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 .
Application Status
Present office action is in response to application filed 12/04/2024. Claims 1-14 are currently pending in the application.
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 2, 3, 4, 6 and dependents thereof 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. In claim 2, it is unclear whether the recitation of “one or more generative artificial intelligence models” is intended to be same as or different from the earlier recitation of “one or more generative artificial intelligence models” in parent claim 1. As a result, the metes and bounds of claim 2 cannot be discerned.
In claim 3, it is unclear whether the recitation of “identify an individual learner pattern from the multi-modal learner features extracted from the learner data” is intended to be same as or different from the earlier recitation of “identify an individual learner pattern from the multi-modal learner features extracted from the learner data” in parent claim 1. As a result, the metes and bounds of claim 3 cannot be discerned.
In claim 4, the recitation of “the provided content parameters” lacks antecedent basis. It is worth noting that the earlier recitation of “receive a set of content parameters” does not establish antecedent basis for an interpretation as “the received content parameters” because, by definition, a set may contain any number of elements (from an empty set to any desired number of elements). As a result, the metes and bounds of claim 4 cannot be discerned.
In claim 6, it is unclear whether the recitation of “a normalized computer-readable format” is intended to be same as or different from the earlier recitation of “a normalized computer-readable format” in parent claim 5. As a result, the metes and bounds of claim 6 cannot be discerned.
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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1: Statutory Category?
Independent claims 1 and 5 respectively recites “an educational computing device” (i.e. a machine) and “a computer-implemented method” (i.e. a process). As such, independent claims 1 and 5 are each directed to a statutory category of invention within § 101, i.e., machine, and process. (Step 1: YES).
Step 2A – Prong 1: Judicial Exception Recited?
Independent claim 1, analyzed as representative of the claimed subject matter, is reproduced below. The limitations determined to be abstract ideas are shown in italics. The additional element(s) recited at a high level of generality are shown in bold. The limitation(s) determined to be extra-solution activity are underlined.
An educational computing device for generating personalized instructional content, the educational computing device including a system memory and a processor in communication with the system memory, the system memory comprising:
[L1] a data acquisition module that causes the processor to: collect instructional data associated with an instructor;
[L2] and translate the instructional data into a normalized computer-readable format;
[L3] a model training module that causes the processor to: receive the instructional data in the selected format from the data acquisition module;
[L4] extract multi-modal features from the data; and
[L5] identify a pedagogical pattern from the multi-modal features extracted from the data, wherein the pedagogical pattern is associated with the instructor;
[L5] a dynamic course generation module that causes the processor to: employ one or more generative artificial intelligence models to generate instructional materials based on the pedagogical pattern associated with the instructor;
[L6] and translate the generated instructional materials into a format capable of display to an individual learner;
[L7] and a content integrity verification module that causes the processor to: collect verified information on the subject matter of the generated instruction material, wherein the verified information comprises information from one or more databases of vetted content;
[L8] compare the verified information with the generated instruction materials; identify differences between the verified information and the generated instruction materials; and
[L9] modify portions of the generated instruction materials which are inconsistent with the verified information such that the modified portions of the generated instruction materials are consistent with the verified information;
[L10] wherein the dynamic course generation module causes the processor to output a personalized instructional course including the updated generated instructional materials in the format capable of display to an individual learner.
The originally filed Specification, as published discloses the claimed invention ‘relates generally to education, and, more specifically, to a system and method that generate personalized courses using artificial intelligence, instructional design best practices, and learning management systems (LMS)” (¶ 2). It is common knowledge that humans/educators have long used pen and paper to generate personalized courses. Thus, other than reciting the “system memory”, “processor”, “one or more generative artificial intelligence models”, and “one or more databases” under the broadest reasonable interpretation, at least the italicized claim limitations may be performed using pen and paper, in the human mind, including observations, evaluations, and judgments and may also be characterized as a certain method of organizing human activity, i.e., managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Accordingly, the claim recites an abstract idea under Step 2A: Prong 1. (Step 2A – Prong 1: YES).
Step 2A – Prong 2: Integrated into a Practical Application?
The “system memory”, “processor”, “one or more generative artificial intelligence models”, and “one or more databases”, are recited at a high level of generality (see originally filed Specification as published (at least ¶ 27: The computing device 16 may comprise any electronic device capable of storing and processing data according to one or more instructions or algorithms. Example computing devices comprise laptop or desktop computers, smartphones, tablet computers, and cloud servers. Computing devices may comprise further hardware, firmware, and software components as disclosed herein; ¶ 28: Example sources from which the data acquisition module may receive or retrieve data include the instructor 12 or learner 14 (which may be facilitated through additional hardware of the computing device 16), database(s) 28, or from external computing devices such as remote servers hosting databases. In some implementations, the data acquisition module 18 further converts the received or retrieved data from a first format to a second format; ¶ 32: The dynamic course generation module 22 comprises a set of software instructions that, when executed on the processor of a computing device 16, cause the processor to execute one or more generative artificial intelligence models to generate instructional content as described further herein; ¶ 53: Model training processes known in the art or later developed may be used; ¶ 55: The content synthesis 135 may comprise employing one or more generative artificial models 30 to create text, images or graphics, audio, video, or other content, or combinations of any two or more content formats, corresponding to the course specification and objectives; ¶ 81: Educational content is culled from public data sources such as open academic repositories, educational forums, and digital libraries …; ¶ 132: … examples of the disclosure may be practiced within a general-purpose computer or in any other circuits or systems; ¶ 153: Verified information may also be received from one or more external computing devices (for example, a cloud server hosting a database of educational texts) via the communication systems of the computing device …; ¶ 195: This written description uses examples to disclose the disclosure, including the best mode, and also to enable any person skilled in the art to practice the disclosure, including making and using any devices or systems and performing any incorporated methods …. The lack of details about the “system memory”, “processor”, “one or more generative artificial intelligence models”, and “one or more databases” indicates that the additional element(s) is/are generic, or part of generic computer elements performing or being used in performing the generic functions claimed. The additional elements [L1]: “collect instructional data associated with an instructor” (data gathering), [L3]: “receive the instructional data” (data gathering), [L7]: “collect verified information on the subject matter” (data gathering) and [L10]: “output a personalized instructional course” (data presentation) simply add insignificant extra-solution activity to the judicial exception, i.e., mere data gathering and data presentation. Each of the data gathering and data presentation is generic and conventional. The claim limitations do not purport to improve the functioning of the “system memory”, “processor”, “one or more generative artificial intelligence models”, and “one or more databases”, do not improve the technology of the technical field, and do not require a “particular machine.” Rather, they are performed using generic components. Further, the claim fails to effect any particular transformation of an article to a different state. The recited steps in the claim fail to provide meaningful limitations to limit the judicial exception. In this case, the claim merely uses the claimed computer elements as a tool to perform the abstract idea.
