DETAILED ACTION
This Office Action has been issued in response to Applicant's RCE filed June 5, 2026.
Claims 1, 8, and 15 have been amended. Claims 1, 2, 4-9, 11-16, and 18-20 have been examined and are pending.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 May 5, 2026 has been entered.
Response to Arguments
Applicant's arguments filed May 5, 2026 have been fully considered but they are not persuasive.
Applicant argues the references do not disclose a privacy cost determined according to an estimated sensitivity to training of a generative model according to a real data set. Paragraph [0043] of Pese discloses the error function module 92 receives as input the vehicle data 102 that is collected from the vehicle 12 (e.g., from the vehicle sensors 16, bus 16, or other data sources 20) and the distorted vehicle data 104. Based on the inputs, the error function module 92 computes an information loss metric (γ) and generates information loss metric data 106 based thereon. Paragraph [0045] of Pese discloses the OEM budget calculation module 94 receives as input the information loss metric data 106. The OEM budget calculation module 94 calculates an OEM privacy budget and generates OEM privacy budget data 18 based thereon.
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, 2, 4-9, 11-16, and 18-20 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 claims recite “estimating a sensitivity to the training of the generative model according to the real data set”. Examiner was unable to find discussion of the sensitivity estimation using the real data set.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 4, 5, 7-9, 11, 12, 14-16, 18, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over US 2023/0137378 to LaTerza et al. (hereinafter “LaTerza”) and further in view of US Pub. No. 2020/0175193 to Pese et al. (hereinafter “Pese”).
As to Claim 1, LaTerza discloses a computer-implemented method, comprising:
training a generative model using a real data set, the real data set comprising a plurality of real data records and information identifying one or more sources of the plurality of real data records (Paragraph [0026] of LaTerza discloses the privacy preserving data generation model 112 may be an ML model trained for generating private synthetic training data from non-private true training data. Paragraph [0039] of LaTerza discloses the true training data 210 may include one or more sets of labeled training data that has been generated, prepared and/or reviewed for training an ML model. As discussed above, because the labeled training data may include private information, it may not be prudent to use the training data directly);
[estimating a sensitivity to the training of the generative model according to the real data set];
[determining, according to the estimated sensitivity, a privacy cost of generating synthetic data samples using the trained generative model];
generating a synthetic data set using the trained generative model, wherein the generated synthetic data set comprises a number of samples of computer-generated data records different from the real plurality of data records (Paragraph [0026] of LaTerza discloses the privacy preserving data generation model 112 may be an ML model trained for generating private synthetic training data from non-private true training data);
wherein [the number of samples is determined according to a privacy budget and the determined privacy cost] to ensure differential privacy of the plurality of data records in the real data set, and wherein the generated synthetic data set excludes the information identifying the one or more sources of the plurality of real data records (Paragraph [0057] of LaTerza discloses to ensure that the synthetic training data generated by private synthetic training dataset preserves privacy at a required level, method 400 may proceed to perform a leakage analysis on the generated synthetic training data, at 440. Paragraph [0058] of LaTerza discloses the leakage analysis may involve analyzing the synthetic training data to ensure that the percentage of private data included in the synthetic training data does not exceed a given leakage threshold).
LaTerza does not explicitly disclose estimating a sensitivity to the training of the generative model according to the real data set and determining, according to the estimated sensitivity, a privacy cost of generating synthetic data samples using the trained generative model and the number of samples is determined according to a privacy budget and the determined privacy cost.
However, Pese discloses this. Paragraph [0043] of Pese discloses the error function module 92 receives as input the vehicle data 102 that is collected from the vehicle 12 (e.g., from the vehicle sensors 16, bus 16, or other data sources 20) and the distorted vehicle data 104. Based on the inputs, the error function module 92 computes an information loss metric (γ) and generates information loss metric data 106 based thereon. Paragraph [0045] of Pese discloses the OEM budget calculation module 94 receives as input the information loss metric data 106. The OEM budget calculation module 94 calculates an OEM privacy budget and generates OEM privacy budget data 18 based thereon. Paragraph [0047] of Pese discloses the application privacy budget calculation module 96 calculates an application specific privacy budget based on the privacy factor (PRF) and the trustworthiness score (TS) of the received data and generates application privacy budget data 110 based thereon. Paragraph [0050] of Pese discloses the application samples calculation module 98 receives as input the OEM privacy budget data 108, and the application privacy budget data 110. The application samples calculation module 98 calculates the application samples and generates application samples data 112 based thereon. The application samples is the number of data points/samples that the third-party application is allowed to retrieve for the selected sensor. It is calculated using the number of allowed OEM data points. Paragraph [0045] of Pese discloses the minimum OEM privacy guarantee is subject to data accuracy requirements and is subtracted at each query from the privacy budget. Paragraph [0046] of Pese discloses where εOEM represents the minimum OEM privacy guarantee which is subject to a sensor accuracy requirement provided by the OEM.
