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 .
All amendments to the claim as filed on 2/12/2026 have been entered and the action follows:
Response to Arguments
Applicant’s arguments with respect to claim(s) 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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-7 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "the foamphysical product" in line 18. There is insufficient antecedent basis for this limitation in the claim.
Claims 2-7 are rejected because they depend on rejected claim 1.
Claim Rejections - 35 USC § 103
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 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 1, 3-4, 6-7, 9-10 and 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over Brauer et al (US 2018/0157933) in view of Wu (CN 112733853).
With respect to claim 1, Brauer discloses A quality assessment system for assessing the quality of a
a) a visual inspection device for providing visual image data of the
a lighting setup comprising a lighting device adapted for lighting the
b) a quality assessment apparatus for assessing the quality of the
an assessment model providing unit adapted to provide a trained machine learning based assessment model, wherein the trained assessment model has been trained based on historical visual image data corresponding to a
an assessment unit adapted to assess a quality of the
trained assessment model to the visual image data, (see paragraph 0004, wherein …Inspection processes are used at various steps during a semiconductor manufacturing process to detect defects on wafers…);
wherein the quality assessment apparatus further comprises a visual image data preparation unit adapted to prepare the visual image data, wherein the preparation of the visual image data comprises segmenting the visual image data into visual image data parts, wherein a visual image data part comprises a coherent part of the visual image data, and wherein the assessment unit is adapted to assess the quality of the
However, Brauer fails to disclose assessing the quality of a foam product by visual inspection, as claimed.
Wu teaches assessing the quality of a foam product by visual inspection, (see Abstract wherein …invention relates to intelligent quality detection …feature map comprises …the foam product part in the image, so as to improve the classification effect), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the two references as they are analogous because they are solving similar problem of quality assessment using image analysis. Teaching of Wu for intelligent quality detection in the manufacturing system to be able to get the foam product classified can be incorporated into the Brauer’s system as suggested (see Brauer figure 3, numerical 36, 102 computer system), for suggestion, and modifying the system will yields a foam product inspection system as claimed, for motivation.
With respect to claims 3 and 4, Brauer and Wu further discloses wherein the applying of the trained assessment model to the visual image data parts individually comprises determining, utilizing the trained assessment model, for a visual image data part a part quality independently of a part quality of other visual image data parts, wherein the assessment unit is adapted to determine the quality of the foam product as an overall quality based on the determined part qualities of the visual image data parts; and wherein the assessment model providing unit is further adapted to provide a trained machine learning based overall quality determinator, wherein the trained overall quality determinator is adapted to determine as quality of a foam product an overall quality based on part qualities determined for segmented visual image data parts, wherein the assessment unit is adapted to apply the overall quality determinator to the part qualities of the visual image data parts to determine the quality of the foam product, (see paragraph 0104, wherein … The one or more software modules may be configured to divide the wafer image into a plurality of reference images. Each reference image associated may be associated with a die in the wafer image. The one or more software modules may be configured to receive one or more test images and create a plurality of difference images by differencing the one or more test images with one or more of the plurality of reference images “a part quality independently of a part quality” and paragraph 0105, wherein …The one or more software modules may be configured to assemble the plurality of reference images and the plurality of difference images into the augmented input data for the CNN “overall quality” and provide the augmented input data to the CNN…), as claimed.
With respect to claims 6 and 7, Brauer and Wu further discloses wherein the lighting setup comprises at least two lighting modes, wherein a lighting mode differs from another lighting mode by providing a differing lighting setting, wherein the camera is adapted to generate visual image data for the at least two lighting modes, and wherein the assessment unit is adapted to assess the quality of the foam product by applying the assessment model to the visual image data generated for the at least two lighting modes; and wherein the trained assessment model is adapted to assess, based on at least two visual image data of the foam product generated for two different lighting modes as input, the quality of the foam product, (see paragraphs 0070-0078, where various illumination or lighting modes are discuss for detecting defects in wafer), as claimed.
