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 .
Response to Amendment
The amendment filed January 22, 2026 has been entered. Claims 1-3, 5-12, and 14-22 remain pending in the application. Claims 1, 10, 15, 16, and 18-19 are noted as amended, claims 4 and 13 are noted as cancelled, and claims 21-22 are noted as newly added. Applicant’s amendments to the claims have overcome all previous objections and 112(b) rejections set forth in the Non-Final Office Action mailed November 12, 2025 and all objections and rejections therein have been withdrawn. However, new rejections are noted below.
With regard to the Claim Interpretation under 35 U.S.C. 112(f), Examiner notes that, while Applicant’s amendments to claim 10 render the interpretation moot for claim 10, some of the limitations are still recited in claim 19 in such a way as to invoke the means-plus-function interpretation, and the interpretation is updated accordingly below.
Claim Objections
Claims 1 and 19 are objected to because of the following informalities:
In claim 1, line 22, “of the of the” should read “of the”.
In claim 19, line 10, “into text” should read “into digital text”.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a data classifier” in claim 19; “a content-to-text constructor” in claim 19; and “a text analyzer” in claim 19.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
“a data classifier” – Interpreted as a software module/engine in the form of a program executed by the microprocessor, configured to fetch training material from the database and classify the material into predefined categories based on content type such as a presentation, recorded video, recorded screen, documents, and chat logs by identifying the format , per paragraphs 36 and 38
“a content to text constructor” – Interpreted as a software module/engine in the form of a program executed by the microprocessor, configured to apply a content-to-text constructions technique such as a text data parser, image to text generator, and/or audio to text generator to the training material to convert the content to text, per paragraphs 36 and 39-43
“a text analyzer” – Interpreted as a software module/engine in the form of a program executed by the microprocessor, configured to parse the text to generate a test script including generating questions, answers, and distractors by applying various submodules, per paragraphs 36 and 44-45
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 15 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 15 recites the limitation "the testing module" in line 1. Due to applicant’s amendments to claim 10, there is insufficient antecedent basis for this limitation in the 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 1-3, 5-12, and 14-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1 and 10 recite a process and a machine/system for performing the process, the process including the steps of obtaining the digital training material from one or more training sources [claim 1]; classifying the digital training material into at least one pre-defined category based on a content type; applying at least one content-to-text construction technique, based on the pre-defined category of the content type, to convert the digital training material into digital text; parsing the digital text for generating a test script including by: performing semantic analysis of the digital text to determine semantics and meaning of phrases of the digital text; generating a first set of questions based on the semantic analysis of the digital text; generating a second set of questions based on the first set of questions by filtering the first set of questions to exclude repetitive questions and amending the first set of questions for at least one of wording, structure, formation, or meaning; selecting a third set of questions from the second set of questions considered relevant based on a topic of training and a specific lesson related to the topic, wherein the third set of questions is a subset of the second set of questions and are selected based on predefined criteria, and the relevance of the third set of questions to the topic is determined based upon a predefined set of rules, and generating the test script including one correct answer to each of the third set of questions and a plurality of incorrect answers to each of the third set of questions based on the semantic analysis of the digital text; and presenting the test script to the user. The recited steps, under their broadest reasonable interpretation, are obtaining training material from a source, classifying the training material into a category based on a content type, applying a content-to-text technique to convert the material into text, parsing the text, performing semantic analysis, generating a first set of questions based on the analysis, generating a second set of questions based on the first by filtering the questions to exclude repetitive questions and amending the questions, selecting a third set from the second set based on a topic, a lesson, and predefined criteria wherein the relevance is determined based upon a predefined set of rules, generating a test script including a correct answer and a plurality of incorrect answers to each of the third set of questions, and presenting the test script to a user. The recited steps, as drafted, are a process that is a method of applying an abstract idea, specifically mental processes (evaluation (applying at least one content-to-text technique; converting the training material to text; performing semantic analysis; filtering the questions to exclude repetitive questions), judgement (classifying the training material; parsing the text; generating a first set of questions; generating a second set of questions; amending the questions; selecting a third set of questions based on predefined criteria; determining relevance based upon a predefined set of rules; generating a test script), observation (obtaining the digital training material)) and/or certain methods of organizing human activity in the form of teaching (obtaining the digital training material; generating a first set of questions; generating a second set of questions; filtering the questions to exclude repetitive questions; selecting a third set of questions; generating a test script; and presenting the test script to a user). If claim limitations, under their broadest reasonable interpretation, include a mental process and/or certain methods of organizing human activity, the limitations fall under the abstract ideas judicial exception and therefore recite ineligible subject matter. Accordingly, claims 1 and 10 recite abstract ideas.
