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. Election/Restrictions Claims 9-13 and 19-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 10 November 2025. Drawings The drawings are objected to because Fig. 10B is erroneously labeled Fig. 11B. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance. Specification The disclosure is objected to because of the following informalities: in ¶217-218, it seems that the applicant intends to refer to Figs. 10A-B, not Figs. 11A-B. Appropriate correction is required. Claim Rejections - 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1 -8, 14-18, and 21-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. At Step 1 of the 101 analysis , all claims are directed to one of the statutory categories of invention. Claim 1 is rejected in response to the following analysis: At Step 2A, Prong One, the judicial exceptions are bolded in the copy of claim 1 below: A method comprising: processing measurement data of a substrate that was processed according to a manufacturing process using a first trained machine learning model to predict a critical dimension (CD) profile for the substrate; generating a CD profile prediction image based on the predicted CD profile for the substrate; and processing the CD profile prediction image using a second trained machine learning model to generate a synthetic microscopy image associated with the substrate. Processing measurement data and a CD profile prediction image both represent mathematical operations , as does predicting a CD profile. At Step 2A, Prong Two, the additional elements include first and second machine learning models, which represent generic computer functions. The CD profile prediction image and synthetic microscopy image associated with the substrate, generated by the first and second machine learning models , respectively, are also additional elements. Furthermore, the processed measurement is of “a substrate that was processed according to a manufacturing process”. These additional elements do not integrate the judicial exceptions into a practical application. Applying machine learning is to perform a judicial exception is just applying a computer to a general problem. A processed substrate could refer to a great number of things, and therefore so too can be said of the synthetic microscopy image associated with it. Therefore, the claim seems to do no more than link the abstract idea of data processing to the field of substrates and microscopy. Moreover, because of the breadth of this field, the images generated amount to no more than insignificant extra-solution activity. At Step 2B, the claim as a whole does not amount to significantly more than the judicial exceptions for the reasons given above. Claims 2 and 3 further define the measurement data but do not significantly narrow the field of use, therefore the arguments for rejecting claim 1 still apply, and claims 2 and 3 are also rejected. Claims 4 and 5 describe the machine learning models but do not change the arguments given in claim 1, therefore claims 4 and 5 are also rejected. Claim 6 further defines the microscopy image but does not significantly limit the field of application, therefore the arguments of claim 1 remain and claim 6 is also rejected. Claims 7 and 8 describe measuring a dimension of a device and causing a corrective action to be performed. However the general language used to describe the manufactured device and corrective action does not significantly narrow the field of use, therefore the arguments of claim 1 remain and claims 7 and 8 are also rejected. Claim 14 recites a non-transitory storage medium with instructions that cause the method of claim 1, and is therefore rejected for the same reasons. Claims 15-18 recite the limitations of claims 2-3 and 5-6, respectively, and are rejected for the same reasons. Claim 21 recites a system with a memory coupled to a processor, where the processor implements the method of claim 1. Claim 21 is rejected for the same reasons as claim 1. Claim 22 recites that the processor implements a method of training the machine learning models (here grouped into a single machine learning model) of claim 1 and is rejected for the same reasons. 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. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness . Claims 1 -8, 14-18, and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Pandev (US 20220352041 A1) in view of Sakai (US 20100314661 A1) . Regarding claim 1 , Pandev discloses a method comprising: processing measurement data of a substrate (¶15: semiconductor structures are measured, and "a trained parameter conditioned measurement model predicts a set of values of each non-conditioning parameter based on measurement data and a corresponding set of predetermined values for each conditioning parameter. In this manner, the trained parameter conditioned measurement model predicts the shape of a measured structure." ¶73 clarifies that the structures may be on a wafer and ¶125 shows that the wa f er generally refers to a substrate) that was processed according to a manufacturing process (¶125: the wafer was formed of a semiconductor material i.e. it was processed, and may be patterned from a manufacturing process) using a first trained machine learning model (¶105: "In some embodiments, a measurement model trained as described herein is implemented as a neural network model.") to predict a critical dimension (CD) profile for the substrate (¶19: "parameters of interest determined based on a trained parameter conditioned measurement model as described herein, include, but are not limited to: geometric parameters characterizing a measured structure…Exemplary geometric parameters include critical dimensions (CD)" ; see also Fig. 6 which depicts an image comprising a critical dimension profile, and ¶15 which states that “the trained parameter conditioned measurement model predicts the shape of a measured structure” ) ; and generating a CD profile prediction image based on the predicted CD profile for the substrate (Figs. 5 and 6 depict CD profile prediction images of a hole structure in a substrate ; also ¶19: “A parameter conditioned measurement model as described herein enables the trained model to reconstruct a two dimensional image or a three-dimensional image of a structure under measurement” ) . Pandev does not explicitly disclose processing the CD profile prediction image using a second trained machine learning model to generate a synthetic microscopy image associated with the substrate. Sakai discloses a SEM cross-section image of a semiconductor substrate (see Fig. 2 and ¶17) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Sakai with the invention of Pandev by processing the CD profile prediction image to generate a synthetic microscopy image associated with the substrate. Doing so would be useful because SEM images are familiar to those of ordinary skill in the art. Furthermore, it would have been obvious to perform the processing using a second trained machine learning model. Machine learning models can be trained on large amounts of correlated data to produce a desired output given an input. Since it is reasonable to assume that the details of a SEM image of a substrate feature would be correlated with the details of a CD profile prediction image, Pandev in view of Sakai would be motivated to use CD profile prediction image and SEM image data to train a machine learning model to generate a synthetic SEM image. The resulting model would be able to consistently and autonomously translate CD prediction profile images into SEM images which are familiar to those of ordinary skill in the art. Regarding claim 2 , Pandev in view of Sakai teaches the limitations of claim 1, and Pandev further discloses that the measurement data comprises a profile map of at least one of a film or a feature on the substrate (¶107: "the methods and systems for metrology of semiconductor devices as described herein are applied to the measurement of memory structures. These embodiments enable optical critical dimension (CD), film, and composition metrology for periodic and planar structures.") . Regarding claim 3 , Pandev in view of Sakai teaches the limitations of claim 1, and Pandev further discloses that the measurement data comprises at least one of spectral data or reflectometry data (¶40: the measurement data can include "soft X-ray reflectometry" data; ¶70: "The trained parameter conditioned measurement model is employed to estimate values of one or more parameters of interest from actual measurement data (e.g., measured spectra)") . Regarding claim 4 , Pandev in view of Sakai teaches the limitations of claim 1, and Pandev further discloses that the first trained machine learning model comprises a deep neural network (¶105: the measurement model may be a "deep network model") . Regarding claim 5 , Pandev in view of Sakai teaches the limitations of claim 1, and furthermore teaches that the second trained machine learning model comprises a generative model (the ML in Pandev is a model and it generates output so it is a generative model) . Regarding claim 6 , Pandev in view of Sakai teaches the limitations of claim 1. Furthermore, Pandev’s image is of a cross section of a substrate ( Pandev, Fig. 5) and Sakai’s image is a scanning electron microscope (SEM) image of a cross section of a substrate ( Sakai, Fig. 2) . Following the arguments of claim 1 for generating a virtual SEM image, then, it follows that the synthetic microscopy image comprises a virtual scanning electron microscopy (VSEM) image of a cross section of the substrate (see rejection of claim 1) . Regarding claim 7 , Pandev in view of Sakai teaches the limitations of claim 1. Furthermore, it would have been obvious to measure a feature of the synthetic microscopy image and calculate a dimension of a manufactured device (i.e. the substrate or a feature on the substrate) based on the measurement of the feature of the synthetic microscopy image. One would