DETAILED ACTION
1. This office action is in response to application 18/830,054 filed on 9/10/2024. Claims 1-28 are pending in this office action.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Allowable Subject Matter
3. Claim 6 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 102
4. 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.
Claim(s) 1-28 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 2023/0125477 (hereinafter Gurumurthy).
As for claim 1 Gurumurthy discloses: A computer implemented method, comprising: a) generating training data from an imaging dataset of a wafer (See paragraph 0057); b) iteratively: i) selecting a hyperparameter value from an associated set of hyperparameter values based on a sampling strategy, the hyperparameter value corresponding to at least one hyperparameter defining a machine learning model for detecting anomalies (See paragraphs 0091 and 0109 note the system iteratively uses hyperparameters to train the system for detection); ii) training the machine learning model defined by the hyperparameter based on a subset of the generated training data (See paragraphs 0054-0059); and iii) evaluating the trained machine learning model by computing an associated objective function value of an objective function (See paragraph 0091 note the objective is training loss); and c) after b), selecting one of the trained machine learning models based on the associated objective function value and applying it to the imaging dataset of a wafer to detect anomalies (See paragraph 0057-0059 note anomaly detection).
As for claim 2 the rejection of claim 1 is incorporated and further Gurumurthy discloses: wherein the machine learning model is trained so that, when presented the machine learning model is presented with a subset of the imaging dataset as input, the machine learning model computes a reconstruction of the subset without anomalies, and the anomalies within the subset are detected based on a comparison between the subset and the reconstructed subset (See paragraphs 0513 and 0523 note the system will perform reconstruction based on the anomaly detection).
As for claim 3 the rejection of claim 1 is incorporated and further Gurumurthy discloses: wherein at least one hyperparameter comprises a design hyperparameter related to the design of the machine learning model (See paragraph 0125).
As for claim 4 the rejection of claim 1 is incorporated and further discloses: wherein all of the hyperparameters comprise design hyperparameters related to the design of the machine learning model (See paragraph 0125).
As for claim 5 the rejection of claim 1 is incorporated and further Gurumurthy discloses: wherein the objective function comprises at least two different model evaluation metrics (See paragraph 0490 note the many different special functions).
As for claim 7 the rejection of claim 1 is incorporated and further Gurumurthy discloses: wherein the objective function comprises a quality value to evaluate the quality of the trained machine learning model, and a user interface is configured to present information on the trained machine learning model to a user and let the user indicate the quality value (See paragraph 0371 note quality engine).
As for claim 8 the rejection of claim 1 is incorporated and further Gurumurthy discloses: wherein the sampling strategy for selecting the number of hyperparameter values comprises taking into account hyperparameter values and corresponding values of the objective function from one or more previous iterations by optimizing one criterion selected from the group consisting of expected improvement, maximum probability of improvement, and upper confidence bound (See paragraph 0178 note the confidence score is a bound).
As for claim 9 the rejection of claim 1 is incorporated and further Gurumurthy discloses: wherein the sampling strategy comprises an early stopping criterion (See paragraph 0180 note stop mode provides a stopping criteria).
As for claim 10 the rejection of claim 1 is incorporated and further Gurumurthy discloses: wherein at least one set of hyperparameter values is associated with a probability distribution indicating a likelihood for each hyperparameter value being selected by the sampling strategy (See paragraph 0178 note a probability is a likelihood).
As for claim 11 the rejection of claim 1 is incorporated and further Gurumurthy discloses: wherein the sampling strategy differs for at least two iterations (See paragraph 0091).
As for claim 12 the rejection of claim 1 is incorporated and further Gurumurthy discloses: 12. The method of claim 1, wherein a size of the subset of the generated training data increases with the number of iterations (See paragraph 0091).
As for claim 13 the rejection of claim 1 is incorporated and further Gurumurthy discloses: wherein the imaging dataset comprises a multisensory image (See paragraph 0069 note sensors).
As for claim 14 the rejection of claim 1 is incorporated and further Gurumurthy discloses: wherein the imaging dataset comprises a member selected from the group consisting of a multibeam SEM image and a focused ion beam SEM image (See paragraph 0202).
As for claim 15 the rejection of claim 1 is incorporated and further Gurumurthy discloses: measuring a property of the detected anomalies (See paragraph 0202).
Claim 16 is a machine readable claim substantially corresponding to the method of claim 1 and is rejected for the same reasons as set forth in the rejection of claim 1.
Claims 17-19 are system claims substantially corresponding to the method of claims 1-15 and are thus rejected for the same reasons as set forth in the rejection of claims 1-5, and 7-15
Claims 20-24 are system claims substantially corresponding to the method of claims 1-15 and are thus rejected for the same reasons as set forth in the rejection of claims 1-5, and 7-15
Claim 25 is a machine readable claim substantially corresponding to the method of claim 20 and is rejected for the same reasons as set forth in the rejection of claim 20.
Claims 26-28 are system claims substantially corresponding to the method of claims 20, 17 and 18, and are thus rejected for the same reasons as set forth in the rejection of claims 20, 17 and 18 and are thus rejected for the same reasons as set forth in the rejection of claims 20, 17 and 18.
Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIYAH STONE HARPER whose telephone number is (571)272-0759. The examiner can normally be reached on Monday-Friday 10:00 am - 6:00 pm.
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/Eliyah S. Harper/Primary Examiner, Art Unit 2166 September 29, 2026
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