DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is in response to claims in application 18/888,699 filed on 9/18/2024.
The instant application claims benefit to provisional application #63/583,378 with a priority date of 9/8/2023.
The Pre-Grant publication # 20250095507 is published on 3/20/2025.
Claims 1-10 are pending.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claimed invention is to a computer devices (20) and thus fall within one of the four statutory categories (Step 1: YES).
Claim 1 is directed towards employing taxonomy-based classifications to generate vocabulary matching assessments for a student in a course curriculum to receive educational content data and institutional organization data, a content loading to associate vocabulary terms with course based on metadata derived from a relationship between the associated vocabulary term. The module configured to administer a plurality of periodic vocabulary matching assessments to the student, wherein each vocabulary matching assessment includes one or more associated vocabulary terms. The steps and software module are drawn to concept categorized as an actions that are receiving, observing, identifying, evaluating and judging of educational inputs. A concept that are mental processes and by including generating configuring module configured to administer a plurality of periodic vocabulary matching assessments to the student and processing of information they falls within the Certain Method of Organizing Human Activity groupings of abstract ideas subject to the 2019 Revised Patent Subject Matter Eligibility Guidance. They are predicted set of requirement for teaching learning environment without any significant improvement in functionality of machines. The use of revision by machine-learned model for taxonomy-based classifications could be categorized as a use mathematical calculations are falling within some mathematical concepts They are generally categorized as a grouping of an abstract idea (Step 2A: Prong 1 YES).
The independent claims do not include additional elements that are sufficient to be significantly more than the judicial exception because the limitations of “a computer system with interface display”, “a processor’, “a memory’, "network remote storage", "metadata derived from a relationship between the associated vocabulary term and the taxonomy-based classification” and “ a content loading module configured to associate vocabulary terms embedded in the educational content data with the course based on, for each associated vocabulary term, metadata derived from a relationship between the associated vocabulary term and the taxonomy-based classifications; and “databases of digital content” are determining for interaction with storage for evaluation. Hence not indicative of integration of a practical application (Step 2A: Prong 2 No).
The steps in the recited claims that are highlighted are a well-understood, routine, and conventional activities known in art. Fig.1,2 of the instant specification depict touchable object movements for a hardware/ software in a standard environment with panel implementing the process claimed here. As an example in case of Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, the activities of storing and retrieving of information in a memory of consumer electronic for a field of use purposes are recognized to be computer functions well-understood, routine, and conventional, when they are claimed in a merely generic manner. Further, there found to be no additional elements here in the claim recitation that improves the functioning of a computer itself to overcome the abstract idea rejection (Step 2B: No).
Claims 2-10 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additionally, taking the claimed elements individually yields no difference from taking them in combination because each element simply performs its respective function as discussed above. In other words, these claims merely apply an abstract idea to a programmable processor or computer and do not improve the performance of the process or computer itself or provide a technical solution to a problem in a technical field. The retrieval augmented generation chatbot (RAGC) module as claimed at a high level for a customizable corpus of supplemental instructional content do not effect a transformation of a particular article to a different state or thing, The underlying computing elements remain the same. Instead, the additional features merely amount to an instruction to apply the abstract idea using generic, functional, and conventional components well-known in the art. Mere instructions to apply an exception using the generic computer components cannot provide an inventive concept. Therefore, for these reasons, it appears that claims 1-10 are not patent-eligible under 35 USC 101.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1,2,7-10 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication Number US 20070172810 A1 to McCallum et al. (McCallum) in view of US Patent Application Publication Number 20170177703 A1 to Liu.
Claim 1. McCallum combination teaches a system for employing taxonomy-based classifications to generate vocabulary matching assessments for a student in a course curriculum, the system comprising:
a systemic database that receives educational content data and institutional organization data from one or more sources of educational information ( McCallum: Para 0009 receiving educational assessment of reading performance) ;
a taxonomy module that processes the institutional organization data to generate taxonomy-based classifications of the institutional organization data, wherein at least one taxonomy-based classification is a course having the course curriculum;
McCallum does not identify a taxonomy module that processes the institutional organization data to generate taxonomy-based classifications of the institutional organization data, wherein at least one taxonomy-based classification is a course having the course curriculum. Liu , however, teaches the identification of a taxonomy module that processes the institutional organization data to generate taxonomy-based classifications of the institutional organization data, wherein at least one taxonomy-based classification is a course having the course curriculum (¶0002 taxonomy mapping based generating a vector semantics; Para 0076 generated from processing institutional organization data; Para 0018 Sequence semantic embedding approach enables capturing contextual information and deep semantic meaning and is capable of handling large discrepancies in words such as synonyms, typos, compound words, split word, and the like for vocabulary). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate a taxonomy module that processes the institutional organization data to generate taxonomy-based classifications of the institutional organization data, as taught by lie, into the vocabulary based classification course curriculum system of McCallum, in order to provide training for a taxonomy-based classifications shared across vocabulary assessment levels.
