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 .
Status of Claims
This action is in reply to the communications filed on 06/12/2025.
Claims 1-19 are currently pending and have been examined.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 06/12/2025 and 09/17/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 15 is objected to because of the following informalities: claim 15 recites “further comprising assessing the skill mastery a plurality of learners in a learner group…”. This limitation should be amended to recite “further comprising assessing the skill mastery of a plurality of learners in a learner group…”. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-19 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
First of all, claims must be directed to one or more of the following statutory categories: a process, a machine, a manufacture, or a composition of matter. Claims 1-13 are directed to a machine (“a system”), and claims 14-19 are directed to a process (“a method”). Thus, claims 1-19 satisfy Step One because they are all within one of the four statutory categories of eligible subject matter. Claims 1-19, however, are directed to an abstract idea without significantly more.
Regarding independent claim 1, the specific limitations that recite an abstract idea are:
[…] receive learner assessment data for the one or more core learning skills being assessed, and […] receiving the learner assessment data […].
[…] a plurality of learner profiles for a plurality of learners, each learner profile comprising a learner identification, the learner assessment data from each learning assessment […] attempted by the learner, and an identification of skill mastery of the learner in the plurality of core learning skills […].
[…] a plurality of learning activities, each learning activity associated with the one or more core learning skills; and
[…] receiving the identification of skill mastery of the learner in the plurality of core learning skills for at least one learner and prescribing at least one learning activity […] based on the skill mastery of the at least one learner in the one or more core learning skills.
Claims 1 and 2-13, by virtue of dependence, recite concepts of mental processes. In particular, the limitations identified above recite concepts of collecting information (i.e., receiving learner assessment data), organizing data (i.e., organizing learner profiles for a plurality of learners comprising a plurality of learner data, and organizing a plurality of learning activities associated with one or more core learning skills), and displaying a result of collecting and analyzing data (i.e., receiving an identification of skill mastery of a learner in a plurality of core learning skills and prescribing at least one learning activity based on the skill mastery of the learner in one or more core learning skills). See MPEP 2106.04(a)(2)(III).
Furthermore, claim 1 recites concepts of certain methods of organizing human activity. As a whole, the limitations identified above are directed towards collecting learner assessment information corresponding to learners (i.e., students) and prescribing learning activities to the learners based on an identification of skill masteries of the learners in one or more core learning skills. These limitations, as a whole, recite concepts of managing personal behavior in the form of teaching and following/providing instructions. See MPEP 2106.04(a)(2)(II)(C). This is further evidenced by the specification at ¶ [0022] and ¶ [0058]-¶ [0060].
The judicial exception recited above is not integrated into a practical application. The additional elements of the claim include a “learning assessment database comprising a plurality of learning assessment modules and a plurality of core learning skills, each learning assessment module associated with and for assessing one or more core learning skills”, “an interactive assessment device comprising a display screen and an assessment application displayed on the display screen”, “a processor”, “a learner profile database”, “learning assessment module”, “learning activities database”, and “prescriptive learning engine”. The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer components on which the abstract idea is implemented. See MPEP 2106.05(f).
Furthermore, the claim recites additional elements involving steps for storing/retrieving information in a memory, i.e., storing information in the “learning assessment database”, “learner profile database”, and “learning activities database”. These additional elements fail to integrate the claim into a practical application because the steps for storing/retrieving information in a memory amount to no more than mere data gathering/outputting, which is insignificant extra-solution activity. See MPEP 2106.05(g).
Finally, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, the additional elements, in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A). Furthermore, the additional elements involving steps for storing/retrieving information in a memory fail to amount to significantly more than the judicial exception because the courts have found storing/retrieving information in a memory to be well-understood, routine, and conventional activities. See MPEP 2106.05(d)(II). Because the invention is merely reciting well-understood, routine, and conventional activity, the additional elements of this claim which involve storing/retrieving information in a memory, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. Thus, claim 1 is not patent eligible.
Regarding independent claim 14, the specific limitations that recite an abstract idea are:
Providing a learner with a learning assessment […] the learning assessment […] associated with a plurality of core learning skills.
