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 .
Claim Objections
Claims 1, 4, 7, 11, 14, 17, and 19 are objected to because of the following informalities:
In claim 1, lines 10-11, “an (MCQ flashcard, a True/False flashcard, a Fill-In-The-Blank flashcard, a Pure Question flashcard, a Raw flashcard, or a Solution flashcard” should read “the MCQ flashcard, the True/False flashcard, the Fill-In-The-Blank flashcard, the Pure Question flashcard, the Raw flashcard, or the Solution flashcard” as the limitations have antecedence in lines 6-8. Examiner recommends amending the recitation of lines 6-8 and 10-11, and other similar recitations throughout the claims, to “flashcard type” to better distinguish between the types/categories of flashcards and actually flashcards like “an MCQ flashcard” recited in lines 12-13.
In claim 4, line 3, “classified as a is Fill-In-The-Blank flashcard” should read “classified as a Fill-In-The-Blank flashcard”.
In claim 7, line 3, “as a Fill-In-The-Blank” should read “as the Fill-In-The-Blank”.
In claim 11, line 8, “flashcard, or a Fill-In-The-Blank” should read “flashcard, a Fill-In-The-Blank”.
In claim 14, line 3, “of a Fill-In-The-Blank” should read “of the Fill-In-The-Blank”.
In claim 17, line 15, “the determining using” should read “the determining the study direction using”.
In claim 17, line 19, “the determining using” should read “the determining the study direction using”.
In claim 19, line 2 ”of a flashcard” should read “of the flashcard”.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites a process, the process including the steps of classifying one or more flashcards of the set of flashcards, the classifying including determining a particular flashcard type of each of the one or more flashcards, wherein the particular flashcard type is a multiple choice question (MCQ) flashcard, a True/False flashcard, a Fill-In-The-Blank flashcard, a Pure Question flashcard, a Raw flashcard, or a Solution flashcard; determining that a flashcard of the one or more flashcards has been classified as an MCQ flashcard; and determining a study direction for the flashcard; wherein determining the study direction for the flashcard comprises determining which one of the first side or the second side of the flashcard is a prompt side that is presented to a user prior to a response side. The recited steps, under their broadest reasonable interpretation, are classifying one or more flashcards by determining a flashcard type such as MCQ, True/False, FITB, Questions, raw, or solution, determining that a flashcard is an MCQ flashcard, and determining a study direction by determining which side is a prompt side. The recited steps, as drafted, are a process that is a method of applying an abstract idea, specifically mental processes (judgement (classifying one or more flashcards; determining a flashcard type; determining that a flashcard has been classified as an MCQ flashcard; determining a study direction; determining which side is a prompt side)). If claim limitations, under their broadest reasonable interpretation, include a mental process, the limitations fall under the abstract ideas judicial exception and therefore recite ineligible subject matter. Accordingly, claim 1 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 receiving a set of flashcards, each flashcard in the set of flashcards having a first side and a second side; using a first machine learning model; wherein the first machine learning model is trained using a first training set of flashcards and each flashcard of the first training set has an associated label corresponding to an MCQ flashcard, a True/False flashcard, a Fill-In-The-Blank flashcard, a Pure Question flashcard, a Raw flashcard, or a Solution flashcard; using a second machine learning model; and wherein the second machine learning model is trained using a second training set of flashcards and each flashcard of the second training set of flashcards has a flashcard side that is labeled as a prompt side or a response side. The additional elements are insignificant extra-solution activity, generally linking the judicial exceptions to the field of machine learning, and instructions for applying the judicial exception with a generic computing device as, under their broadest reasonable interpretation, the additional step(s) is/are mere data gathering (see MPEP 2106.05(g)) and training machine learning models. The other additional elements of using a first and second machine learning model are generic computer components for performing the above method/instructions for applying the judicial exception with a generic computer, per MPEP 2106.05(f). As the machine learning models are recited at a high level of generality, and based on paragraphs 0115-0117 of the specification reciting various types of models, the models are interpreted as computer code/algorithms for applying the judicial exceptions with a generic computing device. As such, these additional elements are interpreted as merely instructions to apply the judicial exception. Further, per Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), training machine learning models is not a technical improvement and is merely generally linking the judicial exceptions with the field of machine learning and does not amount to a practical application. 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 step(s) of receiving a set of flashcards and training the machine learning models is/are insignificant extra-solution activity performed during the abstract idea. The additional elements of using a first and second machine learning model used to perform the process are instructions for applying the judicial exception with a generic computing device 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 1 is not directed to eligible subject matter as it is directed to an abstract idea without significantly more.
