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 § 102
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.
Claim(s) 1-2, 4-6, 9-11, 13-15, 18-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Veskus, (Privacy-Preserving Data Synthesis Using Trusted Execution Environments, 2022), (From IDS of 05/28/2025), hereinafter “Veskus”.
Regarding claims 1, 10, 19, Veskus discloses
executing a first data collaboration stage, comprising:
obtaining a collection of data from a first entity [Veskus, Section 2.4, paragraph 5, As the data synthesis methods rely heavily on the structure of the real data, the common approach is for the data owner to either build the data synthesis models and generate the synthetic data themselves or pass the real data to a trusted third party (TTP), who then analyses, cleans, and processes the data to create a synthesis model];
generating synthetic data from the collection of data [Veskus, Section 2.4, paragraph 1, Data synthesis is the process of generating new realistic looking data, based on real data. Synthetic data is created from real datasets or some knowledge of the shape and structure of the real data, using statistical or machine learning methods. The main goal of data synthesis is for synthetic data to have all the same statistical properties as real data.]; and
generating, by a second entity, code defining one or more operations or queries executable on the collection of data and evaluated with respect to the synthetic data [Veskus, Section 3, paragraph 2, At the highest level, the whole process of synthesizing data with the service can be separated into three steps. First the data owner encrypts their CSV file and uploads it to the Sharemind HI server, Section 2.4, paragraph 2, It is important to note however that synthetic data is not
automatically anonymous simply by the merit of being synthetic data. In order to assert whether or not a specific synthetic dataset is considered to be personal data, a thorough analysis has to be done to confirm whether any single real person is identifiable from it.]; and
executing a second data collaboration stage, comprising:
executing the code generated by the second entity in a secure execution environment, including executing one or more operations on the collection of data to generate one or more corresponding output results [Veskus, Section 3, paragraph 2, At the highest level, the whole process of synthesizing data with the service can be separated into three steps. First the data owner encrypts their CSV file and uploads it to the Sharemind HI server. Secondly the task enclaves in the server decrypt the CSV file, use it to create and train a synthesizer model, and synthesize data based on the trained model. Lastly the data owner can download and decrypt the final synthesized data using the Sharemind HI client, Section 2.4, paragraph 2, It is important to note however that synthetic data is not automatically anonymous simply by the merit of being synthetic data. In order to assert whether or not a specific synthetic dataset is considered to be personal data, a thorough analysis has to be done to confirm whether any single real person is identifiable from it., Section 3.1, paragraph 5, The financial institution can use the service to encrypt and upload their financial data to Sharemind HI, where the synthesized data is generated securely inside the protected SGX enclaves. The synthetic data can then be downloaded and decrypted, and shared to the R&D company creating the machine learning models.]; and
providing the one or more corresponding output results to the second entity [Veskus, Section 3, paragraph 2, At the highest level, the whole process of synthesizing data with the service can be separated into three steps. First the data owner encrypts their CSV file and uploads it to the Sharemind HI server. Secondly the task enclaves in the server decrypt the CSV file, use it to create and train a synthesizer model, and synthesize data based on the trained model. Lastly the data owner can download and decrypt the final synthesized data using the Sharemind HI client.].
Regarding claims 2, 11, 20, Veskus further discloses
wherein generating synthetic data from the collection of data comprises applying a differential privacy operation to the collection of data to generate data having a same schema as the collection of data but with adjusted data values [Veskus, Section 2.3, Differential Privacy, Section 2.4.1].
Regarding claims 4, 13, Veskus further discloses
wherein secure execution comprises using a trusted execution environment to securely provision the collection of data from the first entity and perform computations according to the generated code [Veskus, Section 2.6, paragraph 1, Section 2.6.2].
Regarding claims 5, 14, Veskus further discloses
performing output filtering to the output generated by the secure execution environment, wherein the output filtering evaluates the one or more corresponding output results for privacy leakage through output results containing some of the original collection of data [Veskus, Section 2.4, paragraph 2].
Regarding claims 6, 15, Veskus further discloses
wherein the first entity corresponds to a data provider of a data collaboration system and the second entity corresponds to a data consumer of a data collaboration system [Veskus, Section 3, paragraph 2, Section 2.4, paragraphs 1-2, 5].
Regarding claims 9, 18, Veskus further discloses
performing, by the first entity, code filtering on the generated code before execution in the secure execution environment [Veskus, Section 4.1.3].
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) 3, 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Veskus as applied to claims 1, 10 above, and further in view of Walters et al., (US Publication No. 2019/0327501), hereinafter “Walters”.
Regarding claims 3, 12, Veskus does not specifically disclose, however Walters teaches
wherein generating synthetic data from the collection of data comprises applying a random value to each individual data value while retaining [[the]]a data schema [Walters, paragraphs 47, 185, sensitive portions of a dataset are automatically detected and replaced with synthetic data].
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include replacing data with random values in order to protect the security of the data. It would have been obvious to combine Walters with Veskus as each art relates to a similar concept of synthetic data.
Claim(s) 7, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Veskus as applied to claims 1, 10 above, and further in view of Turgeman et al., (US Publication No. 2020/0334370), hereinafter “Turgeman”.
Regarding claims 7, 16, Veskus does not specifically disclose, however Turgeman teaches
wherein evaluating the generated code with respect to the synthetic data comprises testing the execution of the code including one or more queries on the synthetic data [Turgeman, paragraph 3].
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include testing the generated data with queries in order to ensure the data provided the expected result before placing in production. It would have been obvious to combine Turgeman with Veskus as each art relates to a similar concept of sanitized data for security.
Claim(s) 8, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Veskus as applied to claims 1, 10 above, and further in view of Avanes et al., (US Publication No. 2023/0401333), hereinafter “Avanes”.
Regarding claims 8, 17, Veskus does not specifically disclose, however Avanes teaches
wherein executing the second data collaboration stage comprises auditing the code generated in the first data collaboration stage [Avanes, paragraphs 36-37].
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to include auditing the code generated in order to test the code and ensure the code meets the expectations of correct and secure data. It would have been obvious to combine Avanes with Veskus as each art relates to a similar concept of sanitized data for security.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM J GOODCHILD whose telephone number is (571)270-1589. The examiner can normally be reached M-F 8am-4:30pm.
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/William J. Goodchild/Primary Examiner, Art Unit 2433
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