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 .
Statue of claims: claims 1-20 are pending below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on October 9th 2024 was filed and considered. The submission 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
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.
Claims 1-7, 12-16 and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by He et al (US 2016/0328837).
Claim 1, similar claim 12 and 20:
He et al (US 2016/0328837) anticipated the following subject matter:
An electronic device comprising:
a communication circuit (0082 detail hardware such as processors and devices);
a memory (0011) configured to store a first training dataset and at least one artificial intelligence model, the first training dataset being labeled with a defect shape and a mask pattern of a mask (0047-0048 detail defect image data (mask pattern) with imagery data (one or more images/datasets)); and
at least one processor operatively connected to the communication circuit and the memory, wherein the at least one processor is configured to (0029-0030 detail networked system/computer/memory):
receive, from an external database via the communication circuit (0029-0030 detail networked system/devices, where 0031 detail network with external systems), defect data obtained from at least one facility, the defect data including first defect data, second defect data, and third defect data (0046 detail classification (artificial intelligent) on first, second and third defects);
by using the second defect data, obtain a second training dataset by performing data augmentation on a defect shape for which an amount of labeled data in the first training dataset is determined to be less than a threshold value (above teaches first, second defect using imagery data for comparing; figure 3 and 0055-0056 detail use of classifier on pairing of defect types, where 0063 further detail the use of threshold values for separate defect class and pair);
train a first artificial intelligence model generated to classify a defect type of a mask based on the first training dataset and the second training dataset (0013; 0047-0048 detail use of imagery data as datasets; 0055-0056, 0063; figure 4 and 0072 detail use series of classifiers (artificial intelligent) for further grouping); and
classify a defect type of a mask based on at least one of the first defect data, the third defect data, or the first artificial intelligence model (0009, 0010, 0012-0013 all detail use of classification (AI) with imagery data (mask) for firs, second…etc defects).
Regarding claim 12, He et al teach method in figures 2-4 flowcharts.
Regarding claim 20, He et al teach system in figure 1.
Claim 2, similar claim 13:
The electronic device of claim 1, wherein the at least one processor is configured to classify the defect type of the mask based on the first artificial intelligence model, based on a determination that the defect type of the mask fails to be classified based on at least one of the first defect data or the third defect data (0009, 0010, 0012-0013 all detail use of classification (AI) with imagery data (mask) for firs, second…etc defects types).
Claim 3, similar claim 14:
The electronic device of claim 1, wherein the second defect data is obtained from a mask from which the first defect data was obtained and on which at least one unit process was performed, and wherein the third defect data is obtained from a mask from which the second defect data was obtained and on which at least one unit process was performed (figure 3 and 0055).
Claim 4, similar claim 15:
The electronic device of claim 1, wherein the at least one processor is configured to classify the defect type of the mask based on at least one of height information of a defect of the mask or location information of the defect of the mask, the at least one of the height information or the location information being included in the first defect data (0041 and 0068 detail attributes includes defect coordinate (location) as part of the image feature amounts, history data).
Claim 5, similar claim 16:
The electronic device of claim 3, wherein the at least one processor is configured to classify the defect type of the mask based on the third defect data, based on a determination that the defect type of the mask fails to be classified based on the first defect data (0036 detail defect types and pair where pair having score lower than a pre-defined threshold that are inadequate (fail) for separating two defect types).
Claim 6:
The electronic device of claim 1, further comprising: a display, wherein the at least one processor is configured to: control the display to display a graphical user interface (GUI) representing labeled data through the display, and perform re-labeling based on a user input received while the GUI is displayed (0029 detail display with user interface 110; 0032-0034 detail user display with input method such as touchscreen, keyboard, mouse, trackpad, where 0050 further detail user manual input/label).
Claim 7:
The electronic device of claim 6, wherein the at least one processor is configured to update the first artificial intelligence model based on a relabeled dataset based on a determination that the defect type of the mask classified by using the first artificial intelligence model is different from a defect type pre-stored for the mask (0050 detail user manual input and learning classifier be then aggregated into an integrated database).
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.
Claims 10 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over He et al (US 2016/0328837) in view of ZHANG et al (US 2022/0092359).
Claim 10, similar claim 18:
He et al (US 2016/0328837) teaches all the subject matter above but not the following which is taught by ZHANG et al (US 2022/0092359):
The electronic device of claim 1, wherein the at least one processor is configured to train the first artificial intelligence model by applying a focal loss function (0053-0056, specifically 0056).
He et al and ZHANG et al are both in the field of image analysis, especially use of neural network to recognize type of defect such that the combine outcome is predicative.
Therefore it would have been obvious to one having ordinary skill before the effective filing date to modify He et al by ZHANG et al such adjustment factor and the exponential adjustment factor provided for the membership probability may be used to process the focus loss function with the membership probability as a variable, and determine an improved loss function for training the neural network model as disclose by ZHANG et al in paragraph 0056.
Claims 11 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over He et al (US 2016/0328837) in view of Freytag et al (US 2023/0196189).
Claim 11, similar claim 19:
He et al (US 2016/0328837) teaches all the subject matter above but not the following which is taught by Freytag et al (US 2023/0196189):
The electronic device of claim 1, wherein the at least one processor is configured to train the first artificial intelligence model by applying label-smoothing (0136 teaches use of smoothing label region, where 0015 show application to defect class with machine learning).
He et al and Freytag et al are both in the field of image analysis, especially use of neural network to recognize type of defect such that the combine outcome is predicative.
Therefore it would have been obvious to one having ordinary skill before the effective filing date to modify He et al by Freytag et al such training image is annotated by improved annotation parameter values with high precision as disclosed by Freytag et al in 0136.
Allowable Subject Matter
Claim 8 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 9, similar claim 17, 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Liu et al (US 2022/0358751) teach TOOL FOR LABELLING TRAINING IMAGES FOR A VEHICLE CHARACTERISTIC DETECTION MODEL - statistical image models or classification engines (e.g., the CNN models) that are used to detect damaged areas on a vehicle, to detect damage types and/or to detect segments.
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/TSUNG YIN TSAI/Primary Examiner, Art Unit 2656
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