DETAILED ACTION
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 .
Status of Claims
This office action is in response to arguments and amendments entered on March 10, 2026 for the patent application 18/239,955 originally filed on August 30, 2023. Claims 1-9 are amended. Claims 1-9 are pending. The first office action of November 18, 2025 is fully incorporated by reference into this Final Office Action.
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 1-9 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 – “Statutory Category Identification”
Claim 1 is directed to “a teacher data editing support system” (i.e. “a machine”), claim 8 is directed to “a teacher data editing support method” (i.e. “a process”), and claim 9 is directed to “a teacher data editing support program” (i.e. “a process”), hence the claims are directed to one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). In other words, Step 1 of the subject-matter eligibility analysis is “Yes.”
Step 2A, Prong 1 “Abstract Idea Identification”
However, the claims are drawn to an abstract idea of “data editing,” either in the form of “certain methods of organizing human activity,” in terms of managing personal behavior or relationships or interactions between people (including social activities, teaching and following rules or instructions), or reasonably in the form of “mental processes,” in terms of processes that can be performed in the human mind (including an observation, evaluation, judgement or opinion). Regardless, the claims are reasonably understood as either “certain methods of organizing human activity” or “mental processes,” which require the following limitations:
Per claim 1:
“receive teacher data including discriminatory factors as variables that potentially cause
discrimination, a feature as a variable to be used for prediction, and a correct answer,
calculate a contribution, as an index, indicating contribution of the discriminatory factors to the correct answer,
display evaluation information indicating a relationship between a degree of change of the correct answer in the teacher data and a degree of deviation of the correct answer from an initial value or a discrimination degree based on the contribution, and
accept a designation of how much the correct answer is changed, change the correct answer in the teacher data in response to the designation, and output the changed teacher data.”
Per claim 8:
“receiving teacher data including discriminatory factors as variables that potentially cause discrimination, a feature as a variable to be used for prediction, and a correct answer;
calculating a contribution as an index indicating contribution of the discriminatory factors to the correct answer;
visually presenting evaluation information indicating a relationship between a degree of changing the correct answer in the teacher data and a degree of deviation of the correct answer from an initial value or a discrimination degree based on the contribution;
accepting designation of how much the correct answer is changed;
changing the correct answer in the teacher data in response to the designation; and
outputting the changed teacher data.”
Per claim 9:
“receiving teacher data including discriminatory factors as variables that potentially cause discrimination, a feature as a variable to be used for prediction, and a correct answer;
calculating a contribution as an index indicating contribution of the discriminatory factors to the correct answer;
visually presenting evaluation information indicating a relationship between a degree of changing the correct answer in the teacher data and a degree of deviation of the correct answer from an initial value or a discrimination degree based on the contribution;
accepting designation of how much the correct answer is changed;
changing the correct answer in the teacher data in response to the designation; and
outputting the changed teacher data.”
These limitations simply describe a process of data gathering and manipulation, which is analogous to “collecting information, analyzing it, and displaying certain results of the collection analysis” (i.e. Electric Power Group, LLC, v. Alstom, 830 F.3d 1350, 119 U.S.P.Q.2d 1739 (Fed. Cir. 2016)). Hence, these limitations are akin to an abstract idea which has been identified among non-limiting examples to be an abstract idea. In other words, Step 2A, Prong 1 of the subject-matter eligibility analysis is “Yes.”
Step 2A, Prong 2 – “Practical Application”
Furthermore, the applicants claimed elements of “a processor,” “a memory,” and “an apparatus having a processing device,” are merely claimed to generally link the use of a judicial exception (e.g., pre-solution activity of data gathering and post-solution activity of presenting data) to (1) a particular technological environment or (2) field of use, per MPEP §2106.05(h); and are applying the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, per MPEP §2106.05(f). In other words, the claimed “data editing,” is not providing a practical application, thus Step 2A, Prong 2 of the subject-matter eligibility analysis is “No.”
Step 2B – “Significantly More”
Likewise, the claims do not include additional elements that either alone or in combination are sufficient to amount to significantly more than the judicial exception because to the extent that, e.g. “a processor,” “a memory,” and “an apparatus having a processing device,” are claimed, these are generic, well-known, and conventional data gather computing elements. As evidence that these are generic, well-known, and a conventional data gathering computing elements (or an equivalent term), as a commercially available product, or in a manner that indicates that the additional elements are sufficiently well-known, the Applicant’s specification discloses these in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a), per MPEP § 2106.07(a) III (a). As such, this satisfies the Examiner’s evidentiary burden requirement per the Berkheimer memo.
