Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Interview
The examiner acknowledges that an interview request was made and denied due to examiner’s unavailability. Applicant is welcome to initiate an interview request upon receiving this action.
Responses to Amendment
In response to the reply filed 10/30/2025, claims 1, 16, and 19 have been amended. Claims 1-20 are pending and under examination.
Response to Arguments
Applicant's arguments filed 10/30/2025, pp. 10-20 have been fully considered but they are not persuasive.
In response to the first argument that claim 1 is directed to patent eligible subject matter because claim 1 recites specific and meaningful limitation using a computer (p. 17), it is respectfully disagreed. Claim 1 recites … According to MPEP,
In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement.
MPEP 2106.04(d)(1). Here, Applicant merely asserts that claim 1 recites specific and meaningful limitation even without specifying which limitations are specific and meaningful limitation. Thus, without providing any specific basis, it is unclear what Applicant alleges the claimed “improvement”.
In response to the second argument that claim 1 recites specific operations and patent eligible subject matter under Step 2B because claim 1 recites specific and meaningful limitation using a computer (pp. 18-19), it is respectfully disagreed. According to MPEP,
Although the courts often evaluate considerations such as the conventionality of an additional element in the eligibility analysis, the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973 (rejecting “the Government’s invitation to substitute §§ 102, 103, and 112 inquiries for the better established inquiry under § 101”). As made clear by the courts, the “‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter.”
MPEP 2106.05(I). Here, as infra 101 analysis indicates, the amended portion merely recites another abstract ideas and Applicant fails to show any relevant in determining the patent subject matter eligibility. Thus, the rejection is maintained.
Next, Applicant’s arguments, with respect to the rejection under 35 USC 112(a), that the specification discloses inputting data into a machine learning system and therefore meets the 112(a) requirement, it is respectfully disagreed. See reply pp. 9-10, filed 10/30/2025. According to MPEP,
Similarly, original claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed…
When examining computer-implemented functional claims, examiners should determine whether the specification discloses the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that perform the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing… It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement.
MPEP 2161.01(I). Here, other than merely providing data to a generic machine learning model, the specification is silent as to how to train the model to be the claimed curriculum grading model. Thus, the rejection is maintained.
Applicant’s other arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Interpretation
Claim 19 recites the preamble “A computer program product for an electronic computation device comprising a computer readable storage medium.” Although the term “non-transitory” is not explicitly recited in the preamble, it is noted that the specification explicitly disavows transitory signal(s) from its scope. See Spec. ¶29 (“A computer readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through a fiber optic cable, electrical signals communicated through a wire, and/or other transmission media.”).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 1 recites the limitation "creating a curriculum grading model based on the machine learning system" in line 6. The specification, at best, merely declares that inputting curriculum material, challenge material, and training data into a machine learning system will create the curriculum grading model (Spec. ¶59), and the created model can evaluate and output feedback such as score, hints, tips, and/or other corrections (Spec. ¶¶65-67). However, nowhere in the specification describes how to make the curriculum grading model based on the input. Without providing more detailed creation process for the curriculum grading model, the person skilled in the art would not believe the inventor(s) possessed the “curriculum grading model” or how to “create the curriculum grading model” as claimed. Other independent claims 16 and 19, and dependent claims thereof recite the same limitation and are rejected as well.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 9-12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 9 recites the limitation "the providing the curriculum challenge for display" in line 1. There is insufficient antecedent basis for limitations “the providing” and “the curriculum challenge” in the claim. For the reason, dependent claims thereof are rejected as well.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception(s) without significantly more.
[STEP 1] The claim recites at least one step or structure. Thus, the claim is to a process or product, which is one of the statutory categories of invention (Step 1: YES).
[STEP2A PRONG I] The claim(s) recite(s):
A computer-implemented method [claim 1];
An electronic computation device comprising: a processor; a memory coupled to the processor, the memory containing instructions, that when executed by the processor, cause the electronic computation device to [claim 16];
A computer program product for an electronic computation device comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the electronic computation device to [claim 19];
obtaining curriculum material from a curriculum material corpus;
obtaining challenge material from a challenge corpus;
inputting the curriculum material and the challenge material to a machine learning system;
creating a curriculum grading model1 based on the machine learning system, the curriculum grading model is created based on training data, the curriculum material, and the challenge material;
evaluating a curriculum response using the curriculum grading model, and
generating a score based on the evaluating,
the evaluating includes preprocessing which includes tokenizing a snip of code, to generate a token, from the curriculum response which includes a student's answer to a challenge, and extracting a variable from the token;
the preprocessing includes splitting the token which includes splitting the variable into fragments based on capitalization of letters, and identification of delimiters; and
the preprocessing includes normalization which includes converting the fragments to words2.
