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
This is a Final Office action in response to communications filed on December
30, 2025. Applicant amended claims 1-2 and 6-7 and cancelled claims 3-4 and 8-9. Applicant’s amendments to the claims have overcome each objection set forth in the Non-Final Office Action dated September 30, 2025. Therefore, Examiner withdraws the claim objections. Claims 1-2 and 5-7 remain pending in this application.
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 1-2 and 5-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Does the claimed invention fall inside one of the four statutory categories (process, machine, manufacture, or composition of matter)? Yes.
Claims 1-2 and 5-7 are drawn to a method and system for providing a correctness or incorrectness prediction for a learning question. (i.e., process).
Step 2A - Prong One: Do the claims recite a judicial exception (an abstract idea enumerated in the 2019 PEG, a law of nature, or a natural phenomenon)? Yes
Claim 1 recites:
A method performed in a system for providing a correctness or incorrectness prediction for a learning question, the system comprising one or more processors and the method comprising the steps of: by the one or more processors, acquiring a set of data including a user variable determined on the basis of concept-specific correctness or incorrectness sequence data of at least one user, and at least one variable related to a question and a concept associated with the user variable wherein the user variable and the variable related to the question are grouped on the basis of context information, respectively, and the variable related to the concept includes a user's concept understanding estimated using a concept-specific understanding estimation model trained on the basis of the concept-specific correctness or incorrectness sequence data;
and by the one or more processors, and calculating a probability that a first user corresponding to a specific user variable correctly answers a first question corresponding to a specific concept not encountered by the first user, with reference to the set of data, wherein the concept-specific understanding estimation model is trained such that the concept understanding is estimated by assigning a greater weight to second concept-specific correctness or incorrectness sequence data generated at a second time point than to first concept- specific correctness or incorrectness sequence data generated at a first time point, wherein the second time point follows the first time point by a predetermined amount of time, wherein the first user's understanding of the specific concept is estimated on the basis of a second user's understanding of the specific concept, wherein the second user is different from the first user, and wherein the first user's understanding of the specific concept is estimated by assessing similarity between the specific concept and a concept encountered by the first user.
These steps amount to a form of mental process, organizing human activity, and mathematical concepts (i.e., an abstract idea) because a human can discern and calculate user knowledge deficiencies based on basic and advanced questioning and the resulting responses. Applicant of claimed invention discloses that user learning improvements have been “based on the knowledge or know-how of instructors or educational institutions” [0004]. Dependent claims 2 and 5 are directed towards the prediction method (probability calculation, data grouping, and estimation modeling, etc.). Each claim amounts to a form of mental process, organizing human activity, and mathematical concepts (i.e., an abstract idea). As such, the Examiner concludes that claim 1 recites an abstract idea.
Step 2A – Prong Two: Do the claims recite additional elements that integrate the exception into a practical application of the exception? No
In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “additional element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
The requirement to execute the claimed steps/functions using a computer-readable recording medium (dependent claim 5) is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. Similarly, the limitation of a computer-readable recording medium (dependent claim 5) is recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Use of a computer, processor, memory or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015) (See MPEP 2106.05(f)).
Further, the additional limitations beyond the abstract idea identified above, serve merely to generally link the use of the judicial exception to a particular technological environment or field of use. Specifically, they serve to limit the application of the abstract idea to a computerized environment (e.g., identifying and displaying, etc.) performed by a computing device, processor, and memory, etc. This reasoning was demonstrated in Intellectual Ventures I LLC v. Capital One Bank (Fed. Cir. 2015), where the court determined “an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer”). These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(h)).
Dependent claims 2 and 5 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e., they are part of the abstract idea recited in each respective claim). The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claims are directed to an abstract idea.
Step 2B: Does the claim as a whole amount to significantly more than the judicial exception? i.e., Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea? No
In step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for an “inventive concept.” An “inventive concept” is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amount to significantly more than the judicial exception itself. Alice Corp., 573 U.S. at 27-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966).
