DETAILED ACTION
This communication is in response to the Amendments and Arguments filed on 07/15/2026.
Claim(s) 1-20 are pending 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 . Wuest does not disclose or suggest a first category of synthetic data generation "configured to generate synthetic data that maintains4931-9136-5559.3
referential relationships within each dataset" or a second category "configured to enhance the synthetic data," as recited in amended claim 1.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 07/15/2026 has been entered.
Response to Arguments and Amendments
Amendments to the claims by the Applicant have been considered and addressed below.
With respect to the Objections to the Drawings, 35 USC § 101 and 103 rejections, the Applicant provides several arguments in which the Examiner will respond accordingly, below.
35 USC § 101 rejection(s)
Arguments on page 6-12 of Remarks filed on 07/15/2026
Examiner’s Response to Arguments:
Applicant’s arguments, with respect to the rejection(s) of independent claim(s) 1 and 11 under 35 USC 101 have been fully considered but are not persuasive.
The Applicant argues that:
“These limitations require computational operations that cannot be performed in the human mind. A human cannot mentally "generate synthetic data" that "maintains" referential relationships within and across multiple datasets while processing that data through a generative Al framework.”;
“A human cannot mentally perform the operations of a "generative framework" comprising multiple "categories of synthetic data generation" that process data through Gen AI architectures such as TGAN, CTGAN, CopulaGAN, or TVAE, while maintaining referential relationships across datasets.”; and
“Amended claims provide a specific technical improvement to AI/ML training data.”
However, the Examiner respectfully disagrees because the language in the claims as drafted are still considered a mental process and/or mathematical concept as will be described below.
Additionally, the Examiner suggests that the Applicant considers language from the specification at ¶ [0053], which if tied to the language in the independent claims, could potentially help overcoming the 101 abstract idea issues.
Please see detailed analysis below (Prong Two) for more details on how the Examiner understands the independent claims do not recite additional elements that integrate the judicial exception into a practical application. Hence, not qualifying as patent eligible subject matter under 35 U.S.C. § 101.
Please refer to MPEP 2106.04(1): Eligibility Step 2A: Whether a Claim is Directed to a Judicial Exception: Prong One.
“Prong One asks does the claim recite an abstract idea, law of nature, or natural phenomenon? In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. While the terms "set forth" and "described" are thus both equated with "recite", their different language is intended to indicate that there are two ways in which an exception can be recited in a claim. For instance, the claims in Diehr, 450 U.S. at 178 n. 2, 179 n.5, 191-92, 209 USPQ at 4-5 (1981), clearly stated a mathematical equation in the repetitively calculating step, and the claims in Mayo, 566 U.S. 66, 75-77, 101 USPQ2d 1961, 1967-68 (2012), clearly stated laws of nature in the wherein clause, such that the claims "set forth" an identifiable judicial exception. Alternatively, the claims in Alice Corp., 573 U.S. at 218, 110 USPQ2d at 1982, described the concept of intermediated settlement without ever explicitly using the words "intermediated" or "settlement."”
“An example of a claim that recites a judicial exception is "A machine comprising elements that operate in accordance with F=ma." This claim sets forth the principle that force equals mass times acceleration (F=ma) and therefore recites a law of nature exception. Because F=ma represents a mathematical formula, the claim could alternatively be considered as reciting an abstract idea. Because this claim recites a judicial exception, it requires further analysis in Prong Two in order to answer the Step 2A inquiry. An example of a claim that merely involves, or is based on, an exception is a claim to "A teeter-totter comprising an elongated member pivotably attached to a base member, having seats and handles attached at opposing sides of the elongated member." This claim is based on the concept of a lever pivoting on a fulcrum, which involves the natural principles of mechanical advantage and the law of the lever. However, this claim does not recite these natural principles and therefore is not directed to a judicial exception (Step 2A: NO). Thus, the claim is eligible at Pathway B without further analysis.”
From this analysis, in Step 2A, Prong One, the Examiner has evaluated the independent claims accordingly and determined that the amended independent claims as drafted indeed describe a judicial exception (i.e., an abstract idea), which represent a mental process (which can be performed by a human with pen and paper).
More specifically, similar to what was discussed in the Final Rejection mailed on 03/25/2026:
The limitations of as drafted cover a human (mental process and/or mathematical concept). The independent claim(s) recite(s):
1. An apparatus for synthetic data generation, comprising:
a processor; and
a memory communicatively connected to the processor, the memory containing instructions configuring the processor to:
receive data including a plurality of datasets;
input the data into a generative framework, the generative framework comprising:
a plurality of categories of synthetic data generation, wherein the plurality of categories of synthetic data generation includes at least:
a first category of synthetic data generation configured to generate synthetic data that maintains referential relationships within each dataset of the plurality of datasets; and
a second category of synthetic data generation configured to enhance the synthetic data, wherein the generative framework is configured to input the data and output synthetic data through at least a category of synthetic data generation, wherein the synthetic data retains [[a]] referential integrity
generate, based on the generative framework, the synthetic data from the received data for use in training an artificial intelligence model.
