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 06/22/2026.
Claims 1, 11 and 18 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.
Improper Amendment
3. The current amendment—the claim amendment filed on 06/22/2026—is improper since it does not comply with the proper manner of making amendments (see part (c) of MPEP 1.121 “Manner of making amendments in applications”).
Regarding current claim 11, the limitation, “dividing learning data” (see line 3 of current claim 11, emphasis added), was not previously recited. Instead, previously presented claim 11 recites the limitation, “dividing learner essay text” (see claim 11 of the claims filed on 03/12/2026, emphasis added). However, when filing the current claims, Applicant appears to disregard the proper marking required (e.g., underlining the newly added text; such as, --dividing learning data--).
Thus, at least for the reason above, the current claim amendment fails to comply with the proper manner of making an amendment.
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 into structure units, the learning data including: scoring results of contents, a construction, representations, spelling, labeled structure units and a holistic score of each of entire learning essays according to grade and various subjects;
generate structure tagging information for each of the structure units; 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;
[draft] an essay evaluation model that receives essay text for each of the structure units and the structure tagging information, calculate a score for each of the structure units and a holistic score of an entirety of the essay;
[drafting] the essay evaluation model by using essay text that is included in the structured learner essay text and the structure tagging information;
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;
convert an evaluation score that is included in the structured learner essay text into a value between 0 and 1; and
determine an essay type of the learning data based on major features of the learning data;
wherein structure analysis comprises:
forming the structured essay text by attaching the structure tagging information to a location or range of components of the learner essay text,
labeling the components based on the essay type,
generating discourse marker information based on the major features of the learner essay text, the discourse marker information including information on a location and function of a discourse marker in text along with the discourse marker, and
[providing] the structured learner essay text.
— Claim 11:
evaluate an essay for writing learning:
divide learning data and learner essay text into structure units, the learning data including scoring results of contents, a construction, representations, spelling, labeled structure units and a holistic score of each of entire learning essays according to grade and various subjects;
generate structure tagging information for each of the structure units; and structure the learning data and learner essay text by attaching the structure tagging information to the learning data and learner essay text;
[draft] an essay evaluation model that receives essay text for each of the structure units and the structure tagging information, calculate a score for each of the structure units and a holistic score of an entirety of the essay;
[drafting] the essay evaluation 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;
as [an] essay evaluation step, generate essay evaluation results by inputting the structured learner essay text to the essay evaluation model; and
[provide] the essay evaluation results;
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;
wherein the learner essay structuring step includes:
forming the structured learner essay text by attaching the structure tagging information to a location or range of components of the learner essay text,
labeling the components based on the essay type,
generating discourse marker information based on the major features of the learner essay text, the discourse marker information including information on a location and function of a discourse marker in text along with the discourse marker, and
[providing] the structured learner essay text.
— Claim 18:
divide learning data and essay text that is included in learning data into structure units, the learning data including scoring results of contents, a construction, representations, spelling, labeled structure units and a holistic score of each of entire learning essays according to grade and various subjects;
generate structure tagging information for each of the structure units;
structure the learning data and essay text by attaching the structure tagging information to the learning data and essay text;
[draft] a model that receives essay text for each of the structure units and the structure tagging information, and calculate a score for each of the structure units and a holistic score of an entirety of the essay;
[drafting] the essay evaluation 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;
convert an evaluation score that is included in the structured essay text 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;
form the structured essay text by attaching the structure tagging information to a location or range of components of the essay text,
label the components based on the essay type,
generate discourse marker information based on major features of the essay text, the discourse marker information including information on a location and function of a discourse marker in text along with the discourse marker.
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 limitations identified above correspond to a concept that can practically be performed in the human mind (and/or using a pen and paper). In particular, a human—such as an instructor—can perform the core of the claimed process at least using a pen and paper (e.g., the instructor uses one or more templates or models to evaluate the student’s essay; wherein such evaluation includes: dividing the essay into a plurality of units; tagging or labeling each of the one or more units; drafting one or more templates or models for evaluating the essay; estimating one or more scores regarding each of the one or more units of the essay; estimating a holistic score for the entire essay, etc.).
