Response to Amendment
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 .
This action is responsive to amendment filed on 2/11/26. Claims 1, 3-8, 10-15 and 17-20 are pending.
Specification
The Abstract objection has been overcome and is withdrawn.
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, 3-8, 10-15 and 17-20 are rejected under 35 USC 101 as being directed to an Abstract Idea without significantly more.
For example, with respect to claim 1, the steps of “determining privacy parameters”, “determining a sampling rate ”, “calculating one or more computed results to queries” and “applying differential privacy to… computed results” would be considered mental process, as they are observations/evaluations/judgements that could reasonably be performed by the mind, with the aid of pen/paper.
The additional elements in claim 1 are “initializing…computed results from subsets of a dataset” would be considered insignificant extra solution activity in step 2A, prong 2 (data gathering). The “sampling rate defines a probability of inclusion for each individual data record in a dataset” limitation would be considered as extra solution activity (receiving/transmitting data based on the probability). The element, “wherein determining the sampling rate… randomly selecting individual records from the dataset for inclusion in each subset based on the sampling rate”, the limitation amount to data gathering considered insignificant extra solution activity (MPEP 2106.05(g). The differential privacy framework or processor is a generic computer component. So, the claim limitations, taken along or in combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Regarding claim 3, the limitation “modifying the privacy parameters to account for… sampling the dataset” would be considered mental process, as they are observations/evaluations/judgements that could reasonably be performed by the mind, with the aid of pen/paper.
Regarding claims 4 and 5, the limitation “differential privacy framework is a budget recycling differential privacy framework… and a recycling mechanism” and “determined based on the BR-DP framework” further falls under a generic computer component as rejected in claim 1 above. The components may be integrated to apply different parameters based on data type.
Regarding claim 6, the limitation “applying the differential privacy to each computed result… adding the random noise value to the computed result” is a mere generic transmission and presentation of collected computed result which is considered insignificant extra solution activity (MPEP 2106.05(g).
Regarding system claims 8, 10-14 and media claims 15, 17-20, they comprise substantially the same subject matter as rejected method claims 1, 3-7 above, and are therefore rejected as being directed to an Abstract Idea without significantly more on the merits.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 3-8, 10-15 and 17-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Peng et al. (USPN. 2025/0005179).
Regarding claims 1, 8 and 15, Peng discloses a system, medium and method comprising:
memory, processor and network (figs. 1 and 9, par. 56, memory, cpu and network):
initializing a differential privacy framework for providing differential privacy to computed results from subsets of a dataset (fig. 1, par. 54, differential privacy system 150);
determining privacy parameters for the differential privacy framework (par. 53, query parameters);
determining a sampling rate wherein the sampling rate defines a probability of inclusion for each individual data record in a dataset (fig. 1, par. 55, sampling rates and pars. 137-150, outputs include a tuple with an estimate of n data records of sampling rate), wherein determining the sampling rate comprises iteratively evaluating sampling rate values to determine a sampling rate that maximizes utility based on the privacy parameters and minimizing an overall error caused by sampling the subsets of the dataset (pars. 136 and 150, adaptive sampling and maximum privacy budget, parameters selectors and including the functions of parameter selectors, combinative estimators and stopping criterion with repeating the procedure comprises iterative evaluation. Note that repeating the above steps not only performs the same steps as claimed, but meets the same objective to minimize error or budget for spending);
determining the subsets of the dataset using the sampling rate, wherein determining the subsets comprises randomly selecting records from the dataset for inclusion in each subset based on the sampling rate (pars. 154-155, noise variance at round i or iterations for adaptive privacy budget and sampling rate for sampling X, note par. 150 discussing probability with respect to randomness);
calculating one or more computed results to queries as applied to a corresponding sampled subset (par. 55, adaptive estimation, see fig. 1, items 154 and 158); and
applying differential privacy to each of the one or more computed results according to the differential privacy framework (fig. 3, obtain dataset and generate output, items 302-310).