As discussed above in “Step 2A – Prong Two”, the identified additional elements in independent claims 1 and 6 and dependent claims 2, 5, and 7 are equivalent to adding the words “apply it” on a generic computer, and/or generally link the use of the judicial exception to a particular technological environment or field of use. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer and/or append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, (e.g., mere data gathering, post-solution activity) and/or simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. The dependent claims 2 and 5 fail to include any additional elements. In other words, each of the limitations/elements recited in the respective independent claim is further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea recited in the respective claim). The Examiner has therefore determined that no additional element, or combination of additional claims elements are sufficient to ensure the claims amount to significantly more than the abstract idea identified above. Therefore, claims 1-2 and 5-7 are not eligible subject matter under 35 USC 101.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1 and 5-6 are rejected under 35 U.S.C. 102 as being unpatentable under US 20190272775 A1 (“Noble”).
In regards to claim 1, Noble discloses
A method performed in a system for providing a correctness or incorrectness prediction for a learning question, the system comprising one or more processors ([0204], “One or more processors … may be included in processing unit”) and the method comprising the steps of ([0195], “The recommendation engine can use the skill level of the user to generate a prediction of the likelihood of … users providing a desired response”): by the one or more processors ([0204], “One or more processors … may be included in processing unit”), acquiring a set of data ([0144], “With reference to FIG. 3, an illustrative set of data stores and/or data store servers is shown”) including a user variable ([0185], “a component … may report a user response, but define an identifier of that user as a private variable”) determined on the basis of concept-specific correctness or incorrectness sequence data of at least one user, and at least one variable related to a question and a concept associated with the user variable ([0020], “determining the correctness of the response … includes …: determining if the response … is present in the solution … and categorizing each of the steps as … correct; incorrect; or assisted … determining the correctness of the response … comprises: determining … if: (1) math embodied in the step is accurate; and (2) if the step is relevant.” Examiner notes that a math problem is an application of a concept.) wherein the user variable and the variable related to the question are grouped on the basis of context information ([0159, “the content library database ... can comprise ... a content network ... linked together by ... common user; relation to a common subject, topic, skill, or the like; ... the content ... can comprise a grouping of content ... in the form of ... a flash card and an extraction portion that can comprise the desired response ... an answer to a flash card”.), respectively, and the variable related to the concept includes a user's concept understanding estimated using a concept-specific understanding estimation model trained on the basis of the concept-specific correctness or incorrectness sequence data ([0189], “Internal processing … could perform functions such as providing content to a user, receiving a response from a user, determining the correctness of the received response, updating one or several models based on the correctness of the response, recommending new content for providing to one or several users, or the like” Examiner notes that an internal processor can include an estimation model.);
and by the one or more processors ([0204], “One or more processors … may be included in processing unit”), calculating a probability that a first user corresponding to a specific user variable correctly answers a first question corresponding to a specific concept not encountered by the first user, with reference to the set of data ([0433], “After the student metadata has been retrieved… probabilities are determined … these probabilities can be determined based on user metadata identifying nodes … that have been mastered by the student” Examiner notes that the student metadata may include concepts not previously studied by the student.), wherein the concept-specific understanding estimation model is trained such that the concept understanding is estimated by assigning a greater weight to second concept-specific correctness or incorrectness sequence data generated at a second time point than to first concept- specific correctness or incorrectness sequence data generated at a first time point, wherein the second time point follows the first time point by a predetermined amount of time, wherein the first user's understanding of the specific concept is estimated on the basis of a second user's understanding of the specific concept, wherein the second user is different from the first user, and wherein the first user's understanding of the specific concept is estimated by assessing similarity between the specific concept and a concept encountered by the first user ([0189], “Internal processing ... could perform functions such ... updating ... models based on the correctness of the response ... internal processing ... can decide ... weighting of records from the data stream. To the extent that decisions are made based upon analysis of the data stream, each data record is time stamped to reflect when the information was gathered such that additional credibility could be given to more recent results ... a particular contributor of information may prove to have less than optimal gathered information and that could be weighted very low or removed” Examiner notes that an internal processor can include an estimation model.).
In regards to claim 5, Noble discloses
A non-transitory computer-readable recording medium having stored thereon a computer program for executing the method of Claim 1 ([0062], “FIG. 2 is a … diagram illustrating a computer server and computing environment within a content distribution network.”).
