DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
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.
Claims 1-20 are 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.
Claims 1 and 11 have been amended to include the new limitation of “validate, via the interface, the synthetic data request and the one or more user input parameters”. However, the application as originally filed does not support the new limitation. The application as originally filed states in ph. [0054]-[0055], “The authentication token 505 and the user input parameters 330 are validated 515. In some embodiments, the validation 515 is performed by the secured environment 305. Additionally or alternatively, the validation 515 is performed by the interface 320. The user input parameters 330 may be determined invalid if they are outside of the ranges allowed by the synthetic data generator 205 or would otherwise cause issues with the synthetic data generator 205. If either the authentication token 505 or the user input parameters 330 are not valid, the secured environment 305 and/or the interface 320 return an error message 520. If the authentication token 505 and the user input parameters 330 are validated, the interface 320 provides the user input parameters 330 to the synthetic data generator 205, which generates one or more sets of synthetic data 310 in accordance with the user input parameters 330.” These are the only passages in the application that describe any type of validation.
As seen in the passages above, the application as originally filed describes the validation of the authentication token 505 and the user input parameters 330 from the request. The application as originally filed never describes validating, via the interface, the synthetic data request separately from the authentication token 505 and/or the user input parameters 330. Accordingly, the claims contain 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 inventors at the time the application was filed, had possession of the claimed invention.
Claims 2-10 and 12-20 are rejected as depending from and including the new matter of claims 1 and 11.
Additionally regarding claims 9 and 19, the claims have been amended as follows “receive [[a]] the synthetic data request for the plurality of synthetic data through an application programming interface (API) associated with the interface”. However, the application as originally filed describes the API as the interface rather than having an application programming interface (API) associated with a separate interface. It’s depicted in figure 3 as the API 320. See also ph. [0054]-[0055], “The secured environment 305 transmits an API request 510 to the interface 320… In some embodiments, the validation 515 is performed by the secured environment 305. Additionally or alternatively, the validation 515 is performed by the interface 320… If the authentication token 505 and the user input parameters 330 are validated, the interface 320 provides the user input parameters 330 to the synthetic data generator 205, which generates one or more sets of synthetic data 310 in accordance with the user input parameters 330.” As seen in these passages it’s the API 320 depicted in 320, and there is no description of the API 320 being associated with separate interface from the API 320.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 11 have been amended to include the new limitation of “validate, via the interface, the synthetic data request and the one or more user input parameters”. The claims are indefinite as to what constitutes the metes and bounds of validating the synthetic data request as opposed to and separately from the input parameters of the API request. Especially in light of the fact that the specification never describes validating the synthetic data request separately from the input parameters 330 and authentication token 505.
Claims 2-10 and 12-20 are rejected as depending from and including the new matter of claims 1 and 11.
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.
Claim(s) 1-5, 7-9, 11-15, and 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Villasante Marcos et al. (US 2025/0126497) (hereinafter Villasante) in view of Hong (US 2015/0212931) (hereinafter Hong).
Regarding claims 1 and 11, Villasante teaches a data generation system and method for secure synthetic data generation comprising:
at least one processor (ph. [0095], one or more microprocessors); and
a memory device in operable communication with the at least one processor (ph. [0095], “The processing circuitry may be configured to execute program code stored in memory, which may include one or several types of memory such as read-only memory (ROM), random-access memory, cache memory, flash memory devices, optical storage devices, etc.”), the memory device including computer-executable instructions stored therein, which, when executed by the processor, cause the at least one processor to:
receive a plurality of historical data including one or more trends (fig. 2, real training data 13; ph. [0035], “automatically discover and learn similarities or patterns in input data”’; ph. [0038], “media data such as images, text, audio, and videos.”);
train a data generator with the plurality of historical data and a plurality of noise data to generate data to simulate the one or more trends (fig. 2, training the Generative Adversarial Network (GAN 10) using the historical data 13 and noise data 15; ph. [0035]-[0037], “Generative modeling is a specific ML unsupervised learning task that attempts to automatically discover and learn similarities or patterns in input data, such that the model can be used to generate new data mimicking the original dataset…FIG. 2 depicts a general model of a GAN 10, with the two sub-models: a generator 11 and a discriminator 12. Training data 13, which may for example comprise actual images, music, network traffic, or the like, provides real data. The images or the like are sampled 14, if necessary, and input to the discriminator 12. Random input 15, such as pseudo-random noise or the like”);
execute the data generator to generate a plurality of synthetic data, wherein the plurality of synthetic data includes the one or more trends (fig. 2, generator 11 generates the synthetic data 16; ph. [0035]-[0037], “Generative modeling is a specific ML unsupervised learning task that attempts to automatically discover and learn similarities or patterns in input data, such that the model can be used to generate new data mimicking the original dataset…FIG. 2 depicts a general model of a GAN 10, with the two sub-models: a generator 11 and a discriminator 12.”; and
output the plurality of synthetic data to a user (fig. 2, synthetic data 16 is output to the user from the generator 11).