11. A method of synthetic data generation using a computing device, comprising:
[the limitations as in claim 1, above].
More specifically, this reads on a human (e.g., mentally and/or using pen and paper) based on:
Receive plurality of data (e.g., words/sentences in speech, text, etc.);
Use the received plurality of data to follow a predetermined set of rules or steps (e.g., mathematical steps);
Wherein the predetermined set of rules or steps include:
A plurality of categories for data (e.g., speech, text, etc.) generation within the predetermined set of rules or steps including:
A first category that involves maintaining reference relationship (e.g., label) of each dataset; and
A second category, wherein the predetermined set of rules or steps considers the received data as input in order to obtain or generate data (e.g., speech, text, etc.), wherein evaluation of the reference integrity (e.g., veracity) is performed; and
obtain or generate data (e.g., speech, text, etc.) based on the predetermined set of rules or steps (e.g., mathematical steps), which are to be used in redefining the predetermined set of rules/steps.
Please also refer to MPEP 2106.05(f)(2): Whether the claim invokes computers or other machinery merely as a tool to perform an existing process, and MPEP 2106.06(b): Clear Improvement to a Technology or to Computer Functionality.
Please refer to MPEP 2106.04(2): Eligibility Step 2A: Whether a Claim is Directed to a Judicial Exception: Prong Two.
“Prong Two asks does the claim recite additional elements that integrate the judicial exception into a practical application? In Prong Two, examiners evaluate whether the claim as a whole integrates the exception into a practical application of that exception. If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception (Step 2A: NO) and thus is eligible at Pathway B. This concludes the eligibility analysis. If, however, the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception (Step 2A: YES), and requires further analysis under Step 2B (where it may still be eligible if it amounts to an ‘‘inventive concept’’). For more information on how to evaluate whether a judicial exception is integrated into a practical application, see MPEP § 2106.04(d)(2).”
From this analysis, in Step 2A, Prong Two, the Examiner has evaluated the independent claims accordingly and determined that the amended independent claims as drafted that the claims as a whole do not include additional elements that integrate the exception into a practical application of that exception. (i.e., an abstract idea). As discussed in the Final Rejection mailed on 03/25/2026:
This judicial exception is not integrated into a practical application because for example: claim 1 recites “an apparatus for synthetic data generation”, “a processor”, “a memory” and claim 11 recites “computing device”. As an example, in ¶ [0019] of the as filed specification, it is disclosed: “…In some embodiments, the data 112 may be sent to the processor 104 through an external computing device, such as, but not limited to, a laptop, server, desktop, tablet, and the like. The data 112 may include one or more datasets...”. Therefore, a general-purpose computer or computing device is described and mainly used as an application thereof. Accordingly, these additional elements do not integrate the abstract idea into a practical idea because it does not impose any meaningful limits on practicing the abstract idea.
Please also refer to MPEP 2106.05(f)(2): Whether the claim invokes computers or other machinery merely as a tool to perform an existing process.
Finally, please refer to MPEP 2106.05(A): Relevant Considerations For Evaluating Whether Additional Elements Amount To An Inventive Concept
“Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include:
i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));”
From this analysis, in Step 2B, the Examiner has evaluated the independent claims accordingly and determined that the independent claims as drafted have limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception. Similar to what was discussed in the Final Rejection mailed on 03/25/2026.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of using a computer is listed as a general computing device as noted. The claim is not patent eligible.
In summary, the Examiner respectfully disagrees with the arguments above. Please refer to analysis above as well as to the updated 35 USC § 101 rejection(s) below.
35 USC § 103 rejection(s)
Arguments on pages 12-15 of Remarks filed on 07/15/2026
Examiner’s Response to Arguments:
Applicant’s arguments with respect to claim(s) 1 and 11 under 35 U.S.C. § 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Wuest et al. (US 20250094325 A1) further in view of Singh et al. (US 10339038 A1).
Additionally, the Applicant argues that “Wuest does not disclose or suggest a first category of synthetic data generation "configured to generate synthetic data that maintains. referential relationships within each dataset" or a second category "configured to enhance the synthetic data," as recited in amended claim 1.”