Assessing the learner on the plurality of core learning skills;
For the learner, assigning a skill mastery status for each of the plurality of core learning skills being assessed […];
Storing the skill mastery status for each of the plurality of core learning skills in a learner profile for the learner;
Selecting at least one learning activity […], the at least one learning activity associated with at least one core learning skill for which the learner does not yet have skill mastery status.
Therefore, claims 14 and 15-19, by virtue of dependence, recite concepts of mental processes. In particular, the limitations identified above recite concepts of observation and judgement (i.e., assessing the learner on the plurality of core learning skills, assigning a skill mastery status for each of the plurality of core learning skills being assessed, and selecting at least one learning activity associated with at least one core learning skill for which the learner does not yet have skill mastery status), and organizing information (i.e., storing the skill mastery status for each of the plurality of core learning skills in a learner profile for the learner). See MPEP 2106.04(a)(2)(III).
Furthermore, claim 14 recites concepts of certain methods of organizing human activity. As a whole, the limitations identified above are directed towards assessing learners (i.e., students) on a plurality core learning skills, assigning a skill mastery status for each of the core learning skills being assessed, and selecting learning activities for the learners associated with core learning skills for which the learner does not yet have mastery status. These limitations, as a whole, recite concepts of managing personal behavior in the form of teaching and following/providing instructions. See MPEP 2106.04(a)(2)(II)(C). This is further evidenced by the specification at ¶ [0022] and ¶ [0058]-¶ [0060].
The judicial exception recited above is not integrated into a practical application. The additional elements of the claim include a “learning assessment module associated with a plurality of core learning skills” and a “learning activities database”. The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer components on which the abstract idea is implemented. See MPEP 2106.05(f).
Furthermore, the claim recites additional elements involving steps for storing/retrieving information in a memory, i.e., “selecting at least one learning activity from a learning activities database”. These additional elements fail to integrate the claim into a practical application because the steps for storing/retrieving information in a memory amount to no more than mere data gathering/outputting, which is insignificant extra-solution activity. See MPEP 2106.05(g).
Finally, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, the additional elements, in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A). Furthermore, the additional elements involving steps for storing/retrieving information in a memory fail to amount to significantly more than the judicial exception because the courts have found storing/retrieving information in a memory to be well-understood, routine, and conventional activities. See MPEP 2106.05(d)(II). Because the invention is merely reciting well-understood, routine, and conventional activity, the additional elements of this claim which involve storing/retrieving information in a memory, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. Thus, claim 14 is not patent eligible.
Claim 2 describes the core learning skills for each grade level as being set by one or more of a standard curriculum, educational research, school board requirements, and jurisdictional requirements. Thus, claim 2 merely further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claim 1 from which the claim depends.
Claim 3 recites the same abstract idea as claim 1, by virtue of dependence, and is rejected for substantially the same reasons. The claim further introduces the additional elements of a “learning milestones database comprising a plurality of learning milestones”.
The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer components on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, either alone or in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A).
Claim 4 further describes collecting data pertaining to an assessment, and thus further describes the abstract idea. The claim further introduces the additional elements of steps for collecting information via generic computer components (“wherein the interactive assessment device can receive one or more of notes, audio, video, photograph of assessment, voice to text, and sensor data pertaining to the assessment”).
The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer components on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, either alone or in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A).
Claim 5 further describes collecting additional learner assessment data that is added to a learner profile, and this further describes the abstract idea. The claim further introduces the additional elements of steps for collecting information via generic computer components (“wherein the interactive assessment device is connected to a peripheral assessment tool, and the peripheral assessment tool collects additional learner assessment data”).
The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer components on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, either alone or in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A).
Claim 6 recites the same abstract idea as claims 1 and 5, by virtue of dependence, and is rejected for substantially the same reasons. The claim further introduces the additional elements of “wherein the peripheral assessment tool is a toy, hand puppet, card, tile, or manipulative”.
