DETAILED ACTION
Claims 1-6 are pending. Claims 1-6 are considered in this Office 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 .
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on 2/13/2025 and 10/6/2025 have been acknowledged.
The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. The initialed and dated copies of Applicant’s IDS form 1449 is attached to the instant Office action.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claim 1 of the current application (Hereby known as ‘570) is provisionally rejected on the grounds of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent Application No. 19/207,006 (Hereby known as ‘006). Although the claims at issue are not identical, they are not patentably distinct from each other because:
Regarding Claim 1, Claim 1 of the current application (‘570) recites substantially similar steps of '006 – Claim 1.
Claim 1 of ‘570 recites the steps of:
classifying a plurality of pieces of data into a plurality of groups, based on a first attribute of a plurality of attributes included in each of the plurality of pieces of data;
comparing a positive example rate of data included in a first group of the plurality of groups with a positive example rate of specific data in which a value of a second attribute of the plurality of attributes in data included in a second group of the plurality of groups is included in a range according to a distribution of the value of the second attribute of the data included in the first group; and
executing bias evaluation on data based on comparison between other groups other than the second group of the plurality of groups and the first group, based on a result of the comparing processing.
Whereas Claim 1 of ‘006 states:
classifying a plurality of items of data into a plurality of groups based on a first attribute of a plurality of attributes included in each item of the plurality of items of data;
identifying, from among the plurality of groups, a second group having a lower positive example ratio of data included than a positive example ratio of data included in a first group of the plurality of groups; and
executing data bias evaluation based on comparison of the first group with another group, not including the second group, among the plurality of groups.
These are obvious variants of each other as both recite substantially the same limitations.
Thus, Claim 1 of the current application is an obvious variant of claim 1 in ‘006.
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.
Alice – Claims 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 5, and 6 recite the limitations to classifying a plurality of pieces of data into a plurality of groups, based on a first attribute of a plurality of attributes included in each of the plurality of pieces of data (Collecting and Analyzing the Information, an Observation and Evaluation, a Mental Process; a Fundamental Economic Process, i.e. analyzing data for bias, a Certain Method of Organizing Human Activity),comparing a positive example rate of data included in a first group of the plurality of groups with a positive example rate of specific data in which a value of a second attribute of the plurality of attributes in data included in a second group of the plurality of groups is included in a range according to a distribution of the value of the second attribute of the data included in the first group (Analyzing the Information, an Evaluation, a Mental Process; a Fundamental Economic Process, i.e. analyzing data for bias, a Certain Method of Organizing Human Activity),and executing bias evaluation on data based on comparison between other groups other than the second group of the plurality of groups and the first group, based on a result of the comparing processing (Analyzing the Information, an Evaluation, a Mental Process; a Fundamental Economic Process, i.e. analyzing data for bias, a Certain Method of Organizing Human Activity), which under their broadest reasonable interpretation, covers performance of the limitation in the mind for the purposes of evaluating data using a comparison, but for the recitation of generic computer components. That is, other than reciting a computer-readable medium, computer, apparatus, memory, and processor, nothing in the claim element precludes the step from practically being performed or read into the mind for the purposes of evaluating data for bias, which is a Fundamental Economic Process, a Certain Method of Organizing Human Activity. For example, executing bias evaluation on data based on comparison between other groups encompasses a data analyst or statistician performing bias evaluation on data, an observation, evaluation, and judgment. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas, an observation, evaluation, and judgment. Further, as described above, the claims recite limitations for a Fundamental Economic Process, a “Certain Method of Organizing Human Activity”. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the above stated additional elements to perform the abstract limitations as above. The computer, apparatus, memory, processor, and computer-readable medium are recited at a high-level of generality (i.e., as a generic software/module performing a generic computer function of storing, retrieving, sending, and processing data) such that they amount to no more than mere instructions to apply the exception using generic computer components. Even if taken as an additional element, the collecting above are at best insignificant extra-solution activity as these are receiving, storing, and transmitting data as per the MPEP 2106.05(d). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, when considered both individually and as an ordered combination. As discussed above with respect to integration of the abstract idea into a practical application, the additional element being used to perform the abstract limitations stated above amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible. Applicant’s Specification states:
“[[0107] For example, as illustrated in FIG. 8, the bias evaluation device 1 includes a Central Processing Unit (CPU) 91, a memory 92, a hard disk 93, and a network interface 94. The CPU 91 is coupled to the memory 92, the hard disk 93, and the network interface 94 via a bus.”