Specifically, the Applicant’s claimed “a processor,” is best described as “a processing device” in para. [0040] as follows:
“[0040] The processing device includes, for example, a central processing unit (CPU), a micro processing unit (MPU), a graphics processing unit (GPU), a field-programmable gate array (FPGA), or the like. Various functions of the teacher data editing support system 1 are implemented by the processing device reading various programs and data stored in the storage device and executing the programs.”
As such, the Applicant’s claimed “a processor,” is reasonably interpreted as either a processor or a computer which is further reasonably interpreted to be a generic, well-known, and conventional data computing element.
Likewise, the Applicant’s claimed “a memory,” is best described as “a storage device” in para. [0042] as follows:
“[0042] The storage device is a device that stores programs and data, and is, for example, a random access memory (RAM), a read only memory (ROM), or a non-volatile semiconductor memory (NVRAM).”
Again, the Applicant’s claimed “a memory,” is reasonably interpreted as a form of data storage which is further reasonably interpreted to be a generic, well-known, and conventional data computing element.
Finally, the Applicant’s claimed “an apparatus having a processing device,” is not described with any detail in the written description of the specification as originally filed. Regardless, “a calculator including a processor,” is described in para. [0104] of the Applicant’s written description as originally filed, provides the following: “[0104] FIG. 25 is a conceptual diagram illustrating a hardware configuration example of the calculator. A calculator 2500 corresponds to each of the calculators 100-1, 100-2, and 100-3 illustrated in FIG. 24. The calculator 2500 includes a processor 2501, a main storage device 2502, a sub storage device 2503, and a network interface 2504. The processor 2501 corresponds to the above-described processing device. The main storage device 2502 and the sub storage device 2503 correspond to the above-described storage device. The network interface 2504 is a device for communication with an external device or the like via the network NW illustrated in FIG. 24.” As such, this is broadly described and reasonably interpreted as either a processor or a computer which describes a generic computer component that is commonly provided in commercially available computers.
Therefore, the Applicant’s own specification discloses ubiquitous standard equipment that is (1) generic, routine, conventional, and/or commercially available; and (2) does not provide anything significantly more. Thus, Step 2B, of the subject-matter eligibility analysis is “No.”
In addition, dependent claims 2-7 do not provide a practical application and are insufficient to amount to significantly more than the judicial exception. As such, dependent claims 2-7 are also rejected under 35 U.S.C. § 101, based on their respective dependencies to claim 1. Therefore, claims 1-9 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject-matter.
Response to Arguments
The Applicant’s arguments filed on March 10, 2026 related to claims 1-9 are fully considered, but are not persuasive.
Rejections Under 35 U.S.C. § 101
The Applicant respectfully argues “Applicant respectfully submits that at least Applicant's independent claims 1 and 9, as presented herein, are directed to patent-eligible subject matter under 35 U.S.C. § 101 for at least the following reasons.
When considering claim 1 as a whole, as required, the claim sets forth an improvement to the field of machine learning by automatically adjusting training data to lessen the contributions that discriminatory factors in the training data have on a model to reduce occurrences of discriminatory decisions and unfairness made by a model trained by the adjusted training data.
Consideration of improvements is relevant to the eligibility analysis regardless of the technology of the claimed invention. That is, the consideration applies equally whether it is a computer-implemented invention, an invention in the life sciences, or any other technology. See, e.g., Rapid Litigation Management V. CellzDirect, Inc., 827 F.3d 1042, 119 USPQ2d 1370 (Fed. Cir. 2016). MPEP Section 2106.05(a) II (emphasis added).
The presently claimed invention edits teacher (i.e., training) data for machine learning in order to reduce biased or sensitive decisions by machine learning models. Because historical human activity data used for training may contain past discrimination (such as bias based on age or gender), models trained on such data risk inheriting and amplifying unfair judgments”
The Examiner respectfully disagrees. The Applicant’s argument is not commensurate with the scope of the claims. Specifically, “machine learning” is not claimed. As such, the argument is not persuasive.
The Applicant respectfully argues “The improvement is a technical solution to a technical problem, as explained in the specification. Existing fairness-improvement techniques have limitations. Some approaches assume that increasing the amount of training data improves fairness, which is not always true-especially if the added data still reflects sensitive attributes. See Para. [0006] of the pre-grant publication corresponding to the present application (US 2024/0212517). Other methods can adjust labels to improve fairness but are limited to binary classification problems and cannot handle tasks like regression. Id. To address these shortcomings, the presently claimed invention changes the correct answer in the teaching data to reduce sensitive or discriminatory determinations made by machine learning models, beyond the constraints of prior methods. That is, the presently claimed invention sets forth a computer-implemented specific technical processes under technically relevant conditions.