The abstract idea is not integrated into a practical application because the additional elements are merely generally linking the use of the abstract idea to a particular field of use. See MPEP 2106.05(h).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, either alone or in combination, are merely generally linking the use of the abstract idea to a particular field of use. Because merely generally linking the use of the abstract idea to a particular field of use cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A).
Claim 7 recites the same abstract idea as claims 1 and 5, by virtue of dependence, and is rejected for substantially the same reasons. The claim further introduces the additional elements of “wherein the peripheral assessment tool comprises an external sensor, embedded sensor, microphone, or camera”.
The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer components on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, either alone or in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A).
Claim 8 further describes providing auditory language instruction for completing an assessment, and thus further describes the abstract idea. The claim further introduces the additional elements of “wherein the interactive assessment device further comprises a speaker and the assessment module comprises auditory language instruction for completing the assessment module”.
The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer components on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, either alone or in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A).
Claim 9 further describes the learner assessment data as comprising completion time, error rate, and delay time. Thus, claim 9 further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claim 1 from which the claim depends.
Claim 10 further describes collecting information regarding an identification of skill mastery in a plurality of core learning skills for a group of learners and prescribing a specific learning activity for the group of learners based on the skill mastery in the plurality of core learning skills for the group of learners. Thus, claim 10 further describes the abstract idea.
The claim further introduces the additional elements of steps for collecting information via generic computer components, i.e., “wherein prescriptive learning engine receives […]”. The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer components on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, either alone or in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A).
Claim 11 further describes collecting learner assessment data for the core learning skills being assessed. Thus, claim 11 further describes the abstract idea.
The claim further introduces the additional elements of steps for collecting and displaying information via generic computer components, i.e., “wherein the interactive assessment device displays the learning assessment module and the processor receives the learner assessment data […]”. The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer components on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, either alone or in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A).
Claim 12 further describes the learner profile as comprising learner information and tailoring a learning assessment based on the learner information. Thus, claim 12 further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claim 1 from which the claim depends.
Claim 13 further describes the learner information as comprising a birth date, grade level, school, teacher, school board, educational history, family information, domestic situation, community or extracurricular associations, learner’s interests, cultural group, geographical area, migration history, languages spoked, and a first language. Thus, claim 13 further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claims 1 and 12 from which the claim depends.
Claim 15 further describes assessing the skill mastery of a plurality of learners in a learner group by sorting the plurality of learners in the learner group by a selected core learning skill to identify learners in the learning group who do not yet have skill mastery status in the selected core learning skill. Thus, claim 15 further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claim 14 from which the claim depends.
Claim 16 further describes selecting the at least one learning activity for the learner group based on the core learning skill mastery status for the set of learners in the learner group, and updating the learner profile for each learner in the learner group indicating that the learning activity was completed by each learner in the group of learners. Thus, claim 16 further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claims 14-15 from which the claim depends.
Claim 17 further describes comparing the skill mastery status to learning milestones in a learning milestones database. Thus, claim 17 further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claim 14 from which the claim depends.
Claim 18 further describes the learner profile a comprising learner information and tailoring the learning assessment based on the learner information. Thus, claim 18 further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claim 14 from which the claim depends.
Claim 19 further describes the learner information as comprising a birth date, grade level, school, teacher, school board, educational history, family information, domestic situation, community or extracurricular associations, learner’s interests, cultural group, geographical area, migration history, languages spoked, and a first language. Thus, claim 19 further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claims 14 and 18 from which the claim depends.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim 1 and 3-19 are rejected under 35 U.S.C. § 102(a)(2) as being anticipated by Novatin et al. U.S. Publication No. 2025/0378518, hereafter known as Novatin.
