DETAILED ACTION
This final rejection is responsive to communication filed February 2, 2026. Claims 1, 5 and 6 are currently amended. Claims 2-4 and 7-9 are canceled. Claims 1, 5, and 6 are pending in this application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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, 5, and 6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 5 and 6 recite:
calculating learning ability information of the user; and
selecting a target solution content from among the plurality of pieces of solution contents based on an index of an expected educational effect of the user for each of the plurality of pieces of solution contents;
wherein the calculating of the learning ability information comprises:
allocating a feature value according to whether to search for at least one question included in the learning set information based on the search information,
generating a first matrix based on the reference value of the search database and the feature value related to the user,
transforming the first matrix into a second matrix by performing a block compression based on similarity of the reference value and the feature value,
wherein the first matrix includes user identification information of the user and the plurality of users as rows and question identification information of each of the plurality of questions as columns and includes (i) feature values allocated to the plurality of questions and (ii) reference values allocated to the plurality of users as components of the first matrix,
wherein the performing of the block compression comprises clustering components of questions in the first matrix in which feature values allocated to the questions for the user are the same as reference values allocated to the questions for users among the plurality of users by using the block compression to generate the second matrix including the clustered components, and
calculating a learning ability score of the user based on the second matrix;
wherein the calculating of the learning ability score of the user comprises:
calculating the learning ability score of the target user based on the acquired comparison information;
wherein the allocating of the feature value comprises:
allocating a first value to each question in the first group of questions of the learning set information for which a search was performed by the user through the user terminal, the first value indicating that the user does not understand the each question in the first group of questions, and
allocating a second value to each question in the second group of questions of the learning set information for which a search was not performed by the user through the user terminal, the second value indicating that the user understands the each question in the second group of questions;
wherein the selecting of the target solution content comprises:
selecting the target solution content by comparing the first index and the second index.
The broadest reasonable interpretation of these steps is that the steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. A user can mentally perform the calculating, selecting, allocating, generating, transforming, and comparing steps in the human mind, or with the aid of pen and paper. Further, the steps of calculating and transforming by block compression are mathematical concepts.
This judicial exception is not integrated into a practical application. Claims 1, 5 and 6 recite the following additional elements:
a device including a memory storing instructions, a transceiver that communicates with the user terminal, and a processor configured to execute the instructions to perform recited steps;
acquiring, by the device/computer, from a user terminal, question information on a plurality of questions solved by the user through the user terminal and the search information of the user through the transceiver, wherein the plurality of questions include a first group of questions in which a search for a solution content of each question in the first group of questions was performed by the user through the user terminal and a second group of questions in which a search for a solution content of each question in the second group of questions was not performed by the user through the user terminal, and the search information includes time data related to the search for the solution content of each question in the first group of questions performed by the user and search-related question identification information on each question in the first group of questions;
acquiring, by the device/computer, a solution content set related to the question information, wherein the solution content set includes a plurality of pieces of solution contents for a question among the plurality of questions;
acquiring learning set information based on the search information;
acquiring a search database of a plurality of users based on the learning set information, the search database including user identification information on each of the plurality of users and a reference value allocated according to whether each of the plurality of users searches for a question included in the learning set information;
wherein the acquiring of the learning set information comprises:
acquiring questions on which a search is performed by the user for a first period among the plurality of questions based on the time data and the question identification information, and
acquiring the learning set information by determining the acquired questions as a common learning set,
wherein the calculating of the learning ability score of the user comprises:
acquiring input data from the second matrix having the clustered components,
inputting the input data into a neural network model trained using training data including response comparison information among users and acquiring comparison information indicating a relative learning ability of the user with respect to the plurality of users by using the input data, the input data being in a form corresponding to a form of the response comparison information included in the training data;
obtaining a first index related to the expected educational effect when a first solution content is provided to the user, and
obtaining a second index related to the expected educational effect when a second solution content is provided to the user.
