CTNF 18/561,357 CTNF 94458 DETAILED ACTION 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. PCT/JP2021/020115 , filed on 05/27/2021 . Information Disclosure Statement The information disclosure statement (IDS) submitted on 02/09/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. 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 1-15 rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. Step 1 According to the first part of the analysis, in the instant case, claims 1-13 are directed to an apparatus comprising at least a processor, claim 14 is directed to a method, and claim 15 is directed to a computer-readable non-transitory medium. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). Claim 1 recites: Step 2A, Prong 1 “ a selection process of selecting, from the plurality of training instances, two or more training instances each of which derives one or more uncertain prediction results obtained using one or more machine learning models that output prediction results while using instances as input ” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can select training instances that have uncertain prediction results with the help of pen and paper. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). “ a generation process of generating a synthetic instance by combining the two or more training instances which have been selected by the selection process. ” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can generate a synthetic training sample by adding two other training samples together with the help of pen and paper. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). Step 2A, Prong 2 “ An information processing apparatus, comprising at least one processor, the at least one processor carrying out” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) “ an acquisition process of acquiring a plurality of training instances ” (insignificant extra-solution activity) This judicial exception is not integrated into a practical application. Step 2B “ An information processing apparatus, comprising at least one processor, the at least one processor carrying out” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) “ an acquisition process of acquiring a plurality of training instances ” (This step appears to be directed to transmitting or receiving information, which is well-understood, routine, and conventional. i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); See MPEP 2106.05 (d) (II).) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 2 recites: Step 2A, Prong 1 Claim 2 recites at least the abstract idea identified above in claim 1. Step 2A, Prong 2 “wherein: the at least one processor further carries out a training process of training at least one of or all of the one or more machine learning models using at least one of or all of the plurality of training instances.” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) This judicial exception is not integrated into a practical application. Step 2B “wherein: the at least one processor further carries out a training process of training at least one of or all of the one or more machine learning models using at least one of or all of the plurality of training instances.” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 3 recites: Step 2A, Prong 1 “ wherein: the two or more training instances which are selected by the at least one processor in the selection means process include a training instance that derives variation in a plurality of prediction results obtained using a plurality of machine learning models ” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can select training instances that derive uncertain predictions from ML models. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). Step 2A, Prong 2 “…by the at least one processor…” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) This judicial exception is not integrated into a practical application. Step 2B “…by the at least one processor…” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 4 recites: Step 2A, Prong 1 “ the two or more training instances selected by the at least one processor in the selection process include a training instance that is present near a decision boundary in a feature quantity space of at least one machine learning model ” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can select training instances near a decision boundary. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). “ in the selection process, the at least one processor selects, from the plurality of training instances, training instances which are respectively included in a plurality of spaces partitioned by the decision boundary in the feature quantity space ” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can select training instances in a feature quantity space. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). Step 2A, Prong 2 “…by the at least one processor…” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) This judicial exception is not integrated into a practical application. Step 2B “…by the at least one processor…” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 5 recites: Step 2A, Prong 1 “the at least one processor adds the synthetic instance to the plurality of training instances, and carries out the acquisition process, the training process, the selection process, and the generation process again” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can add a synthetic sample to a training set and repeat the process. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). Step 2A, Prong 2 “…the at least one processor…” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) This judicial exception is not integrated into a practical application. Step 2B “…the at least one processor…” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 6 recites: Step 2A, Prong 1 “in the generation process, the at least one processor generates a plurality of synthetic instances, and integrates, into a single synthetic instance, two synthetic instances that satisfy a similarity condition among the plurality of synthetic instances .” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can combine two synthetic instances based on their similarity. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). Step 2A, Prong 2 “…the at least one processor…” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) This judicial exception is not integrated into a practical application. Step 2B “…the at least one processor…” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 7 recites: Step 2A, Prong 1 “ in the generation means process, the at least one processor outputs , among synthetic instances, one or more synthetic instances each of which derives one or more uncertain prediction results that are obtained using the one or more machine learning models which have been trained the training process.” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can determine a synthetic instance that has an uncertain prediction. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). Step 2A, Prong 2 “…the at least one processor…” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) This judicial exception is not integrated into a practical application. Step 2B “…the at least one processor…” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 8 recites: Step 2A, Prong 1 Claim 8 recites at least the abstract idea identified above in claim 1. Step 2A, Prong 2 “ the one or more machine learning models include a machine learning model to be trained using the synthetic instance ” (linking judicial exception to a field of use. See MPEP 2106.05(h).) This judicial exception is not integrated into a practical application. Step 2B “ the one or more machine learning models include a machine learning model to be trained using the synthetic instance ” (linking judicial exception to a field of use. See MPEP 2106.05(h).) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 9 recites: Step 2A, Prong 1 “ in the selection process, the at least one processor selects, from the plurality of training instances, two or more training instances each of which derives a plurality of uncertain prediction results that are obtained using a plurality of machine learning models” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can select a instance based on the prediction result being uncertain. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). Step 2A, Prong 2 “…the at least one processor…” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) “ at least two of the plurality of machine learning models use machine learning algorithms which are different from each other ” (linking judicial exception to a field of use. See MPEP 2106.05(h).) This judicial exception is not integrated into a practical application. Step 2B “…the at least one processor…” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) “ at least two of the plurality of machine learning models use machine learning algorithms which are different from each other ” (linking judicial exception to a field of use. See MPEP 2106.05(h).) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 10 recites: Step 2A, Prong 1 “ in the selection process, the at least one processor selects, from the plurality of training instances, two or more training instances each of which derives a plurality of uncertain prediction results that are obtained using a plurality of machine learning models” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can select a instance based on the prediction result being uncertain. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). Step 2A, Prong 2 “…the at least one processor…” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) “ the plurality of machine learning models use a single machine learning algorithm ” (linking judicial exception to a field of use. See MPEP 2106.05(h).) This judicial exception is not integrated into a practical application. Step 2B “…the at least one processor…” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) “ the plurality of machine learning models use a single machine learning algorithm ” (linking judicial exception to a field of use. See MPEP 2106.05(h).) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 11 recites: Step 2A, Prong 1 Claim 11 recites at least the abstract idea identified above in claim 1. Step 2A, Prong 2 “ at least one machine learning model to be trained is a decision tree ” (linking judicial exception to a field of use. See MPEP 2106.05(h).) This judicial exception is not integrated into a practical application. Step 2B “ at least one machine learning model to be trained is a decision tree ” (linking judicial exception to a field of use. See MPEP 2106.05(h).) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 12 recites: Step 2A, Prong 1 “the at least one processor further carries out a label assignment process of assigning a label to each of at least one of or all of the plurality of training instances and the synthetic instance.” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can assign a label to a training instance. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). Step 2A, Prong 2 “…the at least one processor…” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) This judicial exception is not integrated into a practical application. Step 2B “…the at least one processor…” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 13 recites: Step 2A, Prong 1 “ in the generation process, the at least one processor generates a plurality of synthetic instances by repeatedly carrying out a process of selecting a first generation process and a second generation process based on a predetermined condition, and carrying out a process which has been selected to generate a synthetic instance, the first generation process being a process of combining a plurality of training instances selected in the selection process, and the second generation process being a process of extracting at least one training instance from the plurality of training instances selected in the selection process, and combining the at least one training instance which has been extracted and a training instance which is present, in a feature quantity space, near the at least one training instance which has been extracted” (This step