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 .
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 are rejected under 35 U.S.C. 103 as being unpatentable over Hebert, publication number: US 2018/0004978 in view of Zaccak, publication number: US 2021/0360010.
As per claim 1, Hebert teaches a system for training a machine learning model comprising:
at least one data processor;
memory storing instructions which, when executed by the at least one data processor, result in operations comprising:
receiving a dataset (Dataset 405, Fig. 4, [0087]);
receiving at least one first user-generated privacy parameter which governs a differential privacy (DP) algorithm to be applied to a function evaluated over the received dataset (Data owner determining risk values, [0018]);
calculating, based on the received at least one first user-generated privacy parameter, at least one second privacy parameter based on a ratio or overlap of probabilities of distributions of different observations (Determining utility quantifiers, [0087]);
applying, using the at least one second privacy parameter, the DP algorithm to the function over the received dataset to result in an anonymized function output (Anonymizing data 445, [0087]);
Hebert does not teach anonymously training at least one machine learning model using the dataset after application of the DP algorithm to the function over the received dataset which, when deployed, is configured to classify input data.
In an analogous art, Zaccak teaches anonymously training at least one machine learning model using the dataset after application of the DP algorithm to the function over the received dataset which, when deployed, is configured to classify input data (machine learning models, [0045-0046][0082][0093], training using differentially processed data, [0061][0096]).
Therefore, it would have been obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention to modify Hebert’s differential privacy system to include machine learning training as described in Zaccak’s privacy system for the advantage of creating a system that improves classification without compromising user privacy.
As per claim 2, the combination teaches wherein the operations further comprise:
deploying the trained at least one machine learning model;
receiving, by the deployed trained at least one machine learning model, input data (Zaccak: trained model, [0096][0116]).
As per claim 3, the combination teaches wherein the operations further comprise:
providing, by the deployed trained at least one machine learning model based on the input data, a classification (Zaccak: determination, [0046]).
As per claim 4, the combination teaches wherein:
the at least one first user-generated privacy parameter comprises a bound for an adversarial posterior belief
ρ
c
that corresponds to a likelihood to re-identify data points from the dataset based on a differentially private function output (Hebert: threshold, [0037]); and
the calculated at least one second privacy parameter comprises privacy parameters ε, 𝛿 (Hebert: risk and utility, [0078]); and
the calculating is based on a conditional probability of distributions of different datasets given a differential private function output which are bound by the posterior belief
ρ
c
as applied to the dataset (Zaccak: multiple rounds, [0091]).
As per claim 5, the combination teaches wherein
the at least one first user-generated privacy parameter comprises privacy parameters ε, 𝛿 (Hebert: risk and utility, [0078]);
the calculated at least one second privacy parameter comprises an expected membership advantage
ρ
α
that corresponds to a probability of an adversary successfully identifying a member in the dataset (Hebert: threshold, [0037]); and
the calculating is based on a conditional probability of different possible datasets (Zaccak: multiple rounds, [0091]).
As per claim 6, the combination teaches wherein
the at least one first user-generated privacy parameter comprises privacy parameters ε, 𝛿 (Hebert: risk and utility, [0078]);
the calculated at least one second privacy parameter comprises an adversarial posterior belief bound
ρ
c
that corresponds to a likelihood to re-identify data points from the dataset based on a differentially private output (Hebert: threshold, [0037]).
As per claim 7, the combination teaches wherein the calculating is based on a conditional probability of different possible datasets (Zaccak: multiple rounds, [0091]).
Claims 8 – 14 are rejected based on claims 1 – 7
Claims 15 – 20 are rejected based on claims 1-6
Conclusion
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/OLUGBENGA O IDOWU/Primary Examiner, Art Unit 2494
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