The judicial exception is not integrated into a practical application because the claims do not recite additional elements that are significantly more than the judicial exception or meaningfully limit the practice of the judicial exception. The additional elements are the text and material being digital; applying a machine learning model stored in memory to the digital text, wherein the machine learning model is applied only to the digital text and not on any generated questions such that none of a plurality of techniques of the semantic analysis have dependency on any of a plurality of question types of questions generated; presenting on a user device; a training database configured to store the digital training material from one or more training sources [claim 10]; and a processor with modules configured to perform the operations [claim 10]. The additional elements are insignificant extra-solution activity and instructions for applying the judicial exception with a generic computing device as, under their broadest reasonable interpretation, the additional step of storing the training material is merely storing data (see MPEP 2106.05(d)(II)). With regard to the machine learning model being applied only to the digital text, the limitation is merely defining the type of data to be manipulated which is insignificant extra-solution activity per MPEP 2106.05(g). The other additional elements of the material being digital, applying a machine learning model, a user device, a training database, and a processor with modules are generic computer components/instructions for performing the above method, per MPEP 2106.05(f), as the elements are modules/algorithms in the form of instructions/programs executed by the processor to perform the functions. Under their broadest reasonable interpretation, the additional elements are generic components/instructions of a computing device used to apply the abstract idea. Specifically, with regard to the machine learning model, as the model is recited at a high level of generality, the model amounts to an algorithm/computer instructions for applying the judicial exceptions. Further, paragraphs 32 and 36 of the specification states the system is a personal computing device such as a smartphone, a laptop, or a desktop and the engines/modules are program instructions executed by one or more processors. As such, these additional elements are interpreted as merely instructions to apply the judicial exception with a generic computing device. Accordingly, the additional elements and steps do not integrate the abstract idea into a practical application because they do not impose any meaningful limitations on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, the additional step(s) of storing the training material and the model being applied only to the digital text is/are insignificant extra-solution activity performed during the abstract idea, and the additional elements of the material being digital, applying a machine learning model, a user device, a training database, and a processor with modules used to perform the process are generic computing components/device used to apply the judicial exception and therefore fall under the “apply it” limitation of the judicial exception and do not amount to significantly more per MPEP 2106.05(f). Further, the limitations, taken in combination, add nothing that is not already present when looking at the elements taken individually. As such, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, under their broadest reasonable interpretation, the additional elements do not meaningfully limit the practice of the abstract idea and do not amount to significantly more than the judicial exceptions. Therefore, claims 1 and 10 are not directed to eligible subject matter as they are directed to abstract ideas without significantly more.
Claims 2-3, 5-9, 11-12, 14-18, and 21-22 are dependent from claims 1 and 10 and include all the limitations of the independent claims. Therefore, the dependent claims recite the same abstract idea. The limitations of the dependent claims fail to amount to significantly more than the judicial exception. For example:
The limitations of claims 2-3, 5, 11, and 13-14 recite clarification of the types of data and processes used/comprising the content types/category, the content-to-text construction technique, the questions, and the training sources. The limitations, under their broadest reasonable interpretation, are merely defining/selecting a type of data to be manipulated which, per MPEP 2106.05(g), is insignificant extra-solution activity. The limitations fail to provide any teaching that integrates the judicial exceptions into a practical application or amounts to significantly more than the judicial exceptions. For this reason, the analysis performed on the independent claims is also applicable on these claims.
The limitations of claims 6, 9, 15, and 18 recite further abstract ideas including presenting a set of one correct answer and one or more incorrect answers for the user to select at least one answer from (CMOHA) and repeating the operations for a first and second student to generate a first and second test script (CMOHA). As the limitations are further abstract ideas, the limitations cannot meaningfully limit or amount to significantly more than the abstract ideas of the independent claims. The limitations fail to provide any teaching that integrates the judicial exceptions into a practical application or amounts to significantly more than the judicial exceptions. For this reason, the analysis performed on the independent claims is also applicable on these claims.
The limitations of claims 7-8, 16-17, and 21-22 recite further insignificant extra-solution activity including implementing the method through a web server, a container, a VM, a plugin, or a preinstalled software, the test script is displayed on a display screen, and the content-to-text technique comprising a programming task, and an image to text constructor. The limitations are instructions for implementing the judicial exceptions on a generic computing device. The limitations fail to provide any teaching that integrates the judicial exceptions into a practical application or amounts to significantly more than the judicial exceptions. For this reason, the analysis performed on the independent claims is also applicable on these claims.
Accordingly, claims 2-3, 5-9, 11-12, 14-18, and 21-22 are directed to abstract ideas without significantly more and are not drawn to eligible subject matter.