Which shows that system has these standard and generic pieces, but has no description of any computer or system other than this, and thus any generic computing device can be used to perform the abstract limitations, such as a laptop, phone, desktop, etc., and from this interpretation, one would reasonably deduce the aforementioned steps are all functions that can be done on generic components, and thus application of an abstract idea on a generic computer, as per the Alice decision and not requiring further analysis under Berkheimer, but for edification the Applicant’s specification has been used as above satisfying any such requirement. This is “Applying It” by utilizing current technologies. For the collecting steps that were considered extra-solution activity in Step 2A above, if they were to be considered additional elements, they have been re-evaluated in Step 2B and determined to be well-understood, routine, conventional, activity in the field. The background does not provide any indication that the additional elements, such as the system, memory, apparatus, etc., nor the collecting steps as above, are anything other than a generic, and the MPEP Section 2106.05(d) indicates that mere collection or receipt, storing, or transmission of data is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). For these reasons, there is no inventive concept. The claim is not patent eligible.
Claims 2-4 contain the identified abstract ideas, further narrowing them, with no new additional elements to be considered as part of a practical application or under prong 2 of the Alice analysis of the MPEP, thus not integrated into a practical application, nor are they significantly more for the same reasons and rationale as above.
After considering all claim elements, both individually and in combination, Examiner has determined that the claims are directed to the above abstract ideas and do not amount to significantly more. Therefore, the claims and dependent claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. v. CLS Bank International, No. 13–298.
Claim Rejections - 35 USC § 103
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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1-6 are rejected under 35 U.S.C. 103 as being unpatentable over Bias (NPL – One vs. One Mitigation of Intersectional Bias) in view of Yadagiri (U.S. Publication No. 2023/039,3960).
Regarding Claims 1, 5, and 6, Bias, Non-Patent Literature for One vs. One Mitigation of Intersectional Bias, teaches a bias evaluation method implemented by a computer (Abstract - method uses machine learning models for diverse decision making where fairness-aware machine learning is used to mitigate the bias in training data) comprising:
classifying a plurality of pieces of data into a plurality of groups, based on a first attribute of a plurality of attributes included in each of the plurality of pieces of data (Bias p.3 col. 3 - page 3, 3. Preliminaries: we assume that the sensitive attributes S and non-sensitive attributes A have multiple attributes (e.g., race, gender, age) and polyvalent attributes (e.g., race, those that have more than two values such as Caucasoid, Negroid and Mongoloid). For example, if there are only two sensitive attributes (race and gender), it can be expressed as follows: Sʳᵃᶜᵉ= {Caucasoid, Negroid, Mongoloid}, Sgᵉⁿᵈᵉʳ ={male, female}, S € {(Caucasoid, male), (Negroid, male), (Mongoloid, male), (Caucasoid,female), (Negroid, female), (Mongoloid, female)}. We also define S as a set of subgroups. This means that each one of the instances of the database is classified in one of the subgroups. This classification is based on a first attribute, for example the race)
comparing a positive example rate of data included in a first group of the plurality of groups with a positive example rate of specific data in which a value of a second attribute of the plurality of attributes in data included in a second group of the plurality of groups is included in a range according to a distribution of the value of the second attribute of the data included in the first group (P. 3 col 3 - page 3, 3. Preliminaries: a favorable class (e.g., accepted in loan applications) is considered as a positive class and an unfavorable class as a negative class (multiple groups); page 3, 3. Preliminaries, Definition 3.1 (Concept of subgroup fairness criteria): p(X, s)=p(X). While p(X,s) calculates specific metrics for specified subgroup S, p(X) calculates the concerned metrics for the dataset as a whole, i.e., all instances. For example, in a decision on a loan application, when p indicates the acceptance rate, if the acceptance rate for all customers is 50%, p(X)=0.5. In this example, when there are three subgroups, a, b, and C on the sensitive attributes of the customers,and these three subgroups have the different acceptance rates: p(X,a)=0.3, p(X,b)=0.5, and p(X,c)=0.6 respectively, this situation is unfair. In contrast, if all three subgroups, have the same acceptance rates: (p(X,a)=0.5, p(X,b)=0.5, and p(X,c)=0.5, it is fair because p(X)=p(X,a),=p(X,b), and =p(X,c) are satisfied. The acceptance rate of (Negroid, female), considered to be "a positive example rate of data included in a first group", is compared with the acceptance rate of (Caucasoid, male), considered to be "a positive example rate of specific data". A second attribute is considered to be the gender – which shows a comparison of a positive and negative example); and
executing bias evaluation on data based on comparison between other groups other than the second group of the plurality of groups and the first group, based on a result of the comparing processing (Bias p. 3 col 3 - three subgroups, a, b, and C on the sensitive attributes of the customers, and these three subgroups have the different acceptance rates: p(X,a)=0.3, p(X,b)=0.5, and p(X,c)=0.6 respectively, this situation is unfair. In contrast, if all three subgroups, have the same acceptance rates: (p(X,a)=0.5, p(X,b)=0.5, and p(X,c)=0.5, it is fair because p(X)=p(X,a),=p(X,b), and =p(X,c) are satisfied; page 3, 3. For example, if there are only two sensitive attributes (race and gender), it can be expressed as follows: S={Caucasoid, Negroid, Mongoloid}, S={male, female}, S € {(Caucasoid, male), (Negroid, male), (Mongoloid, male), (Caucasoid, female), (Negroid, female), (Mongoloid, female)}. We also define S as a set of subgroups. The bias evaluation is based on comparison between other groups other than the second and the first group: the first group (Negroid, female) and the second group (Caucasoid, male), can be compared to the other groups (Negroid, male), (Mongoloid, male), (Caucasoid, female), or (Mongoloid, female)).
