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
2. Claims 1-20 are pending in Instant Application.
Information Disclosure Statement
3. The information disclosure statement (IDS) submitted on 11/09/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 (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 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
4. Claims 1-20 are rejected under 35 U.S.C. 102 (a) (2) as being anticipated by US 2023/0024796 issued to Hazard et al. (Hazard).
As per claim 1, Hazard teaches a computer-implemented method comprising: generating, using a probability distribution of synthetic data (Hazard: Fig. 1A - receive request for synthetic data, select undetermined feature for which to determine value, and determine distribution for undetermined feature and ¶ 0029 - teaches a probability calculation used to determine whether to resample and retest or discard the synthetic data), a first value of a utility measure function, and a second value of the utility measure function, a value of an optimization variable (Hazard: ¶ 0018 - the values for some features are determined based at least in part on the conditions or condition requirements that are placed on the synthetic data; wherein ¶ 0039 - teaches value for first undetermined feature of the synthetic data case is determined based on the focal cases and the value for the second undetermined feature will be conditioned on the UID and the value for the first undetermined feature), the synthetic data generated from a source dataset using a first differential privacy technique, the utility measure function measuring a characteristic of a dataset (Hazard: ¶ 0041 - synthetic data cases may be generated for each table, for each UID (or set of UIDs). This may be beneficial when a synthetic dataset of similar size to the set of training data cases is desired; wherein ¶ 0032 - teaches the techniques may include determining the k-anonymity, t-closeness, I-diversity, and/or other privacy measures for synthetic data cases (e.g., either as the synthetic data cases are generated,); computing, using the value of the optimization variable, a sampling weight, the sampling weight comprising a probability of selecting a portion of data from the synthetic data (Hazard: ¶ 0049 - synthetic data may be generated based on the join sampling weight by combining the probability values from table); sampling, according to the sampling weight, the synthetic data, the sampling resulting in a sampled synthetic dataset (Hazard: ¶ 0051 - the system or operator may request additional data to augment the current training dataset and the synthetic data may be requested to direct sampling via a reinforcement learning process); and training, using the sampled synthetic dataset, a machine learning model, the training resulting in a trained machine learning model (Hazard: ¶ 0144 - determining a dataset quality metric may include determining at least one statistical quality metric that compares the statistical properties of the set of training data cases and the set of two or more synthetic data cases; at least one model comparison metric that quantifies the machine learning model properties and performance of the set of training data cases and the set of two or more synthetic data cases).
As per claim 2, Hazard teaches the computer-implemented method of claim 1, wherein the first value of the utility measure function is computed on the source dataset using a second differential privacy technique (Hazard: ¶ 0032 - the techniques may include determining the k-anonymity, t-closeness, I-diversity, and/or other privacy measures for synthetic data cases (e.g., either as the synthetic data cases are generated, after many or all the synthetic data cases are generated, or a combination of the two)).
As per claim 3, Hazard teaches the computer-implemented method of claim 1, wherein the second value of the utility measure function is computed on the synthetic data (Hazard: ¶ 0040 - UID may allow for creation of values in a single table of synthetic data).
As per claim 4, Hazard teaches the computer-implemented method of claim 1, wherein the value of the optimization variable is generated by solving an optimization problem (Hazard: ¶ 0018 - the values for some features are determined based at least in part on the conditions or condition requirements that are placed on the synthetic data).
As per claim 5, Hazard teaches the computer-implemented method of claim 1, wherein the utility measure function is part of a set of utility measure functions, the optimization variable is part of a set of optimization variables, and the set of utility measure functions has the same number of members as the set of optimization variables (Hazard: ¶ 0025 - synthetic data is identical or too similar to existing training data).
As per claim 6, Hazard teaches the computer-implemented method of claim 1, wherein the sampled synthetic dataset has a first characteristic that matches a first characteristic of the source dataset within a tolerance, the first characteristic measured according to the utility measure function (Hazard: ¶ 0217 - the synthetic and training dataset records that are most similar to each other are at least no closer than the closest 1.0% of data in the training dataset).
As per claim 7, the claim resembles claim 1 and is rejected under the same rationale while Hazard also teaches a computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by a processor to cause the processor to perform operations (Hazard: claim 16 - a computer readable medium storing instructions which, when executed by one or more computing devices, cause the one or more computing devices to perform a method).
As per claim 8, Hazard teaches the computer program product of claim 7, wherein the stored program instructions are stored in a computer readable storage device in a data processing system, and wherein the stored program instructions are transferred over a network from a remote data processing system (Hazard: ¶ 0404 - the remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem).
As per claim 9, Hazard teaches the computer program product of claim 7, wherein the stored program instructions are stored in a computer readable storage device in a server data processing system, and wherein the stored program instructions are downloaded in response to a request over a network to a remote data processing system for use in a computer readable storage device associated with the remote data processing system, further comprising: program instructions to meter use of the program instructions associated with the request; and program instructions to generate an invoice based on the metered use (Hazard: ¶ 0396 - processors programmed to perform the techniques pursuant to program instructions (metered use) in firmware, memory, other storage, or a combination).
As per claim 10, the claim resembles claim 2 and is rejected under the same rationale.
As per claim 11, the claim resembles claim 3 and is rejected under the same rationale.
As per claim 12, the claim resembles claim 4 and is rejected under the same rationale.
As per claim 13, the claim resembles claim 5 and is rejected under the same rationale.
As per claim 14, the claim resembles claim 6 and is rejected under the same rationale.
As per claim 15, the claim resembles claim 1 and is rejected under the same rationale while Hazard also teaches a computer system comprising a processor and one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by the processor to cause the processor to perform operations (Hazard: claim 16 - a computer readable medium storing instructions which, when executed by one or more computing devices, cause the one or more computing devices to perform a method).
As per claim 16, the claim resembles claim 2 and is rejected under the same rationale.
As per claim 17, the claim resembles claim 3 and is rejected under the same rationale.
As per claim 18, the claim resembles claim 4 and is rejected under the same rationale.
As per claim 19, the claim resembles claim 5 and is rejected under the same rationale.
As per claim 20, the claim resembles claim 6 and is rejected under the same rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SM AZIZUR RAHMAN whose telephone number is (571) 270-7360. The examiner can normally be reached on M-F Telework;
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ali Shayanfar can be reached on 571-270-1050. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SM A RAHMAN/Primary Examiner, Art Unit 2434
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