The Examiner respectfully disagrees and notes that Wuest et al. does teach the following limitations in at least the cited portions of their disclosure:
a first category of synthetic data generation configured to generate synthetic data that maintains referential relationships within each dataset of the plurality of datasets (see Wuest et al. ¶ [0038] citation as in limitation above. “…The primary groups can include network traffic tests, file-based tests, and behavioral sequence tests […] The AI model 110 can efficiently classify the test cases based on its initial training, …” and ¶ [0044]: “In one aspect, the generated data is validated for its quality and reliability based on the testing task requirements. A validation process ensures that the data can reliably simulate real-world scenarios and conditions with adequate accuracy. For validation, a predefined criteria can be set. The predefined criteria can be a set of benchmarks of standards that the synthetic test data must meet. For instance, if the synthetic data is meant to mimic user behavior on a website, the criteria can include realistic time intervals between clicks, the sequence of pages visited, or the variety of user agents and devices…”
Examiner’s note: synthetic data meeting criteria like mimicking a variety of users (i.e., a plurality of datasets) as disclosed by Wuest et al. reads on the synthetic data that maintains referential relationships within each dataset of the plurality of datasets); and
a second category of synthetic data generation configured to enhance the synthetic data (see Wuest et al. ¶ [0038 and 0044] citations as in limitation(s) above. “…The primary groups can include network traffic tests, file-based tests, and behavioral sequence tests […] The AI model 110 can efficiently classify the test cases based on its initial training, …”) and further ¶ [0043]: “…This feedback is then used to make iterative improvements to the model, ensuring that subsequent data generation is closer to the desired output.”,
For more details, please refer to updated 35 U.S.C. § 103 rejections for claims 1-20, below.
Claim Rejections - 35 USC § 112
14. The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
15. Claims 1-20 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The Examiner notes that the limitation of “a second category of synthetic data generation configured to enhance the synthetic data…” in independent claims 1 and 11, do not seem to be described in the as filed specification.
Hence, dependent claims 2-10 and 12-20 are also rejected.
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 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. More specifically directed to the abstract idea grouping of: mental process and/or mathematical concept.
The independent claim(s) recite(s):
1. An apparatus for synthetic data generation, comprising:
a processor; and
a memory communicatively connected to the processor, the memory containing instructions configuring the processor to:
receive data including a plurality of datasets;
input the data into a generative framework, the generative framework comprising:
a plurality of categories of synthetic data generation, wherein the plurality of categories of synthetic data generation includes at least:
a first category of synthetic data generation configured to generate synthetic data that maintains referential relationships within each dataset of the plurality of datasets; and
a second category of synthetic data generation configured to enhance the synthetic data, wherein the generative framework is configured to input the data and output synthetic data through at least a category of synthetic data generation, wherein the synthetic data retains [[a]] referential integrity
generate, based on the generative framework, the synthetic data from the received data for use in training an artificial intelligence model.
11. A method of synthetic data generation using a computing device, comprising:
[the limitations as in claim 1, above].
More specifically, this reads on a human (e.g., mentally and/or using pen and paper) based on:
Receive plurality of data (e.g., words/sentences in speech, text, etc.);
Use the received plurality of data to follow a predetermined set of rules or steps (e.g., mathematical steps);
Wherein the predetermined set of rules or steps include:
A plurality of categories for data (e.g., speech, text, etc.) generation within the predetermined set of rules or steps including:
A first category that involves maintaining reference relationship (e.g., label) of each dataset; and
A second category, wherein the predetermined set of rules or steps considers the received data as input in order to obtain or generate data (e.g., speech, text, etc.), wherein evaluation of the reference integrity (e.g., veracity) is performed; and
obtain or generate data (e.g., speech, text, etc.) based on the predetermined set of rules or steps (e.g., mathematical steps), which are to be used in redefining the predetermined set of rules/steps.
This judicial exception is not integrated into a practical application because for example: claim 1 recites “an apparatus for synthetic data generation”, “a processor”, “a memory” and claim 11 recites “computing device”. As an example, in ¶ [0019] of the as filed specification, it is disclosed: “…In some embodiments, the data 112 may be sent to the processor 104 through an external computing device, such as, but not limited to, a laptop, server, desktop, tablet, and the like. The data 112 may include one or more datasets...”. Therefore, a general-purpose computer or computing device is described and mainly used as an application thereof. Accordingly, these additional elements do not integrate the abstract idea into a practical idea because it does not impose any meaningful limits on practicing the abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of using a computer is listed as a general computing device as noted. The claim is not patent eligible.