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, which 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 . . . 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)”; “wherein the structure analysis module comprises a structure tagging unit configured to form the structured essay text by attaching the structure tagging information to a location or range of components of the learner essay text . . . information on a location and function of a discourse marker in text along with the discourse marker, and output the structured learner essay text”); building an evaluation model using the organized text data (e.g., “a learning module . . . generate an essay evaluation model . . . the essay evaluation model being performed through learning by using essay text that is included in the structured 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”; “the learning module converts an evaluation score that is included in the structured learner essay text into a value between 0 and 1 and generates the essay evaluation model by training a learning model, the training of the learning model including learning by using the converted evaluation score as a label”; “the learning module inputs the structured learner essay text . . . to train the learning model”); generating a result(s) based on the analysis of text data using the evaluation model (e.g., “an evaluation module . . . 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, when each of the current claims is considered as a whole (e.g., see the discussion under Prong Two above regarding such consideration of the claim as a whole), none of the claims 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 2017/0140659; 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 06/22/2026). However, the arguments are not persuasive at least for the following reasons:
Firstly, regarding the Office’s finding under Prong One of Step 2A (i.e., mental processes), Applicant has attempted to summarize the August 4, 2025 memorandum and Example 39 of the USPTO guidance. Subsequently, Applicant asserts, “amended claim 1 does not recite a judicial exception for at least the reason that amended claim 1 directly corresponds to the above example” (emphasis added).
However, quite similar to the analysis presented in the previous office action, none of the current claims is analogous to Example 39. This is because Example 39 does not recite any judicial exception, regardless of whether the judicial exception being considered is an abstract idea of not. In contrast, each of the currnet claims do recite an abstract idea—namely, a mental process. This is again because the claims do recite limitations that can practically be performed in the human mind and/or using a pen and paper. For instance, current claim 1 recite the following limitation (see lines 5-9, emphasis added):
“ a structure analysis module including instructions executable by the processor, the instructions in response to execution by the processor configured to divide learning data, the learning data including scoring results of contents, a construction, representations, spelling, labeled structure units and a holistic score of each of entire learning essays according to grade and various subjects, and learner essay text into structure units”
Thus, while excluding the computer elements (e.g., the processor executing instructions, etc.), a human—such as a teacher—can mentally (and/or using a pen and paper) perform the following limitations—such as, dividing each of the “learning data” and the “learner essay text” into one or more structure units. It is worth noting that the claimed “learner essay text”, as readily apparent from its name, is referring to the essay that the learner has written; whereas, claimed “learning data” is referring to essay evaluation data scored by two or more experts (e.g., see [0080] of the specification). In this regard, the “learning data” is essentially a scored essay, which is used as a benchmark—or answer key—to score the learner’s essay. Accordingly, the teacher can easily divide—mentally and/or using a pen and paper—each of the “learner essay text” and the “learning data” into one or more “structure units”, wherein each “structure unit” is referring to a part of the essay. For instance, the teacher divides each essay into various units; such as: (i) the heading/title of the essay; (ii) one or more paragraphs of the essay; (iii) one or more sentences of the essay; (iv) the abstract of the essay; (v) the summary of the essay; (vi) the body of the essay, etc. Note that the interpretation above is consistent with Applicant’s original disclosure (e.g., see [0056] of the specification)
Furthermore, once the teacher has separately identified the different units as discussed above, the teacher can not only tag/label each of the one or more structure units above (e.g., the teacher labels: (i) the heading of the essay as a “heading” or “title”; and (ii) the abstract of the essay as an “abstract”, etc.), but also “calculate a score for each of the structure units and a holistic score of an entirety of the essay” (emphasis added).
The observation above once again confirms that the current claims do recite an abstract idea—namely, a mental process. Although the above is just an exemplary scenario, the same type of analysis applies to the rest of the limitations that recite the abstract idea (see above the findings presented under Prong One of Step 2A). Thus, Applicant’s attempt to challenge the Office’s findings under Prong One, while relying on non-analogous art (Example 39) is not persuasive.
In addition, while referring to the Office’s analysis presented in the previous office action (i.e., page 10 of the office action dated 04/22/2026), which points out the fact that Applicant’s claims “do recite limitations that can practically be performed in the human mind (and/or using a pen and paper)”, Applicant asserts that “the latter assertion is based on an interpretation that disregards subject matter that is explicitly recited” (emphasis added).