Claims 2-10 are dependent from claim 1 and include all the limitations of the independent claim. 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 2-10 recite further abstract ideas including extracting one or more flashcard features (observation MP); determining a language for each side of the flashcard (judgement MP); determining whether a side includes a numerical value (judgement MP); determining a number of tokens (observation MP); determining that a second flashcard has been classified and determining a study direction (judgement MP); determining that a side contains a question and selecting that side as a prompt side (judgement MP); identifying a question mark (observation MP); identifying interrogative pronouns (observation MP); identifying an inverted word order (observation MP); identifying a blank space (observation MP); selecting that side as a prompt side (judgement MP); identifying a response side based on the words True or False (observation MP); determining a set-level preference (judgement MP); replacing the study direction for the flashcard (judgement MP); and determining the set-level preference based on a majority of flashcards (evaluation MP). 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 additional elements of the dependent claims are further instructions for applying the judicial exception using a generic computing device including using a rule-based model. 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 2-10 are directed to abstract ideas without significantly more and are not drawn to eligible subject matter.
Claim 11 recites a process, the process including the steps of classifying one or more flashcards of the set of flashcards as being of a particular flashcard type; determining that the particular flashcard type for a flashcard of the one or more flashcards is the Pure Question flashcard, the Raw flashcard, or the Solution flashcard; and determining a study direction for the flashcard, wherein determining of the study direction for the flashcard comprises determining which one of the first side or the second side of the flashcard is a prompt side that is presented to a user prior to a response side. The recited steps, under their broadest reasonable interpretation, are classifying one or more flashcards by determining a flashcard type, determining that a flashcard is the Pure Question flashcard, the Raw flashcard, or the Solution flashcard, and determining a study direction by determining which side is a prompt side. The recited steps, as drafted, are a process that is a method of applying an abstract idea, specifically mental processes (judgement (classifying one or more flashcards; determining a flashcard type; determining that a flashcard has been classified as a pure question, raw, or solution flashcard; determining a study direction; determining which side is a prompt side)). If claim limitations, under their broadest reasonable interpretation, include a mental process, the limitations fall under the abstract ideas judicial exception and therefore recite ineligible subject matter. Accordingly, claim 11 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 receiving a set of flashcards, each flashcard in the set of flashcards having a first side and a second side; using a first machine learning model; wherein the first machine learning model is trained using a first training set of flashcards and each flashcard of the first training set has an associated label corresponding to an MCQ flashcard, a True/False flashcard, a Fill-In-The-Blank flashcard, a Pure Question flashcard, a Raw flashcard, or a Solution flashcard; using a rule-based model. The additional elements are insignificant extra-solution activity, generally linking the judicial exceptions to the field of machine learning, and instructions for applying the judicial exception with a generic computing device as, under their broadest reasonable interpretation, the additional step(s) is/are mere data gathering (see MPEP 2106.05(g)) and training a machine learning model. The other additional elements of using a first machine learning model and using a rule-based model is a generic computer component for performing the above method/instructions for applying the judicial exception with a generic computer, per MPEP 2106.05(f). As the machine learning models are recited at a high level of generality, and based on paragraphs 0115-0117, 0135, 0138 of the specification recite the models as various types of machine learning and a rule-based algorithm based on flashcard features. Further, per Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), training machine learning models is not a technical improvement and is merely generally linking the judicial exceptions with the field of machine learning and does not amount to a practical application. 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 step(s) of receiving a set of flashcards and training the model is/are insignificant extra-solution activity performed during the abstract idea. The additional elements of using a first machine learning model and using a rule-based model used to perform the process are instructions for applying the judicial exception with a generic computing device 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 11 is not directed to eligible subject matter as it is directed to an abstract idea without significantly more.
