CTNF 18/454,030 CTNF 102081 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Priority 02-27 AIA Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JP2022-183670 , filed on November 16, 2022 . Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Regarding Claim 1 Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03 Claim 1 is machines claim thus it falls into one of the four categories of statutory subject matter. Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II. Regarding independent claim 1, following limitations recite a judicial exception: “ selecting, based on first distribution of data included in a first data group in which a value of a first attribute is a first value among a plurality of data groups obtained by classifying a plurality of pieces of data based on an attribute, first data from a second data group in which the value of the first attribute is a second value among the plurality of data groups ” [Mental Process] – selecting one point or data from a plurality of data groups based on some kind of attribute by comparing that point or data to other data or points, which involves observation, evaluations, judgements and opinions which are capable of being performed in the human mind with the assistance of paper and pen “ generating new data in which the value of the first attribute is the second value based on the first data ” [Mental Process/Mathematical Calculation] – generating new data based on another data, which is just coming up with data by comparing and calculating based on the attributes such that the output can be just written down that involves observation, evaluations, judgements and opinions, which is capable of being performed in the human mind with the assistance of paper and pen. Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception? The claim 1 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea. Regarding Claim 2 Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03 Claim 2 is a dependent claim of 1, thus it falls within the same category of statutory subject matter. Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II. Regarding dependent claim 2, following limitations recite a judicial exception: “a number of pieces of the data in the first group is larger than a number of pieces of data in a data group in which the first attribute has the first value and the second attribute has the fourth value” [Mental Process] – decision making of a single group is bigger than the another is an act of comparing involving observation, evaluations, judgements and opinions, which is capable of being performed in the human mind with the assistance of paper and pen. Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception? The claim 2 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea. Regarding Claim 3 Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03 Claim 3 is a dependent claim of 2, thus it falls within the same category of statutory subject matter. Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II. Regarding dependent claim 3, following limitations recite a judicial exception: “ the generating includes generating the new data based on second distribution of data included in a data group in which the first attribute has the second value and the second attribute has the third value, the data group having a larger number of pieces of data than a number of pieces of data in the second data group ” [Mental Process/Mathematical Calculation] – generating new data based on the second distribution of data having some kind of attributes or values, which is just creating or coming up with data by comparing and calculating based on the attributes such that the output can be just written down that involves observation, evaluations, judgements and opinions, which is capable of being performed in the human mind with the assistance of paper and pen. Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception? The claim 3 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea. Regarding Claim 4 Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03 Claim 4 is a dependent claim of 1, thus it falls within the same category of statutory subject matter. Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II. Regarding dependent claim 4, following limitations recite a judicial exception: “ the selecting includes selecting a plurality of pieces of the first data in descending data order of a distance to the first distribution from the second data group ” [Mental Process] – selecting pieces of data in descending order by first comparing each piece of data to sort them in the order of interest then just select the one on the top of the list, which involves observation, evaluations, judgements and opinions which are capable of being performed in the human mind with the assistance of paper and pen. [Mathematical Calculations] – coming up with the distances of the pieces of the data requires mathematical computation which recites to an abstract idea. Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception? The claim 4 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea. Regarding Claim 5 Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03 Claim 5 is a dependent claim of 1, thus it falls within the same category of statutory subject matter. Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II. Regarding dependent claim 5, following limitations recite a judicial exception: “ the generating includes generating the new data of a number based on a difference between a number of pieces of data in the second data group and a number of pieces of data in a data group that has a larger number of pieces of data than the number of pieces of data in the second data group among the plurality of data groups ” [Mental Process] – generating the new data is simply creating data based on some kind of indicators by simply comparing between pieces of data, which involves observation, evaluations, judgements and opinions which are capable of being performed in the human mind with the assistance of paper and pen. [Mathematical Calculations] – the number of generating the new data is based on difference of the numbers between two groups which is simple math computation which recites to an abstract