least one learner experience type is suitable for a learner environment comprising at least one of print, screen readers, learning management systems, mobile apps, and directly hosted web (Sharma, [1119], “PDF”; [0032], “websites”).
Re claim 7:
7. The system of claim 3, wherein the at least one learner experience type comprises at least one of Print for PDF; Print for Braille; Screen Readers via media-less/text only HTML 5; LMSes via traditional learning technology interoperability standard packages such as SCORM/AICC/xAPI which contain full course materials (the industry standard method); LMSes via “remote lms packages” using a technique to use traditional learning technology interoperability standard packages such as SCORM/AICC/xAPI as a vehicle to distribute iFramed and LTI enabled content which remotely reference full course materials; LMSes via “remotely managed” content where the system builds and updates the iFramed and LTI enabled content dynamically using the LMS’s APIs while maintaining ongoing and active management of the content centrally; and Mobile apps or website experiences without a LMS via direct hosting of APIs, HTML5 and similar technologies (Sharma, [1119], “PDF”; [0032], “websites”; [0876]).
Re claim 11:
11. The method of claim 10, wherein the anonymized event data comprises at least: an anonymized learner universally unique identifier (UUID); a learning topic unique identifier; a mastery score associated with the learner’s progress; an init value indicative of the initial mastery state assigned to the learner prior to commencement of learning; and a slip value associated with a risk of a failure to correctly answer due to inability to recall the learning (Holiday, [0017], “The system also monitors teacher effectiveness at teaching specific lessons or skills based on student progress, time used, feedback and review scores”; [0022], “The system records feedback and uses it to rate the effectiveness of the material, which is used to suggest material most effective at solving a particular problem”; [0051], “Teacher Rating--Cumulative rating of content by teachers (higher weightings for those teachers who are most effective)”; [0148], “materials may be ranked according to their effectiveness”; [0149], “examples or illustrations of the course materials may be marked as to their effectiveness. The examples and materials may be ranked according to their effectiveness, and the most effective materials may be presented to students.”; [0153], “Students may also be able to rate and comment on the teacher effectiveness and the effectiveness of examples and other content provided by the teacher”; the ratings, scores, rankings, comments and feedbacks are used to determine the effectiveness of the content).
Re claim 12:
12. The method of claim 11, wherein the learner knowledge graph the learning topic universally unique identifier; a learning topic label; node relationships defining cross dependencies of related learning topics; prerequisite learning topics for formalized dependencies within the node relationships; and adaptive learning variables related to the learning topic (Sharma, figs. 1 – 3 show a plurality of nodes (learning events); [0205], “completing the course material”; [0337], “first complete a task such as reading a text, viewing an image or video, listening to an audio recording, or responding to a test question in the node”; [0380]; [0258], “The author creates the test node 10/, and prepares appropriate skill test material, such as test questions”; [0260], “the user is simply presented with the test. If the user passes the test, then he continues on the advanced path”).
Conclusion
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/JACK YIP/ Primary Examiner, Art Unit 3715