DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Drawings
The drawings are objected to because Fig. 2 is so blurry as to be illegible. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claims 2-18 are objected to because of the following informalities:
Claims 2-4 and 6-8 separate the preamble from the body of the claim with a comma while this separating comma is missing from claims 5 and 9-18. This decreases clarity. Uniformity is recommended.
Dependent claim 7 inherits the deficiencies of its respective parent claims, and is thus objected to under the same rationale.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 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.
Regarding claim 1, it is unclear whether the memory storing one or more large language models, user history records, and one or more databanks of items is the same element as the computer memory and/or non-transitory computer readable storage medium that is/are coupled with the computer hardware processor or a separate element. Thus, one of ordinary skill in the art would not be apprised of the metes and bounds of the patent protection sought. For the purposes of compact prosecution, the memory, the computer memory, and the non-transitory computer readable storage medium are construed as the same element. Dependent claims 2-18 inherit the deficiencies of their respective parent claims, and are thus rejected under the same rationale.
Further regarding claim 1, it is unclear whether the “computer processor" is the same element as the “computer hardware processor” or a separate element. This lack of clarity is caused by the difference in recitation between these two and other elements which are referencing earlier recited terms. See, for example, “a computer system” and “the system” in claim 1 and “a computer method” and “the method” in claim 19. For purposes of compact prosecution, “the computer processor” is construed as the same element as “a computer hardware processor”. Dependent claims 2-18 inherit the deficiencies of their respective parent claims, and are thus rejected under the same rationale.
Further regarding claim 1, it is unclear how “the prescribed curriculum being a reduced data size for efficient storage and transmission”. In particular,
Claims 19 and 20 each recite the limitation "the one or more large language models" in line 3 of claim 19 and line 4 of claim 20. There is insufficient antecedent basis for this limitation in each claim.
Claims 19 and 20 each recite the limitation "the one or more databanks" in lines 5-6 of claim 19 and line 6 of claim 20. There is insufficient antecedent basis for this limitation in each claim.
Claims 19 and 20 each recite the limitation "the applicant interface" in line 8 of claim 19 and line 9 of claim 20. There is insufficient antecedent basis for this limitation in each claim.
Claims 19 and 20 each recite the limitation "the user history records" in line 12 of claim 19 and line 13 of claim 20. There is insufficient antecedent basis for this limitation in each claim.
Claims 19 and 20 each recite the limitation "the user" in lines 17-18 of the claim 19 and line 18 of claim 20. There is insufficient antecedent basis for this limitation in each claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without including additional elements that are sufficient to amount to significantly more than the judicial exception itself.
Step 1
The instant claims are directed to products and a method which falls under the four statutory categories (STEP 1: YES).
Step 2A, Prong 1
Independent claim 1 recites:
A computer system for cognitive tests for scalable precision education for a user, the system comprising:
a memory storing one or more large language models, user history records, and one or more databanks of items;
a computer hardware processor coupled with computer memory, a non-transitory computer readable storage medium, and an applicant interface residing on an electronic device, the computer processor configured to:
tune the one or more large language models for knowledge and skill classification and extraction using a dataset of labeled examples, wherein each labeled example has a corresponding classification label;
classify and extract skill items and knowledge items from the one or more databanks of items based on categorization confidence scores generated by the tuned one or more large language models;
administer a cognitive test, at the applicant interface residing on the electronic device, using the skill items and knowledge items, the cognitive test producing a set of resulting scores indicating a user evaluation in skill and knowledge, respectively;
generate, by a conversation agent enabled by the one or more large language models, a customized prompting based on the user history records and the user evaluation in skill and knowledge;
receive, by the conversation agent, user response data, the conversation agent classifying the user response data to identify recommended skills and knowledge for development; and
generate a prescribed curriculum customized for the user based at least on the recommended skills and knowledge for development, and transmit the prescribed curriculum for the user, the prescribed curriculum being a reduced data size for efficient storage and transmission while providing effective skills and knowledge development for the user.