However, Applicant’s conclusory assertion above does not appear to make any sense. For instance, Applicant fails to articulate how the Office’s analysis, which points out the limitations that recite the abstract idea, is assumed to disregard the alleged subject matter that the claim is assumed to be explicitly reciting. In fact, Applicant fails to point out this alleged subject matter, which the Office action supposedly disregarded. Consequently, Applicants conclusory assertion, which lacks evidence, is not persuasive.
Applicant has also attempted to challenge the Offices previous response (see page 12 of the previous office action), which explains how the limitations that recite the abstract idea are identified and evaluated. Applicant asserts, “more fully, previous claim 1 recited ‘a structure analysis module configured to divide learning data and learner essay text into structure units . . . 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) ....’ In view of the foregoing, all of previous claim 1 ‘encompass(es) AI,’ because the structure analysis module ‘uses an essay type classification model’ based on Al, and each of the claimed learning module and evaluation module use information generated by the structure analysis module” (emphasis added).
However, quite similar to the point made in the previous office action, here also Applicant appears to fail to properly apply the inquiry under Prong One of Step 2A. In particular, while relying on the claimed computer elements (e.g., the claimed structure analysis module that purportedly implements an AI model), Applicant is attempting to negate the Office’s findings under Prong One of Step 2A. In contrast, the inquiry under Prong One does not require one to consider any of the computer elements. Instead, Prong One requires one to identify merely the limitations that recite the abstract idea (see MPEP 2106.07(a), emphasis added).
For Step 2A Prong One, the rejection should identify the judicial exception by referring to what is recited (i.e., set forth or described) in the claim and explain why it is considered an exception. For example, if the claim is directed to an abstract idea, the rejection should identify the abstract idea as it is recited (i.e., set forth or described) in the claim and explain why it is an abstract idea.
In contrast, while relying on the claimed computer elements, Applicant is attempting to challenge the Office’s findings directed to the limitations that recite a mental process. Thus, Applicant’s arguments are not persuasive. This is because the claimed computer elements are part of the additional elements, but not part of the abstract idea.
Applicant further asserts, “claim 1 is further amended to emphasize that claim 1 does not represent a ‘mental process.’ A human being cannot as practical matter perform, for example, amended claim 1's ‘divide learning data, the learning data including scoring results of contents, a construction, representations, spelling, labeled structure units and a holistic score . . . 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.’ The volume of information that a human would be required to process, and the manipulation of data that a human would need to perform, exceeds anything that would be practical” (emphasis added).
However, Applicant appears to fail to provide a proper rationale (if any) to substantiate the above assertion. For instance, no plausible rationale and/or evidence is presented to demonstrate why a human cannot evaluate the alleged “learning data”, which is merely text printed on a page. It is again worth noting that the “learning data” is merely an answer key, or model essay, which is scored by two or more experts (again see [0080] of the specification). Given the above reality, even basic common sense dictates that a human can indeed mentally evaluate various attributes related to such textual data—such as, scoring results of contents, a construction, representations, spelling, etc. So far, Applicant fails to challenge—much less negate—the fact above.,
In addition, Applicant’s reasoning is not consistent with the eligibility test governing mental processes. In particular, Applicant appears to rely on a subjective theory—namely, “volume of information”—to challenge the Office’s finding regarding the limitations that recite an abstract idea. As an initial matter, the alleged “volume of information”, does not even signify what amount of information is being implied, much less a feature (if any) that cannot practically be performed in the human mind. For instance, a single or a double page model essay (e.g., an answer key that a teacher uses to grade the learner’s essay) can have all the attributes that Applicant has listed; such as: scoring results of contents, a construction, representations, spelling, labeled structure units, etc. This exemplary test confirms that Applicant’s alleged “volume of information” does not have any significance. In fact, prior to any grading, the teacher manually prepares the above model essay, so that he/she can use it as an answer key. Similarly, before starting the scoring process, which involves comparing the learner’s essay against the model essay, the teacher also divides and labels each of the model essay and the student’s essay in to one or more essay structure units (e.g., heading/title, abstract, summary, etc.).