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to combine the private data system as disclosed by LaTerza, with limiting the number of samples as disclosed by Pese. One of ordinary skill in the art would have been motivated to combine to apply a known technique to a known device. LaTerza and Pese are directed toward private data system and as such it would be obvious to use the techniques of one in the other. LaTerza’s privacy would be improved by limiting samples as disclosed by Pese.
As to Claim 2, LaTerza-Pese discloses the method of claim 1, further comprising: training a differentially-private machine learning model according to the sampled synthetic data (Paragraph [0058] of LaTerza discloses when it is determined that the synthetic training data meets the leakage threshold, method 400 may proceed to provide the synthetic training data for training the language classifier model, at 455).
As to Claim 4, LaTerza-Pese discloses the computer-implemented method of claim 1, wherein the plurality of real data records are usable to train the generative model to make inferences, and wherein the plurality of computer-generated data records are usable to train another model to make the inferences (Paragraph [0026] of LaTerza discloses the privacy preserving data generation model 112 may be an ML model trained for generating private synthetic training data from non-private true training data. Paragraph [0058] of LaTerza discloses when it is determined that the synthetic training data meets the leakage threshold, method 400 may proceed to provide the synthetic training data for training the language classifier model, at 455).
As to Claim 5, LaTerza-Pese discloses the computer-implemented method of claim 1, further comprising: estimating a training sensitivity for the generative model according to the real data set; wherein sampling the generated synthetic data set to ensure differential privacy of the data in the real data set is performed according to the estimated training sensitivity (Paragraph [0057] of LaTerza discloses to ensure that the synthetic training data generated by private synthetic training dataset preserves privacy at a required level, method 400 may proceed to perform a leakage analysis on the generated synthetic training data, at 440. Paragraph [0058] of LaTerza discloses the leakage analysis may involve analyzing the synthetic training data to ensure that the percentage of private data included in the synthetic training data does not exceed a given leakage threshold. Paragraph [0047] of Pese discloses the application privacy budget calculation module 96 calculates an application specific privacy budget based on the privacy factor (PRF) and the trustworthiness score (TS) of the received data and generates application privacy budget data 110 based thereon.
Examiner recites the same rationale to combine used for claim 1.
As to Claim 7, LaTerza-Pese discloses the computer-implemented method of claim 5, wherein a number of samples of the sampled generated synthetic data set is determined according to specified amount of differential privacy and the estimated training sensitivity (Paragraph [0050] of Pese discloses the application samples calculation module 98 receives as input the OEM privacy budget data 108, and the application privacy budget data 110. The application samples calculation module 98 calculates the application samples and generates application samples data 112 based thereon. The application samples is the number of data points/samples that the third-party application is allowed to retrieve for the selected sensor. It is calculated using the number of allowed OEM data points. Paragraph [0047] of Pese discloses the application privacy budget calculation module 96 calculates an application specific privacy budget based on the privacy factor (PRF) and the trustworthiness score (TS) of the received data and generates application privacy budget data 110 based thereon.
Examiner recites the same rationale to combine used for claim 1.
