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 June 30, 2026 has been entered. Claims 1, 3-17, and 19-22 remain pending in the application. Claims 1, 3-4, 7, 11, 14, 17, and 19 are noted as amended, claims 2 and 18 are noted as cancelled, and claims 21-22 are noted as newly added. Applicant’s amendments to the claims have overcome all previous objections, except the previous object to claim 11 which is included below, set forth in the Non-Final Office Action mailed April 17, 2026 and all objections therein, excluding the objection to claim 11, have been withdrawn. However, new objections and rejections are noted below.
Claim Objections
Claims 1, 4, 8, 11, and 17 are objected to because of the following informalities:
In claim 1, lines 11-12, “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,” should read “a multiple choice question (MCQ) flashcard type, a True/False flashcard type, a Fill-In-The-Blank flashcard type, a Pure Question flashcard type, a Raw flashcard type, or a Solution flashcard type,”.
In claim 1, lines 17-18, “the MCQ flashcard” should read “the MCQ flashcard type”.
In claim 1, line 19, “into a second machine learning model,” should read “into the second machine learning model”.
In claim 1, line 20, “the MCQ flashcard” should read “the MCQ flashcard type”.
In claim 1, lines 21-22, “for the MCQ flashcard” should read either “for the one or more MCQ flashcards” or “for one of the MCQ flashcards” or “for one of the one or more MCQ flashcards”. Similar correction should be made in line 23.
In claim 4, line 3, “classified as the is Fill-In-The-Blank flashcard, a Pure Question flashcard, a Raw flashcard, or a Solution flashcard” should read “classified as the is Fill-In-The-Blank flashcard type, the Pure Question flashcard type, the Raw flashcard type, or the Solution flashcard type”.
In claim 8, line 3, “as a True/False flashcard” should read “as the True/False flashcard type”
In claim 11, lines 8-10, “classifying, using the first machine learning model, one or more flashcards of the set of flashcards as being of a particular flashcard type of each of the one or more flashcards of the set of flashcard,” is redundant and grammatically confusing. Examiner recommends amending the recitation to “classifying, using the first machine learning model, one or more flashcards of the set of flashcards as being of a particular flashcard type”.
In claim 11, line 12, “to a particular flashcard type” should read “to the particular flashcard type”.
In claim 11, lines 13-14, “multiple choice question (MCQ) flashcard, a True/False flashcard, or a Fill-In-The-Blank flashcard, a Pure Question flashcard, a Raw flashcard or a Solution flashcard” should read “multiple choice question (MCQ) flashcard type, a True/False flashcard type, a Fill-In-The-Blank flashcard type, a Pure Question flashcard type, a Raw flashcard type, or a Solution flashcard type”.
In claim 11, line 18, “into a second machine learning model,” should read “into the second machine learning model”.
In claim 11, lines 19-20, “the Pure Question flashcard, the Raw flashcard or the Solution flashcard” should read “the Pure Question flashcard type, the Raw flashcard type, or the Solution flashcard type”.
In claim 11, lines 22, “flashcard-using” should read “flashcard using”.
In claim 17, line 9-10, “question (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 “question (MCQ) flashcard type, a True/False flashcard type, a Fill-In-The-Blank flashcard type, a Pure Question flashcard type, a Raw flashcard type, or a Solution flashcard type,”.
In claim 17, line 11, “Gradient Bosted Tree” should read “Gradient Boosted Tree”.
In claim 17, lines 13-14, “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;” should read “multiple choice question (MCQ) flashcard type, a True/False flashcard type, a Fill-In-The-Blank flashcard type, a Pure Question flashcard type, a Raw flashcard type, or a Solution flashcard type;”.
In claim 17, line 18, “the particular flashcard type is” should read “the particular flashcard type of the flashcard is”.
In claim 17, line 18, “(MCQ) flashcard” should read “(MCQ) flashcard type”.
In claim 17, line 19, “True/False flashcard” should read “True/False flashcard type”.
In claim 17, line 23, “the particular flashcard type is” should read “the particular flashcard type of the flashcard is”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 11-16 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 11, as amended, recites “inputting into a second machine learning model, the one or more flashcards in the set of flashcards whose particular flashcard type is determined as the Pure Question flashcard, the Raw flashcard or the Solution flashcard; determining, using the second machine learning model, a study direction for the Pure Question flashcard, the Raw flashcard or the Solution flashcard using a rule-based model”. The limitations lack sufficient description in the specification and amount to new matter as the second machine learning model is recited in the specification as being trained on MCQ and T/F flashcards and that the second machine learning model is used to determining the study direction for MCQ and T/F question types, not the Pure, Raw, Solution, or FITB types which are only taught as being determined using the rule-based model, see Paragraphs 0167-0168, 0173. Further, as the limitation is a computer-implemented function, the specification must provide sufficient support for both the hardware and algorithm for performing the claimed function. In this instance, the specification does not provide a sufficient algorithm for the second machine learning model determining a pure question, raw, or solution flashcard in part as the flashcards may not have information on both sides and therefore would not have a prompt side and solution side for training the second machine learning model. Therefore, the specification fails to provide sufficient support for the second machine learning model determining a study direction for Pure Question flashcard, Raw flashcard, or Solution flashcard.