With respect to claims 2 and 12, the claim(s) recite:
2 and 12. The apparatus/method of claims 1 and 11, wherein the processor is further configured to validate an aspect of the synthetic data.
This reads on a human (e.g., mentally and/or using pen and paper):
validating the generated data based on a predefined aspect or rule.
No additional limitations are present.
With respect to claims 3 and 13, the claim(s) recite:
3 and 13. The apparatus/method of claims 2 and 12, wherein the aspect includes evaluation of one of veracity, variety, volume, or a combination thereof, of the synthetic data.
This reads on a human:
validating the generated data based on a predefined aspect or rule (e.g., veracity/accuracy).
No additional limitations are present.
With respect to claims 4 and 14, the claim(s) recite:
4 and 14. The apparatus/method of claims 1 and 11, wherein the first category of synthetic data generating methods includes a plurality of generative artificial intelligence architectures.
This reads on a human:
wherein the first category is a predetermined set of rules or steps (e.g., mathematical steps)
No additional limitations are present.
With respect to claims 5 and 15, the claim(s) recite:
5 and 15. The apparatus/method of claims 1 and 11, wherein the first category of synthetic data generation includes a hierarchical modeling algorithm (HMA).
This reads on a human:
wherein the first category is a predetermined set of rules or steps (e.g., mathematical steps)
No additional limitations are present.
With respect to claims 6 and 16, the claim(s) recite:
6 and 16. The apparatus/method of claims 5 and 15, wherein the processor is further configured to extract combination data of the received data and feed the extracted combination data to the HMA as metadata.
This reads on a human:
extracting data and use it within the predetermined set of rules or steps (e.g., mathematical steps)
No additional limitations are present.
With respect to claims 7 and 17, the claim(s) recite:
7 and 17. The apparatus/method of claims 1 and 11, wherein the second category of synthetic data generation includes a distribution-based generation.
This reads on a human:
wherein the second category is a predetermined set of rules or steps (e.g., mathematical/statistical steps)
No additional limitations are present.
With respect to claims 8 and 18, the claim(s) recite:
8 and 18. The apparatus/method of claims 1 and 11, wherein the synthetic data is anonymized while retaining the referential integrity of the data.
This reads on a human:
anonymizing a portion of the generated data based on a predefined aspect or rule (e.g., veracity/accuracy).
No additional limitations are present.
With respect to claims 9 and 19, the claim(s) recite:
9 and 19. The apparatus/method of claims 1 and 11, wherein the generative framework is further configured to generate a free text variable through a large language model (LLM).
This reads on a human:
wherein the predetermined set of rules or steps (e.g., mathematical steps) instructs to generate text via additional predetermined set of rules or steps
No additional limitations are present.
With respect to claims 10 and 20, the claim(s) recite:
10 and 20. The apparatus/method of claims 1 and 11, wherein the processor is further configured to:
receive user input, the user input including a selection of a category of synthetic data generating methods of the generative framework; and
generate the synthetic data through category of synthetic data generating methods selected from the user input.
This reads on a human:
receive a selection from another human choosing between the first or second categories; and
generating data based on predetermined set of rules or steps (e.g., mathematical steps) for said selection of category.
No additional limitations are present.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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.
Claims 1-3, 8, 11-13, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wuest et al. (US 20250094325 A1) further in view of Singh et al. (US 10339038 A1).
