DETAILED ACTION
This Office Action is sent in response to Applicant’s Communication received 1/2/2026 for application number 18/099,266.
Claims 1-17 are pending.
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 .
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 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claims 1, 11, and 17 recite:
… acquiring sets of data indicating individual attributes each of which is labeled as favorable or unfavorable; calculating a ratio of a number of sets of data labeled as favorable and a number of sets of data labeled as unfavorable with respect to each of a plurality of types determined by values of a combination of a first attribute and a second attribute that are associated with the sets of data; when a difference in the ratio that is calculated with respect to each of the plurality of types is not less than a threshold, with respect to each combination of a first type contained in the plurality of types and each of types other than the first type, based on the ratio, specifying candidate data to be changed from among a plurality of sets of data having values corresponding to the first type; based on the candidate data specified with respect to each of the combinations, selecting first data from among the plurality of sets of data; and generating training data by changing a label of the first data; and training classification device for decision-making by inputting the generated training data
(2A, prong 1) The underlined portions of the claim recite an abstract idea, specifically mathematical calculations and a mental process. Calculating a ratio of favorable to unfavorable for a plurality of types is a mathematical calculation. Furthermore, a human can mentally judge the ratio is above a threshold for a first type, choose a set of data, and create training data by changing a label.
(2A, prong 2) This judicial exception is not integrated into a practical application. The claims recite the additional elements of [a] acquiring sets of data labeled as favorable or unfavorable, [b] generic computer components in claims 1 and 17, like a non-transitory medium, processor, and memory, and [c] training a classification device by inputting the generated training data. Additional element [a] is insignificant extra-solution activity because it is mere data gathering for the abstract idea. Additional element [b] is a mere instruction to apply the exception because the limitations merely add generic computer components to the abstract idea after the fact. Additional element [c] is also a mere instruction to apply the exception because it merely states an outcome (of training a classification device) without details of how the outcome is accomplished (how the training functions or how the generated data is used to train). Even when all of the additional elements are considered together with the abstract idea, they do not integrate the abstract idea into a practical application because the elements merely add insignificant extra-solution activity and mere instructions to apply the exception to the abstract idea.
(2B) The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional element [a] of acquiring sets of labeled data is well-understood, routine, and conventional, analogous to storing and retrieving information in memory, see MPEP 2106.05(d) citing Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). Additional elements [b] and [c] are mere instructions to apply the exception, as explained above. Even when all of the additional elements are considered together in the claim as a whole, they do not amount to significantly more than the abstract idea itself because the elements merely add insignificant extra-solution activity that is well-understood, routine, and conventional and mere instructions to apply the exception to the abstract idea.
With respect to claims 2-10 and 12-16, these claims add additional calculations or mental steps to the abstract idea.
Claims 2 and 12 recite selecting the first type with the largest ratio, which a human can judge and select mentally.
Claims 3 and 13 recite changing labels for a second type, different than the first; a human can mentally change labels for two different groups.
Claims 4-6 and 14-16 recite the calculation includes calculating a fairness metric based on probability, distance, or distribution, and selecting based on the calculated fairness metric (claim 4 and 14), wherein the selection is based on the metric exceeding a threshold (claim 5 and 15) or based on addition or subtraction of the fairness metric with a threshold. The fairness metric, and adding or subtracting the metric is a mathematical calculation, and the selecting based on a fairness metric / the fairness metric relative to a threshold can performed mentally by a human.
Claim 7 recites selecting data using a fairness algorithm that corrects fairness, which is an additional mental step; a human can apply a process to select data that they judge will correct fairness.
Claim 8 recites changing a label when the order of the ratios among the types does not change (e.g. the highest ratio remains the highest), which is determination that a human can judge mentally.
Claim 9 recites the first type is either the type with the most candidates to be changed or the largest ratio, which is a determination that a human can judge mentally.
Claim 10 recites the first and second attributes are protected attributes. A human can make a mental determination if an attribute is protected (i.e. mentally determine that religion, age, sex, etc. are protected attributes).
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-5, 7-15, and 17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kamiran et al. Classifying without Discriminating (see attached NPL).