As to Claim 8, LaTerza discloses one or more non-transitory computer-accessible storage media storing program instructions that when executed on or across one or more computing devices cause the one or more computing devices to implement: generating a differentially private data set, comprising:
training a machine learning model to produce a generative model according to a real data set, the real data set comprising a plurality of real data records and information identifying one or more sources of the real data (Paragraph [0026] of LaTerza discloses the privacy preserving data generation model 112 may be an ML model trained for generating private synthetic training data from non-private true training data. Paragraph [0039] of LaTerza discloses the true training data 210 may include one or more sets of labeled training data that has been generated, prepared and/or reviewed for training an ML model. As discussed above, because the labeled training data may include private information, it may not be prudent to use the training data directly);
[estimating a sensitivity to the training of the machine learning model according to the real data set];
[determining, according to the estimated sensitivity a privacy cost of generating synthetic data samples using the trained machine learning model];
generating a synthetic data set using the trained machine learning model, wherein the generated synthetic data set comprises a number of samples of computer-generated data records different from the real plurality of data records (Paragraph [0026] of LaTerza discloses the privacy preserving data generation model 112 may be an ML model trained for generating private synthetic training data from non-private true training data); and
wherein [the number of samples is determined according to a privacy budget and the determined privacy cost] to ensure differential privacy of the plurality of data records in the real data set, and wherein the generated synthetic data set excludes the information identifying the one or more sources of the plurality of real data records (Paragraph [0057] of LaTerza discloses to ensure that the synthetic training data generated by private synthetic training dataset preserves privacy at a required level, method 400 may proceed to perform a leakage analysis on the generated synthetic training data, at 440. Paragraph [0058] of LaTerza discloses the leakage analysis may involve analyzing the synthetic training data to ensure that the percentage of private data included in the synthetic training data does not exceed a given leakage threshold).
LaTerza does not explicitly disclose estimating a sensitivity to the training of the machine learning model according to the real data set and determining, according to the estimated sensitivity a privacy cost of generating synthetic data samples using the trained machine learning model and the number of samples is determined according to a privacy budget and the determined privacy cost.
However, Pese discloses this. Paragraph [0043] of Pese discloses the error function module 92 receives as input the vehicle data 102 that is collected from the vehicle 12 (e.g., from the vehicle sensors 16, bus 16, or other data sources 20) and the distorted vehicle data 104. Based on the inputs, the error function module 92 computes an information loss metric (γ) and generates information loss metric data 106 based thereon. Paragraph [0045] of Pese discloses the OEM budget calculation module 94 receives as input the information loss metric data 106. The OEM budget calculation module 94 calculates an OEM privacy budget and generates OEM privacy budget data 18 based thereon. Paragraph [0047] of Pese discloses the application privacy budget calculation module 96 calculates an application specific privacy budget based on the privacy factor (PRF) and the trustworthiness score (TS) of the received data and generates application privacy budget data 110 based thereon. Paragraph [0050] of Pese discloses the application samples calculation module 98 receives as input the OEM privacy budget data 108, and the application privacy budget data 110. The application samples calculation module 98 calculates the application samples and generates application samples data 112 based thereon. The application samples is the number of data points/samples that the third-party application is allowed to retrieve for the selected sensor. It is calculated using the number of allowed OEM data points. Paragraph [0045] of Pese discloses the minimum OEM privacy guarantee is subject to data accuracy requirements and is subtracted at each query from the privacy budget. Paragraph [0046] of Pese discloses where εOEM represents the minimum OEM privacy guarantee which is subject to a sensor accuracy requirement provided by the OEM.
Examiner recites the same rationale to combine used for claim 1.
As to Claim 9, LaTerza-Pese discloses the one or more non-transitory computer-accessible storage media of claim 8, further comprising: training a differentially-private machine learning model according to the sampled synthetic data (Paragraph [0058] of LaTerza discloses when it is determined that the synthetic training data meets the leakage threshold, method 400 may proceed to provide the synthetic training data for training the language classifier model, at 455).
As to Claim 11, LaTerza-Pese discloses the one or more non-transitory computer-accessible storage media of claim 8, wherein the plurality of real data records are usable to train the generative model to make inferences, and wherein the plurality of computer-generated data records are usable to train another model to make the inferences (Paragraph [0026] of LaTerza discloses the privacy preserving data generation model 112 may be an ML model trained for generating private synthetic training data from non-private true training data. Paragraph [0058] of LaTerza discloses when it is determined that the synthetic training data meets the leakage threshold, method 400 may proceed to provide the synthetic training data for training the language classifier model, at 455).
As to Claim 12, LaTerza-Pese discloses the one or more non-transitory computer-accessible storage media of claim 8, further comprising: estimating a training sensitivity for the generative model according to the real data set; wherein sampling the generated synthetic data set to ensure differential privacy of the data in the real data set is performed according to the estimated training sensitivity (Paragraph [0057] of LaTerza discloses to ensure that the synthetic training data generated by private synthetic training dataset preserves privacy at a required level, method 400 may proceed to perform a leakage analysis on the generated synthetic training data, at 440. Paragraph [0058] of LaTerza discloses the leakage analysis may involve analyzing the synthetic training data to ensure that the percentage of private data included in the synthetic training data does not exceed a given leakage threshold. Paragraph [0047] of Pese discloses the application privacy budget calculation module 96 calculates an application specific privacy budget based on the privacy factor (PRF) and the trustworthiness score (TS) of the received data and generates application privacy budget data 110 based thereon.