As to independent claim 1, Wuest et al. teaches:
1. An apparatus for synthetic data generation (see ¶ [0008]: “… The present disclosure relates to systems and methods for generating synthetic test data for testing a software solution…”), comprising:
a processor (see ¶ [0020]: “…In one embodiment, the system 100 is implemented on a hardware processor 102 and operably coupled to a memory 104.”); and
a memory communicatively connected to the processor (see ¶ [0020] citation as in limitation above.), the memory containing instructions configuring the processor to:
receive data including a plurality of datasets (see ¶ [0010]: “In an embodiment, a method of generating synthetic test data for testing a software solution on a computing system comprises receiving a testing task from a user, wherein the testing task is indicative of a software solution to be tested and a type of testing to be performed;…” and ¶ [0032]: “…additional initial information or datasets can be collected to enhance the diversity and realism of the synthetic test data from the data sources, such as external threat intelligence feeds or user behaviour analytics...”);
input the data into a generative framework (see ¶ [0010] citation as in limitation above and further: “… identifying test properties of the testing task, including at least one required attribute for synthetic test data; gathering initial information based on the test properties of the testing task; forming a training dataset based on the initial information and the test properties; pretraining a generative AI model based on a large language model (LLM) using the training dataset; configuring synthetic test data based on the test properties; and generating synthetic test data according to the testing task using the generative AI model.”), the generative framework comprising:
a plurality of categories of synthetic data generation (see ¶ [0038]: “In accordance with one aspect, testing tasks can be categorized into various simple groups for testing purposes. In one example, three primary groups can be formed for classification. The primary groups can include network traffic tests, file-based tests, and behavioral sequence tests. […] The AI model 110 can efficiently classify the test cases based on its initial training, where it learned the necessary data collection techniques for scenarios like emails.”), wherein the plurality of categories of synthetic data generation includes at least:
a first category of synthetic data generation configured to generate synthetic data that maintains referential relationships within each dataset of the plurality of datasets (see ¶ [0038] citation as in limitation above. “…The primary groups can include network traffic tests, file-based tests, and behavioral sequence tests […] The AI model 110 can efficiently classify the test cases based on its initial training, …” and ¶ [0044]: “In one aspect, the generated data is validated for its quality and reliability based on the testing task requirements. A validation process ensures that the data can reliably simulate real-world scenarios and conditions with adequate accuracy. For validation, a predefined criteria can be set. The predefined criteria can be a set of benchmarks of standards that the synthetic test data must meet. For instance, if the synthetic data is meant to mimic user behavior on a website, the criteria can include realistic time intervals between clicks, the sequence of pages visited, or the variety of user agents and devices…”
Examiner’s note: synthetic data meeting criteria like mimicking a variety of users (i.e., a plurality of datasets) as disclosed by Wuest et al. reads on the synthetic data that maintains referential relationships within each dataset of the plurality of datasets); and
a second category of synthetic data generation configured to enhance the synthetic data (see ¶ [0038 and 0044] citations as in limitation(s) above. “…The primary groups can include network traffic tests, file-based tests, and behavioral sequence tests […] The AI model 110 can efficiently classify the test cases based on its initial training, …” and further ¶ [0043]: “…This feedback is then used to make iterative improvements to the model, ensuring that subsequent data generation is closer to the desired output.”),
wherein the generative framework is configured to input the data and output synthetic data through at least a category of synthetic data generation (see ¶ [0010, 0038, and 0044] citations as in limitations above. “¶ [0010]: “In an embodiment, a method of generating synthetic test data for testing a software solution on a computing system comprises receiving a testing task from a user, wherein the testing task is indicative of a software solution to be tested and a type of testing to be performed […] identifying test properties of the testing task, including at least one required attribute for synthetic test data; gathering initial information based on the test properties of the testing task; forming a training dataset based on the initial information and the test properties; pretraining a generative AI model based on a large language model (LLM) using the training dataset; configuring synthetic test data based on the test properties; and generating synthetic test data according to the testing task using the generative AI model.” and further ¶ [0067]: “At 314, the method 300 includes generating synthetic test data according to the testing task using the generative AI model 110...” ),
generate, based on the generative framework, the synthetic data from the received data for use in training an artificial intelligence model (see ¶ [0010, 0038, and 0044] citations as in limitations above, more specifically: “[0010] … identifying test properties of the testing task, including at least one required attribute for synthetic test data; gathering initial information based on the test properties of the testing task; forming a training dataset based on the initial information and the test properties; pretraining a generative AI model based on a large language model (LLM) using the training dataset; configuring synthetic test data based on the test properties; and generating synthetic test data according to the testing task using the generative AI model.”
and further ¶ [0067]: “[0067] At 314, the method 300 includes generating synthetic test data according to the testing task using the generative AI model 110. With preliminary training and the specific configurations set, the AI model 110 creates data that mimics real-world data, catering specifically to the testing task provided by the user.”).
However, Wuest et al. does not explicitly teach, but Singh et al. does teach:
wherein the synthetic data retains referential integrity of the received data (see ¶ [col. 3, lines 7-31]: “An embodiment of the present invention is directed to using real data patterns to create synthetic test data. An embodiment of the present invention is directed to generating synthetic data with production test scenarios without exposing sensitive information in lower environments… The system may further provide data patterns using regular expressions (e.g., ^[A-Za-z]). An embodiment of the present invention maintains referential integrity across data entities (e.g., test data file).”).
Wuest et al. and Singh et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in generating data. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wuest et al. to incorporate the teachings of Singh et al. of wherein the synthetic data retains referential integrity of the received data which provides the benefit of improving efficiency, speed and user experience ([col. 2, lines 28-40] of Singh et al.).
As to independent claim 11, Wuest et al. teaches:
11. A method of synthetic data generation using a computing device (see ¶ [0008]: “… The present disclosure relates to systems and methods for generating synthetic test data for testing a software solution…”), comprising:
[the limitations as taught by Wuest et al. and Singh et al. as in claim 1, above.]