Claim 19 recites a machine/system for performing a process, the process including the steps of classify the digital lecture session material into at least one pre-defined category; convert the digital lecture session material into text based on the at least one pre-defined category; parse the digital text to generate a test script; generating a first set of questions based on the parsing; generate a second set of questions based on the first set of questions by filtering the first set of questions to exclude repetitive questions and amending the first set of questions for at least one of wording, structure, formation, or meaning; select a third set of questions from the second set of questions based on a topic of the digital lecture session material, wherein the third set of questions is a subset of the second set of questions and are selected based on predefined criteria, and the relevance of the third set of questions to the topic is determined based upon a predefined set of rules; generate one correct answer to each of the third set of questions based on the parsing; and generate a plurality of incorrect answers to each of the third set of questions. The recited steps, under their broadest reasonable interpretation, are classifying the lecture material into a category, converting the material into text, parsing the text to generate a test script, generating a first set of questions based on the parsing, generating a second set of questions based on the first by filtering the questions to exclude repetitive questions and amending the questions, amending the questions, selecting a third set from the second set based on a topic of the lecture and predefined criteria wherein the relevance is determined based upon a predefined set of rules, and generating a correct answer and a plurality of incorrect answers for each of the third set of questions. The recited steps, as drafted, are a process that is a method of applying an abstract idea, specifically mental processes (evaluation (convert the training material to text; performing semantic analysis; generate a correct answer and a plurality of incorrect answers; filtering the questions to exclude repetitive questions), judgement (classify the lecture session material; parse the text; generate a first set of questions; generate a second set of questions; select a third set of questions)) and/or certain methods of organizing human activity in the form of teaching (generate a first set of questions; generate a second set of questions; amending the questions; select a third set of questions based on predefined criteria; determining relevance based upon a predefined set of rules; and generate a correct answer and a plurality of incorrect answers). If claim limitations, under their broadest reasonable interpretation, include a mental process and/or certain methods of organizing human activity, the limitations fall under the abstract ideas judicial exception and therefore recite ineligible subject matter. Accordingly, claim 19 recites an abstract idea.
The judicial exception is not integrated into a practical application because the claim does not recite additional elements that are significantly more than the judicial exception or meaningfully limit the practice of the judicial exception. The additional elements are a computing device including at least one processor and memory operably coupled to the at least one processor; and instructions that, when executed on the at least one processor, cause the at least one processor to implement the process; a data classifier; a content-to-text constructor; a text analyzer; and using a semantic analysis model by applying a machine learning model stored in memory to the digital text, wherein the machine learning model is applied only to the digital text and not on any generated questions such that none of a plurality of techniques of the semantic analysis have dependency on any of a plurality of question types of questions generated. The additional elements are instructions for applying the judicial exception with a generic computing device as, under their broadest reasonable interpretation, the additional elements of a computing device including at least one processor and memory, instructions to execute on the processor, a data classifier, a content-to-text constructor, a text analyzer, a semantic analysis model, and a machine learning model are generic computer components/instructions for performing the above method, per MPEP 2106.05(f), as the elements are computing components and modules/engines in the form of instructions/programs executed by the microprocessor to perform the functions. Under their broadest reasonable interpretation, the additional elements are generic components/instructions of a computing device used to apply the abstract idea. Further, paragraphs 32 and 36 of the specification states the system is a personal computing device such as a smartphone, a laptop, or a desktop and the engines/modules are program instructions executed by one or more processors. With regard to the machine learning model being applied only to the digital text, the limitation is merely defining the type of data to be manipulated which is insignificant extra-solution activity per MPEP 2106.05(g). With regard to the semantic analysis model and applying a machine learning model, the limitation is recited at a high level of generality which is why it amounts to a computer algorithm or instructions executed by the processor to perform the judicial exceptions. As such, these additional elements are interpreted as merely instructions to apply the judicial exception. Accordingly, the additional elements and steps do not integrate the abstract idea into a practical application because they do not impose any meaningful limitations on practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, the additional elements of a computing device including at least one processor and memory, instructions to execute on the processor, a data classifier, a content-to-text constructor, a text analyzer, and a semantic analysis model applying a machine learning model used to perform the process are generic computing components/device used to apply the judicial exception and therefore fall under the “apply it” limitation of the judicial exception and do not amount to significantly more per MPEP 2106.05(f). Further, the limitations, taken in combination, add nothing that is not already present when looking at the elements taken individually. As such, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, under their broadest reasonable interpretation, the additional elements do not meaningfully limit the practice of the abstract idea and do not amount to significantly more than the judicial exceptions. Therefore, claim 19 is not directed to eligible subject matter as it is directed to an abstract idea without significantly more.