Considering the elements of the claim both individually and as “an ordered combination” the functions implemented by the “system memory”, “processor”, “one or more generative artificial intelligence models”, and “one or more databases” at each step of the method are purely conventional. Each step performed in the claim does no more than require a generic computer/storybook assembly to perform a generic computer function. Thus, the claimed elements have not been shown to integrate the judicial exception into a practical application as set forth in the Revised Guidance which references the Manual of Patent Examining Procedure (“MPEP”) §§ 2106.04(d) and 2106.05(a)–(c) and (e)–(h). Because the abstract idea is not integrated into a practical application, the claim is directed to the judicial exception. (Step 2A, Prong Two: NO).
Step 2B: Claim provides an Inventive Concept?
As discussed with respect to Step 2A Prong Two, the “system memory”, “processor”, “one or more generative artificial intelligence models”, and “one or more databases” in the claim amounts to no more than mere instructions to apply the exception using generic components. The same analysis applies here in Step 2B, i.e., mere instructions to apply an exception using generic computer components cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Because the published Specification, as noted above (for example, ¶¶ 27, 28, 32, 53, 55, 81, 132, 153, 195) describes the “system memory”, “processor”, “one or more generative artificial intelligence models”, and “one or more databases” in general terms, without describing the particulars, the claim limitations may be broadly but reasonably construed as reciting conventional components and techniques, particularly in light of the published Specification sufficiently well-known that the specification does not need to describe the particulars of such additional element(s) to satisfy 35 U.S.C. § 112(a). See MPEP 2106.05(d), as modified by the USPTO Berkheimer Memorandum. Furthermore, the Berkheimer Memorandum, Section III (A)(1) explains that a specification that describes additional element(s) “in a manner that indicates that the additional element(s) is/are sufficiently well-known that the specification does not need to describe the particulars of such additional element(s) to satisfy 35 U.S.C. § 112(a)” can show that the elements are well understood, routine, and conventional); Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017) (“The claimed mobile interface is so lacking in implementation details that it amounts to merely a generic component (software, hardware, or firmware) that permits the performance of the abstract idea, i.e., to retrieve the user-specific resources.”. The generic description of the “system memory”, “processor”, “one or more generative artificial intelligence models”, and “one or more databases” indicates the claim steps are well-known enough that no further description is required for a skilled artisan to understand the process and that the implied component is used in a manner that is well-understood, routine, and conventional in the field. In particular, the recited data gathering steps [L1]: “collect instructional data associated with an instructor”, [L3]: “receive the instructional data” (data gathering), [L7]: “collect verified information on the subject matter” (data gathering) and data presentation step [L10} “output a personalized instructional course” amount to nothing more than well-understood, routine, and conventional activity because these limitations are not distinguished from generic, conventional data gathering and data presentation with a computer. SAP Am., Inc. v. InvestPic, LLC, 890 F.3d 1016, 1021 (Fed. Cir. 2018) (“[M]erely presenting the results of abstract processes of collecting and analyzing information . . . is abstract as an ancillary part of such collection and analysis”); Intellectual Ventures I LLC v. Capital One Financial Corp., 850 F.3d 1332, 1340 (Fed. Cir. 2017) (“[C]ollecting, displaying, and manipulating data” is an abstract idea); Smart Sys. Innovations, 873 F.3d at 1372 (concluding “claims directed to the collection, storage, and recognition of data are directed to an abstract idea.”).
Considered as an ordered combination, the computer components of representative independent claim 1 add nothing that is not already present when the steps are considered separately. The sequence of the steps is equally generic and conventional. See Inventor Holdings, LLC v. Bed Bath & Beyond, Inc., 876 F.3d 1372, 1378 (Fed. Cir. 2017) (sequence of data retrieval, analysis, modification, generation, display, and transmission). Hence, the “system memory”, “processor”, “one or more generative artificial intelligence models”, and “one or more databases” is/are generic, well-known, and conventional computing element(s). The use of the additional element(s) either alone or in combination amounts to no more than mere instructions to apply the judicial exception using generic computer component(s). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept, and thus the claims are patent ineligible. (Step 2B: NO).
In regard to the dependent claims:
Dependents claims 2-4 and 6-14 include all the limitations of corresponding independent claims 1 and 5 from which they depend and, as such, recite the same abstract idea(s) noted above for corresponding independent claims 1 and 5. Each additional claim element, for example, “processor” (claims 2-4, 8, 10-12 and 14) , “one or more generative artificial intelligence models” (claim 2), is recited as a generic computer component used according to its conventional purpose in a conventional manner. The Examiner fails to see any claim activity used in some unconventional manner nor does any produce some unexpected result. An invocation to use known technology in the manner it is intended to be used for its ordinary purpose is both generic and conventional. As per MPEP §§ 2106.05(a)–(c), (e)–(h), none of the limitations of claims 2-4 and 6-14 integrates the judicial exception into a practical application. Additionally, while dependent claims 2-4 and 6-14 may have a narrower scope than corresponding independent claims 1 and 5, no claim contains an “inventive concept” that transforms the corresponding claim into a patent-eligible application of the otherwise ineligible abstract idea(s). Therefore, dependent claims 2-4 and 6-14 are not drawn to patent eligible subject matter as they are directed to (an) abstract idea(s) without significantly more.
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) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claims 1-9, 11 and 14 are rejected under 35 U.S.C. 103 as obvious over Olla (US 20240412654 A1) in view of SINGHAL et al. (US 20200293586 A1) (SINGHAL) and Sivakoff (US 20080228868 A1).
Re claims 1 and 5:
[Claim 1] Olla discloses an educational computing device for generating personalized instructional content, the educational computing device including a system memory and a processor in communication with the system memory (at least ¶ 16: a complete suite of applications (apps) for real-time state of the art AI driven electronic educational method via networked computer systems and cloud-based mobile devices for educators, learners, and other stakeholders … designed for mobile phone and tablet computer platforms on the iOS and Android operating systems as well as traditional desktops and laptops), the system memory comprising: a data acquisition module that causes the processor to: collect instructional data associated with an instructor (at least ¶ 8: educator's input to tailor the updated educational material to the learner; ¶ 18: an educator will input some basic information regarding the course, such as the subject matter, the educational level of the learners, the length of time available for teaching the subject matter, a textbook or other materials that will be used).