The non-highlighted aforementioned, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “computer-implemented” [claim 1], “electronic computation device” and “processor” [claims 16 and 19], “memory” [claim 16], “computer readable storage medium” [claim 19], “obtaining [data] from a corpus,” “inputting [data],” and “machine learning system,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the recited language, the step in the context of this claim encompasses a teacher evaluating and scoring student’s responses based on prior exams as well as questions.
Alternatively, the non-highlighted aforementioned limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation between people but for the recitation of generic computer components. That is, other than reciting the aforementioned additional elements, nothing in the claim element precludes the step from practically being performed between people. For example, but for the recited language, the step in the context of this claim encompasses a teacher receiving responses from students and evaluating and scoring student’s responses based on prior exams as well as question.
If a claim limitation, under its broadest reasonable interpretation, covers managing interactions between people, then it falls within the “Organization of Human Activity” grouping of abstract ideas.
Accordingly, the claim recites a judicial exception, and the analysis must therefore proceed to Step 2A Prong Two.
[STEP2A PRONG II] This judicial exception is not integrated into a practical application. In particular, the claim only recites the additional element(s) – “computer-implemented,” “electronic computation device,” “processor,” “memory,” “computer readable storage medium,” “obtaining [data] from a corpus,” “inputting [data],” and “machine learning system.”
The “computer-implemented,” “electronic computation device,” “processor,” “memory,” “computer readable storage medium,” and “machine learning system” in the aforementioned steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component.
The claim recites the following additional elements: “obtaining [data] from a corpus” and “inputting [data].” The additional element step is recited at a high level of generality, and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Each of the additional limitations is no more than mere instructions to apply the exception using a generic computer component.
Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea and the claim is therefore directed to the judicial exception. (Step 2A: YES).
[STEP2B] The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the aforementioned steps amounts to no more than mere instructions to apply the exception using a generic computer component, which cannot provide an inventive concept.
As noted previously, the claim as a whole merely describes how to generally “apply” the aforementioned concept in a computer environment. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the aforementioned step(s) amounts to no more than adding insignificant extra-solution activity to the judicial exception, which cannot provide an inventive concept.
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be reevaluated in Step 2B. Here, the aforementioned step(s) was/were considered to be extra-solution activity in Step 2A, and thus it is reevaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The background of the specification does not provide any indication that the additional element(s) is/are anything other than a generic, off-the-shelf computer component, and the Symantec, TLI, and OIP Techs. court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or receipt of data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the aforementioned step(s) is/are well-understood, routine, conventional activity is supported under Berkheimer Option 2.
As noted previously, the claim as a whole merely describes how to generally adding insignificant extra-solution activity to the judicial exception. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element of displaying information to perform the aforementioned step(s) amounts to no more than adding insignificant extra-solution activity to the judicial exception, which cannot provide an inventive concept.
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be reevaluated in Step 2B. Here, the aforementioned step(s) was/were considered to be extra-solution activity in Step 2A, and thus it is reevaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The background of the specification does not provide any indication that the additional element(s) is/are anything other than a generic, off-the-shelf computer component, and the Electric Power Group, LLC v. Alstom S.A., and Ameranth, court decisions cited in MPEP 2106.05(g) indicate that displaying data is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the aforementioned step(s) is/are well-understood, routine, conventional activity is supported under Berkheimer Option 2.
The claim is not patent eligible. (Step 2B: NO).