Claims 12-16 are rejected by virtue of their dependency from claim 11.
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, 3-16, and 21-22 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.
Claim 1 recites the limitation "the second machine learning model" in line 7. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation “determining, by the first machine learning model, that the particular flashcard type of the one or more flashcards in the set of flashcards has been classified as the MCQ flashcard”. The limitation as recited means that the determination is that all of the one or more flashcards in the set have the particular flashcard type of an MCQ flashcard type which renders the step unclear and therefore indefinite. Rather, it should read that the determined/identified MCQ flashcards are a subset or separate group of one or more flashcards which corresponds with the subsequent input step. It appears the limitation should read “determining, by the first machine learning model, that one or more flashcards in the set of flashcards has been classified as the MCQ flashcard type” as these one or more flashcards are a subset of the overall classified one or more flashcards.
Claims 3-10 and 21-22 are rejected by virtue of their dependency.
Claim 3 recites the limitation "the flashcard" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 4 recites the limitation "the flashcard" in lines 1-2. There is insufficient antecedent basis for this limitation in the claim.
Claims 5-7 are rejected by virtue of their dependency.
Claim 8 recites the limitation "the flashcard" in lines 1-2. There is insufficient antecedent basis for this limitation in the claim.
Claim 11 recites the limitation "the second machine learning model" in line 7. There is insufficient antecedent basis for this limitation in the claim.
Claim 11 recites the limitation “determining, using the second machine learning model, a study direction for the Pure question flashcard, the Raw flashcard, or the Solution flashcard using a rule-based model”. The amendment to the limitation now recites using both the second machine learning model and the rule-based model. As the use of the second machine learning model is not supported by the specification as discussed above, it is unclear how both models would be used and applied to determine the study direction. Further, this raises issues with claims 12 and 14 which recite using the rule-based model and do not recite the second machine learning model. Thereby, one of ordinary skill in the art would not be able to determine what the inventor or a joint inventor regards as the invention.
Claims 12-16 are rejected by virtue of their dependency from claim 11.
Claim 14 recites the limitation "the flashcard" in line 3. There is insufficient antecedent basis for this limitation in the claim.
Claim 14 recites the limitation “determining that the flashcard is of the Fill-In-The-Blank” flashcard type”. However, claim 11, from which claim 14 depends, recites the flashcard is determined as a Pure Question, Raw, or Solution flashcard. Therefore, it is unclear how the flashcard can be both a pure, raw, or solution flashcard and a FITB flashcard. This renders the claim indefinite and rejected under 35 U.S.C. 112(b).
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, 3-17, and 19-22 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 extracting one or more flashcard features for each flashcard in the set of flashcards, wherein the one or more flashcard features include one or more bullets or a question mark; 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 the particular flashcard type of the one or more flashcards in the set of flashcards has been classified as the MCQ flashcard; and determining a study direction for the MCQ flashcard; wherein determining the study direction for the MCQ 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 extracting one or more flashcard features for each flashcard in a set wherein the features are bullets or question marks, 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 (observation (extracting features); 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; performing steps by/using the first machine learning model utilizing word embeddings; wherein the one or more flashcard features comprise an input to a second machine learning model; wherein the first machine learning model is a Random Forest model or a Gradient Boosted Tree (GBT) model, trained using a first training set of flashcards and each flashcard of the first training set of flashcards has an associated label corresponding to the particular flashcard type; inputting using a second machine learning model the one or more flashcards in the set of flashcards whose particular flashcard type is determined as the MCQ flashcard; and wherein the second machine learning model is a regular expression (Regex) based classifier, 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)), inputting/gathering data, 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. Defining the models as a RF or GBT model and a Regex classifier, respectively, while specific types of models, does not amount to a specific implementation or particular machine as the models are merely being applied to perform the steps that under their broadest reasonable interpretation can be performed mentally. Further, the models are not being improved or adapted beyond using the training data which is insignificant extra-solution activity as discussed below. 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, inputting the flashcards and features into the second machine learning model, 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, which are a RF or GBT model and Regex classifier respectively, 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). Additionally, as evidenced by Yang (US PGPub 20200142948), paragraph 0054, using word embedding/vectorization is well-known in the art for generating vectors/data representing words, punctuation, or other components of text. Therefore, the use of machine learning for these steps is well-understood, routine, and conventional. 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 3-10 and 21-22 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 3-10 and 22 recite further abstract ideas including 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); determining the set-level preference based on a majority of flashcards (evaluation MP); and combining results from the models to obtain a more accurate prediction (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.