In regards to claim 6, Noble discloses
A system for providing a correctness or incorrectness prediction for a learning question, the system comprising ([0195], “The recommendation engine can use the skill level of the user to generate a prediction of the likelihood of … users providing a desired response”) one or more processors configured to ([0204], “One or more processors … may be included in processing unit”): acquire a set of data including a user variable ([0144], “With reference to FIG. 3, an illustrative set of data stores and/or data store servers is shown”) determined on the basis of concept-specific correctness or incorrectness sequence data of at least one user, and at least one variable related to a question and a concept associated with the user variable ([0020], “determining the correctness of the response … includes …: determining if the response … is present in the solution … and categorizing each of the steps as … correct; incorrect; or assisted … determining the correctness of the response … comprises: determining … if: (1) math embodied in the step is accurate; and (2) if the step is relevant.” Examiner notes that a math problem is an application of a concept.), wherein the user variable and the variable related to the question are grouped on the basis of context information ([0159, “the content library database ... can comprise ... a content network ... linked together by ... common user; relation to a common subject, topic, skill, or the like; ... the content ... can comprise a grouping of content ... in the form of ... a flash card and an extraction portion that can comprise the desired response ... an answer to a flash card”.), respectively, and the variable related to the concept includes a user's concept understanding estimated using a concept-specific understanding estimation model trained on the basis of the concept-specific correctness or incorrectness sequence data ([0189], “Internal processing … could perform functions such as providing content to a user, receiving a response from a user, determining the correctness of the received response, updating one or several models based on the correctness of the response, recommending new content for providing to one or several users, or the like” Examiner notes that an internal processor can include an estimation model.);
and calculate a probability that a first user corresponding to a specific user variable correctly answers a first question corresponding to a specific concept not encountered by the first user, with reference to the set of data ([0433], “After the student metadata has been retrieved… probabilities are determined … these probabilities can be determined based on user metadata identifying nodes … that have been mastered by the student” Examiner notes that the student metadata may include concepts not previously studied by the student.), wherein the concept-specific understanding estimation model is trained such that the concept understanding is estimated by assigning a greater weight to second concept-specific correctness or incorrectness sequence data generated at a second time point than to first concept- specific correctness or incorrectness sequence data generated at a first time point, wherein the second time point follows the first time point by a predetermined amount of time, wherein the first user's understanding of the specific concept is estimated on the basis of a second user's understanding of the specific concept, wherein the second user is different from the first user, and wherein the first user's understanding of the specific concept is estimated by assessing similarity between the specific concept and a concept encountered by the first user ([0189], “Internal processing ... could perform functions such ... updating ... models based on the correctness of the response ... internal processing ... can decide ... weighting of records from the data stream. To the extent that decisions are made based upon analysis of the data stream, each data record is time stamped to reflect when the information was gathered such that additional credibility could be given to more recent results ... a particular contributor of information may prove to have less than optimal gathered information and that could be weighted very low or removed” Examiner notes that an internal processor can include an estimation model.).
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:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 2 and 7 are rejected under 35 U.S.C. 103 as being unpatentable under Noble in view of US 20210201205 A1 (“Chatterjee”).
In regards to claim 2, Noble does not disclose wherein in the step of calculating the probability, the probability is calculated using a binary classification algorithm.
Chatterjee discloses
wherein in the step of calculating the probability, the probability is calculated using a binary classification algorithm ([0040], “one of the objectives includes generation of an accurate binary classifier”).
Noble and Chatterjee are considered analogous to the claimed invention because they are in the field of automated evaluations and predictions. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a method for providing a correctness or incorrectness prediction for a learning question, the method comprising the steps of: acquiring a set of data including a user variable determined on the basis of concept-specific correctness or incorrectness sequence data of at least one user, and at least one variable related to a question and a concept associated with the user variable; and calculating a probability that a first user corresponding to a specific user variable correctly answers a first question corresponding to a specific concept, with reference to the data set, as disclosed by Noble, wherein in the step of calculating the probability, the probability is calculated using a binary classification algorithm, as disclosed by Chatterjee, to provide a binary classifier for determining correctness of predictions performed by a deep learning model.
In regards to claim 7, Noble discloses the following limitation with the exception of the underlined limitation wherein the one or more processors are configured to ([0204], “One or more processors … may be included in processing unit”) calculate the probability using a binary classification algorithm.
Chatterjee discloses
calculate the probability using a binary classification algorithm ([0040], “one of the objectives includes generation of an accurate binary classifier”).