Olla appears to be silent on but SINGHAL teaches or at least suggests causing the processor to translate the instructional data into a normalized computer-readable format; a model training module that causes the processor to: receive the instructional data in the selected format from the data acquisition module (at least ¶ 15: search engine computing system 100 may be configured to serve “raw” queries in the form of literal text input by the user … serve “normalized” queries in the form of a computer-readable description of query content, e.g., by processing a computer-readable description indicating an intent of a query representing a question, goal, and/or task of the user indicated by the query, by processing one or more entities in the query, and/or by processing syntactic structure of a query (e.g., a parse tree for the query). Query normalization may be performed by any suitable computer device(s), e.g., by a client computer 102 and/or the search engine computing system 100. Normalized queries may include relevant informational content of a query (e.g., relevant intents/entities) while limiting the amount of variability among queries (e.g., different raw queries that are rephrasings of the same question may be normalized into the same normalized query); ¶ 84: … a dialogue system according to the present disclosure may be trained to interact with different populations of users, using language models that are trained to work well for those populations based on language, dialect, accent, and/or any other features of speaking style of the population; ¶ 87: speech audio input may be processed to recognize user queries for a search engine, e.g., in addition or instead of user input via text in a search bar … speaker devices may be used to output speech audio, e.g., to provide information to the user, interact with the user in spoken conversation). It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have utilized the query normalization feature of SINGHAL to modify Olla as claimed because this would amount to no more than applying known techniques to a known device (method, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Olla in view of SINGHAL teaches or at least suggests extract multi-modal features from the data; and identify a pedagogical pattern from the multi-modal features extracted from the data, wherein the pedagogical pattern is associated with the instructor; a dynamic course generation module that causes the processor to: employ one or more generative artificial intelligence models to generate instructional materials based on the pedagogical pattern associated with the instructor; and translate the generated instructional materials into a format capable of display to an individual learner; and a content integrity verification module that causes the processor to: collect stored information on the subject matter of the generated instruction material, wherein the stored information comprises information from one or more databases of content; compare the stored information with the generated instruction materials; identify differences between the stored information and the generated instruction materials; and modify portions of the generated instruction materials which are inconsistent with the stored information such that the modified portions of the generated instruction materials are consistent with the stored information (at least Olla: at least ¶ 18: The AI based curriculum generator outputs this proposed curriculum for the educator's review, revisions, and approval; ¶ 19: the educator is able to review the progress of the curriculum for a given course as a whole and also of each learner enrolled in a given course … the AI curriculum generator reviews the mastery of the subject matter for each learner in a given course and provides not only feedback to the educator but also suggests revisions to the curriculum as needed. These revisions can then be approved, denied, or modified by the educator; ¶ 20: Personalized Generative AI Key represents a groundbreaking approach to personalizing education, leveraging generative AI to ensure that every learner benefits from an educational journey that is as unique as their own learning style and history. The personalized generative AI key evolves with a learner's educational essence involves a series of steps, combining direct input from the learner with inferred data through interaction analytics … the Generative AI Key allows learners to access different AI-enhanced educational platforms within the system, transferring their personalized learning essence seamlessly, and enabling continuous, adaptive learning experiences; ¶¶ 23, 24: uses generative AI to dynamically adapt and evolve the unique AI key for each user based on the individual's interactions, learning progress, and preferences. That is, as the learner begins to interact with the platform, the method analyzes their behavior to infer additional preferences and learning patterns … the personalized AI key is continuously updated in real-time based on ongoing learner interactions, assessments, and feedback. This ensures that the learner's profile evolves to reflect their current interests, abilities, and learning preferences. These updates are based on performance analytics and engagement metrics as well as periodic surveys to capture changes in interests or new skills acquired; ¶ 25: Learners are also encouraged to provide feedback on the relevance and effectiveness of the content, which is used to further refine their profile. Refinement tools include feedback surveys on content relevance and learning experience satisfaction as well as an option to manually update preferences and interests through the learner profile GUI. The method additionally refines the AI key through inferred information, including detailed engagement patterns … ; ¶ 27: the learner is able to accept or reject these suggestions for modifications; ¶ 29: Knowledge Concept Graphs (KCG) and Cognitive Blueprints (CB) to enhance personalized learning experiences … an educator also has access to a learner's KCGs and CBs via the educator's GUI in the educator portal when that learner is enrolled in course taught by a given educator. An initial KCG and CB are generated for a given learner during the initial assessment phase when a learner sets up their profile, where their knowledge base and preferences are mapped to create the first iteration of the KCG and CB. As learners engage with the system, data on their interactions, progress, and feedback are continuously collected and analyzed. The learner's KCG and CB are dynamically updated based on the latest data … updated KCGs and CBs are reported to an educator of a course in which the learner is enrolled for review and for any modification based on the educator's observations of that learner. Based on the updated KCG and CB, the system, in conjunction with the educator, adapts the learning content and methodologies, recommending personalized learning paths and resources; ¶ 34: robust authentication protocols and access control measures to verify user identities and restrict access to information based on user roles and permissions … All data transmitted between the user's device and the platform, as well as data stored on servers, is encrypted using advanced encryption standards. Users are authenticated through secure login processes, and access to data and platform features is controlled based on predefined roles and permissions. Regularly scheduled audits assess the platform's security posture, identify potential vulnerabilities, and implement remediation strategies to strengthen security measures. Privacy policies and practices are regularly reviewed and updated to ensure compliance with data protection laws, and users are informed about how their data is used and protected …).
