DETAILED ACTION
This communication is a Final Office Action on the merits in response to communications received on 05/21/2026. Claims 5-7 have been canceled. Claims 1 and 10 have been amended. Therefore, claims 1-2 and 8-10 are pending and have been addressed below. 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
1. 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.
2. Claims 1-2 and 8-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
3. Under Step 1 of the two-part analysis from Alice Corp, claim 1 recites a machine (i.e., concrete thing, consisting of parts, or of certain devices and combination of devices) and claim 10 recites a process (i.e., an act or step, or a series of acts or steps). Thus, each of the claims fall within one of the four statutory categories.
4. Under Step 2A – Prong One of the two-part analysis from Alice Corp, the claimed invention recites an abstract idea.
Claims 1 and 10 recite:
“receive problem response information and test score information of a reference test domain…”, “obtain transferable feature from the problem response information and the test score information of the reference test domain”, “wherein the transferable feature indicates user behavior characteristic or user learning characteristic that can be applied in common to the reference test domain and a target test domain, and the transferable feature includes one of (i) a rate of increase in a test score according to increase in the number of problems answered correctly or (ii) a correlation between a probability of a user's departure during learning and a test score when the test score decreases in proportion to an increasing probability of the user's departure during…learning”, “wherein an amount of problem response information and test score information of the target test domain is smaller than an amount of the problem response information and the test score information of the reference test domain”, “generate feature information from the problem response information of the reference test domain, wherein the feature information is information that is usable in common for comparison of relative skills of a plurality of users in the reference test domain and the target test domain, and the feature information includes response comparison information generated by comparing the problem response information about problems solved in common by two different users in the reference test domain, and the response comparison information includes information about (i) a number of problems answered correctly by both of the two different users, (ii) a number of problems answered correctly by only one of the two different users, (iii) a number of problems answered correctly by only the other of the two different users, and (iv) a number of problems answered incorrectly by both of the two different users”, “…using the response comparison information as input …to predict the transferable feature and using the transferable features as input to predict the test score of the user”, “transfer… the target test domain to be used…for predicting the test score of the user in the target test domain”, “wherein the transferring comprises updating…the test target domain using a weight determined…”, “predict transferable feature from feature information in the target test domain…, and “predict the test score of the user from the predicted transferable feature in the target test domain…”
Under the broadest reasonable interpretation, the limitations recite an abstract idea for skill evaluation and predicting a test score for a user which encompasses concepts such as commercial interactions, (i.e., marketing or sales activities, business relations) and mental processes, (i.e., observation, evaluation, judgement, opinion), that fall within the certain methods of organizing human activity and mental processes groupings of abstract ideas. See MPEP 2106.04
The Applicant’s Specification in at [pgs.1-2] Recently, the Internet and electronic devices have been actively used in each field, and the educational environment is also changing rapidly. In particular, with the development of various educational media, learners may choose and use a wider range of learning methods. Among the learning methods, education services through the Internet have become a major teaching and learning method by overcoming time and space constraints and enabling low-cost education. To keep up with the trend, customized education services, which are not available in offline education due to limited human and material resources, are also diversifying. For example, artificial intelligence is used to provide educational content that is subdivided according to the individuality and ability of a learner so that the educational content is provided according to the individual competency of the learner, which departs from standardized education methods of the past. A user skill evaluation model is an artificial intelligence model that models the degree of knowledge acquisition of a student on the basis of a learning flow of the student. Specifically, the user skill evaluation model refers to, given a record of a problem solved by a student and a response of the student, predicting the probability of a next problem being answered correctly and the resulting test score of the user. In order to generate a user skill evaluation model of a certain test domain, a large amount of actual test score information for model training is required. However, in order to collect the actual score, users need to directly take tests, which requires a lot of time and money for data collection. For example, unlike the probability of a correct answer that is predictable directly from problem-solving data collectable by an Al model, when test scores or grades are predicted actual test score information for directly predicting the test scores or grades is insufficient, and collected offline only in a small amount, such that when compared to the prediction of the probability of a correct answer, the prediction of test scores or grades has lower accuracy. In addition, since generating a user skill evaluation model for each test domain and evaluating the user skill evaluation model are both performed manually by model developers, there is a difficulty in ensuring sufficient performance in real service all the time, and a lot of time and effort is taken to generate the model.