Although Bias teaches a first attribute as above, it does not explicitly state a second attribute included in the distribution group or in the data group of the first group, nor does it explicitly cite the computer, apparatus, processor, medium.
Yadagiri, a system and method for reducing bias in machine learning models utilizing a fairness deviation constraint and decision matrix, teaches multiple data attributes, including ones which help to determine fairness of the bias as in [0021] and [0024].
Yadagiri also teaches an apparatus with a processor and memory a non-transitory computer-readable recording medium storing a bias evaluation program for causing a computer to execute processing ([0109] system/apparatus which is a computer with a medium and [0135] memory on a computer)
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the determination of bias in groups and subgroups of the NPL of Bias with the system and method to reduce bias using multiple data attributes of Yadagiri, as they are both analogous art along with the claimed invention which all teach solutions to correction of data bias, and the combination would lead to an improved system which would improve accuracy of the system and reduce bias of the data as taught in [0027] of Yadagiri.
Regarding Claim 2, Bias teaches the comparing processing (As in Claim 1 above) includes processing of
calculating a first fairness score, based on the positive example rate of the specific data included in the second group and the positive example rate of the data in which the second attribute is included in the range, in the data included in the first group (2.2 a fairness metric/score is calculated for multiple attributes and this is positive as in Claim 1 above),
calculating a second fairness score, based on a positive example rate of all data included in the first group and a positive example rate of all data included in the second group (2.2 multiple metrics/scores of fairness for the attributes is calculated in this section), and
comparing the first fairness score and the second fairness score (2.2 these metrics are compared as in section 3 as in Claim 1 above).
Regarding Claim 3, Bias teaches wherein
a plurality of types of attributes is included in the second attribute (in 3.3, groups and subgroups are taught where attributes are in multiple groups)
the comparing processing is executed in each of the plurality of pieces of processing (3.3 these groups and subgroups are compared and deemed valid or invalid as in Claim 1 above),
And although Bias teaches processing of the bias evaluation is executed and integrating of results as in section 3.3 and 4 where there is an integration of methods and attributes, as well as results such as valid or invalid, it does not explicitly state a unified result.
Yadagiri teaches a unified result as in [0087] where multiple fairness attributes of different types and thresholds are used.
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the determination of bias in groups and subgroups of the NPL of Bias with the system and method to reduce bias using multiple data attributes of Yadagiri, as they are both analogous art along with the claimed invention which all teach solutions to correction of data bias, and the combination would lead to an improved system which would improve accuracy of the system and reduce bias of the data as taught in [0027] of Yadagiri.
Regarding Claim 4, Bias teaches the plurality of pieces of data, based on a result of the bias evaluation as in Claims 1 and 3 above, but does not explicitly state a correction.
Yadagiri teaches a correction from the fairness evaluation as in [0051] where there is a correction of unfair results.
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the determination of bias in groups and subgroups of the NPL of Bias with the system and method to reduce bias using multiple data attributes of Yadagiri, as they are both analogous art along with the claimed invention which all teach solutions to correction of data bias, and the combination would lead to an improved system which would improve accuracy of the system and reduce bias of the data as taught in [0027] of Yadagiri.
Conclusion
The prior art made of record is considered pertinent to applicant's disclosure.
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/JOSEPH M WAESCO/Primary Examiner, Art Unit 3625B 6/21/2026
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