Noble and Chatterjee are considered analogous to the claimed invention because they are in the field of automated evaluations and predictions. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the applicant’s invention for a system for providing a correctness or incorrectness prediction for a learning question, the system comprising: a data acquisition unit configured to acquire a set of data including a user variable determined on the basis of concept-specific correctness or incorrectness sequence data of at least one user, and at least one variable related to a question and a concept associated with the user variable; and a probability calculation unit configured to calculate a probability that a first user corresponding to a specific user variable correctly answers a first question corresponding to a specific concept, with reference to the data set, wherein the one or more processors are configured to, as disclosed by Noble, wherein the probability calculation unit is configured to calculate the probability using a binary classification algorithm, as disclosed by Chatterjee, to provide a binary classifier for determining correctness of predictions performed by a deep learning model.
Response to Arguments
Applicant's arguments filed December 30, 2025 have been fully considered, but they are not persuasive. Applicant amended claims 1-2 and 6-7. Claims 1-2 and 5-7 remain pending in this application. With respect to “Rejections Under 35 U.S.C. § 101”, Applicant argues “Step 2A is satisfied because claims 1-2 and 5-7 are not directed to a judicial exception, such as an abstract idea. Recent USPTO 2019 Revised Patent Subject Matter Eligibility Guidance (‘Guidance’) issued on January 4, 2019 requires a two prong test under Step 2A.” (See AMENDMENT AND RESPONSE, REMARKS, Rejections Under 35 U.S.C. § 101, Step 2A, page 6, paragraph 2). Examiner acknowledges Applicant’s remarks. As it relates to Step 2A, claim 1 recites a method performed in a system for providing a correctness or incorrectness prediction for a learning question, the system comprising one or more processors and the method comprising the steps of: by the one or more processors, acquiring a set of data including a user variable determined on the basis of concept-specific correctness or incorrectness sequence data of at least one user, and at least one variable related to a question and a concept associated with the user variable wherein the user variable and the variable related to the question are grouped on the basis of context information, respectively, and the variable related to the concept includes a user's concept understanding estimated using a concept-specific understanding estimation model trained on the basis of the concept-specific correctness or incorrectness sequence data;
and by the one or more processors, and calculating a probability that a first user corresponding to a specific user variable correctly answers a first question corresponding to a specific concept not encountered by the first user, with reference to the set of data, wherein the concept-specific understanding estimation model is trained such that the concept understanding is estimated by assigning a greater weight to second concept-specific correctness or incorrectness sequence data generated at a second time point than to first concept- specific correctness or incorrectness sequence data generated at a first time point, wherein the second time point follows the first time point by a predetermined amount of time, wherein the first user's understanding of the specific concept is estimated on the basis of a second user's understanding of the specific concept, wherein the second user is different from the first user, and wherein the first user's understanding of the specific concept is estimated by assessing similarity between the specific concept and a concept encountered by the first user. These steps amount to a form of mental process, organizing human activity, and mathematical concepts (i.e., an abstract idea) because a human can discern and calculate user knowledge deficiencies based on basic and advanced questioning and the resulting responses. Applicant of claimed invention discloses that user learning improvements have been “based on the knowledge or know-how of instructors or educational institutions” [0004]. Claim 6 is almost identical to claim 1. Dependent claims 2 and 7 are directed towards the probability calculation and dependent claim 5 is directed toward a computer medium for storing the computer program used to calculate the probability. Each claim amounts to a form of mental process, organizing human activity, and mathematical concepts and calculations (i.e., an abstract idea). As such, the Examiner concludes that claims 1 and 6 recite an abstract idea and claims 2, 5, and 7 provide a mechanism for calculating probability.
In prong two of step 2A, an evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the exception into a practical application of that exception. An “additional element” is an element that is recited in the claim in addition to (beyond) the judicial exception (i.e., an element/limitation that sets forth an abstract idea is not an additional element). The phrase “integration into a practical application” is defined as requiring an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
The requirement to execute the claimed steps/functions using a computer-readable recording medium (dependent claim 5) is equivalent to adding the words “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer. Similarly, the limitation of a computer-readable recording medium (dependent claim 5) is recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(f)).
Use of a computer, processor, memory or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015) (See MPEP 2106.05(f)).