The observation above confirms that Applicant’s alleged “volume of information” once again fails to challenge the Office’s findings regarding the claimed limitations that recite the abstract idea.
Moreover, the claimed—and the disclosed—system/method is directed to scoring essays that learners write. In this regard, even basic common sense dictates that teachers have been performing such manual scoring of essays for many years even before the existence of computers. This is in fact sufficient to confirm the Office’s finding that the claimed (and the disclosed) implementation does recite an abstract idea; namely, a mental process. Thus, Applicant’s claimed (and disclosed) implementation is merely providing a tool that is intended to facilitate the above manual scoring task. Consequently, Applicant’s alleged “volume of information” is once again not relevant to even challenge—much less negate—the Office’s findings pointed out above.
Applicant also continues to rely on the computer elements to challenge the Office’s finding regarding mental processes. Applicant asserts, “it would not be practical, or even possible, for a human to perform amended claim 1's ‘generate an essay evaluation model including a structure unit level encoder configured to receive essay text for each of the structure units and the structure tagging information and generate an embedding vector for each of the structure units, a document level encoder configured to receive all of generated embedding vectors . . . an evaluation score that is included in the structured learning data as a label.’ . . . it would not be practical, or even possible, for a human to perform amended claim 1's ‘generate essay evaluation results by inputting, to the essay evaluation model generated by the learning module, essay text that is included in the structured learner essay text and the structure tagging information,’ for at least the reason that the foregoing ‘encompass(es)AI’ along lines previously discussed” (emphasis added).
However, it is immaterial whether the limitations that recite computer elements, which includes (i) the structure unit level encoder that generates an embedding vector for each of the structure units, (ii) the document level encoder that generates a document embedding vector, (iii) the learning module, etc. cannot be performed in the human mind. This is because the Office does not identify any of the computer elements as part of the abstract idea (e.g., see above the limitations identified under Prong One of Step 2A). Accordingly, Applicant’s attempt to challenge the Office’s finding regarding mental processes, while conflating the computer elements with the abstract idea, is once again not persuasive.
Note also that the invalidity of Applicant’s theory, namely Applicant’s attempt to challenge mental processes while relying on the computer elements, can also be verified when considering the court’s decision regarding Electric Power Group. Although the Office presented such analysis in the past (see page 11 of the office action dated 01/28/2026), the analysis is repeated here since Applicant is repeating the same inaccurate theory. In particular, if one applies Applicant’s theory to the claim, namely claim 12, of Electric Power Group, one may incorrectly conclude that claim 12 is not directed to a mental process. For instance, claim 12 of 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 . . . including at least one of frequency instability, voltages, power flows, phase angles, damping, and oscillation modes . . .
Accordingly, when applying Applicant’s incorrect theory, claim 12 above does not recite a mental process. In particular, while relying on the computer elements, one may be tempted to argue that that the 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 being 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 practically be performed in the human mind (and/or using a pen and paper).
In contrast, despite the computer elements 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 once again demonstrates that a claim is not necessarily immune from an abstract idea—such as, a mental process, simply because it recites one or more limitations that recite computer elements. Consequently, Applicant’s arguments are once again not persuasive.
Secondly, regarding Prong Two of Step 2A, Applicant asserts “the claims as amended are not directed to an abstract idea without significantly more for at least the reason that, under Step 2A, Prong Two, the claims integrate a practical application therein and improve the functioning of a technology . . . The Action does not provide any support for the assertion that ‘none of the claims provides an improvement over the relevant existing technology.’ On On the other hand, the present specification provides ample description of improvements. See, e.g., pars. [0031] and [0032]” (emphasis added).
However, Applicant appears to fail to even articulate what is assumed to be integrated into a practical application. It is worth noting that Applicant has already declared that “amended claim 1 does not recite a judicial exception” (see the second page of the current argument, emphasis added). If so, what is then considered to be integrated into a practical application? In particular, Applicant’s assertion, “the claims integrate a practical application therein”, does not address the inquiry under Prong Two since it is merely a generic expression that does not even point out what is assumed to be integrated. This demonstrates the inconsistencies in Applicant’s arguments.