Villasante does not explicitly teach receive, via an interface associated with the data generator, a synthetic data request from a user, wherein the synthetic data request includes one or more user input parameters. However, Hong teaches
receive, via an interface associated with the data generator, a synthetic data request from a user (fig. 5, step 502, receive an API call to access a web service), wherein the synthetic data request includes one or more user input parameters (ph. [0028], “that the particular data fields and/or data values (e.g., data types, operands or arguments or other input) to the API call”);
validate, via the interface, the synthetic data request and the one or more user input parameters (fig. 5, step 504, validate the API call; ph. [0028], “the validation module 210 determines whether a received API call is valid based on whether the access module 212 recognizes the syntax of the API call and that the particular data fields and/or data values (e.g., data types, operands or arguments or other input) to the API call are valid, and/or based on any other criteria of validity.”);
execute the data generator with the validated one or more user input parameters as input to generate a plurality of synthetic data responsive to the synthetic data request (ph. [0027], “For some example schemas, the data repository 110 may include tables arranged to associate an API call and its particular input field(s) to a corresponding API response and its particular output fields. The data repository 110 may further include sample data (e.g., dummy data) to include within the output field(s) of a simulated response to an API call that may be generated by the response simulator 106 of FIG. 1 (e.g., by the response module 214) during testing of the API call.”); and
output the generated plurality of synthetic data to [[a]] the user via the interface (fig 5, step 506, based on validating the API call, provide a simulated AIP response that simulates an API response from the web service).
One of ordinary skill in the art before the effective filing date would have been motivated to modify Villasante in the manner taught by Hong in order to allow programmers to test the synthetic data generation system (Hong, ph. [0016], “Use of an example response simulator for testing, rather than a dedicated network resources may result in the use of fewer hardware and/or software components and thus, use of the response simulator may aid in avoiding testing down-time.”).
Regarding claims 2 and 12, the Villasante/Hong combination teaches the system of claim 1 and method of claim 11. Villasante further teaches the data generator is trained with a plurality of types of data (ph. [0035], “The approach has also been applied to create artificial yet highly realistic media data such as text, audio, and videos.”; ph. [0037], “Training data 13, which may for example comprise actual images, music, network traffic, or the like, provides real data.”).
Regarding claims 3 and 13, the Villasante/Hong combination teaches the system of claim 1 and method of claim 11. Villasante further teaches the plurality of types of data are pre-processed before training the data generator (ph. [0037], “Training data 13, which may for example comprise actual images, music, network traffic, or the like, provides real data. The images or the like are sampled 14, if necessary, and input to the discriminator 12.”).
Regarding claims 4 and 14, the Villasante/Hong combination teaches the system of claim 1 and method of claim 11. Villasante further teaches the plurality of historical data includes a plurality of individual data records (ph. [0037], “Training data 13, which may for example comprise actual images, music, network traffic, or the like, provides real data.”).
Regarding claims 5 and 15, the Villasante/Hong combination teaches the system of claim 4 and method of claim 14. Villasante further teaches plurality of synthetic data is randomized by the plurality of noise data so that the plurality of synthetic data cannot be traced back to the individual data records of the plurality of individual data records (fig. 2, noise input into generator 11 to generate synthetic fake data; ph. [0035], “such that the model can be used to generate new data mimicking the original dataset. For example, generative modeling has been used to create photographs of faces that do not exist in the real world, but are highly realistic to human viewers. The approach has also been applied to create artificial yet highly realistic media data such as text, audio, and videos.”; ph. [0037], “Random input 15, such as pseudo-random noise or the like, are input to the generator 11, which attempts to generate output matching the properties of the training data 13.”).
Regarding claims 7 and 17, the Villasante/Hong combination teaches the system of claim 1 and method of claim 11. Villasante further teaches the data generator is trained with a generative adversarial network (fig. 2, generative adversarial network (GAN) 10).
Regarding claims 8 and 18, the Villasante/Hong combination teaches the system of claim 1 and method of claim 11. Hong further teaches the one or more user input parameters include one or more parameters of the desired plurality of synthetic data (fig. 3, input field table 304; ph. [0031], “The input field table 304 may include a list of input fields defined for all the API calls in the schema. Each input field may be associated with one or more input sub-fields, the names of which may be entered in to the input field table 304. Some example input fields and/or input sub-fields may be valid with particular data values such as certain strings, Boolean operators, numbers or other data input values.”)