Further, the additional limitations beyond the abstract idea identified above, serve merely to generally link the use of the judicial exception to a particular technological environment or field of use. Specifically, they serve to limit the application of the abstract idea to a computerized environment (e.g., identifying and displaying, etc.) performed by a computing device, processor, and memory, etc. This reasoning was demonstrated in Intellectual Ventures I LLC v. Capital One Bank (Fed. Cir. 2015), where the court determined “an abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, such as the Internet [or] a computer”). These limitations do not impose any meaningful limits on practicing the abstract idea, and therefore do not integrate the abstract idea into a practical application (see MPEP 2106.05(h)).
Dependent claims 2 and 5 fail to include any additional elements. In other words, each of the limitations/elements recited in respective dependent claims are further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e., they are part of the abstract idea recited in each respective claim). The Examiner has therefore determined that the additional elements, or combination of additional elements, do not integrate the abstract idea into a practical application. Accordingly, the claims are directed to an abstract idea. Therefore, the rejections of claims 1-2 and 5-7 under 35 U.S.C. § 101, Step 2A are maintained.
With respect to “Rejections Under 35 U.S.C. § 101”, Applicant argues “the Office Action fails to substantiate the rejection under Step 2B as required under the Berkheimer Memo. ‘[T]he Federal Circuit held that ‘[w]hether a claim element or combination of elements is well-understood, routine and conventional to a skilled artisan in the relevant field is a question of fact.’” (See AMENDMENT AND RESPONSE, REMARKS, Rejections Under 35 U.S.C. § 101, Step 2B, page 11, paragraph 3). Examiner acknowledges Applicant’s remarks. As it relates to Step 2B, the claims are analyzed to determine whether any additional element, or combination of additional elements, are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for an “inventive concept.” An “inventive concept” is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amount to significantly more than the judicial exception itself. Alice Corp., 573 U.S. at 27-18, 110 USPQ2d at 1981 (citing Mayo, 566 U.S. at 72-73, 101 USPQ2d at 1966).
As discussed previously, the identified additional elements in independent claims 1 and 6 and dependent claims 2, 5, and 7 are equivalent to adding the words “apply it” on a generic computer, and/or generally link the use of the judicial exception to a particular technological environment or field of use. Therefore, the claims as a whole do not amount to significantly more than the judicial exception itself.
Viewing the additional limitations in combination also shows that they fail to ensure the claims amount to significantly more than the abstract idea. When considered as an ordered combination, the additional components of the claims add nothing that is not already present when considered separately, and thus simply append the abstract idea with words equivalent to “apply it” on a generic computer and/or mere instructions to implement the abstract idea on a generic computer and/or append the abstract idea with insignificant extra solution activity associated with the implementation of the judicial exception, (e.g., mere data gathering, post-solution activity) and/or simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. The dependent claims 2, 5, and 7 fail to include any additional elements. In other words, each of the limitations/elements recited in the respective independent claim is further part of the abstract idea as identified by the Examiner for each respective dependent claim (i.e. they are part of the abstract idea recited in the respective claim). The Examiner has therefore determined that no additional element, or combination of additional claims elements are sufficient to ensure the claims amount to significantly more than the abstract idea identified above. Therefore, the rejections of claims 1-2 and 5-7 under 35 U.S.C. § 101, Step 2B are maintained.