is a recitation of a mental process that is practical to perform in the human mind. A human can select a training instance and generate a synthetic instance by combining the selected instances. (i.e., observation, evaluation, judgement, opinion). See MPEP § 2106.04(a)(2), subsection III.). Step 2A, Prong 2 “…the at least one processor…” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) This judicial exception is not integrated into a practical application. Step 2B “…the at least one processor…” (mere instructions to apply the exception using a generic computer component. See 2106.05(f).) The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Claim 14 recites: Step 2A, Prong 1 & Step 2A, Prong 2 & 2B See rejection of claim 1. Same rationale applies Claim 15 recites: See rejection of claim 1. Same rationale applies. Step 2A, Prong 2 & 2B The claim recites additional elements (“computer-readable non-transitory storage medium storing a program for causing a computer to function as an information processing apparatus”). (Mere instructions to apply the exception using a generic computer component. See 2106.05(f).) This judicial exception is not integrated into a practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. 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 – 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-03-aia AIA Claims 1-5 and 7-15 are r ejected under 35 U.S.C. 102(a)(2) as being a nticipated b y B adjatiya et al. (US-20210117718-A1). R egarding Claim 1 , Badjatiya (US 20210117718 A1) teaches an information processing apparatus, comprising at least one processor, the at least one processor carrying out: an acquisition process of acquiring a plurality of training instances ( para [0013] “The initial training data set includes multiple tuples, each tuple being a training instance (also referred to as training data) and a corresponding training label.” ); a selection process of selecting, from the plurality of training instances, two or more training instances each of which derives one or more uncertain prediction results obtained using one or more machine learning models that output prediction results while using instances as input ( para [0013] “Two training instances are selected from the training data set based on the entropy values for the selected training instances.” para [0021] “Additionally or alternatively, training instances can be sampled based on a strategy favoring sampling of instances with higher entropies.” Para [0039] “The term “entropy” refers to the lack of predictability in a training label or corresponding training instance. A higher entropy indicates that the data classification system is more uncertain about which class to assign the training instance to than a lower entropy indicates.” ); and a generation process of generating a synthetic instance by combining the two or more training instances which have been selected in the selection process ( para [0027] “The synthetic training instance is generated by combining the sampled instances based on the combination ratio.” ) . Regarding Claim 2 , Badjatiya teaches the information processing apparatus according to claim 1, wherein: the at least one processor further carries out a training process of training at least one of or all of the one or more machine learning models using at least one of or all of the plurality of training instances ( para [0014] “More specifically, a training system trains a data classification system by applying a training data set to the classification system and adjusting weights within the classification system based on the training data set. The training data set includes training data in the form of tuples, each tuple including a training instance (the training data to be classified) and a corresponding training label. The training label refers to a probability vector indicating, for each of multiple classes, the probability that the training data is in that class.” ). Regarding Claim 3 , Badjatiya teaches the information processing apparatus according to claim 1, wherein: the two or more training instances which are selected by the at least one processor in the selection process include a training instance that derives variation in a plurality of prediction results obtained using a plurality of machine learning models ( para [0046] “For example, the classification system 108 can employ one or more convolutional neural networks (CNNs).” Plurality of ML models. para [0060] “The entropy determination module 202 generates the entropy 214 for each label 212 and corresponding training instance. The entropy for a label 212 or the corresponding training instance refers to the lack of predictability in the corresponding training instance. A higher entropy indicates that the classification system 108 is more uncertain about which class to assign the corresponding training instance to than a lower entropy indicates.” ). Regarding Claim 4 , Badjatiya teaches the information processing apparatus according to claim 1, wherein: the two or more training instances selected by the at least one processor in the selection process include a training instance that is present near a decision boundary in a feature quantity space of at least one machine learning model ( para [0067] “A decision boundary 308 is also illustrated—the further an entropy is from the decision boundary 308 the lower the entropy (and thus the lower uncertainty that the classification system 108 has about which class to assign the corresponding data to).” para [0072] “Returning to FIG. 3, an example 310 illustrates two classes as portions 304 and 306, a decision boundary 308, and the entropies of multiple instances in each class are illustrated as circles, analogous to example 302 discussed above. The example 310 differs from the example 302 in that the two instances that are sampled, one from each class, have high entropy.” Training instances with higher entropy are closer to the decision border. ); and in the selection process, the at least one processor selects, from the plurality of training instances, training instances which are respectively included in