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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/8/2026 has been entered.
Election/Restrictions
Newly submitted claims 7 – 21 are directed to an invention that is independent or distinct from the invention originally claimed for the following reasons: Claims 1 and 3 – 6 includes selecting portions of the synthetic data by determining a similarity threshold based on a first combination of variables and a first frequency of variables, claims 7 – 13 includes selecting portions of the synthetic data by determining a similarity threshold being based on variables, labels and categories and claims 14 – 21 includes filtering real training data.
Since applicant has received an action on the merits for the originally presented invention, this invention has been constructively elected by original presentation for prosecution on the merits. Accordingly, claims 7 – 21 are withdrawn from consideration as being directed to a non-elected invention. See 37 CFR 1.142(b) and MPEP § 821.03.
To preserve a right to petition, the reply to this action must distinctly and specifically point out supposed errors in the restriction requirement. Otherwise, the election shall be treated as a final election without traverse. Traversal must be timely. Failure to timely traverse the requirement will result in the loss of right to petition under 37 CFR 1.144. If claims are subsequently added, applicant must indicate which of the subsequently added claims are readable upon the elected invention.
Should applicant traverse on the ground that the inventions are not patentably distinct, applicant should submit evidence or identify such evidence now of record showing the inventions to be obvious variants or clearly admit on the record that this is the case. In either instance, if the examiner finds one of the inventions unpatentable over the prior art, the evidence or admission may be used in a rejection under 35 U.S.C. 103 or pre-AIA 35 U.S.C. 103(a) of the other invention.
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 and 3 – 6 are rejected under 35 U.S.C. 103 as being unpatentable over Walter, publication number: US 2023/0091402 in view of Venkataraman (Venk), patent number: US 12 608 648.
As per claim 1, Walter teaches a method comprising:
performing one or more operations using a machine learning model that was trained, at least in part, using synthetic training data generated using one or more generative machine learning models, wherein:
the one or more generative machine learning models were trained to generate the synthetic training data based at least on real training data designated for protection (training using synthetic data, [0007][0008][0054], generating synthetic data from private data [0054][0041]);
Walter does not teach a first portion of synthetic training data generated using the one or more generative machine learning models is used to train was selected for use in training the machine learning model;
a second portion of the synthetic training data was rejected for use in training the
machine learning model; and
the first portion of the synthetic training data was selected and the second portion of the synthetic training data was rejected based at least on:
a first combination of variables and a first frequency of variables in the real training data being compared against a second combination of the variables and a second frequency of the variables in the synthetic training data,
the comparing indicating that the first portion of the synthetic training data satisfied a similarity threshold indicating that the first portion of the synthetic training data was within a certain degree of similarity of the real training data for training the machine learning model, and
the comparing indicating that the second portion of the synthetic training data did not satisfy the similarity threshold indicating that the second portion of the synthetic training data was outside of the certain degree of similarity of the real training data for training the machine learning model
In an analogous art, Venk a first portion of synthetic training data generated using the one or more generative machine learning models is used to train was selected for use in training the machine learning model;
a second portion of the synthetic training data was rejected for use in training the
machine learning model; and
the first portion of the synthetic training data was selected and the second portion of the synthetic training data was rejected based at least on:
a first combination of variables and a first frequency of variables in the real training data being compared against a second combination of the variables and a second frequency of the variables in the synthetic training data,
the comparing indicating that the first portion of the synthetic training data satisfied a similarity threshold indicating that the first portion of the synthetic training data was within a certain degree of similarity of the real training data for training the machine learning model, and
the comparing indicating that the second portion of the synthetic training data did not satisfy the similarity threshold indicating that the second portion of the synthetic training data was outside of the certain degree of similarity of the real training data for training the machine learning model (selecting portions of synthetic data based on a similarity comparison, col. 12, lines 36 – 60, statistical distribution, col. 13 lines 36 – 51, similarity, col. 11, line 51 – col. 12, line 3)
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 Walter’s synthetic data generation system to include relevant data as described in Venk’s synthetic data system for the advantage of preventing drift in the model results.
As per claim 3, the combination teaches wherein:
the real training data includes labeled medical imaging data (Walter: labelled data, [0008][0187], Walter: medical, [0112]); and
the synthetic training data includes data generated using the one or more generative machine learning models and based at least on the labeled medical imaging data (Walter: synthetic data similar in distribution, [0054][0084]).
As per claim 4, the combination teaches wherein a third portion of the synthetic training data was filtered out of consideration for use in training the machine learning model based at least on a first determination that a first distribution corresponding to the third portion of the synthetic training data is different by more than an additional similarity threshold as compared to a second distribution corresponding to the real training data; and
The first portion of the synthetic training data remained after filtering based a least on a second determination that a third distribution corresponding to the first portion of synthetic training data was different by less than the additional similarity threshold as compared to a second distribution corresponding to the real training data (Hazard: multiple metrics, [0126][0129][0224], Venk: selecting portions of synthetic data based on a similarity comparison, col. 12, lines 36 – 60).
As per claim 5, the combination teaches wherein the one or more generative machine learning models were trained using the real training data local to a first location that stores the real training data, and the machine learning model was trained at a second location different from the first location (Walter: secure and local system, Fig. 15, [0152-0153]).
As per claim 6, the combination teaches wherein the one or more generative machine learning models learn one or more first distributions associated with the real training data such that one or more second distributions associated with the synthetic training data are within a threshold similarity to the one or more first distributions (Walter: similar distribution, [0047][0084]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLUGBENGA O IDOWU whose telephone number is (571)270-1450. The examiner can normally be reached Monday-Friday 8am - 5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jung Kim can be reached at 5712723804. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/OLUGBENGA O IDOWU/Primary Examiner, Art Unit 2494
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