Claim(s) 2-15 and 17-18, and 20 is/are dependent on supra claim(s) and includes all the limitations of the claim(s). Therefore, the dependent claim(s) recite(s) the same abstract idea. The claim recites the additional limitations3 of “performing an input-output test on an executable” [claim 8], “text” [claim 13], “audio” [claim 14], and “image” [claim 15], which are no more than mere instructions to apply the exception using a generic computer component, generally linking the use of the judicial exception to a particular technological environment or field of use, insignificant extra-solution activity, or that are well understood, routine and conventional activities previously known to the industry. Accordingly, the additional element(s) do(es) not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea and the claim is therefore directed to the judicial exception. Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity or well-known, routine, and conventional activity in Step 2A should be reevaluated in Step 2B. Here, the aforementioned step(s) “performing an input-output test on an executable”, “text”, “audio”, and “image” was/were considered to be extra-solution activity in Step 2A, and thus it is reevaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The background of the specification does not provide any indication that the additional element(s) is/are anything other than a generic, off-the-shelf computer component, and the Symantec, TLI, and OIP Techs. court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or receipt of data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here), and the Electric Power Group, LLC v. Alstom S.A., and Ameranth, court decisions cited in MPEP 2106.05(g) indicate that displaying data is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Further, the examiner takes OFFICIAL NOTICE that the aforementioned additional elements are well-known, routine and conventional activity such as testing students’ coding skills by compiling and executing codes and receiving inputs as text/audio/image. Accordingly, a conclusion that the aforementioned step(s) is/are well-understood, routine, conventional activity is supported under Berkheimer Option 2. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-6, 8-17, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Digrazia et al (U.S. Patent Application Publication 2021/0216939), hereinafter Digrazia, in view of Tripp et al (U.S. Patent 11,630,919), hereinafter Tripp.
Regarding claims 1, 16, and 19, Digrazia discloses a computer-implemented method/device/product (Abstract) comprising:
obtaining curriculum material from a curriculum material corpus (¶64: “the coding machine-learning module 206 receives training data in the form of labeled high-quality code where the high-quality code is compilable code that did not require modification to become compilable.”);
obtaining challenge material from a challenge corpus (¶72: “The coding machine-learning module 206 received uncompilable code from college seniors majoring in computer science that took a 90 minute assessment in a proctored environment during which they attempted two programming questions.”);
inputting the curriculum material and the challenge material to a machine learning system (¶61: “The coding machine-learning module 206 may modify the parameters of the first coding machine-learning model based on training data that labels code as compilable or uncompilable in order to identify patterns in code that would cause it to be uncompilable.”);
creating a curriculum grading model based on the machine learning system, the curriculum grading model is created based on training data, the curriculum material, and the challenge material (¶61: “the coding machine-learning module 206 generates multiple coding machine-learning models to form an iterative process.”);
evaluating a curriculum response using the curriculum grading model (¶89: “the scoring engine 210 generating a coding score based on the uncompilable code, the compilable code, and the patterns output from the coding machine-learning model.”), and
generating a score based on the evaluating (812 in FIG. 8)
the evaluating includes preprocessing which includes tokenizing a snip of code, to generate a token, from the curriculum response which includes a student's answer to a challenge, and extracting a variable from the token (204 in FIG. 2; ¶54: “A typical compiler performs several steps: tokenization, parsing based on grammar, type checking, linking, and optimization.”; ¶55 further discloses that the variable names are extracted as well: “potentially variable/function names”).
Digrazia does not explicitly disclose the preprocessing includes splitting the token which includes splitting the variable into fragments based on capitalization of letters, and identification of delimiters; and the preprocessing includes normalization which includes converting the fragments to words.
Tripp discloses management of sensitive data using static code analysis (Abstract) comprising the preprocessing includes splitting the token which includes splitting the variable into fragments based on capitalization of letters, and identification of delimiters; and the preprocessing includes normalization which includes converting the fragments to words (col. 4, l. 65 – col. 5, l. 1: “In some embodiments, variable names may be divided into multiple tokens. For example, a variable named accountNumber, might be partitioned into two tokens: account and number.”; col. 5, ll. 11-15: “Model 122 can perform inference on each feature vector and return an indication as to whether the feature vector includes sensitive data and a confidence score associated with the indication, at numeral 6.”).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention was made to modify the invention in Digrazia by adding the code review features as taught in Tripp in order to prevent “potential mishandling of sensitive data” (col. 1, ll. 16-18 of Tripp).
Regarding claim 2, Digrazia discloses that the evaluating includes preprocessing of source code (¶47: “The correction module 202 generates compilable code by: (1) identifying and correcting corrects syntax errors in the uncompilable code; and (2) using an n-gram based token prediction model.”).
Regarding claim 3, Digrazia further discloses that the preprocessing includes token splitting (¶55: “a compiler performs tokenization of code”).
Regarding claim 4, Digrazia further discloses that the preprocessing further includes normalization (¶111: “the coding machine-learning model may be normalized to make it suitable for the uncompilable code.”).
Regarding claim 5, Digrazia further discloses that the curriculum grading model is based on a plurality of correct solutions and a plurality of incorrect solutions (“The coding machine-learning module 206 may modify the parameters of the first coding machine-learning model based on training data that labels code as compilable or uncompilable in order to identify patterns in code that would cause it to be uncompilable.”).