Claims 12-16 are dependent from claims 11 and include all the limitations of the independent claim. 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 12-16 recite further abstract ideas including determining that a side contains a question and selecting that side as a prompt side (judgement MP); identifying a question mark (observation MP); identifying interrogative pronouns (observation MP); identifying an inverted word order (observation MP); determining that the flashcard is of a FITB type (judgement MP); identifying a blank space (observation MP); selecting that side as a prompt side (judgement MP); determining a set-level preference (judgement MP); replacing the study direction for the flashcard (judgement MP); and determining the set-level preference based on a majority of flashcards (evaluation MP). 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.
Accordingly, claims 12-16 are directed to abstract ideas without significantly more and are not drawn to eligible subject matter.
Claim 17 recites a process, the process including the steps of classifying each flashcard of the set of flashcards as being one of a multiple choice question (MCQ) flashcard, a True/False flashcard, a Fill-In-The-Blank flashcard, a Pure Question flashcard, a Raw flashcard, or a Solution flashcard; and determining a study direction for a flashcard from the set of flashcards by determining which one of the first side or the second side of the flashcard is a prompt side that is presented to a user prior to a response side. The recited steps, under their broadest reasonable interpretation, are classifying one or more flashcards by as a flashcard type such as MCQ, True/False, FITB, Questions, raw, or solution; and determining a study direction by determining which side is a prompt side. The recited steps, as drafted, are a process that is a method of applying an abstract idea, specifically mental processes (judgement (classifying one or more flashcards; determining a study direction; determining which side is a prompt side)). If claim limitations, under their broadest reasonable interpretation, include a mental process, the limitations fall under the abstract ideas judicial exception and therefore recite ineligible subject matter. Accordingly, claim 17 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 receiving a set of flashcards, each flashcard in the set of flashcards having a first side and a second side; using a first machine learning model; wherein the first machine learning model is trained using a first training set of flashcards and each flashcard of the first training set has an associated label corresponding to the multiple choice question (MCQ) flashcard, the True/False flashcard, the Fill-In-The-Blank flashcard, the Pure Question flashcard, the Raw flashcard, or the Solution flashcard; for multiple choice question (MCQ) flashcard and the True/False flashcard, performing the determining using a second machine learning model, that is being trained using a second training set of flashcards, wherein each flashcard of the second training set of flashcards has a flashcard side that is labeled as the prompt side or the response side; and for the Fill-In-The-Blank flashcard, the Pure Question flashcard, the Raw flashcard or the Solution flashcard, performing the determining using a rule-based model. The additional elements are insignificant extra-solution activity, generally linking the judicial exceptions to the field of machine learning, and instructions for applying the judicial exception with a generic computing device as, under their broadest reasonable interpretation, the additional step(s) is/are mere data gathering (see MPEP 2106.05(g)) and training machine learning models. The other additional elements of using a first and second machine learning model and using a rule-based model are generic computer components for performing the above method/instructions for applying the judicial exception with a generic computer, per MPEP 2106.05(f). As the machine learning models are recited at a high level of generality, and based on paragraphs 0115-0117, 0135, 0138 of the specification reciting various types of models and a rule-based algorithm based on flashcard features, the models are interpreted as computer code/algorithms for applying the judicial exceptions with a generic computing device. As such, these additional elements are interpreted as merely instructions to apply the judicial exception. Further, per Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025), training machine learning models is not a technical improvement and is merely generally linking the judicial exceptions with the field of machine learning and does not amount to a practical application. 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 step(s) of receiving a set of flashcards and training the machine learning models is/are insignificant extra-solution activity performed during the abstract idea. The additional elements of using a first and second machine learning model and using a rule-based model used to perform the process are instructions for applying the judicial exception with a generic computing device 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 17 is not directed to eligible subject matter as it is directed to an abstract idea without significantly more.