Claim 1: Novatin teaches the following:
A learning assessment database comprising a plurality of learning assessment modules and a plurality of core learning skills, each learning assessment module associated with and for assessing one or more core learning skills;(Abstract: System and methods for adaptive artificial intelligence based course template generation. The system is configured to generate a first course template and a first set of learning course content that adheres to the first course template); (¶ [0003]: the system may generate and display the learning course content via a graphical user interface); (¶ [0016]: the AI engine may adapt course content, including course templates, in real-time, which may offer personalized learning paths for an individual learner user); (¶ [0038]: the content assessment and development system may include one or more databases); (¶ [0044]: the content assessment and development system may develop and maintain instructor profiles); (¶ [0042]: the instructor profiles include learning topics/materials (e.g., fractions, verb tenses, etc.). Instructor profiles additionally include data relating to an instructor’s teaching goals, including learner users’ performance metrics indicating a proficiency with one or more topics (e.g., fractions, multiplication, chemical reactions, balancing equations, etc.)); (¶ [0040]: learner profiles include information relating to performance metrics for a learner user, such as scores for quizzes or tests); (¶ [0069]: course types and categories include, e.g., mathematics, science, literature, philosophy, etc.); (¶ [0045]: The learning content may be authored by third-party users, or may be developed for or by an instructor).
An interactive assessment device comprising a display screen and an assessment application displayed on the display screen to receive learner assessment data for the one or more core learning skills being assessed, and a processor for receiving the learner assessment data relevant to the learning assessment module; (¶ [0003]: the system may generate and display the learning course content via a client device graphical user interface); (¶ [0019]: client device may correspond to a user in a class); (¶ [0045]: the learning course content may include content associated with a learning course, such as webpages, lessons, coursework elements, charts, written work, videos, training materials, syllabi, various training interfaces, assessments, etc. The learning content may be authored by third-party users, or may be developed for or by an instructor); (¶ [0042]: see above); (¶ [0060]: the AI engine may receive feedback data including learner user performance metrics).
A learner profile database comprising a plurality of learner profiles for a plurality of learners, each learner profile comprising a learner identification, the learner assessment data for each learning assessment module attempted by the learner, and an identification of skill mastery of the learner in the plurality of core learning skills in the learning assessment database; (¶ [0038]: the system includes one or more databases, and may store/manage user data); (¶ [0039]: user data includes learner profiles and instructor profiles); (¶ [0073]: a particular learner user is associated with a learner user profile); (¶ [0040]: learner profiles may include user data specific to a particular learner user. Learner profile may include user history, scores, performance metrics, and the like for a particular learner user. Learner profile may include information relating to how a learner user interacts with course content, and content usage patterns of a user); (¶ [0062]: the AI engine may generate a first course template for a group of learner users. As part of the course, the learner users may take an assessment. The AI engine may determine whether learner users achieved a non-satisfactory score on the assessment (e.g. achieved a performance metric or score below a performance threshold indicating a certain proficiency or mastery level) or achieved a satisfactory score on the assessment. As such, the AI engine may adapt the course template and content for learner users based on the results); (¶ [0042]: Instructor profiles include data relating to an instructor’s teaching goals, including learner users’ performance metrics indicating a learner-user’s readiness to advance (e.g., to a subsequent grade level), and performance metrics indicating a proficiency with one or more topics (e.g., fractions, multiplication, chemical reactions, balancing equations, etc.)).
A learning activities database comprising a plurality of learning activities, each learning activity associated with the one more core learning skills; (¶ [0038]: the content assessment and development system may include one or more databases); (¶ [0045]: the databases may include the learning course content associated with a learning course); (¶ [0045]: the learning course content may include content associated with a learning course, such as lessons, coursework elements, charts, written work, videos, training materials, syllabi, various training interfaces, assessments, etc. The learning content may be authored by third-party users, or may be developed for or by an instructor); (¶ [0042]: see above); (¶ [0043]: instructor profiles may include one or more recordings. Recordings may be a recording of the instructor teaching a learning objective (e.g., how to balance an equation, how to simplify fractions, how to use quadratic equations, etc.). Recordings may be viewed on demand by one or more students).