Olla in view of SINGHAL appears to be silent on but Sivakoff teaches or at least suggests causing the processor to: collect verified information on the subject matter of the generated instruction material, wherein the verified information comprises information from one or more databases of vetted content; compare the verified information with the generated instruction materials; identify differences between the verified information and the generated instruction materials; and modify portions of the generated instruction materials which are inconsistent with the verified information such that the modified portions of the generated instruction materials are consistent with the verified information (at least ¶ 12: analyze such access and/or other computer interactions to either guide the student towards subjectively relevant information or prohibit access by the student of subjectively irrelevant or inappropriate information …; ¶ 26: Where received content is inappropriate due to a level of complexity (e.g., academic level), replacement content of appropriate complexity is automatically retrieved and presented to a user in place of the inappropriate content. Where received content is inappropriate due to cultural sensitivity, replacement content may also be retrieved and presented to a use; ¶ 29: The Correlation Engine 138 accepts information from the Recommendation Engine 136. The correlation engine utilizes the student profile to determine if the recommended content is appropriate for the student in accordance with parental controls. Appropriate content is forwarded to the student; ¶ 30: the correlation engine is an artificial intelligence (Al) system adapted to learn from each previous recommendation to improve thereby the relevance of subsequent recommendations. In one embodiment, correlation engine is adapted to improving the quality of the returned content to the user in terms of relevancy as well as appropriateness …; ¶ 39: The recommendation engine performs various processing tasks to determine whether the particular content or content sources are appropriate for the user and relevant to the indicated content interest. As a first approximation, the recommendation engine utilizes profile information, demographic information and the like to determine whether particular content may be useful/appropriate; ¶ 42: The peer-reviewed entity/database 355 comprises one or more organizations tasked with determining the appropriateness of content with respect to particular age groups, moral perspective and the like; ¶ 62: the recommendation engine … suggests appropriate content based upon a student's needs and/or curriculum; ¶ 65: a suite of tools and related services adapted to enable online media companies, content providers, interactive service providers, social networks, application vendors and the like to improve the value and appropriateness of their content to children; ¶ 74: various tools within the suite may be conceptualized as filtering tools wherein inappropriate content is removed from the content set made available to a student. Inappropriate content may be defined as content associated with off-limits topics from the perspective of, for example, a parent of a student. Additional filtering tools further narrow the available content by removing irrelevant content from the content set made available to a student. Irrelevant content may be defined as content associated with time wasting websites, subjects other than a subject presently being worked on by the student and the like …). It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have utilized Sivakoff’s feature of accessing vetted content sources to provide relevant, valuable and appropriate information to modify Olla in view of SINGHAL as claimed because this would amount to no more than applying known techniques to a known device (method, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Olla in view of SINGHAL and Sivakoff teaches or at least suggests wherein the dynamic course generation module causes the processor to output a personalized instructional course including the updated generated instructional materials in the format capable of display to an individual learner (Olla: at least ¶ 28: educational content is delivered in a manner most conducive to each learner's natural inclinations and abilities … dynamically adjusts the presentation of educational material to match the detected intelligences and preferences, incorporating suitable media, activities, and interaction modes, automatically tailoring the material to align with the learner's profile, selecting the types of media, complexity of information, and interaction methods that best suit their identified strengths and preferences; ¶ 32: authors/educators upload their educational content to the platform, where it is processed and formatted for interactive integration. The method prompts the educator/author to review the processed and formatted materials for any revisions that the author/educator would like to see. The AI engine analyzes the content structure and identifies key concepts and learning objectives for enhancement with interactive elements. The method then prompts the educator/author to create additional content for these enhancements and interactive elements. The AI then embeds these multimedia, quizzes, exercises, and simulations within the content based on the AI analysis, ready for learner interaction … As learners interact with the content, the AI engine continually refines and adjusts the interactive elements to match the learner's evolving needs and preferences, iteratively prompting the educator to create additional material enhancements that may then be incorporated into the AI based educational platform).
[Claim 5] Olla discloses a computer-implemented method for generating and delivering personalized instructional content, the computer implemented method implemented by an educational computing device including a system memory and a processor in communication with the system memory (at least ¶ 16: a complete suite of applications (apps) for real-time state of the art AI driven electronic educational method via networked computer systems and cloud-based mobile devices for educators, learners, and other stakeholders … designed for mobile phone and tablet computer platforms on the iOS and Android operating systems as well as traditional desktops and laptops), the computer-implemented method comprising: collecting via a data acquisition module instructional data from one or more data sources (at least ¶ 8: educator's input to tailor the updated educational material to the learner; ¶ 18: an educator will input some basic information regarding the course, such as the subject matter, the educational level of the learners, the length of time available for teaching the subject matter, a textbook or other materials that will be used).
Olla appears to be silent on but SINGHAL teaches or at least suggests processing via the data acquisition module the instructional data into a normalized computer-readable format; storing the processed instructional data on an electronic data storage media (at least ¶ 15: search engine computing system 100 may be configured to serve “raw” queries in the form of literal text input by the user … serve “normalized” queries in the form of a computer-readable description of query content, e.g., by processing a computer-readable description indicating an intent of a query representing a question, goal, and/or task of the user indicated by the query, by processing one or more entities in the query, and/or by processing syntactic structure of a query (e.g., a parse tree for the query). Query normalization may be performed by any suitable computer device(s), e.g., by a client computer 102 and/or the search engine computing system 100. Normalized queries may include relevant informational content of a query (e.g., relevant intents/entities) while limiting the amount of variability among queries (e.g., different raw queries that are rephrasings of the same question may be normalized into the same normalized query); ¶ 84: … a dialogue system according to the present disclosure may be trained to interact with different populations of users, using language models that are trained to work well for those populations based on language, dialect, accent, and/or any other features of speaking style of the population; ¶ 87: speech audio input may be processed to recognize user queries for a search engine, e.g., in addition or instead of user input via text in a search bar … speaker devices may be used to output speech audio, e.g., to provide information to the user, interact with the user in spoken conversation). It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have utilized the query normalization feature of SINGHAL to modify Olla as claimed because this would amount to no more than applying known techniques to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Olla in view of SINGHAL teaches or at least suggests developing via a model training module a pedagogical pattern associated with an instructor based on the stored instructional data; employing via the dynamic course generator one or more generative artificial intelligence models to create instructional materials based on the pedagogical pattern associated with the instructor (at least Olla: at least ¶ 18: The AI based curriculum generator outputs this proposed curriculum for the educator's review, revisions, and approval; ¶ 19: the educator is able to review the progress of the curriculum for a given course as a whole and also of