Examiner recites the same rationale to combine used for claim 1.
As to Claim 14, LaTerza-Pese discloses the one or more non-transitory computer-accessible storage media of claim 12, wherein a number of samples of the sampled generated synthetic data set is determined according to specified amount of differential privacy and the estimated training sensitivity (Paragraph [0050] of Pese discloses the application samples calculation module 98 receives as input the OEM privacy budget data 108, and the application privacy budget data 110. The application samples calculation module 98 calculates the application samples and generates application samples data 112 based thereon. The application samples is the number of data points/samples that the third-party application is allowed to retrieve for the selected sensor. It is calculated using the number of allowed OEM data points. Paragraph [0047] of Pese discloses the application privacy budget calculation module 96 calculates an application specific privacy budget based on the privacy factor (PRF) and the trustworthiness score (TS) of the received data and generates application privacy budget data 110 based thereon.
Examiner recites the same rationale to combine used for claim 1.
As to Claim 15, LaTerza discloses a system, comprising: one or more processors; and a memory storing program instructions that when executed by the one or more processors cause the one or more processors to implement a differentially private data set generator, configured to:
train a machine learning model to produce a generative model according to a real data set, the real data set comprising a plurality of real data records and information identifying one or more sources of the real data (Paragraph [0026] of LaTerza discloses the privacy preserving data generation model 112 may be an ML model trained for generating private synthetic training data from non-private true training data. Paragraph [0039] of LaTerza discloses the true training data 210 may include one or more sets of labeled training data that has been generated, prepared and/or reviewed for training an ML model. As discussed above, because the labeled training data may include private information, it may not be prudent to use the training data directly);
[estimating a sensitivity to the training of the machine learning model according to the real data set];
[determining, according to the estimated sensitivity a privacy cost of generating synthetic data samples using the trained machine learning model]; and
generating a synthetic data set using the trained machine learning model, wherein the generated synthetic data set comprises a number of samples of computer-generated data records different from the real plurality of data records (Paragraph [0026] of LaTerza discloses the privacy preserving data generation model 112 may be an ML model trained for generating private synthetic training data from non-private true training data);
wherein [the number of samples is determined according to a privacy budget and the determined privacy cost] to ensure differential privacy of the plurality of data records in the real data set, and wherein the generated synthetic data set excludes the information identifying the one or more sources of the plurality of real data records (Paragraph [0057] of LaTerza discloses to ensure that the synthetic training data generated by private synthetic training dataset preserves privacy at a required level, method 400 may proceed to perform a leakage analysis on the generated synthetic training data, at 440. Paragraph [0058] of LaTerza discloses the leakage analysis may involve analyzing the synthetic training data to ensure that the percentage of private data included in the synthetic training data does not exceed a given leakage threshold).
LaTerza does not explicitly disclose estimating a sensitivity to the training of the machine learning model according to the real data set and determining, according to the estimated sensitivity a privacy cost of generating synthetic data samples using the trained machine learning model and the number of samples is determined according to a privacy budget and the determined privacy cost.
However, Pese discloses this. Paragraph [0043] of Pese discloses the error function module 92 receives as input the vehicle data 102 that is collected from the vehicle 12 (e.g., from the vehicle sensors 16, bus 16, or other data sources 20) and the distorted vehicle data 104. Based on the inputs, the error function module 92 computes an information loss metric (γ) and generates information loss metric data 106 based thereon. Paragraph [0045] of Pese discloses the OEM budget calculation module 94 receives as input the information loss metric data 106. The OEM budget calculation module 94 calculates an OEM privacy budget and generates OEM privacy budget data 18 based thereon. Paragraph [0047] of Pese discloses the application privacy budget calculation module 96 calculates an application specific privacy budget based on the privacy factor (PRF) and the trustworthiness score (TS) of the received data and generates application privacy budget data 110 based thereon. Paragraph [0050] of Pese discloses the application samples calculation module 98 receives as input the OEM privacy budget data 108, and the application privacy budget data 110. The application samples calculation module 98 calculates the application samples and generates application samples data 112 based thereon. The application samples is the number of data points/samples that the third-party application is allowed to retrieve for the selected sensor. It is calculated using the number of allowed OEM data points. Paragraph [0045] of Pese discloses the minimum OEM privacy guarantee is subject to data accuracy requirements and is subtracted at each query from the privacy budget. Paragraph [0046] of Pese discloses where εOEM represents the minimum OEM privacy guarantee which is subject to a sensor accuracy requirement provided by the OEM.
