DETAILED ACTION
This Office Action is in response to the amendments filed on 04/02/2026.
Claims 1, 4, 7-9, 12, 15-17, and 19 are currently amended.
Claims 1-20 are currently pending in this application and have been examined.
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 .
Response to Arguments
In reference to Applicant’s arguments on page(s) 10 regarding rejections made under 35 U.S.C. 112:
On page 2 of the Office Action, claims 7, 8, 15, and 16 were rejected under 35 U.S.C. 112(b) as allegedly indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor regards as the invention. Without conceding that the claims are unpatentable prior to amendment. Applicant has amended claims 7-8 and 15-16 to remove the word "legitimate." As such, Applicant believes that the 35 USC 112(b) rejections are overcome. Reconsideration and withdrawal of the 35 USC 112(b) rejections are respectfully requested.
Examiner’s response:
Applicant’s arguments have been fully considered and are found to be persuasive.
Applicant’s amendments to the claims have remedied the initial issues raised under 35 U.S.C. 112.
The rejections made under 35 U.S.C. 112 are withdrawn.
In reference to Applicant’s arguments on page(s) 10-11 regarding rejections made under 35 U.S.C. 103:
Applicant notes that the above clarifying amendment overcomes the teachings of Denton, Nica, and Chen. For example, while Denton was relied upon for allegedly teaching "combined GAN," Applicant notes that Denton's LAPGAN model is "a set of generative convnet models... each of which captures the distribution of coefficients hk for natural images at a different level of the Laplacian pyramid." See Denton, section 2.3 on page 3. Thus, Denton's LAPGAN model cannot be said to be "a single GAN model," as it is a series of multiple GAN models. See also, FIG. 2, which depicts the multiple GAN models that comprise the LAPGAN model. Thus, Applicant believes that the clarifying language overcomes the 35 USC 103 rejections, and notes that the Examiner indicated such language would be helpful in the interview of March 9, 2026.
Reconsideration and withdrawal of the 35 USC 103 rejections of claims 1, 9, and 17 are respectfully requested. Additionally, at least because they depend from and further limit claims 1 and 9, Applicant respectfully requests reconsideration and withdrawal of the 35 USC 103 rejections of claims 7-8 and 15-16.
Examiner’s response:
Applicant’s arguments have been fully considered but are found to be moot.
Applicant argues that the applied references of Denton, Nica, and Chen do not teach the amended limitation of “wherein the combined GAN is a single GAN model generated using aspects of the first and second GANs”. Examiner agrees. Further search was conducted to find art that would teach the amended claim limitation.
The rejections made under 35 U.S.C. 103 are maintained and updated below.
Claim Objections
Claims 2-6, 10-14, and 18-20 objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1, 7-9, and 15-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Denton et al (Denton, E., Chintala, S., Szlam, A., & Fergus, R. (2015). Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks. arXiv [Cs.CV]. Retrieved from http://arxiv.org/abs/1506.05751, hereinafter Denton), in view of Nica et al (US 20220374682 A1, hereinafter Nica), in view of Chen et al (Chen, H., Jajodia, S., Liu, J., Park, N., Sokolov, V., & Subrahmanian, V. S. (7 2019). FakeTables: Using GANs to Generate Functional Dependency Preserving Tables with Bounded Real Data. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19, 2074–2080. doi:10.24963/ijcai.2019/287, hereinafter Chen), and in further view of Iashyn et al (US 20200252296 A1, hereinafter Iashyn).
Regarding Claim 1:
Denton teaches
combining the first and second GANs into a combined GAN (Denton [Figure 2]: it can be seen in figure 2 that multiple GANs are trained in series with each other; (EN): in the context of this claim the term “combine” is broad and could be interpreted as training models in series or using the output of one GAN to train a subsequent GAN);
training the combined GAN (Denton [Figure 2 caption]: "The training procedure for our LAPGAN model."; (EN): the training method for the combined network is described in more detail in the Figure 2 caption);
operating the trained combined GAN to generate new fabricated data that: imitate characteristics of the original structured data (Denton [Figure 2 caption]: "It outputs a generated high-pass image h˜0 = G0(z0, l0), which is input to D0. In both the real/generated cases, D0 also receives l0 (orange arrow). Optimizing Eqn. 2, G0 thus learns to generate realistic high-frequency structure h˜0 consistent with the low-pass image l0")
Denton does not distinctly disclose
training a second GAN based on fabricated structured data that adhere to user- defined constraints;
adhere to the user-defined constraints.