each learner enrolled in a given course … the AI curriculum generator reviews the mastery of the subject matter for each learner in a given course and provides not only feedback to the educator but also suggests revisions to the curriculum as needed. These revisions can then be approved, denied, or modified by the educator; ¶ 20: Personalized Generative AI Key represents a groundbreaking approach to personalizing education, leveraging generative AI to ensure that every learner benefits from an educational journey that is as unique as their own learning style and history. The personalized generative AI key evolves with a learner's educational essence involves a series of steps, combining direct input from the learner with inferred data through interaction analytics … the Generative AI Key allows learners to access different AI-enhanced educational platforms within the system, transferring their personalized learning essence seamlessly, and enabling continuous, adaptive learning experiences; ¶¶ 23, 24: uses generative AI to dynamically adapt and evolve the unique AI key for each user based on the individual's interactions, learning progress, and preferences. That is, as the learner begins to interact with the platform, the method analyzes their behavior to infer additional preferences and learning patterns … the personalized AI key is continuously updated in real-time based on ongoing learner interactions, assessments, and feedback. This ensures that the learner's profile evolves to reflect their current interests, abilities, and learning preferences. These updates are based on performance analytics and engagement metrics as well as periodic surveys to capture changes in interests or new skills acquired; ¶ 25: Learners are also encouraged to provide feedback on the relevance and effectiveness of the content, which is used to further refine their profile. Refinement tools include feedback surveys on content relevance and learning experience satisfaction as well as an option to manually update preferences and interests through the learner profile GUI. The method additionally refines the AI key through inferred information, including detailed engagement patterns … ; ¶ 27: the learner is able to accept or reject these suggestions for modifications; ¶ 29: Knowledge Concept Graphs (KCG) and Cognitive Blueprints (CB) to enhance personalized learning experiences … an educator also has access to a learner's KCGs and CBs via the educator's GUI in the educator portal when that learner is enrolled in course taught by a given educator. An initial KCG and CB are generated for a given learner during the initial assessment phase when a learner sets up their profile, where their knowledge base and preferences are mapped to create the first iteration of the KCG and CB. As learners engage with the system, data on their interactions, progress, and feedback are continuously collected and analyzed. The learner's KCG and CB are dynamically updated based on the latest data … updated KCGs and CBs are reported to an educator of a course in which the learner is enrolled for review and for any modification based on the educator's observations of that learner. Based on the updated KCG and CB, the system, in conjunction with the educator, adapts the learning content and methodologies, recommending personalized learning paths and resources; ¶ 34: robust authentication protocols and access control measures to verify user identities and restrict access to information based on user roles and permissions … All data transmitted between the user's device and the platform, as well as data stored on servers, is encrypted using advanced encryption standards. Users are authenticated through secure login processes, and access to data and platform features is controlled based on predefined roles and permissions. Regularly scheduled audits assess the platform's security posture, identify potential vulnerabilities, and implement remediation strategies to strengthen security measures. Privacy policies and practices are regularly reviewed and updated to ensure compliance with data protection laws, and users are informed about how their data is used and protected …).
Olla in view of SINGHAL appears to be silent on but Sivakoff teaches or at least suggests comparing via a verifier the generated instructional materials against verified information sources; modifying, via the verifier, the generated instructional materials to align with the verified information based on the comparison of the generated instructional materials against verified information sources (at least ¶ 12: analyze such access and/or other computer interactions to either guide the student towards subjectively relevant information or prohibit access by the student of subjectively irrelevant or inappropriate information …; ¶ 26: Where received content is inappropriate due to a level of complexity (e.g., academic level), replacement content of appropriate complexity is automatically retrieved and presented to a user in place of the inappropriate content. Where received content is inappropriate due to cultural sensitivity, replacement content may also be retrieved and presented to a use; ¶ 29: The Correlation Engine 138 accepts information from the Recommendation Engine 136. The correlation engine utilizes the student profile to determine if the recommended content is appropriate for the student in accordance with parental controls. Appropriate content is forwarded to the student; ¶ 30: the correlation engine is an artificial intelligence (Al) system adapted to learn from each previous recommendation to improve thereby the relevance of subsequent recommendations. In one embodiment, correlation engine is adapted to improving the quality of the returned content to the user in terms of relevancy as well as appropriateness …; ¶ 39: The recommendation engine performs various processing tasks to determine whether the particular content or content sources are appropriate for the user and relevant to the indicated content interest. As a first approximation, the recommendation engine utilizes profile information, demographic information and the like to determine whether particular content may be useful/appropriate; ¶ 42: The peer-reviewed entity/database 355 comprises one or more organizations tasked with determining the appropriateness of content with respect to particular age groups, moral perspective and the like; ¶ 62: the recommendation engine … suggests appropriate content based upon a student's needs and/or curriculum; ¶ 65: a suite of tools and related services adapted to enable online media companies, content providers, interactive service providers, social networks, application vendors and the like to improve the value and appropriateness of their content to children; ¶ 74: various tools within the suite may be conceptualized as filtering tools wherein inappropriate content is removed from the content set made available to a student. Inappropriate content may be defined as content associated with off-limits topics from the perspective of, for example, a parent of a student. Additional filtering tools further narrow the available content by removing irrelevant content from the content set made available to a student. Irrelevant content may be defined as content associated with time wasting websites, subjects other than a subject presently being worked on by the student and the like …). It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have utilized Sivakoff’s feature of accessing vetted content sources to provide relevant, valuable and appropriate information to modify Olla in view of SINGHAL as claimed because this would amount to no more than applying known techniques to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Olla in view of SINGHAL and Sivakoff teaches or at least suggests wherein the dynamic course generation module causes the processor to output a personalized instructional course including the updated generated instructional materials in the format capable of display to an individual learner (Olla: at least ¶ 28: educational content is delivered in a manner most conducive to each learner's natural inclinations and abilities … dynamically adjusts the presentation of educational material to match the detected intelligences and preferences, incorporating suitable media, activities, and interaction modes, automatically tailoring the material to align with the learner's profile, selecting the types of media, complexity of information, and interaction methods that best suit their identified strengths and preferences; ¶ 32: authors/educators upload their educational content to the platform, where it is processed and formatted for interactive integration. The method prompts the educator/author to review the processed and formatted materials for any revisions that the author/educator would like to see. The AI engine analyzes the content structure and identifies key concepts and learning objectives for enhancement with interactive elements. The method then prompts the educator/author to create additional content for these enhancements and interactive elements. The AI then embeds these multimedia, quizzes, exercises, and simulations within the content based on the AI analysis, ready for learner interaction … As learners interact with the content, the AI engine continually refines and adjusts the interactive elements to match the learner's evolving needs and preferences, iteratively prompting the educator to create additional material enhancements that may then be incorporated into the AI based educational platform).