Regarding claims 9 and 19, the Villasante/Hong combination teaches the system of claim 1 and method of claim 11. Villasante further teaches the at least one processor is further programmed to: receive a request for the plurality of synthetic data through an application programming interface (API), and output the plurality of synthetic data through the API (fig. 6, steps 102 and 106, receive from a network function a request for a synthetic data analytic and sending it; ph. [0006], “The Network Exposure Function (NEF) supports different functionality and specifically in the context of this disclosure, NEF supports different Exposure Application Programming Interfaces (APIs).”; [0011], “The AF interacts with the 3GPP Core Network, and specifically in the context of this disclosure, allows external parties to use the Exposure APIs offered by the network operator.”; ph. [0094], “A request for a SyntheticData analytic is received from a network function (block 102). The request specifies at least an amount of data requested and the type of data requested. A Generative Adversarial Network (GAN) model is used to generate realistic synthetic network traffic data based on actual network traffic collected in the wireless communication network (block 104). The specified amount of synthetic network traffic data, of the specified type, is sent to the requesting network function.”).
Claim(s) 6 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over the Villasante/Hong combination as applied to claims 1 and 11 above, and further in view of Li et al. (US 2022/0147702) (hereinafter Li).
Regarding claims 6 and 16, the Villasante/Hong combination teaches the system of claim 1 and method of claim 11. The combination does not explicitly teach apply the one or more analysis rules to the plurality of synthetic data prior to outputting the plurality of synthetic data. However, Li teaches receive one or more analysis rules, and apply the one or more analysis rules to the plurality of synthetic data prior to outputting the plurality of synthetic data (fig. 1A, formatted template transformation component; ph. [0027], “managing template creation guidance including setting and managing formatting rules applicable for generation of transformed templates”; fig. 1C, post processing of formatted templates 166 applied prior outputting of the transformed template 168; ph. [0071], “The algorithm for refinement may be programmed to evaluate various aspects of transformations under the lens of the formatting rules for formatted templates (previously described). Modifications to the transformed template may be made based on a result of applying the algorithm for refinement. For example, imaging associated with objects may be sharpened (including lines and edges).”). One of ordinary skill in the art before the effective filing date would have been motivated to modify the Villasante/Hong combination in the manner taught by Li in order to perfect the generated synthetic data prior to outputting (Li ph. [0070], “While those transformed templates may be high quality, they may not be perfect and presentation ready. As such, the post-processing phase 166 may comprise processing operations to refine the transformed templates for presentation purposes.”).
Claim(s) 10 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over the Villasante/Hong combination as applied to claims 1 and 11above, and further in view of Wang et al. (US 2023/0139772) (hereinafter Wang).
Regarding claim 10 and 20, the Villasante/Zheng combination teaches the system of claim 1 and method of claim 11. The combination does not explicitly teach the at least one processor is further programmed to, prior to training the data generator, pre-process the plurality of historical data to remove one or more types of bias. However, Wang teaches the at least one processor is further programmed to, prior to training the data generator, pre-process the plurality of historical data to remove one or more types of bias (ph. [0076], “In some cases, the sparse detection data 110 and/or encoded sparse detection data (e.g., a sparse projection image) may include a bias. As such, in some embodiments, the pre-processor 510 may include a normalizer 520 that removes the bias”). One of ordinary skill in the art before the effective filing date would have been motivated to modify the Villasante/Zheng combination in the manner taught by Wang in order to normalize the data being input into the GAN in order to ensure more accurate results.
Response to Arguments
§103 Rejection
Applicants argue that the Villasante/Zheng combination in the office action mailed 12/04/2025 do not teach the newly added receiving and validating steps in the amended claims submitted on 03/03/2026. Applicants’ argument is found to be persuasive.
However, the Villasante/Hong combination teaches the claims as amended as see in the modified §103 rejection above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Gomez et al. (US 2021/0026710) teaches validating data parameters of API calls and a fake backend that generates data to response to call to an API service.
Lopian (US 2016/0140021) teaches isolating software components including making an API call to a mock/synthetic data framework.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN W WATHEN whose telephone number is (571)270-5570. The examiner can normally be reached M-F 9-5:30pm.
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BRIAN W. WATHEN
Primary Examiner
Art Unit 2151
/BRIAN W WATHEN/Primary Examiner, Art Unit 2151
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