Consistent with the disclosure the series steps recite methods for predicting how well a student may score on a test. The acts for predicting the testing score of the user recite “receive”, “obtain”, “generate”, “using”, “transfer”, “predict” which are collecting and comparing historical testing responses and skill data of the user in order to determine a predicted test score for the user. Thus, the series of steps may be reasonably characterized as mental processes that can be practically performed in the human mind, with or without the use of a physical aid such as pen and paper. Additionally, the series of steps for predicting a test score for a user pertain to business relations that teacher/recruiting professionals typically perform when helping individuals identify suitable colleges and making informed decisions about their future. As such, the claim recites an abstract idea.
5. Under Step 2A – Prong Two of the two-part analysis from Alice Corp, this judicial exception is not integrated into a practical application because the additional elements of: “an apparatus, the apparatus comprising”, “memory storing instructions”, “a processor configured to execute the instructions to:”, “a user terminal”, “train an artificial intelligence (Al) model”, “the Al model includes a transferable feature prediction model and a score prediction model”, “the training of the AI model comprises (i) training the transferable feature prediction model”, “(ii) training the score prediction model”, “the trained Al model”, “a skill evaluation Al model”, “training of the AI model”, “the skill evaluation Al model by using an algorithm implemented as a program”, “wherein the processor is further configured to execute the instructions to”, “the skill evaluation model” – see claims 1 and 10 are recited at a high-level of generality in light of the specification [See Fig. 1 and pgs. 1-2, 7-10]. Thus, because the specification describes the additional elements in general terms without describing the particulars, the additional elements may be broadly but reasonably construed as reciting generic computer components being used to aid in performing the abstract idea. Therefore, the additional elements recited in the claim add the words “apply it” with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely use a computer processor as a tool to perform the abstract idea as discussed in MPEP 2106.05 (f).
The other additional element of “in response to a change in data of the reference test domain, repeat a process of obtaining the transferable feature to update” adds insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g)
The other additional elements of: “for predicting a test score through a transferable feature indicating a difference in skills of users in a plurality of test domains” and “the online” as recited is/are an attempt to limit the claimed invention to a field of use or particular technological environment, as discussed in MPEP 2106.05(h).
Thus, the additional claim elements are not indicative of integration into a practical application, because the claims do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition (Vanda Memo), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea and the claims are directed to an abstract idea.
6. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of: “an apparatus”, “memory storing instructions”, “a processor configured to execute the instructions to:”, “from a user terminal”, “train an artificial intelligence (Al) model by”, “the Al model”, “the training of the AI model comprises training the AI model to”, “the trained Al model to a skill evaluation Al model”, “through the skill evaluation Al model by using an algorithm implemented as a program”, “wherein the processor is further configured to execute the instructions to”, “the skill evaluation model” – see claims 1 and 10, amount to nothing more than mere instructions in which to apply the judicial exception and do not provide an inventive concept at Step 2B.
The other additional elements of: “in response to a change in data of the reference test domain, repeat a process of obtaining the transferable feature to update” were considered to be insignificant extra-solution activity in Step 2A, and thus re-evaluated in Step 2B to determine if it is more than well-understood, routine, conventional activity in the field.
The Symantec, Alice Corp, Ultramercial, Versata Dev. Group Inc court decisions cited in MPEP 2106.05(d)(II) indicated that: “receiving or transmitting data over a network”, “electronic recordkeeping”, “storing and retrieving information in memory”, are all well-understood, routine, conventional activity when claimed in a generic manner. Thus, when considering these elements individually and as a whole with the judicial exception, the claimed invention does not provide an inventive concept at Step 2B.
