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 .
DETAILED ACTION
The instant application having Application No. 18/896,901 is presented for examination by the examiner.
Priority
Acknowledgment is made of applicant's claim for foreign priority under 35 U.S.C. 119(a)-(d). The certified copy has been received.
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 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 2, 7, and 8 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by over Sun (US 2023/0078704 A1).
Regarding Claim 1
Sun discloses:
An information providing apparatus comprising:
processing circuitry configured to:
identify a type of a distribution of original data that have been stored in plural servers in a state of being divided and shared by secret sharing (Sun ¶65: teaches that original data (user profiles) are divided into secret shares and stored across plural computing systems of an MPC system. Sun ¶7: further teaches identifying a type of a distribution of the original data by modeling the user profiles using a probability distribution, thereby identifying the distribution type of the original data while the data remains divided and shared by secret sharing across the MPC servers.);
generate pseudo data according to the type of the distribution identified (Sun ¶7 and 98: teaches generating pseudo data according to the identified distribution by generating new centroid feature vectors based on the probability distribution used to model the original data, including generating random feature vectors by sampling from a standard normal distribution, thereby producing pseudo data derived from the identified distribution rather than the original data itself.); and
provide the pseudo data generated (Sun ¶99: teaches providing the generated pseudo data by sharing the newly generated centroid feature vectors, which are randomly generated according to the identified distribution, with external entities, thereby making the pseudo data available for downstream use instead of the original data.).
Regarding Claim 2
The information providing apparatus according to claim 1, wherein in a case where the type of the distribution has been identified as a normal distribution, the processing circuitry is further configured to generate, as the pseudo data, random data conforming to a normal distribution having a mean value and a variance value that have been specified (Sun ¶86 and 97: teaches identifying the type of distribution as a normal distribution by modeling secret-shared user data as an n-dimensional normal distribution. Sun ¶86–¶88: further teaches determining the parameters of the normal distribution, including a mean value (centroid μ) and a variance value (covariance matrix Σ) for the modeled data. Sun ¶89: further teaches generating random data conforming to the identified normal distribution by generating random vectors drawn from a standard normal distribution using a Box-Muller transform and transforming the random vectors using the determined mean and covariance parameters. The resulting data conforms to a normal distribution defined by the specified mean and variance and is used in place of the original data, thereby constituting pseudo data).
Regarding Claim 7
Claim 7 is directed to a method corresponding to the apparatus in claim 1. Claim 7 is similar in scope to claim 1 and is therefore rejected under similar rationale.
Regarding Claim 8
Claim 8 is directed to a computer-executable instruction corresponding to the method in claim 1. Claim 8 is similar in scope to claim 1 and is therefore rejected under similar rationale.
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 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 of this title, 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 3 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Sun (US 2023/0078704 A1) as applied to claims 1-2 above, and in view of Rane (US 2011/0040820 A1).
Regarding Claim 3
Sun (¶7, 65, 98-99) teaches performing computations using secure multi-party computation techniques, including privacy-preserving evaluation of encrypted or secret-shared data, but Sun is silent as to identifying a probability distribution or performing a statistical test to determine a distribution type. On the other hand, Rane teaches identifying a probability distribution through secure computation, specifically by determining a joint empirical probability distribution (JEPD) using secure multi-party computation, and further teaches performing statistical evaluation over that distribution without revealing the underlying data (Rane ¶31–39). Rane further teaches that statistical functions can be expressed and evaluated directly in terms of the computed distribution, thereby enabling statistical testing and distribution-level analysis under secure computation (Rane ¶31).
It would have been obvious to one of ordinary skill in the art at the time of the invention to combine Sun’s secure computation framework with Rane’s secure distribution-based statistical analysis to identify a type of distribution by performing a statistical test through secure computation, as the combination merely applies known secure computation techniques to known statistical distribution analysis methods and yields predictable results, namely privacy-preserving identification and analysis of data distributions.
Regarding Claim 4
Claim 4 is directed to an apparatus corresponding to the apparatus in claim 3. Claim 4 is similar in scope to claim 3 and is therefore rejected under similar rationale.
Claims 5 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Sun (US 2023/0078704 A1), in view of Rane (US 2011/0040820 A1) as applied to claims 3-4 above, and in further view of Lindell (US 2018/0357427 A1).
Regarding Claim 5
Sun (¶7, 65, 98-99) teaches identifying characteristics of data distributions and performing privacy-preserving data analysis using secure computation and Rane (¶31–39) teaches performing secure multi-party computation to derive statistical representations of data, including determining a joint empirical probability distribution (JEPD) and computing normalized statistical results based on that distribution through secure computation. However, Sun and Rane do not explicitly teach computing a predetermined quantile via secure sorting of records as a measure of central tendency. On the other hand, Lindell teaches performing secure computation on encrypted datasets including secure sorting of shared records and secure computation of rank-based and percentile functions, such as PERCENTILE, RANK, and PERCENT_RANK, using secure MPC protocols (¶74–75, 80–81). Lindell further teaches that such percentile values are computed after sorting records under secure computation, thereby enabling quantile-based statistical measures while preserving data privacy.
The claim is obvious because one of ordinary skill in the art would have been motivated to use Lindell’s known secure sorting and percentile computation techniques to implement a quantile-based measure of central tendency within the secure statistical testing frameworks of Sun and Rane, yielding the predictable result of performing a statistical test using a predetermined quantile while maintaining data privacy. Combining known secure distribution analysis with known secure quantile computation represents a routine and predictable design choice in privacy-preserving statistical analysis.
Regarding Claim 6
Claim 6 is directed to an apparatus corresponding to the apparatus in claim 5. Claim 6 is similar in scope to claim 5 and is therefore rejected under similar rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAAD ABDULLAH whose telephone number is (571) 272-1531. The examiner can normally be reached on Monday - Friday, 9:30am - 5:30pm, EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynn Feild can be reached on (571) 272-2092. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SAAD AHMAD ABDULLAH/ Examiner, Art Unit 2431
/LYNN D FEILD/ Supervisory Patent Examiner, Art Unit 2431