Regarding claims 6, 17, and 20, Digrazia further discloses deriving at least one curriculum element from the curriculum material; and wherein the score that is generated is based on the at least one curriculum element (¶72 discloses two problems are selected from a curriculum: “The coding machine-learning module 206 received uncompilable code from college seniors majoring in computer science that took a 90 minute assessment in a proctored environment during which they attempted two programming questions… The topics covered by the questions spanned iterative/recursive algorithms, trees, and graphs and other algorithms like the shortest job first, etc.”; ¶81 further discloses the scoring per each problem).
Regarding claim 8, Digrazia further discloses that the evaluating further comprises performing an input-output test on an executable based on the curriculum response (¶61: “the coding machine-learning module 206 may modify the parameters of a first coding machine-learning model that outputs a determination of whether a first statement has a compiler error or is uncompilable.”).
Regarding claim 9, Digrazia further discloses that the providing the curriculum challenge for display includes setting a challenge mode (¶46 discloses the application can be used for “a job interview”).
Regarding claim 10, Digrazia further discloses that setting the challenge mode comprises setting the challenge mode to a testing mode (¶46 discloses the application can be used for “programming class”).
Regarding claim 11, Digrazia further discloses that setting the challenge mode comprises setting the challenge mode to an appraisal mode (¶46 discloses the application can be used for “any other purpose”).
Regarding claim 12, Digrazia further discloses that the appraisal mode includes generation and presentation of a hint (¶106: “a description of one or more of logical correctness of the uncompilable code, an algorithmic approach used by the uncompilable code, a programming style of the uncompilable code, a maintainability of the uncompilable code, a recommendation for how to correct logic of the uncompilable code, correct code, a number of compiler errors, a number of test cases, a recommendation to make the uncompilable code compilable, or an identification of one or more lines in the uncompilable code that are uncompilable and one or more alternate lines to correct the uncompilable code”).
Regarding claim 13, Digrazia further discloses that the curriculum material corpus includes text (¶116: “uncompilable code is received from a programmer”).
Regarding claim 14, Digrazia further discloses that the curriculum material corpus includes audio (¶84: “The media machine-learning module 208 may receive training data that includes media of a candidate being interviewed, such as a video or audio”).
Regarding claim 15, Digrazia further discloses that the curriculum material corpus includes at least one image (¶84: “The media machine-learning module 208 may receive training data that includes media of a candidate being interviewed, such as a video or audio”).
Claim(s) 7 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Digrazia in view of Tripp, and further in view of Morton et al (U.S. Patent Application Publication 2017/0140660), hereinafter Morton.
Regarding claims 7 and 18, Digrazia does not explicitly disclose performing a parameter randomization on a curriculum challenge.
Morton teaches cyber training system and method (Abstract) comprising performing a parameter randomization on a curriculum challenge (¶78: “Each blueprint contains a range of parameters that allows for randomization of the parameters at the start of each play. At the time of mission instantiation, several variables are chosen to determine the characteristics of the mission/game and then the VEM creates a specific mission instance.”).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention was made to modify the invention in Digrazia by adding the cyber training features as taught in Morton in order to provide “variability to a player in repeated attempts of the same [challenge]” (¶78 of Digrazia).
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 THOMAS J HONG whose telephone number is (571)272-0993. The examiner can normally be reached 9:30AM-6PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sunil Singh can be reached at (571) 272-3460. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
THOMAS J. HONG
Supervisory Patent Examiner
Art Unit 3729
/THOMAS J HONG/ Supervisory Patent Examiner, Art Unit 3729
1 It is noted that the limitation “creating a curriculum grading model based on the machine learning system” does not require the claimed curriculum grading model is a machine learning model and instead, any model created based on the machine learning model. For the purpose of evaluating patent subject matter eligibility, the “curriculum grading model” is considered an abstract idea of either mental process or mathematical model, and not necessarily a machine learning model.
2 The limitations “tokenizing,” “splitting token which includes splitting variable into fragments,” and “converting the fragments to words” are considered mental activities as code reviewers can review the code, find variable names from the tokens, and infer which name can be mapped to the fragments.
3 The limitations “preprocessing of source code” [claim 2], “token splitting” [claim 3], and “normalization” [claim 4] are considered an abstract idea of either mental process, certain organization of human activities, or mathematical processing because those can be surely performed by senior software programmers when they review junior programmers’ codes.
Prosecution Insights