7. Claims 2, 8-9 are dependent of claim 1.
Claim 2 recites “wherein the processor is further configured to execute the instructions to, when a combination of a plurality of transferable features discriminates a difference in skill of the user in the plurality of test domains, allow the transferable feature to include a combination of at least one transferable feature.” which further narrows how the abstract idea may be performed, but does not make the claim any less abstract. Claim 8 recites “wherein the processor is further configured to execute instructions to determine a validity of the AI model according to whether the AI model satisfies basic properties of tests or whether the AI model operates normally; and determine a validity of the skill evaluation AI model according to whether the skill evaluation AI model satisfies basic properties of tests or whether the skill evaluation AI model operates normally.” at a high-level of generality and amounts to mere instructions to apply the abstract idea on a computer, as discussed in MPEP 2106.05(f)”, Claim 9 recites “wherein the processor is further configured to execute instructions to, when a test score of Student i is Si, a test score of Student j is Sj, and transferrable feature is Li/(Li+Lj), predict test scores of users in the target test domain, by using, as the score prediction model, a gradient descent model for finding Li that minimizes a value of
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which further narrows how the abstract idea may be performed, but does not make the claim any less abstract. The additional elements recited in the dependent claims use computer components or other machinery in their ordinary capacity for economic or other tasks (e.g., to receive, process, or output data) and/or simply adds generic computer components after the fact to the abstract idea which does not integrate the judicial exception into a practical application or provide an inventive concept.
Response to Arguments
Applicant’s arguments filed 05/21/2026 have been fully considered but they are not persuasive.
With Respect to Rejections Under 101
Applicant argues “The Examiner asserts that the claim 1 recites an abstract idea relating to evaluating skill and predicting a test score. However, when considered as a whole, the amended Claim 1 is directed to a specific technical implementation of a machine learning system rather than a mental process.” The Examiner respectfully disagrees.
The Applicant’s arguments are not persuasive. After considering the Specification [pgs. 1-2] and the focus of claim 1 it is clear that the ordered combination of limitations recite ineligible abstract subject matter for evaluating skill and predicting a test score of a user. The recitation of a machine learning system, i.e., one or more trained machine learning models, in this claim does not negate the mental nature of the limitations recited because claim 1 merely uses the processor in combination with one or more trained machine learning models as a tool to perform the otherwise mental processes. The specificity of the presently recited techniques recited in claim without any technological details of an advance does not automatically confer patent eligibility. For these reasons, the rejections under 101 are being maintained.
Applicant argues “Even assuming that the claims recite an abstract idea, the claims clearly integrate the concept into a practical technological application. The analysis in the Office Action improperly characterizes Claim 1 and fails to consider the specific technical limitations recited in the claim. (1) Structured Data Transformation
The amended Claim 1 recites: " generating feature information from problem response information, " wherein the feature information includes response comparison information, and " wherein the response comparison information includes structured comparison data between users. See, [0032]-[0034]: feature information defined for comparison of relative skills, [0066]-[0069]: response comparison information including TT/TF/FT/FF, FIG. 4: structured comparison representation This limitation defines a specific transformation of raw user response data into structured comparative data.
Such processing cannot practically be performed in the human mind and therefore does not constitute a mental process.” The Examiner respectfully disagrees.
The Applicant’s arguments are not persuasive. After considering the cited passages from the Specification, the Examiner asserts they provide a general explanation in regards to how the step recited in the abstract idea may be performed. For example, the “generate” step was identified as a feature of the abstract idea under Step 2A Prong One. This step uses results-based functional claim language and merely narrows how the abstract idea may be performed. Features of the abstract idea alone cannot integrate the abstract idea into a practical application. See MPEP 2106.05(a); See also Alice, 573 U.S. at 221 (a claim to an abstract idea must have additional features to ensure that it does not monopolize the abstract idea) The remarks do not discuss any of the additional elements recited in the claim. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “(2) Two-Stage AI Model Architecture The amended Claim 1 recites: " a transferable feature prediction model, " a score prediction model,
" training the transferable feature prediction model using response comparison
information as input to predict a transferable feature, and "training the score prediction model using the transferable feature as input to predict a test score. See, [0062], FIG. 3: dual model structure; [0065]-[0067]: transferable feature prediction model; [0072]-[0075]: score prediction model The amended Claim 1 does not merely recite "an AI model," but instead recites a specific, multi-stage machine learning architecture with defined input/output relationships. This structure separates feature learning from score prediction, and introduces an intermediate representation (transferable feature).