Re claims 2-4, 6-9, 11 and 14:
[Claim 2] Olla in view of SINGHAL and Sivakoff teaches or at least suggests where the system memory of the computing device further comprises: an adaptive learning orchestrator that causes the processor to: collect learner data from one or more inputs regarding an individual learner's interaction with the system; extract multi-modal learner features from the learner data; and identify an individual learner pattern from the multi-modal learner features extracted from the learner data, wherein the individual learner pattern is associated with the individual learner (at least Olla: ¶¶ 20-21: the Personalized Generative AI key is created when a leaner is presented with a series of survey questions designed to gather explicit information about their preferred learning styles (visual, auditory, kinesthetic, etc.), interests including specific subjects or topics the learner is interested in or wishes to avoid, skill levels including existing knowledge or proficiency in specific subject areas or skills, engagement patterns such as preferred content types, session lengths, and learning schedules, and performance data such as outcomes from assessments and quizzes to gauge mastery and areas for improvement; ¶ 23: as the learner begins to interact with the platform, the method analyzes their behavior to infer additional preferences and learning patterns; ¶ 28: If at any point the educator feels it is necessary to further modify the material presented or the learning experience for a particular learner, the educator can add input, thereby modifying or overriding the AI); and a personalization module configured to: employ one or more of the generative artificial intelligence models to at least one of generate supplemental instruction materials or modify the generated instructional materials based on the individual learner pattern (at least Olla: ¶ 8: utilizing the AI Key to inform AI-driven educational tools and services and an educator about the learner's personalized learning profile; and iteratively updating the educational material using the AI-driven educational tools and services and the educator's input to tailor the updated educational material to the learner based on the dynamically updated AI key …prompts the educator to create or provide additional educational content tailored to the learner's personalized learning profile, the learner's interactions with the educational material, and the learner's progress; ¶ 9: an artificial intelligence (AI) driven electronic educational method that adapts and customizes to a learner's particular learning style, preferences, progress, and acquired knowledge; ¶ 27: tailor the learning experience uniquely to each individual learner); and translate the modified or supplemental generated instructional materials into a format capable of display to an individual learner (at least Olla: ¶ 27: utilizes AI algorithms to adjust the learning content and pathways that are presented to a learner in an AI enhanced learning platform in real-time … the preemptive customizations are presented to the learner and/or the educator in an interactive visual format on the respective GUIs and the preemptive customizations can be further customized or modified by either the learner and/or the educator …. Based on the analysis, the method dynamically adjusts the learning content, difficulty levels, and presentation styles to best suit the learner's current state; ¶ 28: the learning preferences of a given learner are integrated into the learner's unique AI key, providing a personalized learning experience that adapts to the diverse ways individuals perceive, process, and engage with information … dynamically adjusts the presentation of educational material to match the detected intelligences and preferences, incorporating suitable media, activities, and interaction modes, automatically tailoring the material to align with the learner's profile).
[Claim 3] Olla in view of SINGHAL and Sivakoff teaches or at least suggests where the system memory of the computing device further comprises: an adaptive learning orchestrator that causes the processor to: collect learner data from one or more inputs regarding an individual learner's interaction with the system; extract multi-modal learner features from the learner data; identify an individual learner pattern from the multi-modal learner features extracted from the learner data, wherein the individual learner pattern is associated with the individual learner (at least Olla: ¶¶ 20-21: the Personalized Generative AI key is created when a leaner is presented with a series of survey questions designed to gather explicit information about their preferred learning styles (visual, auditory, kinesthetic, etc.), interests including specific subjects or topics the learner is interested in or wishes to avoid, skill levels including existing knowledge or proficiency in specific subject areas or skills, engagement patterns such as preferred content types, session lengths, and learning schedules, and performance data such as outcomes from assessments and quizzes to gauge mastery and areas for improvement; ¶ 23: as the learner begins to interact with the platform, the method analyzes their behavior to infer additional preferences and learning patterns; ¶ 28: If at any point the educator feels it is necessary to further modify the material presented or the learning experience for a particular learner, the educator can add input, thereby modifying or overriding the AI); and provide the individual learner pattern to the dynamic course generation module; and wherein the dynamic course generation module causes the processor to: employ one or more of the generative artificial intelligence models to create the generated instructional materials based on the individual learner pattern (at least Olla: ¶ 8: utilizing the AI Key to inform AI-driven educational tools and services and an educator about the learner's personalized learning profile; and iteratively updating the educational material using the AI-driven educational tools and services and the educator's input to tailor the updated educational material to the learner based on the dynamically updated AI key …prompts the educator to create or provide additional educational content tailored to the learner's personalized learning profile, the learner's interactions with the educational material, and the learner's progress; ¶ 9: an artificial intelligence (AI) driven electronic educational method that adapts and customizes to a learner's particular learning style, preferences, progress, and acquired knowledge; ¶ 27: utilizes AI algorithms to adjust the learning content and pathways that are presented to a learner in an AI enhanced learning platform in real-time … the preemptive customizations are presented to the learner and/or the educator in an interactive visual format on the respective GUIs and the preemptive customizations can be further customized or modified by either the learner and/or the educator …. Based on the analysis, the method dynamically adjusts the learning content, difficulty levels, and presentation styles to best suit the learner's current state; ¶ 28: the learning preferences of a given learner are integrated into the learner's unique AI key, providing a personalized learning experience that adapts to the diverse ways individuals perceive, process, and engage with information … dynamically adjusts the presentation of educational material to match the detected intelligences and preferences, incorporating suitable media, activities, and interaction modes, automatically tailoring the material to align with the learner's profile).
[Claim 4] Olla in view of SINGHAL and Sivakoff teaches or at least suggests where the system memory of the computing device further comprises a learning management system integrator that causes the processor to: receive a set of content parameters from a learning management software application; compare the generated instructional materials to the provided content parameters; modify the generated instructional materials based on the provided content parameters; and communicate the instructional materials generated by the system to the learning management software application without manual intervention (at least Olla: ¶ 18: an educator will input some basic information regarding the course, such as the subject matter, the educational level of the learners, the length of time available for teaching the subject matter, a textbook or other materials that will be used … In response to these inputs from the educator and based on the learning keys of the learners in a particular course (discussed below), the AI based curriculum generator generates a proposed curriculum that is a best fit for the subject matter, teaching style, and unique learning characteristics of the learners for a particular course; ¶ 23: the method analyzes their behavior to infer additional preferences and learning patterns; ¶ 24: the personalized AI key is continuously updated in real-time based on ongoing learner interactions, assessments, and feedback … These updates are based on performance analytics and engagement metrics as well as periodic surveys to capture changes in interests or new skills acquired; ¶ 25: refines the AI key through inferred information; ¶ 28: Using AI, the method continuously refines the understanding of a learner's preferences and intelligences based on engagement metrics and feedback, fine-tuning the adaptiveness of the content delivery).