Regarding claims 2 and 12, Wuest et al. in combination with Singh et al. teaches the limitations as in claims 1 and 11, above.
Wuest et al. further teaches:
2 and 12. The apparatus/method of claims 1 and 11, wherein the processor is further configured to validate an aspect of the synthetic data (see ¶ [0044]: “In one aspect, the generated data is validated for its quality and reliability based on the testing task requirements. A validation process ensures that the data can reliably simulate real-world scenarios and conditions with adequate accuracy…”).
Regarding claims 3 and 13, Wuest et al. in combination with Singh et al. teaches the limitations as in claims 2 and 12, above.
Wuest et al. further teaches:
3 and 13. The apparatus/method of claims 2 and 12, wherein the aspect includes evaluation of one of veracity, variety, volume, or a combination thereof, of the synthetic data (see ¶ [0044] citation as in claims 2 and 12 above “accuracy” (i.e., veracity) and further: “…For validation, a predefined criteria can be set. The predefined criteria can be a set of benchmarks of standards that the synthetic test data must meet. For instance, if the synthetic data is meant to mimic user behavior on a website, the criteria can include realistic time intervals between clicks, the sequence of pages visited, or the variety of user agents and devices. ”).
Claims 4-5 and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wuest et al. (US 20250094325 A1) further in view of Singh et al. (US 10339038 A1) as applied to claims 1 and 11, above and further in view of Narayanan et al. (US 20240249158 A1).
Regarding claims 4 and 14, Wuest et al. in combination with Singh et al. teaches the limitations as in claims 1 and 11, above.
Narayanan et al. further teaches:
4 and 14. The apparatus/method of claims 1 and 11, wherein the first category of synthetic data generating methods includes a plurality of generative artificial intelligence architectures (see ¶ [0099]: “For example, one or more tabular synthetic data generation techniques may be leveraged in the event that an evaluation feature value is missing from a tabular dataset. The tabular synthetic data generation techniques may include leveraging one or more generative adversarial (GAN) models such as, conditional GAN-CTGAN implemented in Synthetic Data Vault (SDV), Wasserstein GAN (WGAN), and/or WGAN plus gradient penalty (WGAN-GP) to synthesize one or more missing evaluation feature values for the tabular dataset. In some embodiments, the tabular dataset includes a mix of discrete and continuous columns. In such a case, CTGAN may be leveraged to synthesize the mix of data. Other tabular synthetic data generation techniques may include GaussianCopula, CopulaGAN, triplet-based variational autoencoder (TVAE) and/or the like. In addition, in some examples, hierarchical modeling algorithm 1 (HMA1) and/or like may be leveraged for multi-table evaluation features and/or TimeGAN, autoregressive (AR), periodic autoregressive (PAR), and/or the like may be leveraged for timeseries data. Additionally tabular synthetic data can be derived from third party platforms, such as MDClone, Mostly.ai, and/or the like.”).
Wuest et al., Singh et al. and Narayanan et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in generating data. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wuest et al. in combination with Singh et al. to incorporate the teachings of Narayanan et al. of wherein the first category of synthetic data generating methods includes a plurality of generative artificial intelligence architectures which provides the benefit of provides improved machine learning evaluation, training, and monitoring techniques that overcome the technical challenges of conventional machine learning techniques ([0003] of Narayanan et al.).
Regarding claims 5 and 15, Wuest et al. in combination with Singh et al. teaches the limitations as in claims 1 and 11, above.
Narayanan et al. further teaches:
5 and 15. The apparatus/method of claims 1 and 11, wherein the first category of synthetic data generation includes a hierarchical modeling algorithm (HMA) (see ¶ [0099] citation as in claims 4 and 14, above and further: “… In addition, in some examples, hierarchical modeling algorithm 1 (HMA1) and/or like may be leveraged for multi-table evaluation features and/or TimeGAN, autoregressive (AR), periodic autoregressive (PAR), and/or the like may be leveraged for timeseries data. Additionally tabular synthetic data can be derived from third party platforms, such as MDClone, Mostly.ai, and/or the like.”).
Wuest et al., Singh et al. and Narayanan et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in generating data. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wuest et al. in combination with Singh et al. to incorporate the teachings of Narayanan et al. of wherein the first category of synthetic data generating methods includes a hierarchical modeling algorithm (HMA) which provides the benefit of provides improved machine learning evaluation, training, and monitoring techniques that overcome the technical challenges of conventional machine learning techniques ([0003] of Narayanan et al.).
