DETAILED ACTION
Claims 1-2, 4-5, 7-12, 14-15, and 17-20 are currently pending.
Claims 3, 6, 13, and 16 have been cancelled.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/02/2023 has been considered by the Examiner.
Response to Arguments
Drawings
Examiner agrees the previous objections to the drawings are moot, the objections are withdrawn.
35 U.S.C. 102 and 103
Applicant’s arguments with respect to the Tiemeyer, Honda, and He references as applied in the Non-Final Rejection mailed 12/23/2025 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 4-5, 10-12, and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Wang (US 2024/0062362, effectively filed 12/21/2020) in view of Schmedemann (““Procedural synthetic training data generation for AI-based defect detection in industrial surface inspection”).
Regarding claim 1, Wang teaches:
A processor-implemented method (Wang [0035] processor) with image generation, the method comprising:
receiving a plurality of input parameters for a plurality of images to be generated; (Wang [0059] training condition data acquirer 320 acquires a defect attribute combination of a detect-containing inspection image acquired by a second training image acquirer. A defect attribute combination may comprise one or more defect attributes of a defect included in a defect-containing inspection image)
generating a plurality of defect profiles comprising a size and location of one or more defects to be formed in an image, (Wang [0059] defect attributes may comprise a defect type, defect size, defect location, detect strength…etc)
generating the plurality of images comprising defect information based on the plurality of defect profiles and the plurality of input parameters using an image rendering operation, (Wang [0066] Generator 331 can be configure to synthesize a defect having detect attributes identified by the defect attribute combination with the defect-free inspection image)
Wang fails to teach:
wherein the qeneratinq of the plurality of defect profiles comprises determininq the location and the size of the one or more defects to be formed in each imaqe usinq a random distribution operation;
and
wherein the qeneratinq of the plurality of imaqes comprises performinq the imaqe renderinq operation such that a total number of defects of each type, from amonq a plurality of defect types, is inserted in equal numbers in the plurality of qenerated imaqes.
Schmedemann teaches:
wherein the qeneratinq of the plurality of defect profiles comprises determininq the location and the size of the one or more defects to be formed in each imaqe usinq a random distribution operation; (Schmedemann page 1104 left side, “The defect shape is randomized by varying its size, position, and orientation on the surface of the object”)
and
wherein the qeneratinq of the plurality of imaqes comprises performinq the imaqe renderinq operation such that a total number of defects of each type, from amonq a plurality of defect types, is inserted in equal numbers in the plurality of qenerated imaqes. (Schmedemann paragraph bridging 1103-04, We group our parameters based on the object type the parameter is assigned to. Intervals and distributions for the parameters have to be given as an input for the TDG. If no distribution is given then an equal distribution is assumed)
Before the time of filing, it would have been obvious to add the randomization of defects and distribution of defects (as taught by Schmedemann) to the synthetic training data generation of Wang. The inventions lie in the same field of endeavor of the generation of synthetic training data. The motivation to combine the reference is to remove bias in training data. See Schmedemann Abstract.
Regarding claim 2, the combination of Wang and Schmedemann teaches:
The method of claim 1, wherein the plurality of input parameters comprises one or more of a plurality of defects to be formed in each image and the plurality of defect types. (Wang [0059] training condition data acquirer 320 acquires a defect attribute combination of a detect-containing inspection image acquired by a second training image acquirer. A defect attribute combination may comprise one or more defect attributes of a defect included in a defect-containing inspection image)
Regarding claim 4, the combination of Wang and Schmedemann teaches:
The method of claim 1, wherein each of the plurality of generated images comprises a line pattern corresponding to either one of an optical microscopy image and a scanning electron microscope image of a patterning process. (Wang [0055] a first defect-free inspection image and a second defect-free inspection image, each of which is an SEM image, are shown as an example. See also Figure 8- Generation synthetic defect image base on defect free inspect image and defect attribute combination)
Regarding claim 5, the combination of Wang and Schmedemann teaches:
The method of claim 4, wherein one or more defects of the line pattern comprises any one or any combination of any two or more of a micro bridge, a bridge, a micro gap, an extended gap, and a line-collapse. (Wang [0060] A defect type may comprise a plurality of defect types such as a bridge defect, narrow line defect, wide line defect, etc)
Regarding claim 10, the combination of Wang and Schmedemann teaches:
A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, configure the one or more processors to perform the method of claim 1. (Wang [0035] processor. Please also see a full discussion of claim 1 above)
Regarding claim 11, the combination of Wang and Schmedemann teaches:
An apparatus with image generation, the apparatus comprising: one or more processors (Wang [0035] processor) configured to:
receive a plurality of input parameters for a plurality of images to be generated; (Wang [0059] training condition data acquirer 320 acquires a defect attribute combination of a detect-containing inspection image acquired by a second training image acquirer. A defect attribute combination may comprise one or more defect attributes of a defect included in a defect-containing inspection image)
generate a plurality of defect profiles comprising a size and location of one or more defects to be formed in an image; (Wang [0059] defect attributes may comprise a defect type, defect size, defect location, detect strength…etc) and
generate the plurality of images comprising defect information based on the plurality of defect profiles and the plurality of input parameters using an image rendering operation(Wang [0066] Generator 331 can be configure to synthesize a defect having detect attributes identified by the defect attribute combination with the defect-free inspection image)
wherein, for the generating of the plurality of defect profiles, the one or more processors are configured to determine the location and the size of the one or more defects to be formed in each image using a random distribution operation, and (Schmedemann page 1104 left side, “The defect shape is randomized by varying its size, position, and orientation on the surface of the object”)
wherein for the generating of the plurality of images, the one or more processors are configured to generate the plurality of images such that a total number of defects of each type, from among a plurality of defect types, is inserted in equal numbers in the plurality of generated images. (Schmedemann paragraph bridging 1103-04, We group our parameters based on the object type the parameter is assigned to. Intervals and distributions for the parameters have to be given as an input for the TDG. If no distribution is given then an equal distribution is assumed)
Before the time of filing, it would have been obvious to add the randomization of defects and distribution of defects (as taught by Schmedemann) to the synthetic training data generation of Wang. The inventions lie in the same field of endeavor of the generation of synthetic training data. The motivation to combine the reference is to remove bias in training data. See Schmedemann Abstract.
Regarding claim 12, the combination of Wang and Schmedemann teaches:
The apparatus of claim 11, wherein the plurality of input parameters comprises one or more of a plurality of defects to be formed in each image and the plurality of defect types. (Wang [0059] training condition data acquirer 320 acquires a defect attribute combination of a detect-containing inspection image acquired by a second training image acquirer. A defect attribute combination may comprise one or more defect attributes of a defect included in a defect-containing inspection image)
Regarding claim 14, the combination of Wang and Schmedemann teaches:
The apparatus of claim 11, wherein each of the plurality of generated images comprises a line pattern corresponding to either one of an optical microscopy image and a scanning electron microscope image of a patterning process. (Wang [0055] a first defect-free inspection image and a second defect-free inspection image, each of which is an SEM image, are shown as an example. See also Figure 8- Generation synthetic defect image base on defect free inspect image and defect attribute combination)
Regarding claim 15, the combination of Wang and Schmedemann teaches:
The apparatus of claim 14, wherein one or more defects of the line pattern comprises any one or any combination of any two or more of a micro bridge, a bridge, a micro gap, an extended gap, and a line-collapse. (Wang [0060] A defect type may comprise a plurality of defect types such as a bridge defect, narrow line defect, wide line defect, etc)
Allowable Subject Matter
Claims 7-9 and 17-19 are 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 20 is allowed.
Regarding claim 20, neither the closest known prior art, nor any reasonable combination thereof, teaches:
processing each of a plurality of generated images into a grid comprising a plurality of cells, wherein the plurality of images are generated based on a plurality of defect profiles and a plurality of input parameters using an image rendering operation, and
wherein the image rendering operation is performed such that a total number of defects of each type, from among a plurality of defect types, is inserted in equal numbers in the plurality of generated images;
identifying an object by processing each cell in the grid using an object detection operation; and
training a machine learning model by comparing coordinates of the identified object with coordinates stored in the plurality of defect profiles, wherein a size and a location of one or more defects in the plurality of defect profiles are determined using a random distribution operation.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Refer to PTO-892, Notice of References Cited for a listing of analogous art.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Molly K Wilburn whose telephone number is (571)272-3589. The examiner can normally be reached Monday-Friday 8am-4pm.
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, Emily Terrell can be reached at (571) 270-3717. 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.
/Molly Wilburn/Primary Examiner, Art Unit 2666
Prosecution Insights