[Claim 6] Olla in view of SINGHAL and Sivakoff teaches or at least suggests collecting, via the data acquisition module, data regarding an individual learner's interaction with a computer educational system (at least Olla: ¶ 8: capturing a learner's initial learning preferences, styles, and knowledge; ¶ 20: a learner interacts with the system to create an initial profile that captures their learning styles, preferences, and any existing knowledge or skills … continuously updates the AI Key based on learner interactions, newly acquired knowledge, and evolving preference); translating, via the data acquisition module, the data regarding the individual learner's interaction with the computer educational system into a normalized computer-readable format; storing the processed individual learner data on the electronic data storage media of the computing device (at least SINGHAL: ¶ 15: search engine computing system 100 may be configured to serve “raw” queries in the form of literal text input by the user … serve “normalized” queries in the form of a computer-readable description of query content, e.g., by processing a computer-readable description indicating an intent of a query representing a question, goal, and/or task of the user indicated by the query, by processing one or more entities in the query, and/or by processing syntactic structure of a query (e.g., a parse tree for the query). Query normalization may be performed by any suitable computer device(s), e.g., by a client computer 102 and/or the search engine computing system 100. Normalized queries may include relevant informational content of a query (e.g., relevant intents/entities) while limiting the amount of variability among queries (e.g., different raw queries that are rephrasings of the same question may be normalized into the same normalized query); Olla: ¶ 20: … The key for a given user acts as a passport to personalized learning experiences, carrying detailed profiles of their learning preferences, styles, and historical data; ¶ 30: … offering learners personalized progress reports); developing, via the model training module, an individual learner pattern associated with a particular instructor based on the stored individual learner data; employing via the dynamic course generator one or more generative artificial intelligence models to create or modify instructional materials based on the individual learner pattern (at least Olla: ¶ 20: the Generative AI Key allows learners to access different AI-enhanced educational platforms within the system, transferring their personalized learning essence seamlessly, and enabling continuous, adaptive learning experiences; ¶ 21: … gather explicit information about their preferred learning styles (visual, auditory, kinesthetic, etc.), interests including specific subjects or topics the learner is interested in or wishes to avoid, skill levels including existing knowledge or proficiency in specific subject areas or skills, engagement patterns; ¶ 23: the learner begins to interact with the platform, the method analyzes their behavior to infer additional preferences and learning patterns; ¶ 25: refines the AI key through inferred information, including detailed engagement patterns … By combining direct input with inferred analytics, the GenAI Key becomes a dynamic, evolving representation of the learner's educational profile, enabling personalized learning pathways that adapt over time to meet each learner's unique needs and preferences; ¶ 27: This AI-driven approach to adaptive learning and personalization significantly enhances the effectiveness and engagement of e-learning platforms, offering a highly tailored educational journey that evolves with each learner's individual needs, preferences, and goals).
[Claim 7] Olla in view of SINGHAL and Sivakoff teaches or at least suggests receiving via a learning management system integrator a set of content parameters from a learning management software application; modifying, via the learning management system integrator, instructional materials generated by the method to comply with the provided content parameters; and communicating via the learning management system integrator the instructional materials generated by the method to the learning management software application as such instructional materials are created (at least Olla: ¶ 18: an educator will input some basic information regarding the course, such as the subject matter, the educational level of the learners, the length of time available for teaching the subject matter, a textbook or other materials that will be used … In response to these inputs from the educator and based on the learning keys of the learners in a particular course (discussed below), the AI based curriculum generator generates a proposed curriculum that is a best fit for the subject matter, teaching style, and unique learning characteristics of the learners for a particular course; ¶ 23: the method analyzes their behavior to infer additional preferences and learning patterns; ¶ 24: the personalized AI key is continuously updated in real-time based on ongoing learner interactions, assessments, and feedback … These updates are based on performance analytics and engagement metrics as well as periodic surveys to capture changes in interests or new skills acquired; ¶ 25: refines the AI key through inferred information; ¶ 28: Using AI, the method continuously refines the understanding of a learner's preferences and intelligences based on engagement metrics and feedback, fine-tuning the adaptiveness of the content delivery).
[Claim 8] Olla in view of SINGHAL and Sivakoff teaches or at least suggests where the system memory of the computing device further comprises a normalizer that causes the processor to detect inconsistencies in the format of the instructional data; and translate instructional data not in a first format into the first format (at least Olla: ¶ 28: the method ensures that educational content is delivered in a manner most conducive to each learner's natural inclinations and abilities; SINGHAL: ¶ 84: … a dialogue system according to the present disclosure may be trained to interact with different populations of users, using language models that are trained to work well for those populations based on language, dialect, accent, and/or any other features of speaking style of the population; Sivakoff: ¶ 26: Where received content is inappropriate due to a level of complexity (e.g., academic level), replacement content of appropriate complexity is automatically retrieved and presented to a user in place of the inappropriate content. Where received content is inappropriate due to cultural sensitivity, replacement content may also be retrieved and presented to a user).
[Claim 9] Olla in view of SINGHAL and Sivakoff teaches or at least suggests an instructor profile (at least Olla: ¶ 17: an educator sets up a profile within the system using a username and password. Within each educator profile, the educator is able to set up a course section for each course taught by that educator). Additionally, Olla discloses “capturing a learner's initial learning preferences, styles, and knowledge to generate an AI Key unique to the learner”. Hence, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have modified Olla in view of SINGHAL and Sivakoff where the model training module utilizes the one or more generative artificial intelligence models to develop an instructor profile similarly to doing so for a learner because a person of ordinary skill has good reason to pursue the known options within his or her grasp. If this leads to the anticipated success, it is likely the product not of innovation but of ordinary skill and common sense. It is common knowledge that generative networks include convolutional neural networks (CNNs), transformers, or recurrent neural networks (RNNs). Hence, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have modified Olla in view of SINGHAL and Sivakoff where the model training module utilizes transformer-based neural network architecture to develop an instructor profile because this would have been an obvious matter of choice.