With respect to “Rejections Under 35 U.S.C. §§ 102 and 103”, Applicant argues “the cited references do not disclose, teach or suggest at least ‘wherein the user variable and the variable related to the question are grouped on the basis of context information, respectively, and the variable related to the concept includes a user's concept understanding estimated using a concept-specific understanding estimation model trained on the basis of the concept-specific correctness or incorrectness sequence data,’ as recited in amended independent claim 1” (See AMENDMENT AND RESPONSE, REMARKS, Rejections Under 35 U.S.C. §§ 102 and 103, page 13, paragraph 3). Examiner acknowledges Applicant’s remarks. Regarding claim 1, Noble discloses a method performed in a system for providing a correctness or incorrectness prediction for a learning question, the system comprising one or more processors ([0204], “One or more processors … may be included in processing unit”) and the method comprising the steps of ([0195], “The recommendation engine can use the skill level of the user to generate a prediction of the likelihood of … users providing a desired response”): by the one or more processors ([0204], “One or more processors … may be included in processing unit”), acquiring a set of data ([0144], “With reference to FIG. 3, an illustrative set of data stores and/or data store servers is shown”) including a user variable ([0185], “a component … may report a user response, but define an identifier of that user as a private variable”) determined on the basis of concept-specific correctness or incorrectness sequence data of at least one user, and at least one variable related to a question and a concept associated with the user variable ([0020], “determining the correctness of the response … includes …: determining if the response … is present in the solution … and categorizing each of the steps as … correct; incorrect; or assisted … determining the correctness of the response … comprises: determining … if: (1) math embodied in the step is accurate; and (2) if the step is relevant.” Examiner notes that a math problem is an application of a concept.) wherein the user variable and the variable related to the question are grouped on the basis of context information ([0159, “the content library database ... can comprise ... a content network ... linked together by ... common user; relation to a common subject, topic, skill, or the like; ... the content ... can comprise a grouping of content ... in the form of ... a flash card and an extraction portion that can comprise the desired response ... an answer to a flash card”.), respectively, and the variable related to the concept includes a user's concept understanding estimated using a concept-specific understanding estimation model trained on the basis of the concept-specific correctness or incorrectness sequence data ([0189], “Internal processing … could perform functions such as providing content to a user, receiving a response from a user, determining the correctness of the received response, updating one or several models based on the correctness of the response, recommending new content for providing to one or several users, or the like” Examiner notes that an internal processor can include an estimation model.); and by the one or more processors ([0204], “One or more processors … may be included in processing unit”), calculating a probability that a first user corresponding to a specific user variable correctly answers a first question corresponding to a specific concept not encountered by the first user, with reference to the set of data ([0433], “After the student metadata has been retrieved… probabilities are determined … these probabilities can be determined based on user metadata identifying nodes … that have been mastered by the student” Examiner notes that the student metadata may include concepts not previously studied by the student.), wherein the concept-specific understanding estimation model is trained such that the concept understanding is estimated by assigning a greater weight to second concept-specific correctness or incorrectness sequence data generated at a second time point than to first concept- specific correctness or incorrectness sequence data generated at a first time point, wherein the second time point follows the first time point by a predetermined amount of time, wherein the first user's understanding of the specific concept is estimated on the basis of a second user's understanding of the specific concept, wherein the second user is different from the first user, and wherein the first user's understanding of the specific concept is estimated by assessing similarity between the specific concept and a concept encountered by the first user ([0189], “Internal processing ... could perform functions such ... updating ... models based on the correctness of the response ... internal processing ... can decide ... weighting of records from the data stream. To the extent that decisions are made based upon analysis of the data stream, each data record is time stamped to reflect when the information was gathered such that additional credibility could be given to more recent results ... a particular contributor of information may prove to have less than optimal gathered information and that could be weighted very low or removed” Examiner notes that an internal processor can include an estimation model.).
MPEP § 2111 discusses proper claim interpretation, including giving claims their
broadest reasonable interpretation (“BRI”) in light of the specification during
examination. Under BRI, the words of a claim must be given their plain meaning unless
such meaning is inconsistent with the specification, and it is improper to import claim
limitations from the specification into the claim. Applicant’s argument is not persuasive because the BRI is broader than what is argued. Therefore, the rejections of independent claims 1 and 6 (which is almost identical to claim 1) and respective dependent claims 2, 5, and 7, as anticipated by Noble, are maintained.