Claim 20 is dependent from claim 19 and includes all the limitations of the independent claim. Therefore, the dependent claim recites the same abstract idea. The limitations of claim 20 recite a further abstract idea. The abstract idea is presenting the test script which is a certain method of organizing human activity in the form of teaching. As the limitation is a further abstract idea, the limitation cannot meaningfully limit or amount to significantly more than the abstract ideas of the independent claim. The additional element of the dependent claim is further instructions for applying the judicial exceptions on a generic computing device by implementing a testing module which is interpreted as mere computer code/instructions to execute on a generic processor. The limitations fail to provide any teaching that integrates the judicial exceptions into a practical application or amounts to significantly more than the judicial exceptions. For this reason, the analysis performed on the independent claim is also applicable on this claim.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claim(s) 1-3, 5-12, and 14-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Smith Lewis et al. (US PGPub 20180218627), hereinafter referred to as Smith in view of Niemi et al. (US PGPub 20160293036), hereinafter referred to as Niemi, and further in view of Levinson (US PGPub 20190221131).
With regard to claims 1 and 10, Smith teaches a method [claim 1] (Abstract; Paragraph 0073; “method”) and a system [claim 10] (Abstract; Paragraph 0016; “learning system”) for conducting an active assessment of a user based on training material (Paragraphs 0017, 0026 teach the system and method are for optimizing student performance and determining student learning progress) comprising:
obtaining the digital training material from one or more training sources [claim 1] (Paragraphs 0018, 0020, 0074 teach the system can receive and/or retrieve content including text or other materials from digital source materials such as online textbooks);
a training database configured to store the digital training material from one or more training sources [claim 10] (Paragraphs 0018, 0020, 0074 teach the system can access digital sources in a library (training database) or stored in a local storage (training database) to retrieve content);
a processor with modules configured to perform the operations of [claim 10] (Paragraph 0104 teaches the system can be executed by one or more hardware processors implementing software instructions):
classify the digital training material into at least one pre-defined category based on a content type (Paragraphs 0043-0044, 0074-0075 teach the system can classify the digital source material which can include extracting metadata and format data related to the type of content in order to apply the appropriate techniques to generate the text document);
apply at least one content-to-text construction technique, based on the pre-defined category, to convert the digital training material into digital text (Paragraphs 0075, 0077-0078 teach the system applies one or more various conversion techniques to convert the content into a text document based on the type of content such as OCR for an image of a text document, speech to text for audio, or PDF to text);
parse the digital text to generate a test script (Paragraphs 0044, 0075 teach the system can apply techniques to parse the digital content to extract relevant text), including:
perform semantic analysis of the digital text for determining semantics and meaning of phrases of the digital text (Paragraphs 0081, 0083 teach the system generates a semantic model of the digital content by performing semantic analysis on the tokenized text document of the digital content) by applying a machine learning model stored in memory to the digital text (Paragraphs 0082-0083 teach the semantic model may be generated/implemented by a machine learning algorithm applied to the tokenized text document wherein the software instructions including the algorithm are stored in a memory of the learner device per paragraph 0104), wherein the machine learning model is applied only to the digital text and not on any generated questions such that none of a plurality of techniques of the semantic analysis have dependency on any of a plurality of question types of questions generated (Paragraphs 0075, 0081, 0083-0084, 0086 teaches the semantic model is applied to the digital content, specifically the content turned into the tokenized text document and does not teach the semantic model is applied to any of the generated/selected knowledge items or candidate knowledge items, therefore, implicitly, the semantic model and the machine learning algorithm do not have dependency on the question types of the questions generated),
generate a first set of questions based on the semantic analysis of the text (Paragraphs 0049-0050, 0087-0088 teach the system can filter knowledge items to generate question and answer pairs (first set of questions) as structured learning assets wherein candidate knowledge items are selected from the tokenized text document based on the semantic model),
generate a second set of questions based on the first set of questions (Paragraphs 0034, 0051, 0061 teach the system can generate interactions (second set of questions) based on the structured learning assets such as the question-answer pairs) by filtering the first set of questions to exclude repetitive questions (Paragraphs 0045, 0048 teach the knowledge items are filtered including deduplicating the items to remove duplicate and/or near duplicate items and while the steps in Smith are in a different order, it would have been obvious to one of ordinary skill in the art to perform the steps in the claimed order in the absence of new or unexpected results per MPEP 2144.04. Further, it would have been obvious to apply the step again to remove duplicate items or interactions to maintain the database and items and organize them), and
