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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. EP24382475.2, filed on 04/30/2024.
Claims 1-11, filed on 04/30/2025, are presented for examination.
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 10 and 11 rejected under 35 U.S.C. 101 because the claims are directed to non-statutory subject matters; wherein
Claim 10 is directed to computer program; i.e., software per se; and, Claim 11 is directed to computer readable medium; such that, the specification does not clearly define the medium as non-statutory (non-transient), which could be magnetic tape and/or carrier waves, etc.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
The claims are generally narrative and indefinite, failing to conform with current U.S. practice. They appear to be a literal translation into English from a foreign document and are replete with grammatical and idiomatic errors.
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.
Claim(s) 1-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Carvalho” et al. (NPL Document, “Survey On Privacy-Preserving Techniques for Microdata Publication”) in view of “Di Valentino” et al. (US 2021/0049282 A1).
Carvalho disclose claim 1/10/11. A computer-implemented method/ computer program product/ computer-readable medium for improving data protection in datasets, the method comprising receiving an input anonymized dataset (100) containing a list of data records associated with attributes (see Figure 2 and section 2, page 3; where Carvalho discloses assessing risk of microdata comprising preprocessing, pseudonymizing, and de-identification, etc.), the method characterized by comprising the following steps executed by one or more processors:
for the input anonymized dataset (100), identifying a dataset type from a defined plurality of dataset types and determining a value k of k-anonymity level [Carvalho discloses type of dataset is linked to identifiers; wherein the type of refers to collection of medical records given attributes HIV and flu (page 12); probability of identity is inverse of value k (pages 8-9)],
calculating an aggregate re-identification risk (1000) for the input anonymized dataset (100) based on the identified dataset type, calculating the aggregate re-identification risk (1000) comprising:
calculating a risk of individual re-identification (1100) as the reciprocal of the value k [Carvalho discloses “The probability of identity disclosure of an individual I being in cell Ck, when Fk individuals in the population are known to belong to it, is 1/Fk fork= 1, …, K’. In other works, in case of k-anonymity this would be 1/k which is reciprocal of the value k” (section 4.1.1, page 8)];
calculating a risk of attribute re-identification (1200) for the records having a unique combination of attributes and calculating a risk of inference re-identification (1300) by a log-linear regression [Carvalho discloses, “inferential disclosure risk requires a data mining technique… Regression methods, including, e.g., long-linear regression, is a machine learning tool suited for predicting continuous values” and “For risk estimation, sdcMicro implements, for example, the SUDA2, k-anonymity, and long-linear methods, among others” (section 4, page 7; section 7, page 29)];
Carvalho may not expressly disclose; but, Di Valentino, analogues art, discloses and the aggregate re-identification risk (1000) being calculated as the maximum from among the calculated risk of individual re-identification (1100), risk of attribute re-identification (1200) and risk of interference re-identification (1300) [Di Valentino discloses, “Re-identification Risk can be one of a maximum risk or an average risk of someone randomly choosing a record from the dataset and trying to re-identify it in the population. In the case of average risk, it may be calculated as…”(par.0057)];
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify the system of Carvalho by incorporating the teaching of Di Valentino for improving datasets containing personally identifiable or confidential information and in particular to risk assessment of the datasets.
Carvalho in view of Di Valentino further disclose,
indicating that the input anonymized dataset (100) is valid if the calculated aggregate re-identification risk is below a risk threshold; otherwise, modifying the input anonymized dataset, recalculating the aggregate re-identification risk for the modified anonymized dataset, and indicating that the modified anonymized dataset is valid if the recalculated aggregate re-identification risk is below the risk threshold; otherwise, indicating that the input anonymized dataset (100) is invalid [Carvalho discloses iterative approach as, “After de-identification, the disclosure risk and utility are re-assessed. If the compromise between the two measures are not met, the parameters of the PPT should be re-adjusted or different techniques must be applied. The process is repeated until level of privacy and utility is reached. Otherwise, the data is protected and can be released” (see Figure 2 and page 6)].
Carvalho in view of Di Valentino further disclose claim 2, 3 and 5. The method according to claim 1, wherein modifying the input anonymized dataset comprises at least one of the following steps: anonymizing the data using a value K>k of k-anonymity level, eliminating vulnerable records and eliminating vulnerable attributes; wherein the vulnerable attributes are located by applying a special unique detection algorithm, SUDA; and further comprising eliminating all the records with an aggregation less than the determined value k of k-anonymity level to eliminate false positives [Carvalho discloses, “population naturally have a higher risk of re-identification than non-uniques” (section 4.1.1, page 8)].
Carvalho in view of Di Valentino further disclose claim 4. The method according to claim 1, the k-anonymity level is determined by setting the value k=1 by default [choosing k at 1 is an obvious and well known over cited arts (par.0056-0058 of Di Valentino)].
The motivation to combine is the same as that of claim 1 above.
Carvalho in view of Di Valentino further disclose claim 6. The method according to claim 1, wherein the plurality of dataset types is defined specifying criteria for aggregation, exclusion, interest, and difficulty of attributes for each dataset type [Carvalho discloses “The probability of identity disclosure of an individual I being in cell Ck, when Fk individuals in the population are known to belong to it, is 1/Fk fork= 1, …, K’. In other works, in case of k-anonymity this would be 1/k which is reciprocal of the value k” (section 4.1.1, page 8)].
Carvalho in view of Di Valentino further disclose claim 7. The method according to claim 1, further comprising calculating a severity for each of the risk of individual re-identification, the risk of attribute re-identification and the risk of inference re-identification, and comparing the calculated severity against a severity threshold [Di Valentino discloses “In step 1310, the computing device classifies the remaining identifying variables that were not contained in the simulation of step 1305. The classified identifiers are also used to de-identify to reduce risk below the threshold in subsequent steps” (par.00135 with Fig.13)].
The motivation to combine is the same as that of claim 1 above.
Carvalho in view of Di Valentino further disclose claim 8. The method according to claim 1, wherein the risk of inference re-identification is calculated based on a risk prediction accuracy which is defined as a calculated precision value of the log-linear regression for at least the determined value of k-anonymity level, wherein calculating the precision value comprises: selecting a statistical distribution with the lowest sum of deviation and chi-squared (chi2) values, performing the log-linear regression for each level of k-anonymization using the selected distribution to develop a predictor function; comparing predicted data from the predictor function with real data from the received data records, calculating the precision value as the percentage of predicted data that matches real data in the comparison [These methods are well-known statistical techniques; see for example, Figs.7-9 and 14 of Di Valentino].
The motivation to combine is the same as that of claim 1 above.
Carvalho in view of Di Valentino further disclose claim 9. The method according to claim 1, wherein the statistical distribution used for risk prediction accuracy is selected from Gaussian, Inverse Gaussian, Binomial, Negative Binomial, Gamma, and Poisson [These are also well known see par.0099 with Fig.5 of Di Valentino].
The motivation to combine is the same as that of claim 1 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. (See PTO—892).
For example, US 9215252 B2 is directed to Methods And Apparatus To Identify Privacy Relevant Correlations Between Data Values
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMARE F TABOR whose telephone number is (571) 270-3155. The examiner can normally be reached Mon.—Fri.: 8:00 AM to 5:00 PM.
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/AMARE F TABOR/Primary Examiner, Art Unit 2434
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