idea Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception? The claim 5 does not recite any additional elements other than abstract ideas, so it does not integrate into a practical application. Thus, this claim is directed to the abstract idea. Regarding Claim 6 Claim 6 has similar limitations of Claim 1. For the reasons described above with respect to Claim 1, its judicial exceptions are not meaningfully integrated into a practical application, or significantly more than the abstract ideas. The claim does not provide anything more than the abstract ideas of mental processes and mathematical calculations that are practically capable of being performed with the assistance of pen and paper. Therefore, Claim 6 also recite abstract ideas that do not integrate into a practical application or amount to significantly more than judicial exception, and thus are rejected under U.S.C. 101. Regarding Claim 7 Step 1 – whether the claim falls within any statutory category. See MPEP 2016.03 Claim 7 is an apparatus claim thus it falls into one of the four categories of statutory subject matter. Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II. Regarding independent claim 7, following limitations recite a judicial exception: “ selecting, based on first distribution of data included in a first data group in which a value of a first attribute is a first value among a plurality of data groups obtained by classifying a plurality of pieces of data based on an attribute, first data from a second data group in which the value of the first attribute is a second value among the plurality of data groups ” [Mental Process] – selecting one point or data from a plurality of data groups based on some kind of attribute by comparing that point or data to other data or points, which involves observation, evaluations, judgements and opinions which are capable of being performed in the human mind with the assistance of paper and pen “ generating new data in which the value of the first attribute is the second value based on the first data ” [Mental Process/Mathematical Calculation] – generating new data based on another data, which is just coming up with data by comparing and calculating based on the attributes such that the output can be just written down that involves observation, evaluations, judgements and opinions, which is capable of being performed in the human mind with the assistance of paper and pen. Step 2A Prong 2 – whether the claim recites additional elements that integrate the exception into a practical application of the exception? Regarding Claim 7, the claim recites additional elements of “ a memory ” A memory to store instructions or code-based information is at best the equivalent of merely adding the words apply it to the judicial exception (See MPEP 2106.05(f)). “ a processor coupled to the memory, the processor being configured to perform processing ” A processor to execute the instructions or code-based information is at best the equivalent of merely adding the words apply it to the judicial exception (See MPEP 2106.05(f)). [Even when viewed in combination, the additional elements do not more than automate the mental processes that a person could perform, using computer components as a tool, thus the claim as a whole does not integrate into a practical application.] Step 2B – whether the claim as a whole amount to significantly more than the judicial exception? I.e. Are there any additional elements (features/limitations/step) recited in the claim beyond the abstract idea? The claim does not provide an inventive concept (significantly more than the abstract idea). The claim is ineligible. As explained above, the additional element [1, 2] are merely computer components that are just to store and execute code-based instructions which are considered a mere instruction to apply an exception and amount to storing and receiving information in memory, which is well-understood, routine, conventional activity (See MPEP 2106.05(d), subsection II). This limitation remains a mere instruction to apply an exception even upon reconsideration. Even when considered in combination, the additional element represents a mere instruction to apply an exception, which cannot provide an inventive concept. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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. 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-15 AIA Claim s 1, 5-7 are rejected under 35 U.S.C. 102( a)(1 ) as being anticipated by Miriam at el. ( Miriam ), NPL, “ Cross-Validation for Imbalanced Datasets: Avoiding Overoptimistic and Overfitting Approaches ”, published on 15 October 2018, 18pages . As to independent Claim 1, Miriam teaches a non-transitory computer-readable recording medium storing a data generation program for causing a computer to execute processing comprising: selecting, based on first distribution of data included in a first data group in which a value of a first attribute is a first value among a plurality of data groups obtained by classifying a plurality of pieces of data based on an attribute, first data from a second data group in which the value of the first attribute is a second value among the plurality of data groups( Miriam, Pg5: subsection 11, MWMOTE, Lines7-12, "MWMOTE starts by identifying the harder-to-learn minority examples (Simin), so that each is given a selection weight (Sw), according to their distance to the nearest examples belonging to the majority class", and Lines15-20, "To generate the new synthetic samples, the complete set of minority class examples Smin is clustered into M groups. Then, a minority example x from Simin is selected according to the probability Sp", wherein the data is clustered or grouped into the majority and minority groups. Here it can be interpreted such that the majority group is the first group with first attribute having a first value and the minority group as the second group with the first attribute having a second value. Also, the data from the second group or the minority group here is selected based on the weight which is equivalent to the claimed invention); and generating new data in which the value of the first attribute is the second value based on the first