The limitation of claim 21 recites the additional element of the first machine learning model comprising using a RNN such as a LSTM network and the intended use of processing a sequence of embedding vectors representing text data. Due to the high-level of generality of the recitation of machine learning, the limitation is interpreted as mere computer code for performing the computer functions and falls under the instructions for applying an abstract idea. The intended use is merely a conclusory statement of an intended improvement since the recitation of machine learning does not include further specification of how the steps are performed to show a technological improvement. This is further evidenced by the specification only discussing LSTM network in paragraph 0117 as an alternative to the first machine learning model and does not specify specific steps or techniques performed using the LSTM. Therefore, the limitations fail to provide any teaching that integrates the judicial exceptions into a practical application or amount to significantly more than the judicial exception. For this reason, the analysis performed on the independent claims is also applicable on these claims.
Accordingly, claims 3-10 and 21-22 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 extracting one or more flashcard features for each flashcard in the set of flashcards, wherein the one or more flashcard features include one or more bullets or a question mark; classifying one or more flashcards of the set of flashcards as being of a particular flashcard type of each of the one or more flashcards of the set of flashcards; determining that the particular flashcard type for a flashcard of the one or more flashcards in the set of flashcards is the Pure Question flashcard, the Raw flashcard, or the Solution flashcard; and determining a study direction for the Pure Question flashcard, the Raw flashcard, or the Solution flashcard, wherein determining of the study direction for the Pure Question flashcard, the Raw flashcard, or the Solution 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 extracting one or more flashcard features for each flashcard in a set wherein the features are bullets or question marks, 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 (observation (extracting features); 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; performing steps by/using the first machine learning model utilizing word embeddings; wherein the one or more flashcard features comprise an input to a second machine learning model; wherein the first machine learning model is a Random Forest model or a Gradient Boosted Tree (GBT) model, 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; inputting using a second machine learning model the one or more flashcards in the set of flashcards whose particular flashcard type is determined as the MCQ flashcard; using the second machine learning model; wherein the second machine learning model is a regular expression (Regex) based classifier, and 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)), inputting/gathering data, and training a machine learning model. The other additional elements of using a first machine learning model, a second machine learning model, and 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/rule 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. Defining the models as a RF or GBT model and a Regex classifier, respectively, while specific types of models, does not amount to a specific implementation or particular machine as the models are merely being applied to perform the steps that under their broadest reasonable interpretation can be performed mentally. Further, the models are not being improved or adapted beyond using the training data which is insignificant extra-solution activity as discussed below. 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, inputting the flashcards and features into the second machine learning model, 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 second machine learning model, which are a RF or GBT model and Regex classifier respectively, 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). Additionally, as evidenced by Yang (US PGPub 20200142948), paragraph 0054, using word embedding/vectorization is well-known in the art for generating vectors/data representing words, punctuation, or other components of text. Therefore, the use of machine learning for these steps is well-understood, routine, and conventional. 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 extracting one or more flashcard features for each flashcard in the set of flashcards, wherein the one or more flashcard features include one or more bullets or a question mark; classifying a particular flashcard type of each flashcard of the set of flashcards, the particular flashcard type 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 extracting one or more flashcard features for each flashcard in a set wherein the features are bullets or question marks, classifying one or more flashcards 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 (observation (extracting features); 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; performing steps by/using the first machine learning model utilizing word embeddings; wherein the first machine learning model is a Random Forest model or a Gradient Boosted Tree (GBT) model, 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; when the particular flashcard type is the multiple choice question (MCQ) flashcard or the True/False flashcard, performing the determining the study direction using a second machine learning model, that is a regular expression (Regex) based classifier 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 when the particular flashcard type is the Fill-In-The-Blank flashcard, the Pure Question flashcard, the Raw flashcard or the Solution flashcard, performing the determining the study direction 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. Defining the models as a RF or GBT model and a Regex classifier, respectively, while specific types of models, does not amount to a specific implementation or particular machine as the models are merely being applied to perform the steps that under their broadest reasonable interpretation can be performed mentally. Further, the models are not being improved or adapted beyond using the training data which is insignificant extra-solution activity as discussed below. 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, which are a RF or GBT model and Regex classifier respectively, 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). Additionally, as evidenced by Yang (US PGPub 20200142948), paragraph 0054, using word embedding/vectorization is well-known in the art for generating vectors/data representing words, punctuation, or other components of text. Therefore, the use of machine learning for these steps is well-understood, routine, and conventional. 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 19-20 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 19-20 recite further abstract ideas including 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 19-20 are directed to abstract ideas without significantly more and are not drawn to eligible subject matter.