In addition, except for simply asserting an alleged technological improvement, Applicant fails to provide a valid evidence and/or rationale to substantiate the above assertion. Note that the claimed—and the originally disclosed—system/method is relying on the existing computer/network technology. Accordingly, if Applicant is assuming that the claimed (and/or the disclosed) system/method is integrating the abstract idea into a patent-eligible practical application, then Applicant should identify a feature (if any)—or a combination of features (if any)—that is an advance over the existing computer/network technology. However, simply alleging a technological improvement regarding the claimed system/method, while failing to show an advanced technological feature (if any) that the claimed (or the disclosed) system/method is implementing, does not constitute a persuasive argument.
Furthermore, regarding the analysis under Prong Two of Step 2A, the Office already provides the rationale, which signifies the reason why the claimed and disclosed system/method lacks a technological improvement. In particular, the Office already indicates that the claimed (and the disclosed) system/method is utilizing the existing computer/network technology—merely as a tool—to facilitate an abstract idea, i.e., evaluating or scoring essays. Moreover, besides the Office’s analysis, Applicant’s original disclosure itself provides sufficient evidence confirming the lack of technological improvement regarding the claimed—and the disclosed—system/method. For instance, except for providing generic description regarding the use of existing machine-learning algorithms ([0064], [0067], [0080], etc.), including the existing computer/network system that the disclosed system/method is implementing ([0121] to [0126], etc.), the disclosure is silent regarding any technological feature—or a combination of features—that is assumed to be an advance over the existing computer/network technology. Accordingly, unlike Applicant’s theory, the Office is not necessarily required to provide extra support or evidence regarding the finding under Prong Two. In fact, regarding Prong Two of Step 2A, even the MPEP indicates that “there is no requirement for evidence to support a finding that the exception is not integrated into a practical application”, see MPEP 2106.07(a)(III) (emphasis added). Accordingly, Applicant appears to be relying on a subjective theory—as opposed to a valid one—to challenge the Office’s findings.
In addition, the paragraphs that Applicant cited from the specification (i.e., [0031], [0032], [0096]), which supposedly provide “ample” support regarding the alleged technological improvement that Applicant is implying, have nothing to do with signifying a technological improvement (if any) over the existing computer/network technology. Instead, the paragraphs are merely describing the alleged level of accuracy being achieved regarding essay evaluation, including the feedback that the system is providing to the leaner, so that “the learner can efficiently improve his or her writing ability”. However, such process of analyzing collected information, and subsequently providing the user with more accurate or relevant information, has nothing to do with technological improvement, regardless of whether the system helps the user to improve his/her skills or the “subject suitability evaluation or grammar corrections, can be easily performed on each structure unit of an essay”, etc. If anything, Applicant is confirming, perhaps indirectly, the use of the existing computer technology—merely as a tool—to facilitate an abstract idea; such as, facilitating the presentation of relevant information to the user (i.e., information in the form of essay score and/or feedback), based on the analysis of gathered data (e.g., one or more constructed responses—or essay—received from the user, etc.). Of course, such implementation may arguably help the user to improve his/her skills; however, improving the skills of the user does not necessarily mean providing a technological improvement over the existing computer/network technology.
Note that the content of the feedback provided to the learner; such as “feedback that . . . includes grammar for the entire essay and for each paragraph, subject suitability, and a holistic score of the entire essay . . . a corrected sentence with respect to a sentence having a grammatical error for spelling and grammar feedback”, does not change fact pointed out above regarding the lack of technological improvement. This is because the feedback is just information regardless of how detailed the information is. Thus, Applicant’s conclusory assertion, “[the] improvements in the detail of feedback and the providing of corrected grammar, based on structure units” (emphasis added), is not persuasive.
In addition, while mistaking the information above for the alleged technological improvement, Applicant asserts that the current claims reflect “‘particular solution’ in the detailed recitations of operations to generate structure units and use the structure units to generate and train AI models, as in, for example, amended claim 1” (emphasis added). Furthermore, while citing part of claim 1 that supposedly reflects the alleged “particular solution” (see the 6th and 7th page of the argument), Applicant concludes that “[t]he above further represents an ‘inventive concept’ under Step 2B, noting that criteria for finding an inventive concept under Step 2B include ‘whether any additional element or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception.’ MPEP 2106.05. Here, the amended claims recite subject matter that is well beyond ‘well-understood, routine, conventional activity in the field’ and clearly are not ‘specified at a high level of generality’” (emphasis added).
