DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. The following office action is a Final Office Action in response to the communications received on 12/17/2025.
Claims 1-7, 10-13, 15 and 17-20 have been amended; claims 8, 9, 14 and 16 have been canceled. Therefore, claims 1-7, 10-13, 15 and 17-20 are currently pending in this application. Response to Amendment
3. The amendment to the specification; namely the removal of the reference characters “21’” and “22’” from the original description, is sufficient to overcome the objection to the drawing set forth in the previous office-action. Accordingly, the Office withdraws the above objection.
Claim Rejections - 35 USC § 101
4. Non-Statutory (Directed to a Judicial Exception without an Inventive Concept/Significantly More)
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 1-7, 10-13, 15 and 17-20 are rejected under 35 U.S.C.101 because the claimed invention is directed to an abstract idea without significantly more.
(Step 1)
The current claims fall within one of the four statutory categories of invention (MPEP 2106.03).
(Step 2A) [Wingdings font/0xE0] Prong-One:
The claim(s) recite a judicial exception, namely an abstract idea, as shown below:
— Considering each of claims 1, 11 and 18 as a representative claim, the following claimed limitations recite an abstract idea:
— Claim 1:
evaluate an essay for writing learning: divide learning data and learner essay text in a predetermined structure unit; generate structure tagging information for each structure unit; and structure the learning data and the learner essay text by attaching the structure tagging information to the learning data and the learner essay text; [construct] a model by using essay text that is included in the structured learning data and the structure tagging information as an input value and using an evaluation score that is included in the structured learning data as a label; and generate essay evaluation results by [correlating] essay text that is included in the structured learner essay text and the structure tagging information; receive essay text for each structure unit and the structure tagging information; calculate a score for each structure unit and a holistic score of an entirety of the essay; convert an evaluation score that is included in the structured learning data into a value between 0 and 1; and determine an essay type of the learning data based on major features of the learning data.
— Claim 11:
evaluate an essay for writing learning: divide learner essay text in a predetermined structure unit; generate structure tagging information for each structure unit; and structure the learner essay text by attaching the structure tagging information to the learner essay text; as essay evaluation step, generate essay evaluation results by inputting the structured learner essay text to a model; and [provide] the essay evaluation results; receive essay text for each structure unit and the structure tagging information; calculate a score for each structure unit and a holistic score of an entirety of the essay; and generating the essay evaluation results includes converting an evaluation score that is included in the structured learner essay text into a value between 0 and 1; and determine an essay type based on major features of the learner essay text.
— Claim 18:
divide essay text that is included in learning data in a predetermined structure unit; generate structure tagging information for each structure unit; and structure the learning data by attaching the structure tagging information to the essay text; and [construct] a model using the essay text that has been divided in the predetermined structure unit and the structure tagging information as an input value and using an evaluation score that is included in the structured learning data as a label; receive essay text for each structure unit and the structure tagging; calculate a score for each structure unit and a holistic score of an entirety of the essay; convert an evaluation score that is included in the structured learning data into a value between 0 and 1; determine an essay type of the learning data based on major features of the essay text that is included in the learning data.
Thus, the limitations identified above recite an abstract idea since the limitations correspond to mental processes, which is part of the enumerated groupings of abstract ideas identified according to the current eligibility standard (see MPEP 2106.04(a)).
For instance, the claims recite the process of evaluating an essay for writing learning, wherein an essay evaluation result is generated based on the analysis of the essay using one or more templates; wherein such analysis includes: dividing learning data and/or learner essay into one or more units; tagging/labeling each of the units creating a template based on the structured/tagged information above; and subsequently generating evaluation results, etc. (e.g., see current claim 1, 11 or 18). The observation above demonstrates an evaluation, an observation and/or a judgment process, which signifies the abstract idea group mental processes.