Claims 6 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wuest et al. (US 20250094325 A1) in view of Singh et al. (US 10339038 A1) and Narayanan et al. (US 20240249158 A1) as applied to claims 5 and 15 above, and further in view of Gupta et al. (US 20200110795 A1).
Regarding claims 6 and 16, Wuest et al. in combination with Singh et al. and Narayanan et al. teaches the limitations as in claims 5 and 15, above.
However, Wuest et al. in combination with Singh et al. does not explicitly teach, but Gupta et al. does teach:
6 and 16. The apparatus/method of claims 5 and 15, wherein the processor is further configured to extract combination data of the received data and feed the extracted combination data to the HMA as metadata (see ¶ [0023]: “As used herein, the term “electronic form” is used to refer to an electronic document having a data object (i.e., a field) configured to receive data via an input device and metadata identifying the data object….”
and ¶ [0030]: “…As described in more detail herein with respect to FIG. 6, the electronic form server system 102 aggregates the extracted contents into a hierarchical entity-data model 110.”).
Wuest et al., Singh et al. and Narayanan et al. et al., and Gupta et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in data generation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wuest et al. in combination with Singh et al. and Narayanan et al. to incorporate the teachings of Gupta et al. of wherein the processor is further configured to extract combination data of the data and feed the extracted combination data to the HMA as metadata which provides the benefit of improves data security of autocompletion as the form-filling service is capable of autocompleting input field elements where the data is not explicitly stored on the form-filling service ([0022] of Gupta et al.).
Claims 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wuest et al. (US 20250094325 A1) further in view of Singh et al. (US 10339038 A1) as applied to claims 1 and 11 above, and further in view of Walters et al. (US 10382799 B1).
Regarding claims 7 and 17, Wuest et al. in combination with Singh et al. teaches the limitations as in claims 1 and 11, above.
However, Wuest et al. in combination with Singh et al. does not explicitly teach, but Walters et al. does teach:
7 and 17. The apparatus/method of claims 1 and 11, wherein the second category of synthetic data generation includes a distribution-based generation (see ¶ starting at Col. 13, line 20: “(59) FIG. 5B depicts a process 510 for generating synthetic data using class and subclass-specific models, consistent with disclosed embodiments. Process 510 can include the steps of retrieving actual data, determining classes of sensitive portions of the data, selecting types for synthetic data used to replace the sensitive portions of the actual data, generating synthetic data using a data model for the appropriate type and class, and replacing the sensitive data portions with the synthetic data portions. In some embodiments, the data model can be a generative adversarial network trained to generate synthetic data satisfying a similarity criterion, as described herein. This improvement addresses a problem with synthetic data generation, that a synthetic data model may fail to generate examples of proportionately rare data subclasses. For example, when data can be classified into two distinct subclasses, with a second subclass far less prevalent in the data than a first subclass, a model of the synthetic data may generate only examples of the most common first data subclasses. The synthetic data model effectively focuses on generating the best examples of the most common data subclasses, rather than acceptable examples of all the data subclasses. Process 510 addresses this problem by expressly selecting subclasses of the synthetic data class according to a distribution model based on the actual data.”).
Wuest et al., Singh et al. et al. and Walters et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in data generation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wuest et al. in combination with Singh et al. to incorporate the teachings of Walters et al. of wherein the second category of synthetic data generation methods includes a distribution-based generation which provides the benefit of improving the quality of the synthetic data model (Col. 7, lines 66-67 of Walters et al.).
Claims 8 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wuest et al. (US 20250094325 A1) in view of Singh et al. (US 10339038 A1) as applied to claims 1 and 11 above, and further in view of Forgeat et al. (US 20220076066 A1).
Regarding claims 8 and 18, Wuest et al. in combination with Singh et al. teaches the limitations as in claims 1 and 11, above.
However, Wuest et al. in combination with Singh et al. does not explicitly teach, but Forgeat et al. does teach:
8 and 18. The apparatus/method of claims 1 and 11, wherein the synthetic data is anonymized while retaining the referential integrity of the data (see ¶ [0044]: “...The embodiments provide a process to generate synthetic data from the actual collected network operator data where the synthetic data is anonymized to provide privacy, but the synthesized network operator data maintains other essential properties that make it useful for research and analysis related to machine learning.”
Wuest et al., Singh et al. and Forgeat et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in generating data. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wuest et al. in combination with Singh et al. to incorporate the teachings of Forgeat et al. of wherein the synthetic data is anonymized while retaining the referential integrity of the data which provides the benefit of improving the execution of the computing environment in terms of execution efficiency, storage utilization, bandwidth utilization, timing (e.g., latencies) and similar characteristics of the computing environment ([0027] of Forgeat et al.).