However, except for making generic assertions, Applicant fails to demonstrate whether any of the current claims (or the system/method as originally disclosed) is directed to a non-generic and non-conventional arrangement of the additional elements. Even when considering Applicant’s alleged process of generating or training AI models, neither the current claims nor the original specification contemplates any new or advanced AI technology. Instead, the original disclosure is merely providing generic description regarding existing models that the originally disclosed system/method is implementing—such as, a support vector machine (SVM), a decision tree, a recurrent neural network (RRN), etc. ([0064], [0067]). Of course, the disclosed computer-based system/method is itself directed to the existing or conventional computer/network technology ([0121] to [0126], etc.). Accordingly, when each of the current claims (including the original disclosure) is considered as a whole, the claims fail to implement an inventive concept that amounts to “significantly more” than an abstract idea. Thus, Applicant’s conclusory assertions above are not persuasive.
Accordingly, at least for the reasons discussed above, the Office concludes that none of the current claims complies with section §101.
Applicant’s arguments directed to section §112(f)
Applicant’s arguments directed to section §112(f) have also been fully considered. Applicant is asserting that “contrary to the Action, interpretation under 35 U.S.C. § l 12(f) or 35 U.S.C. § 112 (pre-AIA ), 6th paragraph is avoided in view of the amendments to claim 1 . . . the Action appears to argue that ‘a processor and a memory storing instructions executable by the processor’ is not structure, or that avoiding a means-plus-function interpretation requires that each recited module be defined in terms structure specific to that module, and that the structures be ‘different’ from each other. There is no such requirement. The claim defines the modules in terms of a processor and instructions: these are structural. Notwithstanding, to advance prosecution, claim 1 is amended to recite that each of the claimed modules ‘include(es) instructions executable by the processor, the instructions in response to execution by the processor,’ etc., thereby reciting structure for each module specifically” (emphasis added).
However, instead of properly construing the plain finding presented in the office action, Applicant appears to be drafting a new subjective assumption in an attempt to mischaracterize the Office’s analysis. For instance, unlike Applicant’s erroneous assumption, the office action does not state that “‘a processor and a memory storing instructions executable by the processor’ is not structure” (emphasis added). In contrast, the office action is stating that the same structure (e.g., a system comprising a processor and a memory, wherein the memory storing instructions executable by the processor) is representing both the “structure analysis module” and the “learning module”. Thus, Applicant’s attempt to mischaracterize the plain explanation above is invalid.
Similarly, again unlike Applicant’s erroneous assertion, the office action does not necessarily state that “avoiding a means-plus-function interpretation requires that each recited module be defined in terms structure specific to that module, and that the structures be ‘different’ from each other” (emphasis added). Instead, the office action is simply pointing out the facts noted per the original disclosure. In particular, per the original disclosure, each claimed module is not associated with a structure that is specific to that module only. Instead, all the modules share the same structure. Accordingly, simply pointing out these facts does not necessarily mean that the office action is imposing the new requirement that Applicant is alleging. In fact, there is no reason for the Office to make such requirement. This is again because each claimed—and disclosed—module cannot have its own corresponding structure per the original system. In particular, per the originally disclosed system, the modules are already locked to share the same structure. Given this basic reality, there is no need for the Office to make the alleged requirement that Applicant is speculating above. Thus, besides the attempt made to mischaracterize the Office’s analysis, Applicant appears miscomprehend the scope of the original disclosure.
Note also that the current amendment does not remedy the issues noted in the first office action under section §112(f) (see pages 7-9 of the office action dated 10/02/2025). This is again because each of the modules currently claimed still invoke section §112(f) for the same reasons discussed in the first office action above. Thus, the office still maintains the findings presented under section §112(f).
Prior Art
4. 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 from 7:00 AM - 3:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PETER VASAT can be reached on (571) 270-7625. 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
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