With respect to “Rejections Under 35 U.S.C. §§ 102 and 103”, Applicant argues “the cited references do not disclose, teach or suggest ‘wherein the concept-specific understanding estimation model is trained such that the concept understanding is estimated by assigning a greater weight to second concept-specific correctness or incorrectness sequence data generated at a second time point than to first concept-specific correctness or incorrectness sequence data generated at a first time point, wherein the second time point follows the first time point by a predetermined amount of time,’ as recited in amended independent claim 1.” (See AMENDMENT AND RESPONSE, REMARKS, Rejections Under 35 U.S.C. §§ 102 and 103, page 13, paragraph 6-page 14, lines 1-3). Examiner acknowledges Applicant’s remarks. Regarding claim 1, Noble discloses a method performed in a system for providing a correctness or incorrectness prediction for a learning question, the system comprising one or more processors ([0204], “One or more processors … may be included in processing unit”) and the method comprising the steps of ([0195], “The recommendation engine can use the skill level of the user to generate a prediction of the likelihood of … users providing a desired response”): by the one or more processors ([0204], “One or more processors … may be included in processing unit”), acquiring a set of data ([0144], “With reference to FIG. 3, an illustrative set of data stores and/or data store servers is shown”) including a user variable ([0185], “a component … may report a user response, but define an identifier of that user as a private variable”) determined on the basis of concept-specific correctness or incorrectness sequence data of at least one user, and at least one variable related to a question and a concept associated with the user variable ([0020], “determining the correctness of the response … includes …: determining if the response … is present in the solution … and categorizing each of the steps as … correct; incorrect; or assisted … determining the correctness of the response … comprises: determining … if: (1) math embodied in the step is accurate; and (2) if the step is relevant.” Examiner notes that a math problem is an application of a concept.) wherein the user variable and the variable related to the question are grouped on the basis of context information ([0159, “the content library database ... can comprise ... a content network ... linked together by ... common user; relation to a common subject, topic, skill, or the like; ... the content ... can comprise a grouping of content ... in the form of ... a flash card and an extraction portion that can comprise the desired response ... an answer to a flash card”.), respectively, and the variable related to the concept includes a user's concept understanding estimated using a concept-specific understanding estimation model trained on the basis of the concept-specific correctness or incorrectness sequence data ([0189], “Internal processing … could perform functions such as providing content to a user, receiving a response from a user, determining the correctness of the received response, updating one or several models based on the correctness of the response, recommending new content for providing to one or several users, or the like” Examiner notes that an internal processor can include an estimation model.); and by the one or more processors ([0204], “One or more processors … may be included in processing unit”), calculating a probability that a first user corresponding to a specific user variable correctly answers a first question corresponding to a specific concept not encountered by the first user, with reference to the set of data ([0433], “After the student metadata has been retrieved… probabilities are determined … these probabilities can be determined based on user metadata identifying nodes … that have been mastered by the student” Examiner notes that the student metadata may include concepts not previously studied by the student.), wherein the concept-specific understanding estimation model is trained such that the concept understanding is estimated by assigning a greater weight to second concept-specific correctness or incorrectness sequence data generated at a second time point than to first concept- specific correctness or incorrectness sequence data generated at a first time point, wherein the second time point follows the first time point by a predetermined amount of time, wherein the first user's understanding of the specific concept is estimated on the basis of a second user's understanding of the specific concept, wherein the second user is different from the first user, and wherein the first user's understanding of the specific concept is estimated by assessing similarity between the specific concept and a concept encountered by the first user ([0189], “Internal processing ... could perform functions such ... updating ... models based on the correctness of the response ... internal processing ... can decide ... weighting of records from the data stream. To the extent that decisions are made based upon analysis of the data stream, each data record is time stamped to reflect when the information was gathered such that additional credibility could be given to more recent results ... a particular contributor of information may prove to have less than optimal gathered information and that could be weighted very low or removed” Examiner notes that an internal processor can include an estimation model.).
MPEP § 2111 discusses proper claim interpretation, including giving claims their
broadest reasonable interpretation (“BRI”) in light of the specification during
examination. Under BRI, the words of a claim must be given their plain meaning unless
such meaning is inconsistent with the specification, and it is improper to import claim
limitations from the specification into the claim. Applicant’s argument is not persuasive because the BRI is broader than what is argued. Therefore, the rejections of independent claims 1 and 6 (which is almost identical to claim 1) and respective dependent claims 2, 5, and 7, as anticipated by Noble, are maintained.