Response to Arguments
Applicant's arguments, filed June 30, 2026, with respect to the rejection(s) of claim(s) 1, 2-17, and 19-20 under 35 U.S.C. 101 have been fully considered but they are not persuasive. First, Applicant’s assertion that all claims have not been considered is incorrect as all claims including the dependent claims have been considered and discussed previously and above. The analysis has been simplified for the sake of compact prosecution as the claims recite further judicial exceptions in the form of abstract ideas, as noted previously and above, the limitations are predominantly mental processes (MP) under their broadest reasonable interpretation. The dependent claims have been analyzed for additional elements and do not include additional elements which could amount to a practical application or significantly more, and the claim limitations have been considered individually and in combination and do not result in a practical application or significantly more. This is why the analysis of the independent claims is also applicable to the independent claims as the additional limitations of the dependent claims do not integrate the judicial exceptions of the independent claims into a practical application or amount to significantly more than the judicial exceptions. Applicant has not provided substantive arguments for the majority of the claims for the Examiner to rebut. The arguments with regard to claims 5 and 14 are merely reiterating the limitations of the claims and have been considered, but as discussed previously and above, the limitations of claims 5 and 14 are further judicial exceptions as a judgment mental process and observation/judgement mental process respectively. Therefore, the limitations cannot integrate the judicial exceptions into a practical application as they are further abstract ideas. New claims 21 and 22 have been considered above.
Regarding Applicant’s arguments directed to the independent claims, Applicant argues A) the claims do not recite judicial exceptions because they are “particularized and solution-specific”, B) the claims are integrated into a practical application by B1) reciting an improvement to computing technology specifically machine-learning systems, and B2) in view of the Kim memorandum and Ex Parte Desjardins by providing a technical improvement for machine learning models for formatting and analysis of user-generated data for accuracy, and C) the claim limitations amount to significantly more by conferring a technological improvement to the technical problem. Regarding argument A, Examiner notes that under Step 2A Prong one the claims are interpreted under their broadest reasonable interpretation and the analysis is to determine if the broadest reasonable interpretation recite abstract ideas regardless of the implementation of a computer/computing device as discussed in MPEP 2106.04(a)(2) as “a claim that requires a computer may still recite a mental process” and even particular and detailed steps can recite mental processes. The claimed steps are mental processes as the discussed steps are gathering/collecting information in the form of flashcard features and text and analyzing the information/data to classify the flashcards by type and to determine a “study direction” based on the type of flashcard. Regarding arguments B and C, Applicant’s claims of a technological improvement are a conclusory statement and evidence of a technological improvement is not present in the claims as recited. Specifically, while the claims recite the additional elements of machine learning models, the models being RF, GBT, and/or Regex classifiers, and training the models, the additional elements are being applied to perform the abstract ideas as recited. If an individual was given flashcards, asked to identify/recognize the features, classify/determine the flashcards by a question type, and determine a study direction by identifying a prompt and solution/response side, the individual could mentally perform the steps. The application of the machine learning models does not inherently amount to a technical improvement, practical application, or significantly more purely for performing the steps in place of a human mind. Further, as discussed previously, 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 discussed in Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, Case No. 23-2437, (Fed. Cir. 2025) as training machine learning models is well-understood in the art and insignificant extra solution activity as it would be obvious to one of ordinary skill in the art to train the models with relevant data to performed the abstract ideas. Overall, it appears Applicant’s argument is the technical solution is the models being able to perform the abstract ideas/the process of identifying the flashcard type and study directions. Applicant’s arguments and claims do not specify a technical improvement to the performance or functionality of the models and are merely applying the models as instructions for performing the abstract ideas. This is also why Ex Parte Desjardins and the Kim memorandum are not applicable to the instant application as the cited precedent and memorandum are directed toward technical improvements to AI/ML as a technology and not applications of AI/ML to perform judicial exceptions. If Applicant recited specific technical steps that are not mental processes under their broadest reasonable interpretation and/or recited improvements to the models, such as the improved performance/memory of Ex Parte Desjardins, then the argument would be applicable. Therefore, the claims, as discussed above, recite judicial exceptions without significantly more, and the claims stand rejected under 35 U.S.C. 101.
Conclusion
Accordingly, claims 1, 3-17, and 19-22 are rejected.
As previously discussed and included again for clarity of the record, Examiner notes that claims 1, 3-17, and 19-22 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 placed 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.
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.
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/CORRELL T FRENCH/Examiner, Art Unit 3715
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