A prescriptive learning engine for receiving the identification of skill mastery of the learner in the plurality of core learning skills for at least one learner and prescribing at least one learning activity from the learning activities database based on the skill mastery of the at least one learner in the one or more core learning skills. (¶ [0042]: see above); (¶ [0062]: see above); (¶ [0040]: learner profiles may include user data specific to a particular learner user. Learner profile may include user history, scores, performance metrics, and the like for a particular learner user); (¶ [0016]: the AI engine may adapt course content, including course templates, in real-time, which may offer personalized learning paths for an individual learner user. For example, AI engine can dynamically adjust a course template by automatically adjusting a difficulty level of assessments); (¶ [0074]: a server may synthesize relevant user data and learning content to identify one or more relationships among learning objectives, instructors, students, students facing challenges or achieving high scores, etc.); (¶ [0075]: the server may determine recommendations based on the determined relationships/patterns of the synthesized data, such as assignments tailored to student performance metrics, and generate the course templates based on the recommendations).
Claim 3: Novatin teaches the limitations of claim 1. Furthermore, Novatin teaches the following:
Further comprising a learning milestone database comprising a plurality of learning milestones. (¶ [0085]: the AI engine may access/retrieve profiles stored in a database); (¶ [0042]: the instructor profile may include information or data relating to an instructor user’s teaching goals or outcomes, including, e.g., learner users achieving a skill proficiency necessary for obtaining a professional certification or license (e.g., a human resources certification, a nursing certification, a CPA certification, etc.), learner users achieving a passing score on an advanced placement examination (e.g., AP Literature, AP Biology, etc.)); (¶ [0016]: the AI engine may adapt course content, including course templates, in real-time, which may offer personalized learning paths for an individual learner user).
Claim 4: Novatin teaches the limitations of claim 1. Furthermore, Novatin teaches the following:
Wherein the interactive assessment device can receive one or more of notes […] pertaining to the assessment. (¶ [0040]: the learner profile may include information relating to qualitative feedback provided by the learner user, such as, e.g., survey responses, forum discussions, unsolicited feedback, and the like); (¶ [0041]: the content assessment and development system may develop and maintain individual learner profiles based on learner user’s interactions with course materials, direct feedback, etc. For instance, the system may update a learner profile as additional user data becomes available, e.g., the learner user submits qualitative feedback, completes a new assessment, etc.).
Claim 5: Novatin teaches the limitations of claim 1. Furthermore, Novatin teaches the following:
Wherein the interactive assessment device is connected to a peripheral assessment tool, and the peripheral assessment tool collects additional learner assessment data which is added to the learner profile. (¶ [0029]: the system includes one or more user interface input devices integrated with the computing system. Input devices include a voice command recognition system, microphone, digital camera, webcam, eye gaze tracking device, a digital musical instrument, MIDI keyboard, and the like); (¶ [0041]: the content assessment and development system may develop and maintain individual learner profiles based on learner user’s interactions with course materials, assessment performances, and the like).
Claim 6: Novatin teaches the limitations of claim 5. Furthermore, Novatin teaches the following:
Wherein the peripheral assessment tool is […] or manipulative. (¶ [0029]: the system includes one or more user interface input devices integrated with the computing system. Input devices include a digital musical instrument, MIDI keyboard, and the like); (¶ [0041]: the content assessment and development system may develop and maintain individual learner profiles based on learner user’s interactions with course materials, assessment performances, and the like).
Claim 7: Novatin teaches the limitations of claim 5. Furthermore, Novatin teaches the following:
Wherein the peripheral assessment tool comprises an external sensor, embedded sensor, microphone, or camera. (¶ [0029]: the system includes one or more user interface input devices integrated with the computing system. Input devices include a voice command recognition system, microphone, digital camera, webcam, eye gaze tracking device, a digital musical instrument, MIDI keyboard, and the like).
Claim 8: Novatin teaches the limitations of claim 1. Furthermore, Novatin teaches the following:
Wherein the interactive assessment device further comprises a speaker and the assessment module comprises auditory language instruction for completing the assessment module. (¶ [0050]: the AI engine may generate a course template that aligns with an example-based teaching style, i.e., the course template may include, as learning course content, a large number of example problems to be worked through as part of teaching a course or portion thereof); (¶ [0029]: the system includes one or more user interface output devices integrated with the computing system. Output devices include audio output devices and speakers); (¶ [0040]: learner profiles may include information relating to effectiveness of different types of content for a learner user, including, e.g., audio).