Thus, the amended Claim 1 is directed to a technical improvement in how machine learning models are structured and trained, not a mere application of an abstract idea.” The Examiner respectfully disagrees.
The Applicant’s arguments are not persuasive. After considering the cited passages from the Specification, the Examiner asserts the two-stage AI model architecture is described at a high-level of generality as reproduced below:
[0062] More specifically, the basic model training unit 220 may train a transferable feature prediction model for predicting a transferable feature from feature information and a score prediction model for predicting a test score from the transferable feature.
[0065] The transferable feature prediction model training unit 221 may perform an operation of training the transferable feature prediction model for predicting a transferable feature from feature information. [0066] In an embodiment, the feature information may include response comparison information. In this case, the transferable feature prediction model training unit 221 may allow an AI model to learn a weight indicating the relationship between the response comparison information and the extracted transferable feature. [0067] The basic model may predict transferable features from response comparison information of a plurality of users on the basis of the determined weight. [0072] The score prediction model training unit 222 may perform an operation of training the score prediction model for predicting a user's test score from a transferable feature. [0073] As described above, since the transferable feature includes information about a difference in skill between different users, the user's test score may be predicted when the transferable feature is known. [0074] Score prediction may be performed according to various algorithms that may be implemented as a program. In the example described above, when Student 1 has a test score of S1, Student 2 has a test score of S2, and the transferable feature is L1/(L1+L2), a gradient descent model that finds Lis that minimize Expression 1 below may be used as the score predictive model
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[0075] The basic model training unit 220 may train the basic model to predict a transferable feature from problem response information or response comparison information, and to predict a test score from the transferable feature.
As can be seen, the disclosure describes features for training and updating the models for predicting a test score devoid any technological improvements. The machine learning technology described in the disclosure is generic as the Specification demonstrates various algorithms may be implemented. The requirements that the score prediction machine learning models be “trained” and “updated” does not represent a technological improvement. Thus, the machine learning models as described at best aid in performing the abstract idea, however, they do not integrate the judicial exception into a practical application. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “(3) Transfer of AI model Using Weight The amended Claim 1 further recites:" transferring the trained AI model to a target test domain, and "updating the skill evaluation AI model using a weight determined in training. See, [0080]: updating weights to the target model; [0141]: transfer to target domain This corresponds to transfer learning and addresses a technical problem of data scarcity in the target domain.” The Examiner respectfully disagrees.
The Applicant arguments are not persuasive. After considering the cited passages from the Specification, the support for the transfer steps has reproduced below:
[0080] Model transfer may include an operation of updating the weight determined in the training of the basic model to the skill evaluation model of the target domain, or using the basic model as the skill evaluation model of the target domain.
[0141] The model transfer to the skill evaluation model may include updating a weight determined in the training of the basic model to the skill evaluation model of the target domain, or using the basic model itself as the skill evaluation model of the target domain.
As can be seen, the features regarding model transfer are described at a high-level of generality in light of the Specification. The disclosure does not support a finding that the model transfer limitations provide a technological solution to a technological problem. The ability to transfer weights between models to update scores in not a technological improvement. Thus, the remarks do not show how these features integrate the judicial exception into a practical or solve any technical problem. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “(4) Repeating Model Update Mechanism The amended Claim 1 further recites repeating a process of obtaining the transferable feature to update the model in response to a change in data. See, [0129]-[0143], FIG. 9 This defines a dynamic and adaptive system rather than a static prediction process.” The Examiner respectfully disagrees.