In reference to claim 1, Kamiran discloses a non-transitory computer-readable recording medium (a person having ordinary skill in the art would understand the computer-implemented method of Kamiran would be stored in a memory) having stored therein a training data generation program that causes a computer to execute a process comprising: acquiring sets of data indicating individual attributes each of which is labeled as favorable or unfavorable (data acquired, each entry comprising a plurality of attributes and good or bad class labels, table 1, page 3); calculating a ratio of a number of sets of data labeled as favorable and a number of sets of data labeled as unfavorable with respect to each of a plurality of types determined by values of a combination of a first attribute and a second attribute that are associated with the sets of data (ratio / balance of good:bad, pages 3-4, for a plurality of types of combinations of attributes like age, gender, house ownership, page 3, foreign worker status, page 5); when a difference in the ratio that is calculated (a formula calculates a number of modifications that are required:
M
=
Y
o
u
g
×
A
g
e
d
∧
G
o
o
d
-
(
A
g
e
d
×
Y
o
u
n
g
∧
G
o
o
d
)
Y
o
u
n
g
+
A
g
e
d
, pages 3-4) with respect to each of the plurality of types (the formula counts all of the types in Aged and Good / Young and Good) is not less than a threshold (when this formula indicates the number to be changed is greater than zero, then it indicates the difference in the ratio of good:bad is above the threshold), with respect to each combination of a first type contained in the plurality of types and each of types other than the first type (all of the types within Aged and Young, like Young + own, Young + rent, etc., are counted in the formula above), based on the ratio, specifying candidate data to be changed from among a plurality of sets of data having values corresponding to the first type; based on the candidate data specified with respect to each of the combinations, selecting first data from among the plurality of sets of data; and generating training data by changing a label of the first data (data is sorted by probability of “good,” then the label is flipped from good to bad for the lowest probability data item with aged attribute, and bad to good with young attribute, pages 3-4); and training classification device for decision-making on individuals by inputting the generated training data (a model can be trained using the training data, pages 5-6).
In reference to claim 2, Kamiran teaches the non-transitory computer-readable recording medium according to claim 1, wherein the specifying includes selecting, as the first type, a type with the difference in the ratio most distant from the threshold among the types (Kamiran teaches balancing the ratios of all the groups, pages 2-4, so the group with the highest or lowest ratio would be selected for balancing).
In reference to claim 3, Kamiran discloses the non-transitory computer-readable recording medium according to claim 1, wherein the specifying includes, after the first data is selected by the selecting and the label of the first data is changed by the generating, with respect to each combination of another first type different from the first type among the types and each of all other types, based on the ratio, specifying candidate data to be changed from among the sets of data with which the first attribute and the second attribute corresponding to the another first type are associated (another first type, like young, can also have its label flipped based on the ratio, pages 3-4).
In reference to claim 4, Kamiran discloses the non-transitory computer-readable recording medium according to claim 1, wherein the calculating includes calculating, as the difference in the ratio, a fairness metric that is a value based on at least one of a probability, a distance, and a distribution between the two types, and the specifying includes selecting the first type based on the fairness metric that is calculated by the calculating (metric is the difference in probability between a good and bad decision for the two types, i.e. aged and young, pages 2-4).
In reference to claim 5, Kamiran discloses the non-transitory computer-readable recording medium according to claim 4, wherein the specifying includes selecting the first type from types with the fairness metrics exceeding a threshold among the types (aged exceeds a fairness of zero, i.e. aged has a higher probability of being “good” relative to young, pages 2-4).
In reference to claim 7, Kamiran discloses the non-transitory computer-readable recording medium according to claim 1, wherein the selecting includes selecting the first data, using a fairness algorithm that corrects fairness between the two types (see Kamiran pages 1-6 generally: the Kamiran discloses an algorithm that is designed to correct fairness between two groups, like young and old, by swapping labels).
In reference to claim 8, Kamiran discloses the non-transitory computer-readable recording medium according to claim 1, wherein the generating includes changing label of the first data when an order in the ratio among the types does not change even when the label of the first data that is selected by the selecting is changed (ratio / balance of good:bad, pages 3-4, is mean to be maintained, so the aged group’s ratio of good:bad would not fall below the young group’s ratio).
In reference to claim 9, Kamiran teaches the non-transitory computer-readable recording medium according to claim 1, wherein the specifying includes, when there are a plurality of types with the differences in the ratio most distant from the threshold among the types, regarding a type with the largest number of candidates to be changed or the largest ratio as the first type (Kamiran teaches balancing the ratios of all the groups, pages 2-4, so the group with the largest would be selected for balancing).
In reference to claim 10, Kamiran discloses the non-transitory computer-readable recording medium according to claim 1, wherein both the first attribute and the second attribute are protected attributes (age and foreign worker status are protected attributes, page 5).
In reference to claim 11, this claim is directed to a method associated with the non-transitory medium claimed in claim 1 and is therefore rejected under a similar rationale.
In reference to claim 12, this claim is directed to a method associated with the non-transitory medium claimed in claim 2 and is therefore rejected under a similar rationale.
In reference to claim 13, this claim is directed to a method associated with the non-transitory medium claimed in claim 3 and is therefore rejected under a similar rationale.
In reference to claim 14, this claim is directed to a method associated with the non-transitory medium claimed in claim 4 and is therefore rejected under a similar rationale.
In reference to claim 15, this claim is directed to a method associated with the non-transitory medium claimed in claim 5 and is therefore rejected under a similar rationale.
In reference to claim 17, this claim is directed to an apparatus associated with the non-transitory medium claimed in claim 1 and is therefore rejected under a similar rationale.