select a third set of questions from the second set of questions considered most relevant based on topic of training and a specific lesson related to the topic (Paragraphs 0034, 0047, 0051, 0072, 0084 teach the system can determine the relevance of the knowledge items to the learning objectives such as a desired learning topic or content topic such that the generated interaction/question is relevant to the learning objective and transmitted (selected) to the user), wherein the third set of questions are selected based on a predefined criteria (Paragraphs 0051-0053, 0072 teach the interactions can be generated in part based on user data such as learning objectives in order to generate a personalized learning experience such that the interactions (questions) are selected based on the user’s data (predefined criteria)), and the relevance of the third set of questions to the topic is determined based upon a predefined set of rules (Paragraphs 0047, 0084 teach the system can assign knowledge items, and thereby the interactions generated from them, relevance scores and concept scores based on the analysis of the material wherein the relevance is based on relationships between a concept and the semantic model and how central the concept is to a topic and/or objective (rules); Further paragraph 0047 teaches the relevance can be determined with regard to a threshold thereby removing items that do not meet a minimum threshold which per paragraph 46 of the instant application is a pre-defined rule (degree of relevance)), and
generate:
one correct answer to each of the third set of questions based on semantic analysis of the text (Paragraphs 0034, 0069 teach the generated question may be a multiple choice question including a plurality of selectable choices including the correct answer wherein the correct answer is generated based on the structured learning assets generated based on the semantic model), and
a plurality of incorrect answers to the third set of questions (Paragraphs 0034, 0069 teach the generated question may be a multiple choice question including a plurality of selectable choices including a plurality of incorrect answers); and
present the test script to the user on a user device (Paragraphs 0025, 0034, 0053 teach the system includes an interaction application for transmitting and displaying the interactions (questions) to the user via the user/learner device).
Smith may not explicitly teach amending the first set of questions for at least one of wording, structure, formation, or meaning; wherein the third set of questions is a subset of the second set of questions. However, Niemi teaches an adaptive system and method for determining a user’s level of proficiency and generating questions using IRT wherein the stored/created questions can be updated to correct spelling and/or grammar as well as adjust a difficulty level of the questions (Paragraphs 0045, 0049, 0054).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Smith to incorporate the teachings of Niemi by applying the technique of amending/updating the questions to correct spelling and/or grammar as well as adjust the difficulty of Niemi to the question pairs of Smith, as both references and the claimed invention are directed to learning management systems that include generating knowledge and question items and presenting adaptive questions/interactions to learners. One of ordinary skill in the art would modify Smith by coding the system to include amending the selected question pair items for an interaction to correct spelling and grammar and/or adjusting the difficulty of the question to calibrate the interaction for the user. Upon such modification, the method and system of Smith would include amending the first set of questions for at least one of wording, structure, formation, or meaning. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Niemi with Smith’s system and method in order to improve question syntax and wording, improve user interaction by ensuring correct spelling and grammar, and improve customization and adaptability by calibrating the question items for the specific user.
Smith in view of Niemi may not explicitly teach wherein the third set of questions is a subset of the second set of questions. However, Levinson teaches a system and method for generating and providing educational digital media modules including questions wherein the questions can be created and stored in a question database related to the subject matter and a subset of questions can be selected for presentation to the user based on the segment of the media and based on stored rules related to user-specific criteria and other factors such as a difficulty of the content, user proficiency, and/or user learning style (Paragraphs 0026, 0047, 0090, 0092, 0094-0098).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Smith in view of Niemi to incorporate the teachings of Levinson by applying the teaching of selecting a subset of the stored/created questions for presentation to the user of Levison to the created interactions/questions of Smith, as both references and the claimed invention are directed to learning management systems that include generating questions related to presented content and subject matter and presenting adaptive questions/interactions to learners. One of ordinary skill in the art would modify Smith in view of Niemi by coding the system to include selecting a subset of the generated interactions/questions related to the content based on the relevance determinations of Smith and further based on the rules related to user-specific criteria and other factors. Upon such modification, the method and system of Smith would include wherein the third set of questions is a subset of the second set of questions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Levinson with Smith in view of Niemi’s system and method in order to provide more relevant and user-specific questions and better engage the learner.
With regard to claims 2 and 11, Smith further teaches wherein the pre-defined category of the content type includes at least one of a presentation (Paragraph 0043 teaches the content can be a lecture), a recorded video (Paragraph 0043 teaches the content can be a video representation of a learning experience), or a document (Paragraph 0043 teaches the content can be textual representation of learning material (a document)).
With regard to claims 3 and 12, Smith further teaches wherein the applying at least one content-to-text construction technique comprises applying an audio-to-text generator (Paragraphs 0044, 0066, 0075 teach the system can apply audio analysis or speech recognition to audio content/files), applying a text data parser (Paragraphs 0044, 0075 teach the system can perform character recognition and parsing (text data parser) of the material), or applying an image to text generator (Paragraphs 0044, 0066, 0075 teach the system can perform image analysis or recognition or optical character recognition to convert images to text).