However, Nica teaches
training a second GAN based on fabricated structured data that adhere to user- defined constraints (Nica [0023]: "during the adversarial training process, generator model 120 learns the probability distribution of real data, and generates fake samples, synthetic data record 304, that can deceive discriminator model 130. At the same time, discriminator model 130 can receive real samples 306 and synthetic data record 304"; [0020]: "Data constraint 202 can include one or more atomic constraints, e.g., an atomic constraint C1, an atomic constraint C2, and an atomic constraint C3. C1 is specified as T.A>T.C, which means the value of column A in the table is greater than the value of column C in the table. In the current example, the value of column C is the education time, and the value of column A is the age of the person. Hence, the age of the person at column A must be greater than the education time of the person at column C. C2 is specified as T.C>5, which means that a person has received at least 5 years education since the value of column C represents the education time"; (EN): data constraints of Nica are analogous to the user constraints of the instant application)
adhere to the user-defined constraints (Nica [0020]: "Data constraint 202 can include one or more atomic constraints, e.g., an atomic constraint C1, an atomic constraint C2, and an atomic constraint C3. C1 is specified as T.A>T.C, which means the value of column A in the table is greater than the value of column C in the table. In the current example, the value of column C is the education time, and the value of column A is the age of the person. Hence, the age of the person at column A must be greater than the education time of the person at column C. C2 is specified as T.C>5, which means that a person has received at least 5 years education since the value of column C represents the education time"; (EN): data constraints of Nica are analogous to the user constraints of the instant application).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Denton and Nica before him or her, to modify the systems and techniques for generative image models using Laplacian a pyramid of Denton to include the techniques for supporting database constraints for synthetic data generation as shown in Nica. The motivation for doing so would have been to use the database constraints of Nica in order to guide the GAN to generate data while considering the constraints (Nica [0012]: “the current approaches based on GAN or other techniques for generating synthetic data often fail to consider database constraints of the real data, which should be maintained on generated synthetic data. Database constraints are different from the probability distribution of variables of a data table. Classical statistical distributions cannot describe complex and mixed distributions in relational databases. Instead, database constraints may often be represented by Boolean functions”).
Denton + Nica does not distinctly disclose
A computer-implemented method comprising: training a first Generative Adversarial Network (GAN) based on original structured data;
However, Chen teaches
A computer-implemented method comprising: training a first Generative Adversarial Network (GAN) based on original structured data (Chen [Page 2074, section 1, par. 1]: “In this paper, we study an incomplete table synthesis (ITS) problem for tabular data augmentation, where we wish to augment the released incomplete sub-table of records X0 by synthesizing a new table Y of records, so that a machine learning model trained on the augmented table X0 ∪Y works better for the full table X (which X0 originated from) than a model that is trained solely on X0.”; [Page 2076, section 4, par. 2]: "Second, it incorporates the table statistics and FD constraints in training the generator so that the generator establishes a trade-off between the two contradicting objectives mentioned above"; (EN): it is noted that the "structured data" of the instant application refers to tabular data);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Denton + Nica and Chen before him or her, to modify the systems and techniques for generative image models using Laplacian a pyramid of Denton + Nica to include the techniques for GAN training using real, bounded data as shown in Chen. The motivation for doing so would have been to use the bounded data of Chen in order to train the GAN with the real data to generate synthetic data while preserving any functional dependencies of the data (Chen [Abstract]: “In this paper, our goal is to find a way to augment the sub-table by generating a synthetic table from the released sub-table, under the constraints that the generated synthetic table (i) has similar statistics as the entire table, and (ii) preserves the functional dependencies of the released sub-table”).