The Applicant arguments are not persuasive. Here the remarks directed towards (i.e., a dynamic and adaptive system) are an attempt to limit the claimed invention to a particular technological environment or field of use, as discussed in MPEP 2106.05(h) The requirements that the machine learning models be “updated” in response to change data do not represent a technological improvement to machine learning functionality. The cited passages from the Specification [i.e., [¶ 0129]-[¶ 0143], FIG. 9] and claims recite these features using results based functional claim language devoid any technological details. These features for updating the model in response to change data cannot be relied upon alone to integrate the judicial exception into a practical application. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “Even assuming that the amended claims are considered abstract, the amended claims recite an inventive concept (Step 2B). Even assuming that the claim 1 is considered to recite an abstract idea, the claim 1 includes significantly more. The amended Claim 1 recites a specific combination of: " response comparison-based feature generation " transferable feature as an intermediate representation
" a two-stage AI model architecture " transfer of AI model using a weight across domains " repeating operation for updating based on data changes See, [0033]-[0034]: response comparison; [0052]-[0053]: transferable feature, [0062]: dual model, [0080], [0141]: transfer, [0129]-[0143]: update loop This specific combination is not well-understood, routine and conventional. Under USPTO guidance, the analysis must consider the ordered combination of elements, which is not shown to be conventional.” The Examiner respectfully disagrees.
The Applicant’s arguments are not persuasive. The Examiner maintains individually and in combination the additional elements recited in claim 1 do not add an inventive concept. The passages cited from the Specification above evidence that the additional elements are generic computing components and machine learning models being used to aid in predicting a test score for a user. As for the remarks directed towards not being well-understood, routine and conventional, the lack of prior art in this case is insufficient to provide an inventive concept. The claimed methods are not rendered patent eligible by the fact that using existing machine learning technology they perform a task previously undertaken by humans with greater speed and efficiency than could previously be achieved. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “The Office Action asserts that the previous claim 1 is result-oriented. However, as amended, Claim 1 explicitly recites: " how feature information is generated " how response comparison information is constructed " how each AI model is trained " how the trained model is transferred and updated " how prediction is performed in the target domain Accordingly, the amended Claim 1 now recites a concrete sequence of technical operations rather than a mere desired result.
For at least the reasons set forth above, the amended Claim 1 is not directed to an abstract idea, but the claim integrates the abstract idea into a practical application and includes significantly more than any alleged abstract idea.” The Examiner respectfully disagrees.
The Applicant’s arguments are not persuasive. The Examiner maintains the series of steps recite an abstract idea for predicting a test score for a user rather than a technological solution to a technological problem. See BSG Tech LLC v. BuySeasons, Inc., 899 F.3d 1281, 1286 (Fed. Cir. 2018) ("[C]laims are not saved from abstraction merely because they recite components more specific than a generic computer.") Here, the remarks describe how the processor and machine learning models perform their functions at a high-level of generality and in a results-oriented manner without describing how to achieve the results in a non-abstract way. See Int’l Bus. Machs. Corp. v. Zillow Grp., Inc., 50 F.4th 1371, 1378 (Fed. Cir.2022) Merely adding a sequence of machine learning models or steps to be performed by the machine learning models in a claim does automatically integrate the judicial exception into a practical application or provide an inventive concept. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “Independent Claim 10 recites features analogous to those of the amended Claim 1 and is patentable for reasons similar to those for Claim 1. Accordingly, Applicant respectfully requests the rejection to be withdrawn.” The Examiner respectfully disagrees.
The Applicant’s arguments are not persuasive. The Examiner presented findings for claim 10 also explaining why the claims recite an abstract idea and why the additional elements recited by these claims do not impart subject matter eligibility. See rejection above. The Applicant does not persuasively identify error in these findings. For these reasons, 101 rejections are being maintained.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EHRIN PRATT whose telephone number is (571)270-3184. The examiner can normally be reached 8-5 EST Monday-Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynda Jasmin can be reached at 571-272-6782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/EHRIN L PRATT/Examiner, Art Unit 3629
/LYNDA JASMIN/Supervisory Patent Examiner, Art Unit 3629
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