(Step 2A) [Wingdings font/0xE0] Prong-Two:
Given the interpretation of the claimed limitations in light of the specification, the current claims recite additional element(s); wherein a computer system that comprises computer component (i.e., a processor, a memory, etc.) executes computer instructions; and thereby the computer system is utilized to facilitate the recited functions/steps regarding one or more of: collecting and organizing text data (e.g., “a structure analysis module . . . divide learning data and learner essay text in a predetermined structure unit, generate structure tagging information for each structure unit, and structure the learning data and the learner essay text by attaching the structure tagging information to the learning data and the learner essay text”; “wherein to determine an essay type of the learning data based on major features of the learning data, the structure analysis module uses an essay type classification model including any one of a support vector machine (SVM), a decision tree, a recurrent neural network (RNN), and a convolutional neural network (CNN)”); building an evaluation model using the organized text data (e.g., “generate an essay evaluation model through learning by using essay text that is included in the structured learning data and the structure tagging information as an input value and using an evaluation score that is included in the structured learning data as a label”; “wherein the essay evaluation model . . . receive essay text for each structure unit and the structure tagging information and generate an embedding vector for each structure unit . . . receive all of generated embedding vectors for each structure unit and generate a document embedding vector . . . receive the document embedding vector and calculate a score for each structure unit and a holistic score of an entirety of the essay . . . converts an evaluation score that is included in the structured learning data into a value between 0 and 1 and generates the essay evaluation model through learning by using the converted evaluation score as a label”); generating a result(s) based on the analysis of text data using the evaluation model (e.g., “generate essay evaluation results by inputting, to the essay evaluation model, essay text that is included in the structured learner essay text and the structure tagging information”), etc.
However, the claimed additional element(s) fail to integrate the abstract idea into a practical application since the additional element(s) are utilized merely as a tool to facilitate the abstract idea. Thus, when each claim is considered as a whole, the additional element(s) fail to integrate the abstract idea into a practical application since they fail to impose meaningful limits on practicing the abstract idea. For instance, when each of the claims is considered as a whole, none of the claims provides an improvement over the relevant existing technology.
The observations above confirm that the claims are indeed directed to an abstract idea.
(Step 2B)
Accordingly, when the claim(s) is considered as a whole (i.e., considering all claim elements both individually and in combination), the claimed additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to “significantly more” than the abstract idea itself (also see MPEP 2106). The claimed additional elements are directed to conventional computer elements, which are serving merely to perform conventional computer functions. Accordingly, none of the current claims, when considered as a whole, recites an element—or a combination of elements—directed to an inventive concept.
Note also that the utilization of the conventional computer/network technology to facilitate the process of analyzing information, including the process of automatically scoring essays using one or more machine-learning models, etc., is already directed to a well-understood, routine, conventional activity in the art (e.g., see US 2017/0140689; US 2015/0199913; US 2014/0370485, etc.).
The above observation confirms that the current claimed invention fails to amount to “significantly more” than an abstract idea.
It is worth noting that the above analysis already encompasses each of the current dependent claims (i.e., claims 2-7, 10, 12, 13, 15, 17, 19 and 20). Particularly, each of the dependent claims also fails to amount to “significantly more” than the abstract idea since each dependent claim is directed to a further abstract idea, and/or a further conventional computer element(s) utilized to facilitate the abstract idea.
Accordingly, the findings above demonstrate that none of the claims implements an element—or a combination of elements—directed to an inventive concept (e.g., none of the current claims is reciting an element—or a combination of elements—that provides a technological improvement over the existing/conventional technology).
► Applicant’s arguments directed to section §101 have been fully considered (i.e., the arguments filed on 12/17/2025). However, the arguments are not persuasive at least for the following reasons:
Firstly, while referring to paragraphs from of the specification (e.g. [0008], [0031], [0032]), Applicant appears to be attempting to challenge the Office’s findings under prong-two of Step 2A. Applicant asserts, “the claims reflect an improvement to a technology or technical field, and thereby integrate the alleged judicial exception into a practical application . . . the prior art is deficient in accuracy of the evaluation of various types of essay in one system, and in providing a learner with sufficiently detailed feedback . . . improvements represented by the present claims, including increased accuracy of essay evaluation and improved feedback” (emphasis added).
However, unlike Applicant’s assertion, none of the paragraphs above demonstrates any technological improvement over the relevant existing technology. In particular, the alleged improvement in accuracy, which Applicant is alleging that the claimed (or the disclosed) system/method is achieving, has nothing to do with a technological feature (if any) that the relevant existing technology is lacking. This is because the claimed (and the originally disclosed) system/method is still utilizing the existing computer/network technology—merely as a tool—to facilitate the process of grading essays. Thus, unlike the prior art, the claimed (and disclosed) system/method may utilize a new grading approach/strategy to grade essays (e.g., one or more new grading steps, procedures, and/or parameters, etc.). Of course, the claimed (and/or the disclosed) new grading approach/strategy, which is itself a new abstract idea, may be different from the grading approach/strategy that the prior art is implementing. However, this does not necessarily imply a technological improvement. This is because a claim for a new abstract idea is still an abstract idea. See Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151 (Fed. Cir. 2016). In particular, as already pointed out above, the currently claimed (or the originally disclosed) system/method is still utilizing the existing computer/network technology (e.g., a computer system that executes one or more machine-learning algorithms, etc.)—merely as a tool—to facilitate the new grading approach/strategy. Consequently, regardless of Applicant’s assumption regarding the alleged capability of one system to evaluate various types of essays, and/or the alleged “increased accuracy of essay evaluation and improved feedback”, etc., none of the current claims, when considered as a whole, provides any technological improvement over the relevant existing technology. Of course, given the above lack of technological improvement, none of the current claims, when considered as a whole, is implementing any claim element—or a combination of claim elements—that imposes meaningful limits on practicing the claimed abstract idea. Consequently, Applicant’s arguments are not persuasive.