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) 6 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kamiran et al. Classifying without Discriminating (see attached NPL) as applied to claims 4 and 14 above, and further in view of Luong et al., k-NN as an Implementation of Situation Testing for Discrimination Discovery and Prevention (see attached NPL).
In reference to claim 6, Kamiran does not explicitly teach the non-transitory computer-readable recording medium according to claim 4, wherein the specifying includes selecting the first type based on a result of making an addition or a subtraction of subtotals of excesses of the fairness metrics with respect to thresholds that are set for the first attribute and the second attribute, respectively.
Luong teaches the non-transitory computer-readable recording medium according to claim 4, wherein the specifying includes selecting the first type based on a result of making an addition or a subtraction of subtotals of excesses of the fairness metrics with respect to thresholds that are set for the first attribute and the second attribute, respectively (distance between a plurality of attributes is determined headings 4.1 – 4.3 on pages 504-508, and they are compared to a threshold, heading 5, page 508-509).
It would have been obvious to one of ordinary skill in art, having the teachings of Kamiran and Luong before the earliest effective filing date, to modify the fairness metric as disclosed by Kamiran to include the subtotals as taught by Luong.
One of ordinary skill in the art would have been motivated to modify the fairness metric of Kamiran to include the subtotals of Luong because it helps provide better discrimination prevention (Luong, heading 2.1, page 503).
In reference to claim 16, this claim is directed to a method associated with the non-transitory medium claimed in claim 6 and is therefore rejected under a similar rationale.
Response to Arguments
Applicant's arguments filed 1/2/2026 have been fully considered but they are not persuasive.
First, with respect to the 101 rejection, Applicant argues that the independent claims are directed to patent-eligible subject matter because they are integrated into a practical application and solve a technical problem in machine learning of improved bias correction in training data. “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements.” See MPEP § 2106.05(a). Here, the entirety of the technical improvement of correcting bias in training data is provided solely by the abstract idea: the additional elements in the claim of acquiring data, training a classification device, and generic computer components are not sufficient, even in combination with the recited abstract idea, to integrate the abstract idea into a practical application or provide a technical improvement.
Applicant compares the claims in this case to Claim 3 of example 47 of the Office’s July 2024 Subject Matter Eligibility Examples. However, the abstract idea recited in claim 3 of example 47 was integrated into a practical application at step 2A, prong 2 through the additional elements of (d) detecting a source address of malicious packets, (e) dropping the packets, and (f) blocking the address. The claims in the instant application do not recite comparable additional elements. Instead, the claims here are analogous to claim 2 of example 47: in that example, the additional elements of (a) receiving training data, (f) outputting anomaly data from a trained ANN, and general recitations of steps being performed by a computer were not sufficient to integrate the abstract idea into a practical application or amount to significantly more than the abstract idea itself.
Next, with respect to the 102 rejection of the independent claims, Applicant argues that Kamiran does not teach, “specifying candidate data is specified by exhaustively comparing a ‘first type’ against ‘each of types other than the first type’ within a multi-dimensional group of types determined by combinations of attributes,” or “pairwise aggregation,” (Applicant Arguments of 1/2/2026, page 9). The Examiner respectfully disagrees. Here, the claims recite:
calculating a ratio of a number of sets of data labeled as favorable and a number of sets of data labeled as unfavorable with respect to each of a plurality of types determined by values of a combination of a first attribute and a second attribute that are associated with the sets of data; when a difference in the ratio that is calculated with respect to each of the plurality of types is not less than a threshold, with respect to each combination of a first type contained in the plurality of types and each of types other than the first type, based on the ratio, specifying candidate data to be changed from among a plurality of sets of data having values corresponding to the first type
The claims recite “a ratio” of favorable to unfavorable is calculated for “a plurality of types,” the “types” each being the “values of a combination of a first attribute and a second attribute.” (The claims do not require that the plurality of types comprise every possible combination of values for the first and second attribute) Next, the claims recite if “the ratio” is not less than a threshold – the threshold being “with respect to each combination” of the types – data to be changed is specified. In other words, the ratio of favorable to unfavorable for a first type is compared to a threshold that is based on a comparison of the first type to each of the other types. Kamiran discloses calculating a ratio of good:bad for a plurality of types, the types being based on different combinations of attributes like age, foreign worker status, house ownership, etc. (see Table II, page 3, page 5). Then, it uses a formula to determine if a difference the ratio of good:bad between the aged types and young types is above a threshold, the formula being based on a count of all the types, such that the good / bad labels for the two types should be switched to debias the data. The Examiner notes that the disclosed invention operates differently (specifically figs. 3- 10 and the corresponding description), however, although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
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 Andrew T. Chiusano whose telephone number is (571)272-5231. The examiner can normally be reached M-F, 10am-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, Tamara Kyle can be reached at 571-272-4241. 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.
/ANDREW T CHIUSANO/Primary Examiner, Art Unit 2144
Prosecution Insights