a plurality of spaces partitioned by the decision boundary in the feature quantity space ( para [0072] “Returning to FIG. 3, an example 310 illustrates two classes as portions 304 and 306, a decision boundary 308, and the entropies of multiple instances in each class are illustrated as circles, analogous to example 302 discussed above.” Classes (i.e., spaces). ). Regarding Claim 5 , Badjatiya teaches the information processing apparatus according to claim 2, wherein: the at least one processor adds the synthetic instance to the plurality of training instances ( para [0086] “The synthetic training data 120 is added to the training data set 114, so the training data set 114 becomes an augmented training data set. The training system 106 re-trains 210 the classification system 108 by applying the training data set 114 to the classification system 108 and adjusting weights within the classification system 108 based on the training data set 114.” ), and carries out the acquisition process, the training process, the selection process, and the generation process again ( para [0028] “A tuple including the synthetic training instance and the corresponding synthetic training data label is added to a synthetic training data set. This process is repeated for multiple additional training labels and corresponding training instances, resulting in multiple synthetic training instances being generated and multiple tuples being added to the synthetic training data set.” ). Regarding Claim 7 , Badjatiya teaches the information processing apparatus according to claim 2, wherein: in the generation process, the at least one processor outputs, among synthetic instances, one or more synthetic instances each of which derives one or more uncertain prediction results that are obtained using the one or more machine learning models which have been trained the training process ( para [0060] “The entropy determination module 202 generates the entropy 214 for each label 212 and corresponding training instance. The entropy for a label 212 or the corresponding training instance refers to the lack of predictability in the corresponding training instance. A higher entropy indicates that the classification system 108 is more uncertain about which class to assign the corresponding training instance to than a lower entropy indicates.” ) . Regarding Claim 8 , Badjatiya teaches the information processing apparatus according to claim 1, wherein: the one or more machine learning models include a machine learning model to be trained using the synthetic instance ( para [0086] “The synthetic training data 120 is added to the training data set 114, so the training data set 114 becomes an augmented training data set. The training system 106 re-trains 210 the classification system 108 by applying the training data set 114 to the classification system 108 and adjusting weights within the classification system 108 based on the training data set 114.” ). Regarding Claim 9 , Badjatiya teaches the information processing apparatus according to claim 1, wherein: in the selection process, the at least one processor selects, from the plurality of training instances, two or more training instances each of which derives a plurality of uncertain prediction results that are obtained using a plurality of machine learning models ( para [0060] “The entropy determination module 202 generates the entropy 214 for each label 212 and corresponding training instance. The entropy for a label 212 or the corresponding training instance refers to the lack of predictability in the corresponding training instance. A higher entropy indicates that the classification system 108 is more uncertain about which class to assign the corresponding training instance to than a lower entropy indicates.” ); and at least two of the plurality of machine learning models use machine learning algorithms which are different from each other ( para [0045] “In particular, machine learning systems can include a system that utilizes algorithms to learn from, and make predictions on, known data by analyzing the known data to learn to generate outputs that reflect patterns and attributes of the known data. For instance, a machine learning system can include decision trees, support vector machines, linear regression, logistic regression, Bayesian networks, random forest learning, dimensionality reduction algorithms, boosting algorithms, artificial neural networks, deep learning, and so forth.” ). Regarding Claim 10 , Badjatiya teaches the information processing apparatus according to claim 1, wherein: in the selection process, the at least one processor selects, from the plurality of training instances, two or more training instances each of which derives a plurality of uncertain prediction results that are obtained using a plurality of machine learning models ( para [0060] “The entropy determination module 202 generates the entropy 214 for each label 212 and corresponding training instance. The entropy for a label 212 or the corresponding training instance refers to the lack of predictability in the corresponding training instance. A higher entropy indicates that the classification system 108 is more uncertain about which class to assign the corresponding training instance to than a lower entropy indicates.” ); and the plurality of machine learning models use a single machine learning algorithm ( para [0046] “For example, the classification system 108 can employ one or more convolutional neural networks (CNNs).” ). Regarding Claim 11 , Badjatiya teaches the information processing apparatus according to claim 8, wherein: at least one machine learning model to be trained is a decision tree ( para [0045] “For instance, a machine learning system can include decision trees, support vector machines, linear regression, logistic regression, Bayesian networks, random forest learning, dimensionality reduction algorithms, boosting algorithms, artificial neural networks, deep learning, and so forth.” ). Regarding Claim 12 , Badjatiya teaches the information processing apparatus according to claim 1, wherein: the at least one processor further