Claims 18-20 are dependent from claim 17 and include all the limitations of the independent claim. 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 18-20 recite further abstract ideas including extracting one or more flashcard features (observation MP); determining that a side contains a question and selecting that side as the prompt side (judgement MP); identifying a question mark (observation MP); identifying interrogative pronouns (observation MP); and identifying an inverted word order (observation MP). 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.
Accordingly, claims 18-20 are directed to abstract ideas without significantly more and are not drawn to eligible subject matter.
Conclusion
Accordingly, claims 1-20 are rejected.
Examiner notes that claims 1-20 have not been rejected in view of 35 U.S.C. 102 or 103 in view of prior art. As further discussed below with regard to searched and cited art, no combination of searched or cited prior art teaches the limitations of the claimed invention as a whole. Specifically, with regard to independent claims 1, 11, and 17, the prior art fails to teach determining a study direction for a flashcard by determining a prompt side of the flashcard using a second machine learning model [claims 1 and 17] or a rule-based model [claims 11 and 17]. The art teaches methods of generating flashcards by processing an input educational material or document, determining question types based on scanned educational material or quizzes or flashcards, and processing flashcards as training material for machine learning models as discussed below. The art fails to teach determining a study direction as the art teaching generating flashcards focuses on creating new cards wherein the type of question being used or desired is already known or inputted rather than receiving flashcards and determining a prompt and response side as claimed in the instant application. Examiner notes that flashcards are well-known study tools/practice in the art and are known for having a question/prompt side and an answer/response side, as evidenced by McGregor et al (US 5842869, “Description of the Related Art”), but it would not be obvious to one of ordinary skill in the art to “reverse” the flashcard making process and train a first and second machine learning model to classify the question/flashcard type and then determine a study direction by determining which side is a prompt side based on the searched and cited art.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Oros et al. (US PGPub 20170293693), considered the closest art of record, teaches a system and method for generating personalized content for a user/student wherein the system receives content in the form of data packets which can include flashcards/flashcard materials, extracting features and words from the data packets using natural language processing including identifying questions and types of questions from the data packet including true/false, multiple choice, and short answer (pure question/raw) questions, and generating new personal content which can include flashcards/flashcard sets. Oros et al. fails to teach using machine learning or rule-based models, training machine learning models, the flashcards being labelled with their type/format, and determining a study direction and which side of the flashcard is a prompt side.
Sunderland et al. (US PGPub 20090248960) teaches a system and method for creating and using virtual flash cards wherein the cards are created using input and processed data from educational content/material wherein the data includes related pairs such as state and state capital, word and definition, and question and answer wherein the pairs are used to generate the flashcard/sides of the flash card. Sunderland et al. fails to teach using machine learning or rule-based models, training machine learning models, the flashcards being labelled with their type/format, and determining a study direction and which side of the flashcard is a prompt side.
Lee et al. (US PGPub 20240135835) teaches a system and method for augmented-reality tutoring utilizing optical character recognition and machine learning models wherein the models can identify types of problems based on one or more identified features and wherein the models may be trained on flashcards. Lee et al. fails to teach the flashcards are labeled by their particular flashcard type for training the model and fails to teach determining a study direction and which side of the flashcard is a prompt side.
Hall (US PGPub 20110091859) teaches a system and method for creating question and answer pairs that can be placed on electronic flashcards wherein the question is place on one side of a card and the answer is placed on the other and can be in the form of a multiple choice, general question (pure question), or true false question. Hall fails to teach training machine learning models, the flashcards being labelled with their type/format, and determining a study direction and which side of the flashcard is a prompt side.
Beaty et al. (US PGPub 20200302811) teaches a system and method for personalized learning including real-time flashcard sessions wherein the flashcards are stored in a database and define a question and a correct answer wherein new questions can be created and turned into flashcards. Beaty fails to teach receiving the flashcards, classifying the cards by type, training machine learning models, the flashcards being labelled with their type/format, and determining a study direction and which side of the flashcard is a prompt side.
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/CORRELL T FRENCH/Examiner, Art Unit 3715
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