Applicant has also attempted to challenge the Office’s finding presented under prong-one of Step 2A; namely, the finding regarding the group mental processes. Applicant asserts, “the claims are not directed to mental processes . . . it is noted that the USPTO's August 4, 2025 memorandum titled ‘Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101’ . . . claim 1 includes ‘wherein to determine an essay type of the learning data based on major features of the learning data . . . including any one of a support vector machine (SVM), a decision tree, a recurrent neural network (RNN), and a convolutional neural network (CNN).’ Accordingly, claim 1 does not belong to the mental processes group for at least the reason that claim 1 ‘encompass[es] AI in a way that cannot be practically performed in the human mind’” (emphasis added).
However, unlike Applicant’s assertion, the finding per prong-one of Step 2A, which identifies the limitations that recite an abstract idea, does not include “limitations that encompass AI in a way that cannot be practically performed in the human mind” (emphasis added); rather, it only identifies limitations that can be practically performed in the human mind (and/or using a pen and paper). For instance, as part of the test, a human—such as a teacher—can draft one or more models/templates (e.g., an outline that defines one or more attributes to be considered when grading an essay, etc.); and subsequently, the teacher uses such models to grade one or more types of essays. However, the above does not necessarily imply that the teacher is mimicking a computer system by executing one or more computer-executable models—such as, the recurrent neutral network (RNN), the conventional neural network (CNN), etc. Thus, Applicant’s arguments are not persuasive. In particular, besides misapplying the memo (i.e., the August 4, 2025 memorandum), Applicant also appears to misconstrue the memo. For instance, the memo specifies, “Claim limitations that encompass AI in a way that cannot be practically performed in the human mind”; and thus, if a claim recites limitations that encompasses AI in a way that can be practically performed in the human mind (and/or using a pen and paper), it does recite an abstract idea.
Note that the inaccuracy of Applicant’s logic above, namely Applicant’s attempt to challenge a mental process while relying on the claimed computer elements, can also be demonstrated when considering the court’s decision regarding Electric Power Group. For instance, if one considers Applicant’s theory, one may incorrectly conclude that Electric Power Group is not directed to a mental process. For instance, the claim that the court has considered regarding Electric Power Group recites, at least in part, the following limitations (emphasis added),
12. A method of detecting events on an interconnected electric power grid in real time over a wide area and automatically analyzing the events on the interconnected electric power grid . . . receiving a plurality of data streams, each of the data streams comprising sub-second, time stamped synchronized phasor measurements . . . detecting and analyzing events in real-time from
the plurality of data streams . . . measurements from the data streams including at least one of frequency instability, voltages, power flows, phase angles, damping, and oscillation modes . . .
Accordingly, if one applies Applicant’s theory to claim 12 above, one may be tempted to conclude that the claim above is not a mental process. In particular, one may be tempted to argue that that the claimed process of detecting and automatically analyzing events on an electric power grid in real-time, including: (a) receiving multiple data streams that include synchronized phasor measurements that are collected in real-time; (b) detecting and analyzing limits, sensitiveness or rate of changes of at least one of frequency instability, voltages, phase angles, etc., are functions/steps that cannot be practically performed in the human mind (and/or using a pen and paper).
In contrast, despite such limitations above, the court has concluded that the claim is reciting an abstract idea; namely, a mental process. This is because the claim is using the existing technology—merely as a tool—to facilitate an abstract idea; such as, collecting information, analyzing the information, and displaying certain results.
The observation above demonstrates that it is not valid to attempt to challenge a finding regarding a mental process while relying merely on the limitations that recite computer elements.
Secondly, while attempting to summarize various decisions, including the precedential decision of September 26, 2025 (Ex parte Desjardins), Enfish, Applicant is asserting that “claim 1 includes the limitations of claims 8 and 9 . . . the Action oversimplifies the dependent claims, particularly claims 8 and 9 which, as noted above, are incorporated into claim 1. Similarly, in rejecting the independent claims, the Action (page 5) reduces significant details to ‘collecting and organizing text data,’ ‘building an evaluation model using the organized text data’ and ‘generating a result(s) based on the analysis of text data using the evaluation model.’ Accordingly, the Action is at odds with the USPTO guidance cited above . . . the claims reflect an improvement to a technology or technical field, and thereby integrate the alleged judicial exception into a practical application that imposes a meaningful limit on the alleged judicial exception. The claims are therefore not directed to a judicial exception without significantly more, and fully comply with 35 U.S.C. § 101” (emphasis added).