carries out a label assignment process of assigning a label to each of at least one of or all of the plurality of training instances and the synthetic instance ( para [0078] “In one or more implementations, the synthetic training instance generation module 208 generates the synthetic training instance 220 ({tilde over (X)}) and a synthetic training label 222 ({tilde over (Y)}) as a linear transform,” ). Regarding Claim 13 , Badjatiya teaches the information processing apparatus according to claim 1, wherein: in the generation process, the at least one processor generates a plurality of synthetic instances by repeatedly carrying out a process of selecting a first generation process and a second generation process based on a predetermined condition, and carrying out a process which has been selected to generate a synthetic instance ( para [0083]-[0084] “This process is repeated for multiple additional labels 212, resulting in multiple synthetic training instances 220 being generated and multiple new tuples being added to the synthetic training data set 120.” ), the first generation process being a process of combining a plurality of training instances selected in the selection process ( para [0078] “The synthetic training instance generation module 208 generates a synthetic training instance 220 by combining the sampled instances 216 based on the combination ratio 218.” ), and the second generation process being a process of extracting at least one training instance from the plurality of training instances selected in the selection process, and combining the at least one training instance which has been extracted and a training instance which is present, in a feature quantity space, near the at least one training instance which has been extracted ( para [0084] “In one or more implementations, different training instances are sampled for each synthetic training instance 220, thus resulting in the synthetic training instances 220 being generated from a first collection of training instances (e.g., from the minority class) and a second collection of training instances (e.g., from the majority class). Additionally or alternatively, the same training instance can be sampled and used to generate multiple synthetic training instances 220.” ). Regarding Claim 14 , Claim 14 is the method corresponding to the apparatus of claim 1. Claim 14 is substantially similar to claim 1 and is rejected on the same grounds. Regarding Claim 15 , Claim 15 is the storage medium corresponding to the apparatus of claim 1. Claim 15 is substantially similar to claim 1 and is rejected on the same grounds . 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. 07-21-aia AIA Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Badjatiya et al. (US-20210117718-A1) in view of Tsai et al. (US-20210104159-A1) . Regarding Claim 6 , Badjatiya teaches the information processing apparatus according to claim 1. Badjatiya does not explicitly disclose wherein: in the generation process, the at least one processor generates a plurality of synthetic instances, and integrates, into a single synthetic instance, two synthetic instances that satisfy a similarity condition among the plurality of synthetic instances. However, Tsai (US 20210104159 A1) teaches wherein: in the generation process, the at least one processor generates a plurality of synthetic instances, and integrates, into a single synthetic instance, two synthetic instances that satisfy a similarity condition among the plurality of synthetic instances ( para [0054] “In example embodiments, depending on the input received, multiple clubs of images may be merged (e.g., based on their similarities transgressing a similarity threshold) and specific images may be removed from the club. Additionally, when clubs are modified, they may be resubmitted as training data to a machine-learning algorithm to improve the performance of future similarity detection.” ). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the machine learning model of Badjatiya with the merging of training data of Tsai. Doing so would allow for improving the performance of similarity detection of the machine learning model (Tsai para [0054] ). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to HENRY K NGUYEN whose telephone number is (571)272-0217. The examiner can normally be reached Mon - Fri 7:00am-4:30pm. 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, Li B Zhen can be reached at 5712723768. 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. /HENRY NGUYEN/ Examiner, Art Unit 2121 Application/Control Number: 18/561,357 Page 2 Art Unit: 2121 Application/Control Number: 18/561,357 Page 3 Art Unit: 2121 Application/Control Number: 18/561,357 Page 4 Art Unit: 2121 Application/Control Number: 18/561,357 Page 5 Art Unit: 2121 Application/Control Number: 18/561,357 Page 6 Art Unit: 2121 Application/Control Number: 18/561,357 Page 7 Art Unit: 2121 Application/Control Number: 18/561,357 Page 8 Art Unit: 2121 Application/Control Number: 18/561,357 Page 9 Art Unit: 2121 Application/Control Number: 18/561,357 Page 10 Art Unit: 2121 Application/Control Number: 18/561,357 Page 11 Art Unit: 2121 Application/Control Number: 18/561,357 Page 12 Art Unit: 2121 Application/Control Number: 18/561,357 Page 13 Art Unit: 2121 Application/Control Number: 18/561,357 Page 14 Art Unit: 2121 Application/Control Number: 18/561,357 Page 15 Art Unit: 2121 Application/Control Number: 18/561,357 Page 16 Art Unit: 2121 Application/Control Number: 18/561,357 Page 17 Art Unit: 2121 Application/Control Number: 18/561,357 Page 18 Art Unit: 2121 Application/Control Number: 18/561,357 Page 19 Art Unit: 2121 Application/Control Number: 18/561,357 Page 20 Art Unit: 2121 Application/Control Number: 18/561,357 Page 21 Art Unit: 2121 Application/Control Number: 18/561,357 Page 22 Art Unit: 2121 Application/Control Number: 18/561,357 Page 23 Art Unit: 2121 Application/Control Number: 18/561,357 Page 24 Art Unit: 2121 Application/Control Number: 18/561,357 Page 25 Art Unit: 2121 Application/Control Number: 18/561,357 Page 26 Art Unit: 2121
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