Claims 9 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wuest et al. (US 20250094325 A1) further in view of Singh et al. (US 10339038 A1) as applied to claims 1 and 11 above, and further in view of Kasthurirathne et al. (US 20200312457 A1).
Regarding claims 9 and 19, Wuest et al. in combination with Singh et al. teaches the limitations as in claims 1 and 11, above.
However, Wuest et al. in combination with Singh et al. does not explicitly teach, but Kasthurirathne et al. does teach:
9 and 19. The apparatus/method of claims 1 and 11, wherein the generative framework is further configured to generate a free text variable through a large language model (LLM) (see ¶ [0067-0068]: “[0067] The following hypothetical scenario is proposed to demonstrate how the approach could be applied in a real-life setting; An organization that possesses rich free-text data sources, but lacks adequate machine learning expertise can leverage the approach to create synthetic data. They de-identify and share the synthetic data with experts who use it to build machine learning models. Once optimal models have been identified, they can be implemented across the original dataset with compatible performance measures. [0068] The test dataset consisted of structurally similar reports describing a very specific illness. This, together with the overall simplicity of the predictive outcomes (positive vs. negative for salmonella) may have contributed to the positive results. Datasets that are not structurally similar, nor restricted to a specific illness, or consist of more colloquial language may be harder mimic, and thus, produce less optimal results. Such datasets may require more robust decision models built using other free-text friendly GAN models such as Maximum-Likelihood augmented discrete Generative Adversarial Networks (MaliGAN) or Long Text Generative Adversarial Networks (LeakGAN), and more complex feature vectors consisting of n-grams. Further, the approach was restricted to mimicking synthetic free-text data. The models can learn or mimic the significance of various numeric values such as age or other measurements present in free-text data. This feature may not impact the performance of the current effort as no numerical values were selected as top features. However, it may impact models built using other datasets.”).
Wuest et al., Singh et al. et al., and Kasthurirathne et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in data generation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wuest et al. in combination with Singh et al. to incorporate the teachings of Kasthurirathne et al. of wherein the generative framework is further configured to generate a free text variable through a large language model (LLM) which provides the benefit of improving synthetic data generation ([0029] of Kasthurirathne et al.).
Claims 10 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wuest et al. (US 20250094325 A1) further in view of Singh et al. (US 10339038 A1) as applied to claims 1 and 11 above, and further in view of Carbune et al. (US 20250069617 A1).
Regarding claims 10 and 20, Wuest et al. in combination with Singh et al. teaches the limitations as in claims 1 and 11, above.
However, Wuest et al. in combination with Singh et al. does not explicitly teach, but Carbune et al. does teach:
10 and 20. The apparatus/method of claims 1 and 11, wherein the processor is further configured to:
receive user input, the user input including a selection of a category of synthetic data generating methods of the generative framework (see ¶ [0004]: “[0004] Implementations of the disclosure may include one or more of the following optional features. In some implementations, the operations further include, identifying, by the assistant interface, an intermediate list of candidate business LLMs each capable of performing at least a portion of the action. Here, the intermediate list of candidate business LLMs includes a first business LLM and a different second business LLM that are both capable of performing a same respective portion of the action. In these implementations, the operations further include, prompting, by the assistant interface, the user to select which one of the first business LLM or the second business LLM the user prefers for the assistant interface to interact with to fulfill performance of the respective portion of the action; and receiving, at the assistant interface, a selection input indication by the user that indicates selection of the first business LLM for the assistant interface to interact with to fulfill performance of the respective portion of the action. Here, the one or more business LLMs selected by the assistant interface for the assistant interface to interact with to fulfill performance of the action includes the first business LLM and excludes the second business LLM.”); and
generate the synthetic data through category of synthetic data generating methods selected from the user input (see ¶ [0004] citation as in limitation above, and further ¶ [0011]: “…The operations also include providing, for output from the user device, presentation content based on the corresponding response content received from each corresponding business LLM of the selected one or more business LLMs.”).
Wuest et al., Singh et al. et al. and Carbune et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in data generation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Wuest et al. in combination with Singh et al. to incorporate the teachings of Carbune et al. of receive user input, the user input including a selection of a category of synthetic data generating methods of the generative framework and generate the synthetic data through category of synthetic data generating methods selected from the user input which provides the benefit of enabling robust interaction with business LLMs ([0001] of Carbune et al.).
Conclusion
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Keisha Y. Castillo-Torres
Examiner
Art Unit 2659
/Keisha Y. Castillo-Torres/Examiner, Art Unit 2659
Prosecution Insights