Denton + Nica + Chen does not distinctly disclose
wherein the combined GAN is a single GAN model generated using aspects of the first and second GANs
However, Iashyn teaches
wherein the combined GAN is a single GAN model generated using aspects of the first and second GANs (Iashyn [0090]: "The unified GAN training system 306 can generate the unified GAN model 312 using the generator of the first GAN model 308 received from the first network edge device 302 and the generator of the second GAN model 310 received from the second network edge device 304. FIG. 6 illustrates a flow 600 by which generators for different GAN models can be combined to form a unified GAN model. The example flow 600 shown in FIG. 6 can be utilized by the unified GAN training system 306 to generate the unified GAN model 312.") It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Denton + Nica + Chen and Iashyn before him or her, to modify the systems and techniques for generative image models using Laplacian a pyramid of Denton + Nica + Chen to include the techniques for knowledge aggregation for anomaly detectors as shown in Iashyn. The motivation for doing so would have been to use the aggregate GAN models of Iashyn in order to generate a combination GAN model (Iashyn [Abstract]: “A generator of the first GAN model can be trained using real telemetry data of a first computing node and a generator of the second GAN model can be trained using real telemetry data of a second IoT device. The generator of the first GAN model and the generator of the second GAN model can be received. Additionally, a unified generator of a unified GAN model can be trained using the generator of the first GAN model and the generator of the second GAN model. Subsequently, the unified GAN model can be deployed to a third computing node for monitoring operation of the third IoT device.”).
Regarding Claim 7:
Denton does not distinctly disclose
The method of claim 1, wherein: the characteristics of the original structured data comprise properties, dependencies, and intrinsic constraints;
and the first GAN, following its training, is configured to generate legitimate data that imitate the properties, dependencies, and intrinsic constraints of the original structured data.
However, Chen teaches
The method of claim 1, wherein: the characteristics of the original structured data comprise properties, dependencies, and intrinsic constraints (Chen [Page 2076, section 4.2, par. 1]: "To handle the functional dependency and table statistics constraints, we propose the following adapted loss function for the generator: {Eqn. 2} where LG is the original loss function in Eq. (1). kY¯ − X¯k1 is an error term which penalizes the difference between the column-wise average of the generated table Y and the original table X";);
and the first GAN, following its training, is configured to generate legitimate data that imitate the properties, dependencies, and intrinsic constraints of the original structured data (Chen [Page 2078, section 6.2, par. 1]: "To evaluate whether a generated table Y is close to the original full table X, we first compare the cumulative distribution functions (CDFs) of X and Y . We compare with the state-of-the-art table synthesis approach, TableGAN [Park et al., 2018]. Figure 2 displays the CDFs of some selected schema (columns)").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Denton and Chen before him or her, to modify the systems and techniques for generative image models using Laplacian a pyramid of Denton to include the techniques for GAN training using real, bounded data as shown in Chen. The motivation for doing so would have been to use the bounded data of Chen in order to train the GAN with the real data to generate synthetic data while preserving any functional dependencies of the data (Chen [Abstract]: “In this paper, our goal is to find a way to augment the sub-table by generating a synthetic table from the released sub-table, under the constraints that the generated synthetic table (i) has similar statistics as the entire table, and (ii) preserves the functional dependencies of the released sub-table”).
Regarding Claim 8:
Denton does not distinctly disclose
The method of claim 1, wherein: the second GAN, following its training, is configured to generate legitimate data that adhere to the user-defined constraints.
However, Nica teaches
The method of claim 1, wherein: the second GAN, following its training, is configured to generate legitimate data that adhere to the user-defined constraints (Nica [0025]: "discriminator model 130 attempts to distinguish between real and generated samples, while generator model 120 attempts to generate realistic fake samples that discriminator model 130 cannot distinguish from real samples."; [0020]: "Data constraint 202 can include one or more atomic constraints, e.g., an atomic constraint C1, an atomic constraint C2, and an atomic constraint C3. C1 is specified as T.A>T.C, which means the value of column A in the table is greater than the value of column C in the table. In the current example, the value of column C is the education time, and the value of column A is the age of the person. Hence, the age of the person at column A must be greater than the education time of the person at column C. C2 is specified as T.C>5, which means that a person has received at least 5 years education since the value of column C represents the education time"; (EN): data constraints of Nica are analogous to the user constraints of the instant application).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Denton and Nica before him or her, to modify the systems and techniques for generative image models using Laplacian a pyramid of Denton to include the techniques for supporting database constraints for synthetic data generation as shown in Nica. The motivation for doing so would have been to use the database constraints of Nica in order to guide the GAN to generate data while considering the constraints (Nica [0012]: “the current approaches based on GAN or other techniques for generating synthetic data often fail to consider database constraints of the real data, which should be maintained on generated synthetic data. Database constraints are different from the probability distribution of variables of a data table. Classical statistical distributions cannot describe complex and mixed distributions in relational databases. Instead, database constraints may often be represented by Boolean functions”).