Independent claim 19 recites:
A computer method for cognitive tests for scalable precision education, the method comprising:
tuning the one or more large language models for knowledge and skill classification and extraction using labeled examples;
classifying and extracting skill items and knowledge items from the one or more databanks of items based on categorization confidence scores generated by the one or more large language models;
administering a cognitive test, at the applicant interface, using the skill items and knowledge items, the cognitive test producing a set of resulting scores indicating a user evaluation in skill and knowledge, respectively;
generating, by a conversation agent enabled by the one or more large language models, a customized prompting based on the user history records and the user evaluation in skill and knowledge;
receiving, by the conversation agent, user response data, the conversation agent classifying the user response data to identify recommended skills and knowledge for development; and
generating and outputting at the applicant interface, a prescribed curriculum for the user based at least on the recommended skills and knowledge for development.
Independent claim 20 recites:
A non-transitory computer readable medium storing computer interpretable instructions, which when executed by a processor, cause the processor to execute a method for cognitive tests for scalable precision education, the method comprising:
tuning the one or more large language models for knowledge and skill classification and extraction using labeled examples;
classifying and extracting skill items and knowledge items from the one or more databanks of items based on categorization confidence scores generated by the one or more large language models;
administering a cognitive test, at the applicant interface, using the skill items and knowledge items, the cognitive test producing a set of resulting scores indicating a user evaluation in skill and knowledge, respectively;
generating, by a conversation agent enabled by the one or more large language models, a customized prompting based on the user history records and the user evaluation in skill and knowledge;
receiving, by the conversation agent, user response data, the conversation agent classifying the user response data to identify recommended skills and knowledge for development; and
generating and outputting at the applicant interface, a prescribed curriculum for the user based at least on the recommended skills and knowledge for development.
All of the foregoing underlined elements identified above amount to the abstract idea grouping of a certain method of organizing human activity because they amount to managing personal behavior or interactions between people (including social activities, teaching, and following rules or instructions) by collecting information, analyzing the collected information, and outputting the results of the collection and analysis. Additionally, steps identified above are interpreted as a series of steps that could reasonably be performed by mental processes with the aid of pen and paper because the claims, under their broadest reasonable interpretation, cover performance of the limitations in the mind but for the recitation of generic computer components. See MPEP 2106.04(a)(2)(III)(C) - A Claim That Requires a Computer May Still Recite a Mental Process. Even if humans would use a physical aid to help them complete the recited steps, the use of such physical aid does not negate the mental nature of these limitations.
The dependent claims merely further define the judicial exception.
Therefore, the claims recite a judicial exception. (STEP 2A, PRONG 1: YES).
Step 2A, Prong 2
This judicial exception is not integrated into a practical application because the independent and dependent claims do not include additional elements that are sufficient to integrate the exception into a practical application under the considerations set forth in MPEP 2106.04(d). The elements of the claims above that are not underlined constitute additional elements.
The following additional elements, both individually and as a whole, merely generally link the judicial exception to a particular technological environment or field of use: a computer system comprising a memory and a computer hardware processor coupled with computer memory, an non-transitory computer readable storage medium, and an applicant interface residing on an electric device (claim 1); a conversation agent enabled by the one or more large language models (claims 1, 19, and 20); a client web application with a curriculum interface (claim 12); a client web application with a reviewer interface (claim 13); a client web application with an applicant interface (claim 14); reciting the adaptive test is a “computer adaptive test” (claim 14); reciting the method is a “computer method (claim 19); the applicant interface (claims 19 and 20); . This is evidenced by the manner in which these elements are disclosed in the drawings and the instant specification. For example, Fig. 1 and 6 illustrate the elements as collections of conventionally arranged, non-descript black boxes, while Fig. 3-5 illustrate the claimed invention as purely software (Fig. 2 is illegible). Similarly, at least para. 47-61, 90-94, 141-147, and 152-161 of the specification provide stock descriptions of generic computer hardware and software components in any arrangement and the mere use of language learning models (LLMs) reciting conventional tuning steps to merely apply the LLMs to the desired task. Furthermore, the computer components are merely an attempt to link the abstract idea to a particular technological environment, but do not result in an improvement to the technology or computer functions employed. For example, the claims make clear that the claimed invention is directed towards providing a personalized curriculum to the user based on an analysis of test scores, not any improvement to the computerized technology used itself. This amounts to an admission that the use of LLMs and a conversational agent are merely tools to link the judicial exception to a computerized environment, but that the claimed invention does not provide any improvements to the technology or computer functions employed. This further evidences that the judicial exception is not implemented with a particular machine or manufacture, and thus is not providing any technical solution to a technical problem particularly when the mere use of LLMs are common features for data analysis and content generation. The claims do not recite any specific rules with specific characteristics that improve the functionality of the computer system. Again, this is evidenced by the disclosure of the mere use of natural language processing and LLMs in results-based language. None of the hardware offer a meaningful limitation beyond generally linking the performance of the steps to a particular technological environment, that is, implementation via computers. Again, this is evidenced by the manner in which these elements are disclosed in the drawings and specification as identified above. It is noted that claim 1 reciting in results-based language “the prescribed curriculum being a reduced data size for efficient storage and transmission while providing effective skills and knowledge development for the user” is not an additional element nor does it amount to an improvement to computer functionality at least because the disclosure is silent regarding the requisite technical details necessary to carry out the function. See MPEP 2106.05(a)(I). Additionally, the claims do not apply or use a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition nor do they apply or use a judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of the additional elements does not affect this analysis. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224-26 (2014). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (STEP 2A, PRONG 2: NO).