data( Miriam, Pg5: subsection 11), MWMOTE, Lines25-28, "this approach is performed as many times as required, according to the necessary number N of synthetic samples to be generated for complete balance", wherein as mentioned above, the data is selected from the minority class and the minority group is always subject to the data synthesis, so generated data will have the first attribute having the second value.) As to dependent Claim 5, Miriam teaches all the limitations discussed above in Claim 1 and also teaches generating the new data of a number based on a difference between a number of pieces of data in the second data group and a number of pieces of data in a data group that has a larger number of pieces of data than the number of pieces of data in the second data group among the plurality of data groups( Miriam, Pg5: subsection 11, MWMOTE, Lines25-28, "this approach is performed as many times as required, according to the necessary number N of synthetic samples to be generated for complete balance", wherein the number N inherently indicates the difference between the number of data in the majority group/cluster and the number of data in the minority group/cluster that is subject to the data generation.) As to independent Claim 6, it is a method/process claim that contains similar limitations of Claim 1 and thus rejected under the same rationale. As to independent Claim 7, it is an apparatus claim that contains similar limitations of Claim 1 and thus rejected under the same rationale . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-23-aia AIA 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. 07-21-aia AIA Claim s 2 and 3 are rejected under 35 U.S.C. 103 as being unpatentable over Miriam , as discussed in Claim 1, in view of Salazar et al. ( Salazar) , NPL, “FAWOS: Fairness-Aware Oversampling Algorithm Based on Distribution of Sensitive Attributes”, published on May 27 2021, Pages: 10 . As to dependent Claim 2 Miriam teaches, as discussed above, all the limitation of Claim 1. Miriam, however, does not teach the following limitations, but in the same field of endeavor, Salazar teaches the non-transitory computer-readable recording medium according to claim 1, wherein a second attribute has a third value in the first data group and the second attribute has a fourth value in the second data group ( Salazar, Pg81372, Right Column, Figure1, PNG media_image1.png 572 624 media_image1.png Greyscale and Equation 1, Paragraph5, Lines5-6, “FAWOS's objective is to satisfy this condition: PNG media_image2.png 62 484 media_image2.png Greyscale ”, wherein each group has an attribute of Y and S such that Y can be treated as a first attribute and S can be treated as a second attribute which that the first group be P(Y = 1 ^ S = 1, first attribute = first value, second attribute = third value) and the second group be P(Y = 0 ^ S = 0, first attribute = second value, second attribute = fourth value), which is equivalent to the claimed invention), and a number of pieces of the data in the first data group is larger than a number of pieces of data in a data group in which the first attribute has the first value and the second attribute has the fourth value ( Salazar, Pg81373, Right Column, Subsection b German Credit Dataset, “This dataset contains 1000 credit records which consider individuals as having good or bad credit risk [8]. It has 20 features with the sensitive attributes being the gender and age. The sensitive attribute age was converted into a categorical feature by considering the value of Adult (A) (when the age is equal or more than 25 years old) and Youth (Y). This conversion was based on the work in [25] which proves that this provided the most discriminatory effects. Adult is considered to be the privileged group and Youth is considered to be the unprivileged group. In addition, the gender feature was generated from the personal status feature since it was not directly included in the dataset. In the gender feature, Male (M) is the privileged group and Female (F) the unprivileged group. Each dataset is divided into 70% and 30% for training and testing. We report the average performance results of running 10 different training-test splits. Both datasets are tested on the same test dataset” and Pg6, Table 3, PNG media_image3.png 203 405 media_image3.png Greyscale , wherein this example uses three attributes of age, gender, and credit risk which uses more attributes than the claimed invention. But if we focus on the adult group only, we can divide it into four groups as the claimed invention where [A,M,+], [A,F,-], [A,F,+], [A,M,-] (or equivalent to the equation 1 above as Y = +/- and S = M/F) are the groups from one to four listed in the claimed invention accordingly which the first group, 47, (the first attribute = the first value, the second attribute = the third value) is greater than the third group, 15, (the first attribute = the first value, the second attribute = the fourth value) which is equivalent to the claimed invention). Miriam and Salazar are analogous to the claimed invention as they are both from the same field of endeavor of oversampling to generate unbalanced. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine CBO and MWMOTE of Miriam with oversampling with multi-attributed classed data of Salazar. The motivation is as recited by Salazar (Salazar, Pg81371, Left Column, Paragraph3, Lines1-2, “FAWOS can effectively increase the fairness results while maintaining the classifier’s performance) such that conventional oversampling methods relied on binary groups of majority and minority thus it was difficult to classify data with multiple attributes, but FAWOS can effectively handle such cases. As to dependent Claim 3, Miriam teaches all the limitations discussed above in Claims 1 and 2. However, Miriam alone does not teach the following limitations but the combination of Miriam and Salazar teaches the non-transitory computer-readable recording medium according to claim 2, wherein the generating includes generating the new data based on second distribution of data included in a data group in which the first attribute has the second value and the second attribute has the third value, the data group having a larger