Claim 9: Novatin teaches the limitations of claim 1. Furthermore, Novatin teaches the following:
Wherein the learner assessment data comprises one or more of assessment module completion time, error rate, and delay time. (¶ [0040]: the learner profile includes information relating to how a learner user interacts with course content or materials, such as, e.g., clicks, dwell time, time duration on a particular content section or learning objective, and the like. The learn profile further includes performance metrics, such as test scores).
Claim 10: Novatin teaches the limitations of claim 1. Furthermore, Novatin teaches the following:
Wherein the prescriptive learning engine receives the identification of skill mastery in the plurality of core learning skills for a group of learners and prescribes a specific learning activity for the group of learners based on the skill mastery in the plurality of core learning skills for the group of learners. (¶ [0016]: the AI engine may adapt course content in real time for individual learner users, a group of learner users, etc.); (¶ [0062]: the AI engine may generate a first course template for a group of learner users. As part of the course, the learner users may take an assessment. Based on the results of the assessment, the AI engine may generate a second course template (as a revised version of the first course template) for a subgroup of the learner users enrolled in the course, where the subgroup of learner users achieved a non-satisfactory score on the assessment (e.g., achieved a performance metric or score below a performance threshold indicating a certain proficiency or mastery level). The AI engine may generate the second course template such that the second course template includes supplementary material directed towards a learning objective or topic of the assessment such that the subgroup of learner users may further develop their proficiency or mastery level of that learning objective or topic. The learner users that achieved a satisfactory score on the assessment may continue to follow the first course template).
Claim 11: Novatin teaches the limitations of claim 1. Furthermore, Novatin teaches the following:
Wherein the interactive assessment device displays the learning assessment module and the processor receives the learner assessment data for the core learning skill being assessed by the learning assessment module. (¶ [0025] - ¶ [0026]: the computing system may correspond to any of the computing devices described and disclosed. The computing system includes one or more processors); (¶ [0003]: the system may generate and display the learning course content via a graphical user interface); (¶ [0016]: the AI engine may adapt course content, including course templates, in real-time, which may offer personalized learning paths for an individual learner user); (¶ [0060]: the AI engine may receive feedback data including learner user performance metrics); (¶ [0040]: learner profiles may include user data specific to a particular learner user. Learner profile may include user history, scores, performance metrics, and the like for a particular learner user)
Claim 12: Novatin teaches the limitations of claim 1. Furthermore, Novatin teaches the following:
Wherein the learner profile comprises learner information, and wherein the learning assessment module is tailored based on the learner information. (¶ [0040]: learner profiles may include user data specific to a particular learner user. Learner profile may include user history, scores, performance metrics, and the like for a particular learner user); (¶ [0016]: the AI engine may adapt course content, including course templates, in real-time, which may offer personalized learning paths for an individual learner user. For example, AI engine can dynamically adjust a course template by automatically adjusting a difficulty level of assessments); (¶ [0075]: the system server may determine recommendations, such as assignments tailored to student performance metrics, and generate the course templates based on the recommendations).
Claim 13: Novatin teaches the limitations of claim 12. Furthermore, Novatin teaches the following:
Wherein the learner information comprises one or more of […] learner’s interests […]. (¶ [0040]: The learner profile may include user preferences for a particular learner user); (¶ [0085]: Examples of preferences include interaction level, type of content, etc.).
Claim 14: Novatin teaches the following:
Providing a learner with a learning assessment module, the learning assessment module associated with a plurality of core learning skills; (Abstract: System and methods for adaptive artificial intelligence based course template generation. The system is configured to generate a first course template and a first set of learning course content that adheres to the first course template); (¶ [0003]: the system may generate and display the learning course content via a graphical user interface); (¶ [0016]: the AI engine may adapt course content, including course templates, in real-time, which may offer personalized learning paths for an individual learner user); (¶ [0069]: course types and categories include, e.g., mathematics, science, literature, philosophy, etc.).