With respect to “Rejections Under 35 U.S.C. §§ 102 and 103”, Applicant argues “the cited references do not disclose, teach or suggest ‘wherein the first user's understanding of the specific concept is estimated on the basis of a second user's understanding of the specific concept, wherein the second user is different from the first user, and wherein the first user's understanding of the specific concept is estimated by assessing similarity between the specific concept and a concept encountered by the first user,’ as recited in amended independent claim 1” (See AMENDMENT AND RESPONSE, REMARKS, Rejections Under 35 U.S.C. §§ 102 and 103, page 14, paragraph 2). Examiner acknowledges Applicant’s remarks. Regarding claim 1, Noble discloses a method performed in a system for providing a correctness or incorrectness prediction for a learning question, the system comprising one or more processors ([0204], “One or more processors … may be included in processing unit”) and the method comprising the steps of ([0195], “The recommendation engine can use the skill level of the user to generate a prediction of the likelihood of … users providing a desired response”): by the one or more processors ([0204], “One or more processors … may be included in processing unit”), acquiring a set of data ([0144], “With reference to FIG. 3, an illustrative set of data stores and/or data store servers is shown”) including a user variable ([0185], “a component … may report a user response, but define an identifier of that user as a private variable”) determined on the basis of concept-specific correctness or incorrectness sequence data of at least one user, and at least one variable related to a question and a concept associated with the user variable ([0020], “determining the correctness of the response … includes …: determining if the response … is present in the solution … and categorizing each of the steps as … correct; incorrect; or assisted … determining the correctness of the response … comprises: determining … if: (1) math embodied in the step is accurate; and (2) if the step is relevant.” Examiner notes that a math problem is an application of a concept.) wherein the user variable and the variable related to the question are grouped on the basis of context information ([0159, “the content library database ... can comprise ... a content network ... linked together by ... common user; relation to a common subject, topic, skill, or the like; ... the content ... can comprise a grouping of content ... in the form of ... a flash card and an extraction portion that can comprise the desired response ... an answer to a flash card”.), respectively, and the variable related to the concept includes a user's concept understanding estimated using a concept-specific understanding estimation model trained on the basis of the concept-specific correctness or incorrectness sequence data ([0189], “Internal processing … could perform functions such as providing content to a user, receiving a response from a user, determining the correctness of the received response, updating one or several models based on the correctness of the response, recommending new content for providing to one or several users, or the like” Examiner notes that an internal processor can include an estimation model.); and by the one or more processors ([0204], “One or more processors … may be included in processing unit”), calculating a probability that a first user corresponding to a specific user variable correctly answers a first question corresponding to a specific concept not encountered by the first user, with reference to the set of data ([0433], “After the student metadata has been retrieved… probabilities are determined … these probabilities can be determined based on user metadata identifying nodes … that have been mastered by the student” Examiner notes that the student metadata may include concepts not previously studied by the student.), wherein the concept-specific understanding estimation model is trained such that the concept understanding is estimated by assigning a greater weight to second concept-specific correctness or incorrectness sequence data generated at a second time point than to first concept- specific correctness or incorrectness sequence data generated at a first time point, wherein the second time point follows the first time point by a predetermined amount of time, wherein the first user's understanding of the specific concept is estimated on the basis of a second user's understanding of the specific concept, wherein the second user is different from the first user, and wherein the first user's understanding of the specific concept is estimated by assessing similarity between the specific concept and a concept encountered by the first user ([0189], “Internal processing ... could perform functions such ... updating ... models based on the correctness of the response ... internal processing ... can decide ... weighting of records from the data stream. To the extent that decisions are made based upon analysis of the data stream, each data record is time stamped to reflect when the information was gathered such that additional credibility could be given to more recent results ... a particular contributor of information may prove to have less than optimal gathered information and that could be weighted very low or removed” Examiner notes that an internal processor can include an estimation model.).
MPEP § 2111 discusses proper claim interpretation, including giving claims their
broadest reasonable interpretation (“BRI”) in light of the specification during
examination. Under BRI, the words of a claim must be given their plain meaning unless
such meaning is inconsistent with the specification, and it is improper to import claim
limitations from the specification into the claim. Applicant’s argument is not persuasive because the BRI is broader than what is argued. Therefore, the rejections of independent claims 1 and 6 (which is almost identical to claim 1) and respective dependent claims 2, 5, and 7, as anticipated by Noble, are maintained.
Conclusion
Applicant's amendment necessitated the new grounds of rejections 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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Any inquiry concerning this communication or earlier communications from the
examiner should be directed to Lisa Antoine whose telephone number is
(571) 272-4252 and whose email address is lantoine@uspto.gov. The examiner can be reached Monday-Thursday, 7:30 am-5:30 pm CT. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xuan Thai, can be reached on (571) 272-7147. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/LISA H ANTOINE/
Examiner, Art Unit 3715
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715