Step 2B
The independent and dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under the considerations set forth in MPEP 2106.05. As identified in Step 2A, Prong 2, above, the claimed system and the process it performs does not require the use of a particular machine, nor does it result in the transformation of an article. Although the claims recite computer components, identified above, for performing at least some of the recited functions, these elements are recited at a high level of generality in a conventional arrangement for performing their basic computer functions (i.e., receiving, processing, transmitting/outputting data). This is evidenced by the manner in which these elements are disclosed in the instant specification. For example, Fig. 1 and 6 illustrate the elements as collections of non-descript black boxes, while Fig. 3-5 (Fig. 2 is illegible) illustrate the claimed invention as purely software. Similarly, at least para. 47-61, 90-94, 141-147, and 152-161 of the specification provide stock descriptions of generic computer hardware and software components in any arrangement. Furthermore, the computer components are merely an attempt to link the abstract idea to a particular technological environment, but do not result in an improvement to the technology or computer functions employed. For example, the claims make clear that the claimed invention is directed towards providing a personalized curriculum to the user based on an analysis of test scores, not any improvement to the computerized technology used itself. This amounts to an admission that the use of LLMs and a conversational agent are merely tools to link the judicial exception to a computerized environment, but that the claimed invention does not provide any improvements to the technology or computer functions employed. This further evidences that the judicial exception is not implemented with a particular machine or manufacture, and thus is not providing any technical solution to a technical problem particularly when the mere use of machine learning models are common features for data analysis and content generation. The claims do not recite any specific rules with specific characteristics that improve the functionality of the computer system. Again, this is evidenced by the disclosure of the mere use and conventional tuning of LLMs in results-based language. None of the hardware offer a meaningful limitation beyond generally linking the performance of the steps to a particular technological environment, that is, implementation via computers. Again, this is evidenced by the manner in which these elements are disclosed in the instant specification as identified above. Viewed as a whole, these additional claim elements do not provide meaningful limitation to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea of itself (STEP 2B: NO).
Therefore, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Rowe et al. (US 2006/0166174, hereinafter referred to as Rowe).