3. The method of claim 1, further comprising: modifying the privacy parameters to account for an amplified privacy parameter provided by sampling the dataset (pars. 150-155, maximum privacy budget, using different ranges of parameters toggling between truth and estimate).
4. The method of claim 1, wherein the differential privacy framework is a budget recycling-differential privacy framework that separates the privacy parameters between a differential privacy mechanism and a recycling mechanism (pars. 136 and 150, adaptive sampling and maximum privacy budget, parameters selectors).
5. The method of claim 4, wherein the privacy parameters are determined based on the BR-DP framework (par. 129-136, adaptive sampling using differential privacy and Gaussian observation with variance and update standard deviation).
6. The method of claim 1, wherein applying differential privacy to each computed result comprises generating a random noise value and adding the random noise value to the computed result (pars. 32-33, random number value within a noise range).
7. The method of claim 1, wherein determining the sampling rate depends on a type of query being applied to the dataset (fig. 1 , items 150, 154-158, pars. 55 and 56, plurality of sampling rates using a noise value and query).
Response to Arguments
Applicant's arguments filed 2/11/26 have been fully considered but they are not persuasive. See remarks for details below:
Applicant alleges the claimed invention provides practical application.
Examiner disagrees. The independent claims merely apply an abstract privacy methodology using sampling. The claim does not include or provide any detailed steps with regard creating utility of any resulted computations besides applying it according to the differential framework. Note that determining a sampling rate that maximizes utility based on the privacy parameters does not comprise a detailed structure and method for achieving the desired results. The sampling rate especially over a smaller dataset may be performed in the human mind. Similarly, “iteratively evaluating sampling rate…”as claimed over a smaller dataset may also be performed in the human mind. Vital details comprising filtering and conditions used on the privacy parameters may advance the utility aspect of the claimed system, but has not been added to the claims.
In summary, the practical application has not been established by the broadness of the claim limitations, instead probability and randomness is part of noise data processing and not a detailed structure and error processing method. The common features claimed are further anticipated by Peng et al. For details, please refer to the updated rejection.
Applicant alleges claim 1 is not taught by Peng, especially “randomly select... based on the sampling rate”.
In response, the office action reads,
“determining the subsets of the dataset using the sampling rate, wherein determining the subsets comprises randomly selecting records from the dataset for inclusion in each subset based on the sampling rate (pars. 154-155, noise variance at round i or iterations for adaptive privacy budget and sampling rate for sampling X, note par. 150 discussing probability with respect to randomness).
The functions determine subsets of the dataset using a sampling rate (see function composition of “n = gi…” and sampling rate used for adaptive sampling, pars. 154-155). The randomly selecting records/values from the dataset for inclusion is included in the function comprising listed above. Randomness of selecting records/values is further described in pars. 54 and 150.
Applicant alleges claim 1 is not taught by Peng comprising “results to queries as applied to a corresponding sampled subset”.
Examiner disagrees.
Peng discloses a noise system 156 which handle queries from content provider and retrieves records. Using the noise system 156, noise range values are added to the relevant set of records which are then provided to the provider as a result (see pars. 54-55).
As previously states, Peng determines the inclusion of each individual data record by using a tuple with an estimate of n data records of sampling rate and applying adaptive sampling with a maximum budgeting wherein Gaussian model can be used (par. 43).
Similarly, the claimed sampling rate comprising iteratively evaluating sampling rate values is performed in adaptive sampling and maximum privacy budget wherein multiple functions are repeated to minimize error for budget pending and can also use Gaussian model.
No other allegation were made.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure in the field of data sampling:
USPN. 2024/0135025: claims, data sampling.
USPN. 20230019745: pars. 81 and 83: sampling and randomness/probability
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARCIN R FILIPCZYK whose telephone number is (571)272-4019. The examiner can normally be reached M-F 7-4 EST.
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May 12, 2026
/MARCIN R FILIPCZYK/Primary Examiner, Art Unit 2153
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