[Claim 11] Olla in view of SINGHAL and Sivakoff teaches or at least suggests where the adaptive learning orchestrator causes the processor to: collect an individual learner's input of natural language; and employ one or more natural language processing algorithms to extract learner data from the natural language input (at least SINGHAL: ¶ 11: enable a client computer 102 to perform searches using any suitable queries (e.g., natural language keywords, regular expression patterns, Boolean operators for composing multiple queries, etc.); ¶ 12: receive queries via natural language speech utterances received at the microphone, and to output search results via speech audio output at the speaker … any other search interface, e.g., a speech-based natural language search interface). It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have utilized SINGHAL’s natural language search features to modify Olla in view of SINGHAL and Sivakoff as claimed because this would amount to no more than applying known techniques to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
[Claim 14] Olla in view of SINGHAL and Sivakoff teaches or at least suggests where the content integrity verification module further causes the processor to: collect one or more educational content standards; compare the generated instructional materials to the educational content standards; identify whether the generated instructional materials comply with the educational content standards; modify portions of the generated instruction materials which are inconsistent with the educational content standards such that the modified portions of the generated instruction materials are consistent with the educational content standards (at least Olla: ¶ 28: the method ensures that educational content is delivered in a manner most conducive to each learner's natural inclinations and abilities; SINGHAL: ¶ 62: Whenever data is stored, accessed, and/or processed, the data may be handled in accordance with privacy and/or security standards; Sivakoff: ¶ 21: … the compliance engine 159 associates the identified patterns with relevant federal, state or local statutes, school codes of conduct and the like to determine whether the activity may be legally actionable or actionable within the context of a school disciplinary system. Optionally, the activity and the relevant federal, state or local statutes, school codes of content and the like are combined in a report for use by law enforcement or education authorities; ¶ 26: Where received content is inappropriate due to a level of complexity (e.g., academic level), replacement content of appropriate complexity is automatically retrieved and presented to a user in place of the inappropriate content. Where received content is inappropriate due to cultural sensitivity, replacement content may also be retrieved and presented to a user). Hence, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify Olla in view of SINGHAL and Sivakoff as claimed because this would amount to no more than applying known techniques to a known method (device, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Claims 10 and 12-13 are rejected under 35 U.S.C. 103 as obvious over Olla in view of SINGHAL and Sivakoff, as applied to claims 1 and 2 above, and further in view of Peirce et al. (US 20200175058 A1) (Peirce).
Re claims 10 and 12-13:
[Claim 10] Olla in view of SINGHAL and Sivakoff discloses “educational content is delivered in a manner most conducive to each learner's natural inclinations and abilities … dynamically adjusts the presentation of educational material to match the detected intelligences and preferences, incorporating suitable media, activities, and interaction modes, automatically tailoring the material to align with the learner's profile, selecting the types of media, complexity of information, and interaction methods that best suit their identified strengths and preferences”. See Olla, ¶ 28. However, Olla in view of SINGHAL and Sivakoff appears to be silent on but Peirce teaches or at least suggests causing the processor to: receive data regarding the comparative effectiveness of two or more formats of instructional materials; and select from among the generated instructional materials the materials only those materials in the format having the highest comparative effectiveness (at least ¶ 12: FIG. 1 shows … a system that detects that a user needs to learn something, determines learning parameters, and curates a content bundle based on the learning parameters; ¶ 13: FIG. 2 shows another … system in which a user inputs information relating to something the user needs to learn, the system determines learning parameters, and the user receives a curated content bundle based on the learning parameter; ¶ 98: compare how quickly and effectively the user's proficiency level improved across different content formats. Control circuitry 304 may incorporate which content formats are most effective for which types of events. For example, control circuitry 304 may determine that a text-based content format improved the user's proficiency level most efficiently in the past for events similar to the event (e.g., event 215). In response, control circuitry may determine that these content format preferences (e.g., content format preferences 245) should be used to prepare the user for the event (e.g., event 215). In another example, control circuitry may determine that video-based content formats with particular endorsements were effective in the past and should be used again … determine effectiveness … then pair the most effective content format preferences (e.g., content format preferences 245) for a particular type of event (e.g., event 215) in a database). It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have utilized the most effective content format preferences pairing feature\ of Peirce to modify Olla as claimed because this would amount to no more than applying known techniques to a known device (method, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
[Claim 12] Olla in view of SINGHAL and Sivakoff discloses “educational content is delivered in a manner most conducive to each learner's natural inclinations and abilities … dynamically adjusts the presentation of educational material to match the detected intelligences and preferences, incorporating suitable media, activities, and interaction modes, automatically tailoring the material to align with the learner's profile, selecting the types of media, complexity of information, and interaction methods that best suit their identified strengths and preferences”. See Olla, ¶ 28. However, Olla in view of SINGHAL and Sivakoff appears to be silent on but Peirce teaches or at least suggests where the adaptive learning orchestrator further causes the processor to: compare two or more formats of instructional materials with respect to an individual learner as part of the individual learner pattern to criteria; select one of the two or more formats based on the comparison; and modify the generated instructional materials into the selected format (at least ¶ 12: FIG. 1 shows … a system that detects that a user needs to learn something, determines learning parameters, and curates a content bundle based on the learning parameters; ¶ 13: FIG. 2 shows another … system in which a user inputs information relating to something the user needs to learn, the system determines learning parameters, and the user receives a curated content bundle based on the learning parameter; ¶ 98: compare how quickly and effectively the user's proficiency level improved across different content formats. Control circuitry 304 may incorporate which content formats are most effective for which types of events. For example, control circuitry 304 may determine that a text-based content format improved the user's proficiency level most efficiently in the past for events similar to the event (e.g., event 215). In response, control circuitry may determine that these content format preferences (e.g., content format preferences 245) should be used to prepare the user for the event (e.g., event 215). In another example, control circuitry may determine that video-based content formats with particular endorsements were effective in the past and should be used again … determine effectiveness … then pair the most effective content format preferences (e.g., content format preferences 245) for a particular type of event (e.g., event 215) in a database). It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have utilized the most effective content format preferences pairing feature\ of Peirce to modify Olla as claimed because this would amount to no more than applying known techniques to a known device (method, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
[Claim 13] SINGHAL discloses “correlation criteria 136 may be based on natural language processing, machine learning, artificial intelligence, data mining, according to direct one-to-one matching, “fuzzy” matching (e.g., matching with at least a threshold similarity), and/or probabilistic matching”. However, Olla in view of SINGHAL, Sivakoff and further in view of Peirce appears to be silent on where the adaptive learning orchestrator utilizes a multi-armed bandit algorithm to select the instructional material format based on the comparison of the two or more formats to the criteria. Nonetheless, a “multi-armed bandit algorithm” is a particular type of algorithm used in probability theory and machine learning. Hence, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the invention, to have modified Olla in view of SINGHAL, Sivakoff and further in view of Peirce as claimed because this would amount to no more than applying known techniques to a known device (method, or product) ready for improvement to yield predictable results. See KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416 (2007) (“The combination of familiar elements according to known methods is likely to be obvious when it does no more than yield predictable results.”).
Conclusion
The prior art made of record and not relied upon is listed in the attached PTO
Form 892 and is considered pertinent to applicant's disclosure.
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/EDDY SAINT-VIL/Primary Examiner, Art Unit 3715
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