Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive.
Applicant argues that the claim overcomes the USC 101 rejection because claim 1 improves the security and privacy aspects of a database, and further increases the privacy of individuals who may have personal data stored in the database.
Respectfully, Examiner asserts that this is intended use and not part of the actual invention. More importantly, Examiner asserts that the claims do not recite any mention of privacy, security, personal data, or any practical application. The claims merely state that there is an algorithm and an output is produced with noise. The claims fail to make clear what “an input data set” is, what “data records” are, what “first query output” might pertain to.
Applicant argues that Klucar JR and Lang fail to teach input of unsampled data and a target of sampled data records. Examiner asserts that Klucar JR teaches determining differential privacy [0003][0004] which teach the sampled/unsampled data as a measurement to preserve accurate data results and personal privacy. The databases D and D’ are representative of sampled and unsampled. Klucar JR arguably teaches partitioning which would be a target of sampled and a plurality of unsampled partitions.
Examiner has included SUN US 2021/0089882 as a supplemental teaching of differential privacy and to determine a sensitivity to a certain level.
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 are rejected as directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to an abstract idea without significantly more. The claims involve querying a dataset to obtain a result. These steps can be performed mentally or manually by a human being and do not require a machine.
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Klucar JR US 2019/0065775 in view of SUN US 2021/0089882 in view of Lang US 2017/0193109
As per claims 1, 7, 13. (Currently Amended) Klucar JR teaches A method, comprising: determining, based on a query algorithm, a first query output corresponding to an input data set; [0022]-[0033] (teaches a query to a database)
Klucar JR teaches determining, based on a second query output of an unsampled data record in the input data set and the query algorithm, to obtain a sensitivity corresponding to the input data set, wherein the unsampled data record and the first target quantity of sampled data records constitute the input data set; and, [0022]-[0033] (teaches random sampling of a database to obtain data sets, and observation of multiple queries in order to obtain a sensitivity level which effects the output)
Klucar teaches adding noise to the first query output based on the sensitivity to obtain a noised first query output; and outputting the noised first query output. [0022]-[0033] (teaches a privacy framework that incorporates sensitivity, uses randomized sampling and outputting a noised output based on the privacy sensitivity)
Klucar fails to teach an unsampled data record in the input data set and a perturbation of each of a first target quantity of sampled data records.
Sun teaches an input data set wherein the input data set comprises an unsampled data record and a first target quantity of sampled data records; determining based on providing the unsampled data record as a second input to the query algorithm, a second query output; a perturbation that each of the first target quantity of sampled data records cause to the first query output; obtaining based on each perturbation, a sensitivity corresponding to the input data. [0006] (ensuring privacy) [0016] (differential privacy) [0026]-[0037] (teaches sensitivity measuring based on query algorithm and results of privacy level of adjacent data sets D, D’)
It would have been obvious to one of ordinary skill in the art before the priority date of the instant application to use the teaching of Sun with the prior art because it efficiently computes a privacy cost function.
Lang teaches a query operation and determining an output by determining an unsampled data record in the input data set and a perturbation of each of a first target quantity of sampled data records [0033][0034] (teaches that a database is partitioned into subsets and randomly sampled to select a subset of data from a larger database)
It would have been obvious to one of ordinary skill in the art to use the teaching of Lang with the prior art because it improves efficiency.
As per claims 2, 8, 14 (Currently Amended) Lang teaches The method of claim 1, wherein before determining the first query output, the method further comprises: receiving a data query request, comprising the query algorithm; and randomly sampling a second target quantity of data records from the input data set corresponding to the data query request, to obtain the first target quantity of sampled data records and the unsampled data record in the input data set. [0033][0034] (teaches that a database is partitioned into subsets and randomly sampled to select a subset of data from a larger database)
As per claims 3, 9, 15. (Currently Amended) Klucar JR teaches The method of claim 1, wherein determining the first query output comprises: determining, based on the query algorithm, the second query output and a third query output corresponding to the first target quantity of sampled data records; and determining, based on the second query output and the third query output, the first query output.
[0022]-[0033] (teaches qualifying the first output based on prior requests and outputs, including returning an identical output, a noisier output based on sensitivity, or denying results to a request)
As per claims 4, 10, 16. (Currently Amended) Klucar JR teaches The method of claim 1, wherein determining, a perturbation comprises:determining, based on the query algorithm, the third query output corresponding to the first target quantity of sampled data records; determining, based on the query algorithm, a fourth query output provided after each of the first target quantity of sampled data records is deleted; determining the perturbation based on the second query output of the unsampled data record, the third query output, and the fourth query output; and determining, a maximum perturbation among perturbations of the first target quantity of sampled data records to the first query output as the sensitivity. [0046]-[0054] (teaches determining historically identical queries and partitions, determining the partition/database has substantially changed/records deleted, determining additional queries and adding noise based on the history of queries)
As per claims 5, 11, 17. (Currently Amended) Klucar JR teaches The method according toof claim 1, wherein before adding the noise and outputting the noised first query output, the method further comprises: splitting the input data set into at least two partitions based on partitions to which data records from the input data set belong; determining current query outputs of the at least two partitions based on the query algorithm; and determining a difference between each of the current query outputs and a first historical query output of each of the at least two partitions. [0032] (randomized partitions)
As per claims 6, 12, 18. (Currently Amended) Klucar JR teaches The method of claim 5, comprising: deleting when a first current query output of a target partition is the same as a second historical query output of the target partition, at least one data record from the target partition, to make a second current query output of each of the at least two partitions and the first historical query output; deleting the at least one data record from the target partition determining, based on the query algorithm, a third query output; a fourth query output of the data input set, adding the noise to the fourth query output based on the sensitivity to obtain a noised fourth query output; and outputting the noised fourth query output. [0046]-[0054] (teaches determining historically identical queries and partitions, determining the partition/database has substantially changed/records deleted, determining additional queries and adding noise based on the history of queries)
As per claim 19. (New) Klucar JR teaches The computer program product of claim 13, wherein the computer-executable instructions further cause the computing device to:add a small amount of the noise to the first query output when the sensitivity is low; and add a large amount of the noise to the first query output when the sensitivity is high. [0004] [0023][0033] (scaling noise based on sensitivity)
As per claim 20. (New) Klucar JR teaches The computer program product of claim 13, wherein the noise comprises Laplace noise. [0005][0061] (Laplace noise)
Conclusion
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 CHRISTOPHER BROWN whose telephone number is (571)272-3833. The examiner can normally be reached M-F 8-5.
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/CHRISTOPHER J BROWN/Primary Examiner, Art Unit 2439