However, unlike Applicant’s assertion, none of the decisions that Applicant identified above cures the deficiencies of the current claims. In particular, Applicant fails to demonstrate whether any of the current claims, or even the disclosure as a whole, is implementing an element—or a combination of elements—that provides a technological improvement over the relevant existing technology. Of course, as already pointed out above, the lack of technological improvement confirms that none of the current claims, when considered as a whole, integrates the claimed abstract idea into a patent-eligible practical application. Consequently, Applicant’s arguments are not persuasive.
In addition, again unlike Applicant’s assertion, the Office’s analysis does not oversimplify any of the claims. For instance, the Office’s remark, “each of the dependent claims also fails to amount to ‘significantly more’ than the abstract idea since each dependent claim is directed to a further abstract idea, and/or a further conventional computer element(s) utilized to facilitate the abstract idea”, does not oversimplify any of the dependent claims. Instead, it is signifying the fact. In this regard, the analysis is not necessarily required to repeat each of the claims, word-by-word, while duplicating the same finding above. In fact, the practice of analyzing the eligibility of a claimed system/method, based on considering one representative claim (usually an independent claim), is a common practice that even the courts apply. Accordingly, if Applicant assumes that any of the current claims is patent-eligible, it is Applicant’s burden to demonstrate the eligibility (if any) of that claim, regardless of whether the claim is an independent claim or a dependent claim.
Note also that the previous limitations of claims 8 and 9, which Applicant is emphasizing above to support the alleged eligibility of the current claims, also do not demonstrate a technological improvement. This is because it is part of the existing computer/network technology to implement one or more machine-learning models and/or artificial intelligence models to analyze information. For instance, neither the current claims nor the original disclosure as a whole is implementing any advanced support vector machine (SVM), decision tree, a recurrent neural network (RNN), and/or a convolutional neural network (CNN). Instead, each of the current claims, including the original disclosure, is utilizing the existing computer/network technology, which already encompasses the above algorithms/modes, merely as a tool to facilitate the process of grading essays. Consequently, Applicant’s conclusory assertions, “the claims reflect an improvement to a technology or technical field”, “integrate the . . . judicial exception into a practical application that imposes a meaningful limit on the . . . judicial exception”, etc., are all not persuasive. Of course, given the generic and conventional arrangement of the claimed additional elements, none of the claims—when considered as whole—provides an inventive concept that amounts to “significantly more” than an abstract idea.
Thus, at least for the reasons discussed above, the Office concludes that none of the current claims is patent-eligible per section §101.
Response to Section §112(f)
► Applicant’s arguments directed to section §112(f) are also fully considered. Applicant asserts, “interpretation under 35 U.S.C. § 112(f) or 35 U.S.C. § 112 (pre-AIA ), 6th paragraph of claim 1-10 is avoided in view of the amendment to claim 1 to add ‘a processor and a memory storing instructions executable by the processor to implement the following,’ based on pars. [00120]-[00122] of the specification, thereby reciting sufficient structure that the interpretation is not invoked. In claim 16, the terminology alleged to invoke the interpretation is deleted” (emphasis added).
However, the current amendment does not cure the findings presented in the previous office-action under section §112(f). This is because the original specification itself confirms the lack of specific structure regarding the identified claimed features. For instance, current claim 1 recites, “a structure analysis module”; however, there is no structure that is specific to “a structure analysis module”. Similarly, current claim 1 also recites, “a learning module”; however, here also there is no structure that is specific to “a learning module”. Instead, each of the above is a form of software component, which the same processor is executing (see [0132]). Thus, the finding presented in the previous office-action is still relevant.
Prior Art
5. Considering each of claims 1, 11 and 18 as a whole (including their respective dependent claims), the prior art does not teach or suggest the claims as currently presented (regarding the state of the prior art, see the office-action dated 10/02/2025).
Conclusion
Applicant’s amendment necessitated the new grounds of rejection presented in this final 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 filled 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRUK A GEBREMICHAEL whose telephone number is (571) 270-3079. The examiner can normally be reached on 7:00AM-3:00PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, DAVID LEWIS can be reached on (571) 272-7673. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRUK A GEBREMICHAEL/Primary Examiner, Art Unit 3715