Assessing the learner on the plurality of core learning skills; (¶ [0060]: the AI engine may receive feedback data including learner user performance metrics); (¶ [0040]: learner profiles include information relating to performance metrics for a learner user, such as scores for quizzes or tests).
For the learner, assigning a skill mastery status for each of the plurality of core learning skills being assessed by the learning assessment module; (¶ [0062]: the AI engine may generate a first course template for a group of learner users. As part of the course, the learner users may take an assessment. The AI engine may determine whether learner users achieved a non-satisfactory score on the assessment (e.g. achieved a performance metric or score below a performance threshold indicating a certain proficiency or mastery level) or achieved a satisfactory score on the assessment. As such, the AI engine may adapt the course template and content for learner users based on the results); (¶ [0040]: learner profiles include information relating to performance metrics for a learner user, such as scores for quizzes or tests).
Storing the skill mastery status for each of the plurality of core learning skills in a learner profile for the learner; (¶ [0040]: Learner profiles may include user data specific to a particular learner user. Learner profile may include user history, scores, performance metrics, and the like for a particular learner user. Learner profiles include information relating to performance metrics for a learner user, such as scores for quizzes or tests).
Selecting at least one learning activity from a learning activities database, the at least one learning activity associated with at least one core learning skill for which the learner does not yet have skill mastery status. (¶ [0038]: the content assessment and development system may include one or more databases); (¶ [0045]: the databases may include the learning course content associated with a learning course); (¶ [0062]: the AI engine may generate a first course template for a group of learner users. As part of the course, the learner users may take an assessment. Based on the results of the assessment, the AI engine may generate a second course template (as a revised version of the first course template) for a subgroup of the learner users enrolled in the course, where the subgroup of learner users achieved a non-satisfactory score on the assessment (e.g., achieved a performance metric or score below a performance threshold indicating a certain proficiency or mastery level). The AI engine may generate the second course template such that the second course template includes supplementary material directed towards a learning objective or topic of the assessment such that the subgroup of learner users may further develop their proficiency or mastery level of that learning objective or topic. The learner users that achieved a satisfactory score on the assessment may continue to follow the first course template).
Claim 15: Novatin teaches the limitations of claim 14. Furthermore, Novatin teaches the following:
Assessing the skill mastery of a plurality of learners in a learner group by sorting the plurality of learners in the learner group by a selected core learning skill to identify learners in the learning group who do not yet have skill mastery status in the selected core learning skill. (¶ [0062]: the AI engine may generate a first course template for a group of learner users. As part of the course, the learner users may take an assessment. Based on the results of the assessment, the AI engine may generate a second course template (as a revised version of the first course template) for a subgroup of the learner users enrolled in the course, where the subgroup of learner users achieved a non-satisfactory score on the assessment (e.g., achieved a performance metric or score below a performance threshold indicating a certain proficiency or mastery level). The AI engine may generate the second course template such that the second course template includes supplementary material directed towards a learning objective or topic of the assessment such that the subgroup of learner users may further develop their proficiency or mastery level of that learning objective or topic. The learner users that achieved a satisfactory score on the assessment may continue to follow the first course template).
Claim 16: Novatin teaches the limitations of claim 15. Furthermore, Novatin teaches the following:
Selecting the at least one learning activity for the learner group based on the core learning skill mastery status for the set of learners in the learner group, and updating the learner profile for each learner in the learner group indicating that the learning activity was completed by each learner in the group of learners. (¶ [0062]: see above); (¶ [0040]: Learner profiles may include user data specific to a particular learner user. Learner profile may include user history, scores, performance metrics, and the like for a particular learner user. Learner profiles include information relating to performance metrics for a learner user (such as test scores) and time duration on particular content sections, learning objectives, and the like).