be motivated to do this in order to determine a dimension of interest displayed on the image . Regarding claim 8 , Pandev in view of Sakai teaches the limitations of claim 7. Furthermore, Pandev discloses causing performance of a corrective action in view of determined parameters (¶106: "corrections to process parameters determined based on measured device parameter values determined using a trained parameter conditioned measurement model may be communicated to the process tool.") . An example is given of adjusting etch time to achieve desired etch depth (¶106) . It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Pandev with the invention of Pandev in view of Sakai by causing performance of a corrective action in view of the calculated dimension of the manufactured device . Doing so would be useful to address a manufacturing problem such as insufficient etch depth. Furthermore, it would have been obvious for one of ordinary skill in the art to configure the corrective action to comprise one or more of: scheduling maintenance; updating a process recipe; or providing an alert to a user. Scheduling maintenance would enable the method to address a problem determined to be caused by machine failure. Updating a process recipe would enable the method to address a problem determined to be solvable by autonomously changing a setting (such as etch depth in the above example) . Providing an alert to a user would be useful in case a problem is determined to be urgent or unsolvable without human interaction. Regarding claim 14 , the limitations of claim 1 are found in claim 14. Claim 14 also discloses a non-transitory computer-readable storage medium storing instructions which, when executed, cause a processing device to perform the method of claim 1. Noting that the rejection of claim 1 involves computers receiving data and performing operations, these limitations would have been obvious. Claim 14 is therefore rejected on the same grounds as claim 1. Regarding claim 21 , the limitations of claim 1 are found in claim 21. Claim 21 also discloses a memory and a processing device coupled to the memory, which implements the method of claim 1. Noting that the rejection of claim 1 involves computers receiving data and performing operations, these limitations would have been obvious. Claim 21 is therefore rejected on the same grounds as claim 1. Regarding claims 15-16 and 17-18 , these recite the same limitations as claims 2-3 and 5-6, respectively, and are rejected for the same reasons. Regarding claim 2 2 , Pandev in view of Sakai teaches the limitations of claim 21. Furthermore, the limitations of claim 22 are encompassed by training the first and second machine learning (ML) models recited in claim 1. That is, the “machine learning model” of claim 22 may be interpreted as comprising the “first machine learning model” and “second machine learning model” of claim 1. In claim 1, the first ML model was provided with “measurement data of a substrate”; therefore it would have be en reasonable to train the first ML model with “a plurality of CD measurements associated with a substrate”. The method of claim 1 generated a “CD profile prediction image,” which is analogous to the “plurality of CD profile images” generated in claim 22. Then, since the second ML model outputs synthetic microscopy images (in the rejection of claim 1 those images are SEM images) , it would have be en natural to train the second ML model by providing it with a “plurality of SEM images…as target output” and a “plurality of CD profile images” as input. If one defined a ML model comprising the first and second ML models, it would have be en reasonable to conclude that such a model would be trained using an input data set comprising the plurality of SEM images and the plurality of CD profile images, and that training the ML model would comprise providing the plurality of CD measurements to the ML model as training input (i.e. as input into the first ML model), and providing the plurality of SEM images to the ML model as target output (i.e. as target output for the second ML model). Therefore, it would have been obvious to one of ordinary skill in the art practicing the invention of Pandev in view of Sakai to perform the limitations of claim 22 in order to train the machine learning models to perform the method of claim 1 (see rejection of claim s 1 and 21 ) . Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT ETHAN WESLEY EDWARDS whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-0266 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Monday - Friday, 7:30am-5pm . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, FILLIN "SPE Name?" \* MERGEFORMAT Andrew Schechter can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT (571) 272-2302 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. FILLIN "Examiner Stamp" \* MERGEFORMAT ETHAN WESLEY EDWARDS Examiner Art Unit 2857 /E.W.E./ Examiner, Art Unit 2857 /ANDREW SCHECHTER/ Supervisory Patent Examiner, Art Unit 2857