number of pieces of data than a number of pieces of data in the second data group( Miriam, Pg5: subsection 11, MWMOTE, Lines7-12, "MWMOTE starts by identifying the harder-to-learn minority examples (Simin), so that each is given a selection weight (Sw), according to their distance to the nearest examples belonging to the majority class", and Lines15-20, "To generate the new synthetic samples, the complete set of minority class examples Smin is clustered into M groups. Then, a minority example x from Simin is selected according to the probability Sp", and Salazar, Pg81375, Right Column, Table 3, PNG media_image3.png 203 405 media_image3.png Greyscale , wherein as mentioned in Claim 2 that the group (the fourth data group, [A,M,-]), with the first attribute having second value and the second attribute having the third value, has larger number of samples, 17, compared to the second group, [A,F,-], 5, as the claimed invention. Also, as mentioned in Claim 1, the data synthesis is based on the distance to the nearest examples belonging to the majority class, where the male adult group here can be interpreted as the majority group since it has larger number of samples compared to the adult female group. Thus, it can inferred that the data generation is based on this fourth group, [A,M,-], or the second distribution, which is equivalent to the claimed invention. Miriam and Salazar are analogous to the claimed invention as they are both from the same field of endeavor of oversampling to generate unbalanced. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine CBO and MWMOTE of Miriam with oversampling with multi-attributed classed data of Salazar. The motivation is as recited by Salazar (Salazar, Pg81371, Left Column, Paragraph3, Lines1-2, “FAWOS can effectively increase the fairness results while maintaining the classifier’s performance) such that conventional oversampling methods relied on binary groups of majority and minority thus it was difficult to classify data with multiple attributes, but FAWOS can effectively handle such cases . 07-21-aia AIA Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Miriam , as discussed in Claim 1, in view of Bennin et at. ( Bennin) , NPL, “MAHAKIL: Diversity Based Oversampling Approach to Alleviate the Class Imbalance Issue in Software Defect Prediction”, published on 2017, Pages: 17 . As to dependent Claim 4, Miriam teaches, as discussed above, all the limitations in Claim 1. Miriam t eaches identifying harder to learn minority examples and assigning weights in accordance to a distance to the nearest example from a majority class distance between the groups ( Miriam, Pg5: subsection 11, MWMOTE, Lines7-12, "MWMOTE starts by identifying the harder-to-learn minority examples (Simin), so that each is given a selection weight (Sw), according to their distance to the nearest examples belonging to the majority class” and Lines15-20, "To generate the new synthetic samples, the complete set of minority class examples Smin is clustered into M groups. Then, a minority example x from Simin is selected according to the probability Sp" .) However, Miriam does not teach that the selecting pieces of data in descending order of distance to the first distribution. However, in the same field of endeavor, Bennin teaches this limitation ( Bennin, Pg537, Left Column, Paragraph1, Lines3-6, “This involves computing of the Mahalanobis distance (D2) for the minority samples and arranging the samples in a descending order according to their D2 values.”, wherein the Mahalanobis distance (D 2 ) is a measure distance to a distribution (the center of the minority class), which directly corresponds to the claimed invention. Also, Bennin sorts the samples based on this distance in descending order prior to the oversampling/generation process, which is equivalent to the claimed invention.) Miriam and Bennin are analogous to the claimed invention as both of them are from the same field of endeavor of Oversampling based on the distance of the samples. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the distance-weight based oversampling of Miriam with descending sorted distance oversampling of Bennin. The motivation to combine is as recited by Bennin (Bennin, Pg534, Abstract, Lines4-5, “they mostly result in over-generalization (high rates of false alarms) and generate near-duplicated data instances (less diverse data)” and Lines7-9, “MAHAKIL interprets two distinct sub-classes as parents and generates a new instance that inherits different traits from each parent and contributes to the diversity within the data distribution”), such that unlike conventional methods where resulted in less diverse data, MAHAKIL arranges minority samples in descending order based on their Mahalanobis distance, the method effectively identifies and prioritize atypical instances for the oversampling process. This deterministic ranking mechanism allows for the creation of more diverse and informative synthetic data. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to DONG YOON JUNG whose telephone number is (571)270-0198. The examiner can normally be reached 8am-5pm. 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, Cesar Paula can be reached at (571) 272-4128. 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. /DONG YOON JUNG/Examiner, Art Unit 2145 /CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145 Application/Control Number: 18/454,030 Page 2 Art Unit: 2145 Application/Control Number: 18/454,030 Page 3 Art Unit: 2145 Application/Control Number: 18/454,030 Page 4 Art Unit: 2145 Application/Control Number: 18/454,030 Page 5 Art Unit: 2145 Application/Control Number: 18/454,030 Page 6 Art Unit: 2145 Application/Control Number: 18/454,030 Page 7 Art Unit: 2145 Application/Control Number: 18/454,030 Page 8 Art Unit: 2145 Application/Control Number: 18/454,030 Page 9 Art Unit: 2145 Application/Control Number: 18/454,030 Page 10 Art Unit: 2145 Application/Control Number: 18/454,030 Page 11 Art Unit: 2145 Application/Control Number: 18/454,030 Page 12 Art Unit: 2145 Application/Control Number: 18/454,030 Page 13 Art Unit: 2145 Application/Control Number: 18/454,030 Page 14 Art Unit: 2145
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