Regarding claim 1, Rowe teaches a computer system for cognitive tests for scalable precision education for a user, the system comprising:
a memory storing one or more neural networks, user history records, and one or more databanks of items (Rowe, Fig. 6, Data Storage, AI Engine; Fig. 10, Database, Content Activities, Cognitive Data Model, AI Engine);
a computer hardware processor coupled with computer memory, a non-transitory computer readable storage medium, and an applicant interface residing on an electronic device (Rowe, Fig. 10, Server, Learning Management System (LMS)), the computer processor configured to:
tune the one or more neural networks for knowledge and skill classification and extraction using a dataset of labeled examples, wherein each labeled example has a corresponding classification label (Rowe, para. 48-49 describe this. Para. 79, “the dataset is large”);
classify and extract skill items and knowledge items from the one or more databanks of items based on categorization confidence scores generated by the tuned one or more neural networks ((Rowe, para. 45, “Its output is a classification system that is used to predict the best lessons to assign to the new students, given only their entrance cognitive state.”);
administer a cognitive test, at the applicant interface residing on the electronic device, using the skill items and knowledge items, the cognitive test producing a set of resulting scores indicating a user evaluation in skill and knowledge, respectively (Rowe, Fig. 8, Initial Placement Test; para. 34, “The 'Assessment Results' module contains the results of any assessment instruments applied outside the context of a lesson (any kind of pre-tests…”);
generate, by a conversation agent enabled by the one or more neural networks, a customized prompting based on the user history records and the user evaluation in skill and knowledge (Rowe, at least para. 37-43 describe this);
receive, by the conversation agent, user response data, the conversation agent classifying the user response data to identify recommended skills and knowledge for development (Rowe, Fig. 7, Agent assigns lessons based on assessment and refines styles based on results (learning); Fig. 8, Identify Skill Range; Teach New Skills and Reinforce Prior Skills); and
generate a prescribed curriculum customized for the user based at least on the recommended skills and knowledge for development, and transmit the prescribed curriculum for the user, the prescribed curriculum being a reduced data size for efficient storage and transmission while providing effective skills and knowledge development for the user (Rowe, Fig. 7, Agent assigns lessons based on assessment and refines styles based on results (learning); Fig. 8, Identify Skill Range; Teach New Skills and Reinforce Prior Skills; para. 51, “These knowledge and skill areas in the domains of reading, writing, and mathematics provide a framework for organizing and sequencing the instructional curriculum. Artificial intelligence sub-systems and Agents flexibly move students through the curriculum, considering the skill gaps, mastery retention, preferred learning styles, and other performance and demographic predictors of domain and content area development needs.”).
Rowe does not explicitly teach the one or more neural networks are one or more large language models.
However, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention for the one or more neural networks in Rowe to be one or more large language models because a large language model is a neural network trained on a large data set of text and Rowe’s one or more neural networks are trained on a large dataset of text (Rowe, para. 79, “the dataset is large”).
Regarding claims 19 and 20, Rowe teaches a computer method (claim 19) and a non-transitory computer readable medium storing computer interpretable instructions, which when executed by a processor, cause the processor to execute a method (claim 20) for cognitive tests for scalable precision education, the method comprising:
tuning the one or more neural networks for knowledge and skill classification and extraction using labeled examples (Rowe, para. 48-49 describe this. Para. 79, “the dataset is large”);
classifying and extracting skill items and knowledge items from the one or more databanks of items based on categorization confidence scores generated by the one or more neural networks (Rowe, para. 45, “Its output is a classification system that is used to predict the best lessons to assign to the new students, given only their entrance cognitive state.”);
administering a cognitive test, at the applicant interface, using the skill items and knowledge items, the cognitive test producing a set of resulting scores indicating a user evaluation in skill and knowledge, respectively (Rowe, Fig. 8, Initial Placement Test; para. 34, “The 'Assessment Results' module contains the results of any assessment instruments applied outside the context of a lesson (any kind of pre-tests…”);
generating, by a conversation agent enabled by the one or more neural networks, a customized prompting based on the user history records and the user evaluation in skill and knowledge (Rowe, at least para. 37-43 describe this);
receiving, by the conversation agent, user response data, the conversation agent classifying the user response data to identify recommended skills and knowledge for development (Rowe, Fig. 7, Agent assigns lessons based on assessment and refines styles based on results (learning); Fig. 8, Identify Skill Range; Teach New Skills and Reinforce Prior Skills); and
generating and outputting at the applicant interface, a prescribed curriculum for the user based at least on the recommended skills and knowledge for development (Rowe, Fig. 7, Agent assigns lessons based on assessment and refines styles based on results (learning); Fig. 8, Identify Skill Range; Teach New Skills and Reinforce Prior Skills; para. 51, “These knowledge and skill areas in the domains of reading, writing, and mathematics provide a framework for organizing and sequencing the instructional curriculum. Artificial intelligence sub-systems and Agents flexibly move students through the curriculum, considering the skill gaps, mastery retention, preferred learning styles, and other performance and demographic predictors of domain and content area development needs.”).
Rowe does not explicitly teach the one or more neural networks are one or more large language models.
However, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention for the one or more neural networks in Rowe to be one or more large language models because a large language model is a neural network trained on a large data set of text and Rowe’s one or more neural networks are trained on a large dataset of text (Rowe, para. 79, “the dataset is large”).