With regard to claims 5 and 14, Smith further teaches wherein the one or more training sources comprises a video recording of a lecture (Paragraph 0043; video representation of a lecture), a presentation file containing graphics and text data (Paragraph 0043; multimodal content combining text and images), a document (Paragraph 0043; textual representation of learning material), material stored during a lecture session (Paragraph 0043; audio and/or video of a lecture), a PDF file (Paragraphs 0018, 0074; PDFs), and a webpage (Paragraph 0043; a webpage).
With regard to claim 6, Smith further teaches wherein presenting the test script to the user comprises presenting a set of one correct answer and one or more incorrect answers for the user to select at least one answer from the set of one correct answer and the one or more incorrect answers (Paragraphs 0025, 0034, 0053 teach the system includes an interaction application for transmitting and displaying the interactions (questions) to the user wherein a user can select an answer/response among the multiple choice options).
With regard to claim 15, Smith further teaches wherein the testing module further comprises presenting a set of one correct answer and one or more incorrect answers for the user to select at least one answer from the presented answers (Paragraphs 0025, 0034, 0053 teach the system includes an interaction application for transmitting and displaying the interactions (questions) to the user wherein a user can select an answer/response among the multiple choice options generated including the correct answer and a plurality of incorrect answers).
With regard to claims 7 and 16, Smith further teaches wherein the system and method implements through a web server (Paragraphs 0021, 0094-0095 teach the system may be implemented via a network wherein the adaptive engine (server) is separate from the learner devices), a container (Paragraphs 0104, 0117; software), a virtual machine (Paragraph 0104; “may be run as virtual machines on learner device”), a plugin (Paragraphs 0104, 0117; software), or a preinstalled software (Paragraphs 0104, 0117; software).
With regard to claims 8 and 17, Smith further teaches wherein the test script is displayed on a display screen operated by the user (Paragraphs 0025, 0092 teach the learner devices include displays and/or other output hardware on which the interactions (questions/test script) are displayed).
With regard to claims 9 and 18, Smith further teaches wherein a first student user and a second student user are associated with a lecture session (Paragraphs 0026, 0095 teach the system can include a plurality of learners via a plurality of learner devices such that learners can be organized in classes managed by an instructor/lecturer (lecture session)) and the operations of claim 1 and 10 are repeated for the first student user and the second student user, wherein a first test script associated with the first student user includes a unique set of questions different than a second test script associated with the second student user (Paragraphs 0051-0053, 0062, 0107 teach the system generates personalized learning experiences based in part on the personal user data such that different users/students of a class/lecture would receive different interactions/questions as the process is performed (repeated) for each learner/student).
With regard to claim 19, Smith teaches a system (Abstract; Paragraph 0016; “learning system”) for digital lecture session material synthesis (Paragraphs 0026, 0043 teach the system is for determining student learning progress including in a lecture/class and processing digital source material/content including lectures), the system comprising:
a computing device including at least one processor and memory operably coupled to the at least one processor (Paragraphs 0029-0032, 0104 teach the system can include learner devices such as a tablet or smartphone and execute software instructions stored in a memory on one or more processors); and
instructions that, when executed on the at least one processor, cause the at least one processor to implement (Paragraph 0104; “software instructions”):
a data classifier configured to classify the digital lecture session material into at least one pre-defined category (Paragraphs 0043-0044, 0074-0075 teach the system can classify the digital source material which can include extracting metadata and format data related to the type of content in order to apply the appropriate techniques to generate the text document),
a content-to-text constructor configured to convert the digital lecture session material into text based on the at least one pre-defined category (Paragraphs 0075, 0077-0078 teach the system applies one or more various conversion techniques to convert the content into a text document based on the type of content such as OCR for an image of a text document, speech to text for audio, or PDF to text), and
a text analyzer configured to: parse the digital text to generate a test script using a semantic analysis model (Paragraphs 0044, 0075, 0081, 0083 teach the system can apply techniques to parse the digital content to extract relevant text and generate a semantic model of the digital content by performing semantic analysis on the tokenized text document of the digital content) by applying a machine learning model stored in memory to the digital text (Paragraphs 0082-0083 teach the semantic model may be generated/implemented by a machine learning algorithm applied to the tokenized text document wherein the software instructions including the algorithm are stored in a memory of the learner device per paragraph 0104), wherein the machine learning model is applied only to the digital text and not on any generated questions such that none of a plurality of techniques of the semantic analysis have dependency on any of a plurality of question types of questions generated (Paragraphs 0075, 0081, 0083-0084, 0086 teaches the semantic model is applied to the digital content, specifically the content turned into the tokenized text document and does not teach the semantic model is applied to any of the generated/selected knowledge items or candidate knowledge items, therefore, implicitly, the semantic model and the machine learning algorithm do not have dependency on the question types of the questions generated),
generate a first set of questions based on the parsing (Paragraphs 0049-0050, 0087-0088 teach the system can filter knowledge items to generate question and answer pairs (first set of questions) as structured learning assets wherein knowledge items are selected from the tokenized text document based on the semantic model),