Claim 17: Novatin teaches the limitations of claim 14. Furthermore, Novatin teaches the following:
Comparing the skill mastery status to learning milestones in a learning milestones database. (¶ [0085]: the AI engine may access/retrieve profiles stored in a database); (¶ [0042]: an instructor profile may include information or data relating to an instructor user’s teaching goals or outcomes, including, e.g., learner users achieving a passing score on an advanced placement examination (e.g., AP Literature, AP Biology, etc.)), learner users performance metrics indicating the learner users readiness to advance (e.g., to a subsequent grade level), and learner users ability to performance metrics indicating a proficiency with one or more learning objective or topics (e.g., fractions, multiplication, etc.)); (¶ [0016]: the AI engine may adapt course content, including course templates, in real-time, which may offer personalized learning paths for an individual learner user).
Claim 18: Novatin teaches the limitations of claim 14. Furthermore, Novatin teaches the following:
Wherein the learner profile comprises learner information, and wherein the method further comprises tailoring the learning assessment module based on the learner information. (¶ [0042]: see above); (¶ [0040]: learner profiles may include user data specific to a particular learner user. Learner profile may include user history, scores, performance metrics, and the like for a particular learner user); (¶ [0016]: the AI engine may adapt course content, including course templates, in real-time, which may offer personalized learning paths for an individual learner user. For example, AI engine can dynamically adjust a course template by automatically adjusting a difficulty level of assessments); (¶ [0074]: a server may synthesize relevant user data and learning content to identify one or more relationships among learning objectives, instructors, students, students facing challenges or achieving high scores, etc.); (¶ [0075]: the server may determine recommendations based on the determined relationships/patterns of the synthesized data, such as assignments tailored to student performance metrics, and generate the course templates based on the recommendations).
Claim 19: Novatin teaches the limitations of claim 18. Furthermore, Novatin teaches the following:
Wherein the learner information comprises one or more of […] learner’s interests […]. (¶ [0040]: The learner profile may include user preferences for a particular learner user); (¶ [0085]: Examples of preferences include interaction level, type of content, etc.).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 2 is rejected under 35 U.S.C. § 103 as being unpatentable over Novatin, in view of Askour et al. U.S. Publication No. 2025/0218307, hereafter known as Askour.
Claim 2: Novatin teaches the limitations of claim 1. Furthermore, Novatin does not explicitly teach, however Askour does teach, the following:
Wherein the plurality of core learning skills for each grade level are set by one or more of a standard curriculum, educational research, school board requirements, and jurisdictional requirements. (Abstract: An AI-based system generates personalized educational content tailored to individual students' abilities and needs. The platform analyzes academic standards, curriculum guidelines, and student data including IEP status using natural language processing to build customized profiles. AI algorithms are leveraged to dynamically generate lessons, assignments, recommendations, visual aids, and other educational content adapted for each learner. Administrative controls enable customization across districts and schools); (¶ [0014]: The invention is a web and mobile application called EZDucate platform. EZDucate is composed of 3 sub modules, EZDucate Flashcards, EZDucate Language and Arts, and EZDucate Mathematics); (¶ [0024]: FIG. 1 d is a response from the backend service after it successfully downloaded data from Virginia FCPS educational standards website according to the grade input by the administrator).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Novatin with the teachings of Askour by incorporating the features for setting the core learning skills for each grade level by a standard curriculum, school board requirements, and jurisdictional requirements, as taught by Askour, into the system of Novatin that is configured to generate learning content of core skills for students. One of ordinary skill in the art would have been motivated to make this modification with the purpose of “improving educational outcomes” (Abstract), as suggested by Askour. Furthermore, one of ordinary skill in the art would have recognized that the teachings of Askour are compatible with the system of Novatin as they share capabilities and characteristics. In particular, they are both systems directed towards adaptively generating educational content for learners.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORGE G DEL TORO-ORTEGA whose telephone number is (571)272-5319. The examiner can normally be reached Monday-Friday 9:00AM-6:00PM.
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/JORGE G DEL TORO-ORTEGA/Examiner, Art Unit 3628
/SHANNON S CAMPBELL/Supervisory Patent Examiner, Art Unit 3628