Examiner recites the same rationale to combine used for claim 1.
As to Claim 16, LaTerza-Pese discloses the system of claim 15, wherein the differentially private data set generator is configured to: train a differentially-private machine learning model according to the sampled synthetic data (Paragraph [0058] of LaTerza discloses when it is determined that the synthetic training data meets the leakage threshold, method 400 may proceed to provide the synthetic training data for training the language classifier model, at 455).
As to Claim 18, LaTerza-Pese discloses the system of claim 15, wherein the plurality of real data records are usable to train the generative model to make inferences, and wherein the plurality of computer-generated data records are usable to train another model to make the inferences (Paragraph [0026] of LaTerza discloses the privacy preserving data generation model 112 may be an ML model trained for generating private synthetic training data from non-private true training data. Paragraph [0058] of LaTerza discloses when it is determined that the synthetic training data meets the leakage threshold, method 400 may proceed to provide the synthetic training data for training the language classifier model, at 455).
As to Claim 19, LaTerza-Pese discloses the system of claim 15, wherein the differentially private data set generator is configured to: estimate a training sensitivity for the generative model according to the real data set; wherein sampling the generated synthetic data set to ensure differential privacy of the data in the real data set is performed according to the estimated training sensitivity (Paragraph [0057] of LaTerza discloses to ensure that the synthetic training data generated by private synthetic training dataset preserves privacy at a required level, method 400 may proceed to perform a leakage analysis on the generated synthetic training data, at 440. Paragraph [0058] of LaTerza discloses the leakage analysis may involve analyzing the synthetic training data to ensure that the percentage of private data included in the synthetic training data does not exceed a given leakage threshold. Paragraph [0047] of Pese discloses the application privacy budget calculation module 96 calculates an application specific privacy budget based on the privacy factor (PRF) and the trustworthiness score (TS) of the received data and generates application privacy budget data 110 based thereon).
Examiner recites the same rationale to combine used for claim 1.
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over LaTerza-Pese and further in view of US Pub. No. 2021/0216902 to Sutcher-Shepard et al. (hereinafter “Sutcher”).
As to Claim 6, LaTerza-Pese discloses the computer-implemented method of claim 5. LaTerza-Pese does not explicitly discloses wherein the estimating is based at least in part on a Hessian of a loss function of the real data set.
However, Sutcher discloses this. Paragraph [0045] of Sutcher discloses differentially private federated learning process can incorporate two assumptions. The Hessian of the loss function at the local minima to which the loss converges can be sufficiently similar.
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to combine the private data creating system as disclosed by LaTerza, with using the Hessian as disclosed by Sutcher. One of ordinary skill in the art would have been motivated to combine to apply a known technique to a known device. LaTerza and Sutcher are directed toward differential privacy systems and as such it would be obvious to use the techniques of one in the other.
As to Claim 13, LaTerza-Pese discloses the one or more non-transitory computer-accessible storage media of claim 12. LaTerza-Pese does not explicitly discloses wherein the estimating is based at least in part on a Hessian of a loss function of the real data set.
However, Sutcher discloses this. Paragraph [0045] of Sutcher discloses differentially private federated learning process can incorporate two assumptions. The Hessian of the loss function at the local minima to which the loss converges can be sufficiently similar.
Examiner recites the same rationale to combine used for claim 6.
As to Claim 20, LaTerza-Pese discloses the system of claim 19. LaTerza-Pese does not explicitly discloses wherein the estimating is based at least in part on a Hessian of a loss function of the real data set.
However, Sutcher discloses this. Paragraph [0045] of Sutcher discloses differentially private federated learning process can incorporate two assumptions. The Hessian of the loss function at the local minima to which the loss converges can be sufficiently similar.
Examiner recites the same rationale to combine used for claim 6.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Kevin S Mai whose telephone number is (571)270-5001. The examiner can normally be reached Monday to Friday 9AM to 5PM.
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/KEVIN S MAI/Primary Examiner, Art Unit 2499
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