generate a second set of questions based on the first set of questions (Paragraphs 0034, 0051, 0061 teach the system can generate interactions (second set of questions) based on the structured learning assets such as the question-answer pairs) by filtering the first set of questions to exclude repetitive questions (Paragraphs 0045, 0048 teach the knowledge items are filtered including deduplicating the items to remove duplicate and/or near duplicate items and while the steps in Smith are in a different order, it would have been obvious to one of ordinary skill in the art to perform the steps in the claimed order in the absence of new or unexpected results per MPEP 2144.04. Further, it would have been obvious to apply the step again to remove duplicate items or interactions to maintain the database and items and organize them), and
select a third set of questions from the second set of questions based on a topic of the digital lecture session material (Paragraphs 0034, 0047, 0051, 0072, 0084 teach the system can determine the relevance of the knowledge items to the learning objectives such as a desired learning topic or content topic such that the generated interaction/question is relevant to the learning objective and transmitted (selected) to the user), wherein the third set of questions are selected based on a predefined criteria (Paragraphs 0051-0053, 0072 teach the interactions can be generated in part based on user data such as learning objectives in order to generate a personalized learning experience such that the interactions (questions) are selected based on the user’s data (predefined criteria)), and the relevance of the third set of questions to the topic is determined based upon a predefined set of rules (Paragraphs 0047, 0084 teach the system can assign knowledge items, and thereby the interactions generated from them, relevance scores and concept scores based on the analysis of the material wherein the relevance is based on relationships between a concept and the semantic model and how central the concept is to a topic and/or objective (rules); Further paragraph 0047 teaches the relevance can be determined with regard to a threshold thereby removing items that do not meet a minimum threshold which per paragraph 46 of the instant application is a pre-defined rule (degree of relevance)),
generate one correct answer to each of the third set of questions based on the parsing (Paragraphs 0034, 0069 teach the generated question may be a multiple choice question including a plurality of selectable choices including the correct answer wherein the correct answer is generated based on the structured learning assets generated based on the semantic model), and
generate a plurality of incorrect answers to each of the third set of questions (Paragraphs 0034, 0069 teach the generated question may be a multiple choice question including a plurality of selectable choices including a plurality of incorrect answers).
Smith may not explicitly teach amending the first set of questions for at least one of wording, structure, formation, or meaning; wherein the third set of questions is a subset of the second set of questions. However, Niemi teaches an adaptive system and method for determining a user’s level of proficiency and generating questions using IRT wherein the stored/created questions can be updated to correct spelling and/or grammar as well as adjust a difficulty level of the questions (Paragraphs 0045, 0049, 0054).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Smith to incorporate the teachings of Niemi by applying the technique of amending/updating the questions to correct spelling and/or grammar as well as adjust the difficulty of Niemi to the question pairs of Smith, as both references and the claimed invention are directed to learning management systems that include generating knowledge and question items and presenting adaptive questions/interactions to learners. One of ordinary skill in the art would modify Smith by coding the system to include amending the selected question pair items for an interaction to correct spelling and grammar and/or adjusting the difficulty of the question to calibrate the interaction for the user. Upon such modification, the method and system of Smith would include amending the first set of questions for at least one of wording, structure, formation, or meaning. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Niemi with Smith’s system and method in order to improve question syntax and wording, improve user interaction by ensuring correct spelling and grammar, and improve customization and adaptability by calibrating the question items for the specific user.
Smith in view of Niemi may not explicitly teach wherein the third set of questions is a subset of the second set of questions. However, Levinson teaches a system and method for generating and providing educational digital media modules including questions wherein the questions can be created and stored in a question database related to the subject matter and a subset of questions can be selected for presentation to the user based on the segment of the media and based on stored rules related to user-specific criteria and other factors such as a difficulty of the content, user proficiency, and/or user learning style (Paragraphs 0026, 0047, 0090, 0092, 0094-0098).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Smith in view of Niemi to incorporate the teachings of Levinson by applying the teaching of selecting a subset of the stored/created questions for presentation to the user of Levison to the created interactions/questions of Smith, as both references and the claimed invention are directed to learning management systems that include generating questions related to presented content and subject matter and presenting adaptive questions/interactions to learners. One of ordinary skill in the art would modify Smith in view of Niemi by coding the system to include selecting a subset of the generated interactions/questions related to the content based on the relevance determinations of Smith and further based on the rules related to user-specific criteria and other factors. Upon such modification, the method and system of Smith would include wherein the third set of questions is a subset of the second set of questions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate these teachings from Levinson with Smith in view of Niemi’s system and method in order to provide more relevant and user-specific questions and better engage the learner.