The improvement is also recited in the claims. For example, claim 1 recites "calculate a contribution, as an index, indicating contribution of the discriminatory factors to the correct answer, [...] and accept a designation of how much the correct answer is changed, change the correct answer in the teacher data in response to the designation, and output the changed teacher data."
In this respect, we note that this potential technical effect solves the technical problem of inherent bias and discrimination in training data and, in doing so, goes beyond the alleged abstract idea and goes beyond merely using a computer as a tool.
Further, claim 1 does not merely recite only the idea of a solution or outcome (i.e., the claim fails to recite details of how a solution to a problem is accomplished). Rather, claim 1 covers a particular solution to a problem or a particular way to achieve a desired outcome, as described above.
The MPEP also states:
In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. V. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018)). Thus, it is important for examiners to analyze the claim as a whole when determining whether the claim provides an improvement to the functioning of computers or an improvement to other technology or technical field.
Even further, the improvement set forth in claim 1 is not to the alleged abstract idea itself. Additionally, this is not a case like Alice Corp. Pty. Ltd. V. CLS Bank Int'l, 573 U.S. 208, 216, 110 USPQ2d 1976, 1980 (2014) where the computer is merely used as a tool to perform an existing process. Rather, computers/processors are necessarily integral to machine learning and adjusting training data.
Thus, Applicant's claim 1 is not directed to an abstract idea because claim 1 includes additional elements that integrate the alleged abstract idea into a practical application of the abstract idea demonstrated by a particular improvement to the field of machine learning by automatically adjusting training data to lessen the contributions that discriminatory factors in the training data have on a model to reduce occurrences of discriminatory decisions and unfairness made by a model trained by the adjusted training data. Therefore, under the Step 2A Prong Two analysis, claim 1 is not directed to an abstract idea.”
The Examiner respectfully disagrees. First, the Applicant is not providing any advancement in technology, but instead using technology to advance the abstract idea of “data editing,” as applied to teaching. Specifically, the Applicant is merely using a computer combined and other commonly available devices as a tool to carry out the abstract idea of “data editing” (see MPEP § 2106.05(f)).
Second, the Applicant’s described “improvement” is merely describing what teaching professionals do in the analog. Further, the Applicant is presenting a mankind burden and automating known tasks to alleviate the burden. As such, the argument is not persuasive.
The Applicant respectfully argues “Similar to the concepts discussed in BASCOM, Applicant's claim 1 includes additional elements that are sufficient to ensure that the claims amount to significantly more than an abstract idea.
For example, Applicant's amended clam 1 recites:
calculate a contribution, as an index, indicating contribution of the discriminatory factors to the correct answer,
display evaluation information indicating a relationship between a degree of change of the correct answer in the teacher data and a degree of deviation of the correct answer from an initial value or a discrimination degree based on the contribution, and
accept a designation of how much the correct answer is changed, change the correct answer in the teacher data in response to the designation, and output the changed teacher data.”
These elements are significant, at least because the claim includes a specific technique for improving the field of machine learning by automatically adjusting training data to lessen the contributions that discriminatory factors in the training data have on a model. Accordingly, the sum of the functions of the additional elements of Applicant's claim 1, at least when viewed as an ordered combination, are significantly more than when each is taken alone. Therefore, similar to the claims in BASCOM, Applicant's claim 1, at least as an ordered combination, includes a non-conventional and non-generic arrangement of features comprising an inventive concept. As such, Applicant's claim 1 is not directed to routine, conventional, or well-known activities. Consequently, even if Applicant's claim 1 includes an abstract idea, the claim includes additional elements that singly and as an ordered combination amount to significantly more than the mere abstract idea, and therefore, Applicant's claim 1 is patent-eligible for these reasons as well.
For at least the above reasons, the presently pending claims are patent-eligible under 35 U.S.C. § 101 and therefore the rejection of the claims under 35 U.S.C. § 101 should be withdrawn.”
The Examiner respectfully disagrees. The Applicant’s claimed “a processor,” “a memory,” and “an apparatus having a processing device,” are merely generic, well-known, and a conventional data gathering computing elements (or an equivalent term), and quite possibly a commercially available product such as a personal computer or smartphone. The Applicant is not providing any advancement in technology with regard to “a processor,” “a memory,” and “an apparatus having a processing device.” As such, the argument is not persuasive. Therefore, the rejection of claims 1-9 under 35 U.S.C. §101 is not withdrawn.
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 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 ROBERT P BULLINGTON whose telephone number is (313)446-4841. The examiner can normally be reached on Mon.-Fri. 8:00-4:00. 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, 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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/Robert P Bullington, Esq./
Primary Examiner, Art Unit 3715