Regarding claim 2, Rowe teaches the system of claim 1, wherein the conversation agent is fine-tuned using the recommended skills and knowledge for development, the conversation agent fine-tuned to generate a future customized prompting based on a pattern recognized between the user and the recommended skills and knowledge for development (Rowe, para. 7, “The Agents fine-tune and adapt to the current, and ongoing, cognitive state of the student to provide real-time alignment to the best ways that skills are imprinted.”).
Regarding claim 3, Rowe teaches the system of claim 1, wherein the one or more large language models query development resource databases using query codes converted from natural language corresponding to the recommended skills and knowledge for development, the one or more large language models receiving a development resource or a combination of development resources to generate the prescribed curriculum for the user (Rowe, para. 83, “The Server side AI Engine now patterns the new student cognitive model against the cognitive models of all existing students to predict the best course of study for that particular student. Using an AI Engine means that perfect matches do not need to be made to the extent that the AI Engine may generate a new course combination that has never been recommended before.” Para. 87, “the Server side AI Engine initially predicts a best course of study for the student based on an AI pattern of their cognitive model with cognitive models of a previous population of students. The AI engine then recommends an initial program that it has found to be historically the most successful for this Student Cognitive Model. Once the student begins the program the Agents then take over the role of dynamically refining the cognitive model and adapting the program to the needs of the student.”).
Regarding claim 4, Rowe teaches the system of claim 1, wherein the prescribed curriculum comprises a selection of: on line courses, reading materials, practice exercises, and individual lessons (Rowe, para. 50, “The framework of the present invention learning system curriculum consists of specific instructional knowledge and skill areas derived from the research literature (see FIG. 9).” Para. 77, “The Instructional Content is contained in Macromedia Flash-based 'Activities' which consist of 1 to several 'Lessons'. Each Activity teaches a specific area of study while each Lesson focuses on different areas within this study area.”).
Regarding claim 5, Rowe teaches the system of claim 1 wherein the one or more databanks of items comprise labeled items with assigned categories and unlabeled items, wherein the processor tunes the one or more large language models with the labeled items with assigned categories, automatically categorizes the unlabeled items using the tuned one or more large language models, and outputs item categorizations generated by the tuned one or more large language models (Rowe, para. 48-49 describe this.).
Regarding claim 6, Rowe teaches the system of claim 1, wherein if the categorization confidence scores are below a predetermined threshold, the processor generates an alert requesting a human review (Rowe, para. 48-49 describe this.).
Regarding claim 7, Rowe teaches the system of claim 6, wherein the processor transmits the skill items and knowledge items to a reviewer interface for manual labeling and feedback, and tunes the one or more large language models using the feedback (Rowe, para. 48-49 describe this.).
Regarding claim 8, Rowe teaches the system of claim 1, wherein the processor automatically assigns one or more categories to each item of at least a portion of the items using the tuned one or more large language models, wherein the process generates an alert when an item cannot be automatically categorized (Rowe, para. 48-49 describe this.).
Regarding claim 9, Rowe teaches the system of claim 1 wherein the processor automatically categorizes learning resources using the one or more large language models (Rowe, para. 48-49 describe this.).
Regarding claim 10, Rowe teaches the system of claim 1 wherein the processor provides the conversation agent to provide a recommendation to a user about resources to address particular knowledge or skills for development and justifications on how the recommendation was generated (Rowe, Fig. 7, Agent assigns lessons based on assessment and refines styles based on results (learning); Fig. 8, Identify Skill Range; Teach New Skills and Reinforce Prior Skills; para. 51, “These knowledge and skill areas in the domains of reading, writing, and mathematics provide a framework for organizing and sequencing the instructional curriculum. Artificial intelligence sub-systems and Agents flexibly move students through the curriculum, considering the skill gaps, mastery retention, preferred learning styles, and other performance and demographic predictors of domain and content area development needs.”).
Regarding claim 11, Rowe teaches the system of claim 1 wherein the processor tunes the one or more large language models to categorize at least a portion of items based on what knowledge or skill a respective item is assessing (Rowe, para. 7, “as a student progresses through a program of study, wherein responses and results can be measured, a series of Intelligent Pedagogical Software Agents or "Agents" are assigned to, and learn more about, each individual student. The Agents fine-tune and adapt to the current, and ongoing, cognitive state of the student to provide real-time alignment to the best ways that skills are imprinted.”).