Regarding Claim 9:
Due to claim language similar to that of Claim 1, Claim 9 is rejected for the same reasons as presented above in the rejection of Claim 1, with the exception of the limitation(s) covered below.
Denton does not distinctly disclose
A system comprising: a processor set;
one or more computer readable storage media;
and program instructions stored on the one or more computer readable storage media to cause the processor to perform operations
However, Nica teaches
A system comprising: a processor set (Nica [0056]: "Computer system 600 includes one or more processors (also called central processing units, or CPUs), such as a processor 604.");
one or more computer readable storage media (Nica [0059]: "Computer system 600 also includes a main or primary memory 608, such as random access memory (RAM). Main memory 608 may include one or more levels of cache. Main memory 608 has stored therein control logic (i.e., computer software) and/or data.");
and program instructions stored on the one or more computer readable storage media to cause the processor to perform operations (Nica [0059]: "Computer system 600 also includes a main or primary memory 608, such as random access memory (RAM). Main memory 608 may include one or more levels of cache. Main memory 608 has stored therein control logic (i.e., computer software) and/or data.")
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, and having the teachings of Denton and Nica before him or her, to modify the systems and techniques for generative image models using Laplacian a pyramid of Denton to include the techniques for supporting database constraints for synthetic data generation as shown in Nica. The motivation for doing so would have been to use the database constraints of Nica in order to guide the GAN to generate data while considering the constraints (Nica [0012]: “the current approaches based on GAN or other techniques for generating synthetic data often fail to consider database constraints of the real data, which should be maintained on generated synthetic data. Database constraints are different from the probability distribution of variables of a data table. Classical statistical distributions cannot describe complex and mixed distributions in relational databases. Instead, database constraints may often be represented by Boolean functions”).
Regarding Claim 15:
Due to claim language similar to that of Claim 7, Claim 15 is rejected for the same reasons as presented above in the rejection of Claim 7.
Regarding Claim 16:
Due to claim language similar to that of Claim 8, Claim 16 is rejected for the same reasons as presented above in the rejection of Claim 8.
Regarding Claim 17:
Due to claim language similar to that of Claims 1 and 9, Claim 17 is rejected for the same reasons as presented above in the rejection of Claims 1 and 9.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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Zhao, Z., Kunar, A., Birke, R., & Chen, L. Y. (2022). CTAB-GAN+: Enhancing Tabular Data Synthesis. arXiv [Cs.LG]. Retrieved from http://arxiv.org/abs/2204.00401 – CTAB-GAN+ a novel conditional tabular GAN
L. Hu, M. Kan, S. Shan and X. Chen, "Duplex Generative Adversarial Network for Unsupervised Domain Adaptation," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 1498-1507, doi: 10.1109/CVPR.2018.00162. – a novel GAN architecture with duplex adversarial discriminators (referred to as DupGAN), which can achieve domain-invariant representation and domain transformation
Y. Yuan and Y. Guo, "A Review on Generative Adversarial Networks," 2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT), Shenyang, China, 2020, pp. 392-401, doi: 10.1109/ISCTT51595.2020.00074. – A Review on Generative Adversarial Networks
A. Ahmetoğlu and E. Alpaydın, "Hierarchical Mixtures of Generators for Adversarial Learning," 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, 2021, pp. 316-323, doi: 10.1109/ICPR48806.2021.9413249. – we propose the hierarchical mixture of generators, inspired from the hierarchical mixture of experts model, that learns a tree structure implementing a hierarchical clustering with soft splits in the decision nodes and local generators in the leaves.
THIS ACTION IS MADE FINAL. 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 COREY M SACKALOSKY whose telephone number is (703)756-1590. The examiner can normally be reached M-F 7:30am-3:30pm EST.
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/COREY M SACKALOSKY/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
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