With regard to claim 20, Smith further teaches wherein the instructions that, when executed on the at least one processor, cause the at least one processor to further implement a testing module configured to present the test script (Paragraphs 0025, 0034, 0053 teach the system includes an interaction application for transmitting and displaying the interactions (questions) to the user).
With regard to claim 21, Smith further teaches wherein applying the at least one content-to-text construction technique, based on the pre-defined category of the content type, to convert the digital training material into text comprises a programming task specific to the pre-defined category of the content type to separate the content into digital text (Paragraphs 0075, 0077-0078 teach that various conversion techniques (programming task) may be used to convert images and/or speech to text wherein a technique such as OCR would apply for converting an image to text (specific technique) or speech-to-text converters for speech recognition).
With regard to claim 22, Smith further teaches wherein the programming task comprises an image to text constructor to convert a digital image to digital text (Paragraphs 0075, 0077-0078 teach that various conversion techniques (programming tasks) may be used to convert images wherein a technique (task) such as OCR (text constructor) would apply for converting an image to text).
Response to Arguments
Applicant's arguments, see Remarks, pages 12-15, filed January 22, 2026, with respect to the rejection(s) of claim(s) 1-3, 5-12, and 14-20 under 35 U.S.C. 101 have been fully considered but they are not persuasive. Applicant’s arguments are as follows: A) that the claims do not recite abstract ideas as the human mind cannot apply a machine learning model and the claims are not directed towards “teaching” (see pages 12-13), B) the claims integrate the judicial exceptions by amounting to a technological improvement/solution by applying the semantic analysis only to the initial data and not on the questions itself (see pages 14-15), and C) the claims integrate the judicial exceptions by applying the judicial exception in some other meaningful way beyond generally linking the judicial exception to a particular technological environment (see page 15). With regard to argument A, Examiner notes that Applicant’s specification (see paragraphs 2-3 and 31) states the intended use of the present disclosure is to provide frequent and flexible testing which “helps students to learn faster and more efficiently as compared to conventional methods”. By Applicant’s own admission, the claimed invention is directed to a method of teaching. Regardless, one of ordinary skill would recognize that the generating questions and answers for presentation to a user is a method of teaching and would fall under the certain methods of organizing human activity as discussed above. Further, Examiner agrees with Applicant that a human mind “cannot apply a machine learning model stored in memory” which is why the element is analyzed as an additional element and discussed under Step 2 as it is merely using a computer algorithm/instructions for applying the judicial exceptions. Even if Argument A was persuasive, it ignores the other Mental Process abstract ideas which would still recite judicial exceptions and require integration into a practical application or significantly more to overcome the rejection. Therefore, Argument A is not persuasive.
With regard to Applicant’s Arguments B and C, Examiner notes that Applicant’s claims of a technological improvement and more than generally linking are conclusory statements without substantiative support in the claims or specification. Specifically, paragraph 45 of the specification is the only discussion of the application of the semantic model and merely states the “techniques used in semantic analysis have no dependencies on question types”. No discussion or evidence of a technical improvement are provided. Further, “creating directed questions” is not necessarily a technological improvement but rather an improvement experienced by the user as a result of the process. The cited additional elements, in combination, are not evidence of a practical application or significantly more as the cited elements recite abstract ideas and are insignificant extra-solution activity as discussed above. Applicant has not provided substantiative arguments for how the cited limitations are “an application of any purported abstract idea in a meaningful way beyond generally linking”. As such, there is nothing for the Examiner to rebut. Therefore, in view of the discussion above, the claims stand rejected under 35 U.S.C. 101.
Applicant’s arguments, see Remarks, filed January 22, 2026, with respect to the rejection(s) of claim(s) 1-3, 5-12, and 14-20 under 35 U.S.C 102 have been fully considered and are persuasive by virtue of Applicant’s amendments to the claims. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of 35 U.S.C. 103 in view of the newly cited combination of prior art discussed above.
Conclusion
Accordingly, claims 1-3, 5-12, and 14-22 are rejected.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CORRELL T FRENCH whose telephone number is (571)272-8162. The examiner can normally be reached M-Th 7:30am-5pm; Alt Fri 7:30am-4pm EST.
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/CORRELL T FRENCH/Examiner, Art Unit 3715
/KANG HU/Supervisory Patent Examiner, Art Unit 3715
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