Regarding claim 12, Rowe teaches the system of claim 1 further comprising a client web application with a curriculum interface to provide the prescribed curriculum (Rowe, para. 75, “The Client receives appropriate instructional material over the network for delivery to the student and sends results of the student's interaction with this material back to the Server.” Para. 76, “The LMS also provides the User Interface or UI of lessons to the Student.”).
Regarding claim 13, Rowe teaches the system of claim 1 further comprising a client web application with a reviewer interface to display alerts for categorizations and receive feedback, wherein the processor tunes the one or more large language models based on the feedback (Rowe, para. 48-49 describe this. Para. 82, “The parent/teacher also receives an online assessment of the results of the student test, and further recommendations, should issues such as the potential for dyslexia be identified.”).
Regarding claim 14, Rowe teaches the system of claim 1 further comprising a client web application with an applicant interface to provide a computer adaptive test to collect response data for each user, wherein the memory comprises a user history record storing the collected response data and corresponding evaluation data (Rowe, Fig. 7, Agent assigns lessons based on assessment and refines styles based on results (learning); Fig. 8, Identify Skill Range; Teach New Skills and Reinforce Prior Skills; para. 51, “These knowledge and skill areas in the domains of reading, writing, and mathematics provide a framework for organizing and sequencing the instructional curriculum. Artificial intelligence sub-systems and Agents flexibly move students through the curriculum, considering the skill gaps, mastery retention, preferred learning styles, and other performance and demographic predictors of domain and content area development needs.”).
Regarding claim 15, Rowe teaches the system of claim 1 further comprising a client web application with a rater interface that provides response data for a computer adaptive test and collects corresponding evaluation data for the response data for the computer adaptive test (Rowe, Fig. 7, Agent assigns lessons based on assessment and refines styles based on results (learning); Fig. 8, Identify Skill Range; Teach New Skills and Reinforce Prior Skills; para. 51, “These knowledge and skill areas in the domains of reading, writing, and mathematics provide a framework for organizing and sequencing the instructional curriculum. Artificial intelligence sub-systems and Agents flexibly move students through the curriculum, considering the skill gaps, mastery retention, preferred learning styles, and other performance and demographic predictors of domain and content area development needs.”).
Regarding claim 16, Rowe teaches the system of claim 1 wherein the computer processor is configured to tune the one or more large language models for knowledge and skill classification and extraction using a dataset of labeled knowledge examples and another data set of skill examples (Rowe, para. 48-49 describe this.).
Regarding claim 17, Rowe teaches the system of claim 1 wherein the categorization confidence scores generated by the one or more large language models comprise numerical values that represent the level of certainty the one or more large language models have in its predictions or classifications for a given input data point (Rowe, para. 35, “All information that can be predicted is tagged with a certainty rating. A certainty rating of -1 means no information is known about the data, while a certainty rating between 0 and 1 means some information is known (with 0 meaning the value is based purely on a predictive model and 1 meaning that the value is known as fact).”).
Regarding claim 18, Rowe teaches the system of claim 1 wherein the prescribed curriculum is specific to a user and different from prescribed curriculums for other users, wherein the prescribed curriculum is an education plan, a set of courses and educational content for the user (Rowe, para. 56, “The AI Engine and the actions of the Agents drive the present invention curriculum. These systems use an array of student demographic, learning preference, and performance data, shown in FIG. 7, to make strategic decisions about the order and nature of instructional lessons to provide to each student. Personalization of the curriculum is achieved, in part, through the actions of these systems.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Reiter et al. (US 2023/0126133 and US 2023/0129430) are closely related to the instant application with publication dates more than 1 year prior to the effective filing date of the instant application.
Martin et al. (US 2018/0240015 and US 2018/0247549) discloses network-based systems and methods for monitoring user behaviors and performances and aggregating behavior and performance related data into workable data sets for processing and generating recommendations using natural language processing and deep learning.
Dalton et al. (US 11,605,384 B1) discloses an individualized language learning using semi-supervised training neural networks and natural language processing.
Sastry (US 2023/0215282) discloses a generative artificial intelligence learning method and system for an online course.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL LANE whose telephone number is (303)297-4311. The examiner can normally be reached Monday - Friday 8:00 - 4:30 MT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xuan Thai can be reached at (571) 272-7147. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL LANE/ Examiner, Art Unit 3715
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