The “acquiring” and “obtaining” limitations are mere data gathering recited at a high level of generality, and thus are insignificant extra-solution activity. Further, some limitations are recited as being performed by a device including a memory, processor (in claim 1), and transceiver (in claim 6) or computer (in claim 5). The device (including the memory, processor and transceiver) and computer are recited at a high level of generality and are used as a tool to perform the generic computer function of acquiring data and used to perform the abstract idea, as discussed above. As such, they amount to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Further, the step of inputting the input data into a neural network model trained using training data including response comparison information among users to acquire comparison information provides nothing more than mere instructions to implement an abstract idea on a generic computer (see MPEP 2106.05(f)) and also merely indicates a field of use or technological environment (neural network) in which the judicial exception is performed (see MPEP 2106.05(h)). Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claim is directed to the judicial exception.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the “acquiring” and “obtaining” limitations are recited at a high level of generality. These elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. As discussed above, the recitation of a computer and device (comprising the memory, processor and transceiver) to perform limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Further, the step of inputting the input data into a neural network model trained using training data provides nothing more than mere instructions to implement an abstract idea on a generic computer and also merely indicates a field of use or technological environment in which the judicial exception is performed. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
Response to Arguments
Applicant's arguments filed February 2, 2026 have been fully considered but they are not persuasive. Applicant argues that the underlying idea in the claims is not merely an abstract idea and additional elements or combination of elements in the claims are sufficient to ensure that the claim amounts to significantly more than the judicial exception. The examiner disagrees. As outlined in the rejection above, the claims recite several limitations that are drawn to mental processes and thus recite an abstract idea. Further, the additional elements, even when considered in combination, represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept and thus do not amount to significantly more than the judicial exception.
Applicant argues that amended claim 1 recites specific technical operations that define specific data structures and transformations that cannot be performed mentally and are not merely-results oriented. The examiner disagrees. The first two operations listed on page 17 of Applicant’s remarks (i.e. “generating of a first matrix…” and “transformation of the first matrix…”) are considered mental processes because a user can mentally or manually, with the aid of pen and paper, generate a matrix based on a reference values and a feature value and transform a generated matrix into a second matrix by using a block compression. The matrix and transformed matrix are not new, inventive data structures and reducing the size of a matrix using block compression does not transform the data into a new, different data structure. Further, inputting data into a trained neural network model to acquire comparison information is merely applying the abstract idea using a computer. The claim appears to recite a high-level application of a previously trained model to determine information.
Applicant further argues that the claims integrate the alleged abstract idea into a practical application. The examiner disagrees. Applicant argues that the present invention solves a technological problem of providing the best educational content based on the user’s learning ability by quantifying learning ability information of the user and recommending content based on user’s learning ability. However, the examiner does not find the claims to provide a technical solution to a problem. If anything, the claims provide an improvement to an abstract idea. The problem of providing the best educational content is not limited to computer-based implementation. Instead, the computer is used as a tool to implement the abstract steps.
Applicant again argues that the claims recite concrete technical operations including generating and transforming a first matrix and inputting data into a training model. As argued above, the generating and transforming steps are mental processes and thus cannot integrate the abstract idea into a practical application. The step of inputting input data into a trained neural network model to acquire information is merely adding the words “apply it” or mere instructions to implement the abstract idea using a computer. It also merely indicates a field of use or technological environment (neural network) in which the judicial exception is performed and thus also does not integrate the abstract idea into a practical application.
Applicant further argues that claim 1 recites significantly more than the abstract idea because the invention achieves improvement in a data processing architecture. The examiner disagrees. Using a compression technique to transform a matrix that is a similarity based matrix structure suitable for neural network input does not represent an improvement to a data processing architecture. As stated above, block compression of a matrix can be performed manually with the aid of pen and paper. The claimed matrix is not a new or improved data structure, but instead stores a specific type of data that does not change the data structure.
Applicant argues that the transformation of a matrix using a compression technique provides a more accurate and computationally efficient way and provides an improvement over the prior art. However, providing an improvement over the prior art does not equate to patent eligibility, especially when the improvement is based on using a computer to perform a task that can be performed manually. These alleged improvements do not constitute a technical improvement to computer functionality. Instead, it appears as though the computer is used as a tool to provide the improved functionality.
Applicant argues that claim 1 recites an improved data structure, similar to Enfish. The examiner disagrees. The data structures of claim 1 do not function differently from conventional data structures or provide concrete benefits in computer performance. The claimed data transformation can be performed mentally/manually and inputting data into a trained neural network model to produce results is not a different function or benefit in computer performance. Applicant states that the claimed invention improves reliability and efficiency of computer-implemented learning ability evaluation. However, any improvement in learning ability evaluation appears to be an improvement to an abstract idea of evaluating learning ability, and the computer-based implementation is merely using the computer as a tool to achieve an improvement. The examiner does not find any improvement to the functioning of the computer or to a computer-based technology. Even when considered as an ordered combination as a whole, claim 1 does not recite significantly more than the abstract idea.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALICIA M WILLOUGHBY whose telephone number is (571)272-5599. The examiner can normally be reached 9-5:30, EST, M-F.
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, Ajay Bhatia can be reached at 571-272-3906. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALICIA M WILLOUGHBY/Primary Examiner, Art Unit 2156 March 2, 2026