{Liu is in the same field of educational adaptive assessment of vocabulary or word development by a method for automatically mapping taxonomies based on sequence semantic embedding, involves generating publication page, and inserting first elements into second elements based on assigned mapped category of first elements}
McCallum in combination with Liu teaches further a content loading module configured to associate vocabulary terms embedded in the educational content data with the course based on, for each associated vocabulary term, metadata derived from a relationship between the associated vocabulary term and the taxonomy-based classifications (McCallum: Fig.2 assessment loading content and parameters; Para 0021 loading module presents new sub-sets based on continuous assessment on changing difficulty level as such; Liu: Fig.3 match a category from a first taxonomy to a second taxonomy); and
a probe module configured to administer a plurality of periodic vocabulary matching assessments to the student, wherein each vocabulary matching assessment includes one or more associated vocabulary terms (McCallum: para 0023-0024Vocabulary test such as diagnostic online reading assessment; Para 0029 vocabulary matching assessments).
{Note: A taxonomy of vocabulary matching assessment can be organized by the cognitive process it tests, the context provided, and the specific format used. These factors determine what kind of word knowledge is being measured, ranging from simple recall to the ability to apply words in a complex context}
Claim 2. McCallum combination teaches the system of claim 1, wherein the probe module is further configured to establish, from previously administered vocabulary matching assessments, a baseline and a trendline based on each of the previously administered vocabulary matching assessments, wherein the trendline is updated in real-time (para 0009, 00029 adaptive diagnostic matching assessment and repeating the process ).
Claim 7. McCallum combination teaches the system of claim 2, further comprising an instructor insight module configured to generate a report comprising the baseline and the trendline (Para 0010 aggregate reporting).
Claim 8. McCallum combination teaches the system of claim 1, wherein the relationship between the associated vocabulary term and the taxonomy-based classifications is based on vector indexing (Liu: Fig.3).
Claim 9. McCallum combination teaches the system of claim 1, wherein the institutional organization data comprises a plurality of educational settings, a plurality of subjects, a plurality of courses of which the course is one, and one or more course levels for each course of the plurality of courses, wherein the taxonomy module defines a matrix of nested hierarchies from said pluralities of educational settings, subjects, courses, and course levels (McCallum Para 0009 educational settings with plurality of sub-sets reading test and assessment system such as illustrated from Fig. 3A-3FLiu: para 0043 hierarchical association and taxonomical module.
Claim 10. McCallum combination teaches the system of claim 9, wherein the plurality of educational settings comprises educational levels from kindergarten to post-secondary education, and wherein each said educational level defines a nested hierarchy of at least one subject, course and course level, respectively (Para 0026 words from first-grade to twelfth-grade difficulty).
Claims 3-6 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication Number US 20070172810 A1 to McCallum et al. (McCallum) in view of US Patent Application Publication Number 20170177703 A1 to Liu and further in view of US Patent Application Publication Number US 9275148 B1 to Elassaad.
Claim 3. McCallum in combination with Liu teaches the system of claim 2, without further comprising a retrieval augmented generation chatbot (RAGC) module. Elassaad, however, teaches augmented generation module having a customizable corpus of supplemental instructional content, and wherein the module is configured to provide conversational feedback to the student, wherein the conversational feedback is based on the customizable corpus of supplemental instructional content and the trendline (col. 3 lines 33 a customizable corpus of supplemental instructional content where an augmentation System relies on the data sources along with the user feedback and interests to generate on the fly relevant augmentation data for the task at hand such as to provide conversational feedback from customizable corpus of supplemental instructional content and the trendline to a student). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate a retrieval augmented generation chatbot (RAGC) module configured to provide conversational feedback to the student, wherein the conversational feedback is based on the customizable corpus of supplemental instructional content and the trendline, as taught by Elassaad, into the vocabulary based classification course curriculum system of McCallum as modified by Liu, in order to use augmented generated chatbot as an effective conversational and feedback tool.
Claim 4. McCallum in combination with and Elassaad teaches the system of claim 3, wherein the RAGC module is configured to provide conversational feedback to an instructor of the student, wherein the conversational feedback is based on the customizable corpus of supplemental instructional content and the trendline (Elassaad: col. 3 lines 9-18 customizable feedback e.g. to an instructor or a doctor).
Claim 5. McCallum in combination with and Elassaad teaches the system of claim 4, wherein the RAGC module is further comprises a hierarchical navigable functionality, wherein the customizable corpus of supplemental instructional content comprises a corpus of instructional strategies, and wherein the hierarchical navigable functionality identifies at least one of the instructional strategies of the corpus of instructional strategies so that the conversational feedback comprises identified instructional strategies (Elassaad col.13 lines 28-30 nested navigation capability)
Claim 6. McCallum in combination with and Elassaad teaches the system of claim 5, wherein the hierarchical navigable functionality builds a layered graph structure where vectors connect strategic content of each of the instructional strategies and trendline content of the trendline, wherein trendline content is based on at least one associated vocabulary term of at least one previously administered vocabulary matching assessments (Liu: Para 0064 the historical data is related to previous item as that well be from one previously administered vocabulary matching assessments) .
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20110065082 A1 Gal et al.
Device, system, and method of educational content generation.
US 20160323693 A1 METHOD AND SYSTEM FOR DYNAMIC APPLICATION MANAGEMENT Rathod
US 11610109 B2 Csar; Sebastian Alexander et al.
Language agnostic machine learning model for title standardization
US 10503569 B2 Bharti; Harish et al.
Feature-based application programming interface cognitive comparative benchmarking
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/S.Z/Examiner, Art Unit 3715 August 23, 2025
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715