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 .
Claims 1-4, 6-11, 13-18 and 20 have been examined.
Response to Arguments
Applicant's arguments filed 4/6/26 have been fully considered but they are not persuasive.
Regarding Applicant’s remarks filed on 4/6/26, Applicant mainly argues that the prior art of record does not explicitly disclose the step of defining surrogate privacy metric. However, the disputed limitation merely describes a surrogate privacy metric calculated based on original data and released data. The particular surrogate privacy metric does not affect the data release mechanism recited in the claim because it minimizes the distortion metric subject to a constraint on the privacy metric, not surrogate privacy metric. Therefore, the step defining surrogate privacy metric is interpreted as nonfunctional descriptive material that does not distinguish the claim from the prior art in terms of patentability, which will be given little/no weight in the examination process. See MPEP 2111.05.
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.
Claims 1-4, 6-11, 13-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Fawaz et al. U.S. 2016/0203333 (hereinafter Fawaz) in view of Bi et al. U.S. 2022/0327238 (hereinafter Bi).
As per claim 1, 8 and 15, Fawaz discloses a method/system/CRM for protecting confidential aggregate dataset information when sharing data by utilizing one or more processors along with allocated memory, the method comprising:
receiving confidential dataset from a data owner via a communication interface, the confidential dataset including data that the data owner does not want to reveal when sharing data, and the confidential dataset being generated from an original distribution of dataset as released distribution dataset (Fawaz: [0006]-[0007]: preserve privacy of collected data by releasing modified data according to privacy preserving mappings);
defining a privacy metric as a probability of an attacker guessing the privacy data by applying a first data processing algorithm onto the confidential dataset (Fawaz: [0081]: privacy preserving mapping decision module collects distribution data and generate probability measure to determine level of privacy; Fig. 7 and [0087]: different privacy metrics…using divergence privacy to advantageously guarantee a small probability of inferring private data based on the released data; [0091]: worse-case divergence privacy);
defining a distortion metric of a data release mechanism as worst-case distance between the original distribution dataset and the released distribution dataset by applying a second data processing algorithm (Fawaz: [0030]-[0032]: define accuracy notion/distortion metric based on Hamming distance; [0091]-[0100]: worst-case divergence privacy calculation); and
implementing the data release mechanism that minimizes the distortion metric subject to a constraint on the privacy metric for protecting the confidential aggregate dataset information when sharing data (Fawaz: [0019]: release distorted version of data, designed under a utility constraint as well known in the art to prevent inference of private data; [0025]-[0028]: optimize tradeoff between privacy level and utility constraint; [0033]: limit the amount of private information that can be inferred, given a utility constraint).
Fawaz discloses applying privacy preserving functions to nxn matrix (Fawaz: [0064]). Fawaz does not explicitly recite “multi-dimensional data.” However, Bi discloses transforming p-dimensional distribution data that is subject to differential privacy analysis to allow distribution regardless of the number of variables (Bi: Abstract; [0012]: alter multivariate/multidimensional structure of an original sample after privatization; [0015]: univariate and multivariate). It would have been obvious to one having ordinary skill in the art to apply data privatization to multidimensional data because Fawaz and Bi are analogous art involving privacy protection that requires released data to satisfy a certain confidentiality standard. The motivation to combine would be to provide flexible/scalable data privatization on distribution data of various complexities.
Fawaz as modified does not explicitly disclose the step of defining surrogate privacy metric based on the equation recited. However, the limitation merely describes a surrogate privacy metric calculated based on original data and released data. The particular surrogate privacy metric does not affect the data release mechanism recited in the claim because it minimizes the distortion metric subject to a constraint on the privacy metric, not the surrogate privacy metric. Therefore, the content of the tag is interpreted as nonfunctional descriptive material that does not distinguish the claim from the prior art in terms of patentability.
As per claim 2, 9 and 16, Fawaz as modified discloses the limitations according to claims 1, 8 and 15 respectively. Fawaz as modified further discloses implementing an algorithm for sharing data generated from a single-dimensional Gaussian distribution to preserve privacy and output the multi-dimensional privacy data (Bi: Abstract; [0015]: address all types of univariate and multivariate data; [0020]: p-dimensional normal/gaussian distribution). It would have been obvious to one having ordinary skill in the art to share data generated from single-dimension to preserve privacy and output the multi-dimensional privacy data because Fawaz and Bi because both disclose privacy protection that requires released data to satisfy a certain confidentiality standard, i.e. differential privacy. The motivation to combine would be to provide flexible/scalable data privatization on distribution data of various complexities.
As per claim 3, 10 and 17, Fawaz as modified discloses the limitations according to claims 2, 9 and 16 respectively. Fawaz as modified further discloses implementing an algorithm for sharing data generated from multi-dimensional Gaussian distribution with diagonal covariance matrix to preserve privacy and output the multi-dimensional privacy data (Fawaz: [0081]; Bi: [0084]).
As per claim 4, 11 and 18, Fawaz as modified discloses the limitations according to claims 3, 10 and 17 respectively. Fawaz as modified further discloses implementing an algorithm for sharing data generated from a two-dimensional Gaussian distribution to preserve privacy and output the multi-dimensional privacy data (Bi: [0012]; [0015]). Same rationale applies here as above in rejecting claim 1.
As per claim 6 and 13, Fawaz as modified discloses the limitations according to claims 1 and 8 respectively. Fawaz as modified does not explicitly disclose wherein smaller value corresponds to stronger privacy. However, assigning ascending or descending values to privacy level appears to be a matter of design choice since any indicator would work equally to adjust level of distortion to preserve privacy.
As per claim 7, 14 and 20, Fawaz as modified discloses the limitations according to claims 1, 8 and 15 respectively. Fawaz as modified further discloses defining a surrogate distortion metric as a distance between the original distribution dataset and the released distribution dataset by applying a third data processing algorithm (Fawaz: [0068]-[0071]: determine privacy metrics based on difference/distortion of released data and original data).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhang et al. U.S. 2022/0215116 discloses utility parameter that defines difference between original data set and released data set, such that a cost function can be specified to determine noisy result desired.
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 SHIN HON (ERIC) CHEN whose telephone number is (571)272-3789. The examiner can normally be reached Monday to Thursday 9am- 7pm.
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/SHIN-HON (ERIC) CHEN/ Primary Examiner, Art Unit 2431
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