With respect to claim 12, Brauer discloses A quality assessment method for assessing a quality of a
providing visual image data corresponding to an image of the
preparing the visual image data, wherein the preparation of the visual image data comprises segmenting the visual image data into visual image data parts, wherein a visual image data part comprises a coherent part of the visual image data, (see paragraph 0025, wherein …software modules may be configured to divide “segmenting” the wafer image into a plurality of reference images “visual image data parts”…; and paragraph 0089, wherein …the processor to divide 109 the wafer image into a plurality of reference images …information may be associated with each of the plurality of reference images, such as wafer information, image location, image capture parameters…),
providing a trained machine learning based assessment model, wherein the trained assessment model has been trained based on historical visual data corresponding to a
assessing a quality of the
However, Brauer fails to disclose assessing the quality of a foam product by visual inspection, as claimed.
Wu teaches assessing the quality of a foam product by visual inspection, (see Abstract wherein …invention relates to intelligent quality detection …feature map comprises …the foam product part in the image, so as to improve the classification effect), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the two references as they are analogous because they are solving similar problem of quality assessment using image analysis. Teaching of Wu for intelligent quality detection in the manufacturing system to be able to get the foam product classified can be incorporated into the Brauer’s system as suggested (see Brauer figure 3, numerical 36, 102 computer system), for suggestion, and modifying the system will yields a foam product inspection system as claimed, for motivation.
Claims 9 and 14 are rejected for the same reasons as set forth in the rejections for claim 12, because claims 9 and 14 are claiming subject matter of similar scope as claimed in claim 12. Furthermore, Brauer discloses an apparatus and computer program in figures 3 and 4 respectively.
With respect to claim 13, Brauer discloses assessment model training method for training a machine learning based assessment model (see Abstract , a CNN), wherein the training method comprises:
providing historical visual image data of
wherein the historical visual image data is prepared, wherein the preparation of the historical visual image data comprises segmenting the visual image data into visual image data parts, wherein a visual image data part comprises a coherent part of the visual image data,
providing a trainable assessment model that is to be trained by utilizing machine learning, training the provided assessment model based on the provided historical visual image data and corresponding quality such that the trained assessment model is adapted to determine the quality of a
However, Brauer fails to disclose assessing the quality of a foam product by visual inspection, as claimed.
Wu teaches assessing the quality of a foam product by visual inspection, (see Abstract wherein …invention relates to intelligent quality detection …feature map comprises …the foam product part in the image, so as to improve the classification effect), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the two references as they are analogous because they are solving similar problem of quality assessment using image analysis. Teaching of Wu for intelligent quality detection in the manufacturing system to be able to get the foam product classified can be incorporated into the Brauer’s system as suggested (see Brauer figure 3, numerical 36, 102 computer system), for suggestion, and modifying the system will yields a foam product inspection system as claimed, for motivation.
With respect to claim 10, Brauuer and Wu further discloses An assessment model training apparatus for training a machine learning based assessment model, wherein the training apparatus (see Abstract) comprises:
a visual image data providing unit adapted to provide historical visual image data of foam products with a known quality, (see paragraph 0017, wherein …method may further comprise performing a wafer scan using an image data acquisition subsystem. The image data acquisition subsystem converts the wafer scan into the wafer image…),
an assessment model providing unit adapted to provide a trainable assessment model that is to be trained by utilizing machine learning, a training unit adapted to train the provided assessment model based on the provided historical visual image data and corresponding quality such that the trained assessment model is adapted to determine the quality of a foam product based on visual image data of the foam product, wherein the assessment model training apparatus performs the method of claim 13, (see paragraph 0006, wherein …detect defects is to use computer vision. In computer vision, a model, such as a convolutional neural network (CNN) may be used to identify defects. A CNN may be provided with a variety of images from a wafer and a set of known defects. One of the most common tasks is to fit a model to a set of training data, with the goal of making reliable predictions on unseen test data. Usually one needs several hundred examples of each at a minimum…), as claimed.
Claim 15 is rejected for the same reasons as set forth in the rejections for claim 13, because claim 15 is claiming subject matter of similar scope as claimed in claim 13. Furthermore, Brauer discloses a computer program in figure 4.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Brauer et al (US 2018/0157933) in view of Wu (CN 112733853) as applied to claim 4 above, and further in view of Fitzgibbon et al (US Pub. 2009/0285544).
With respect to claim 5, Brauer and Wu discloses all the limitation as claimed and rejected in claim 4 above. However, Brauer and Wu fail to explicitly disclose a gaussian process classifier parameters, as claimed.
Fitzgibbon teaches a gaussian process classifier parameters, (see paragraph 0023, wherein …As will be familiar to the person skilled in the art, this function (which can be seen a measure of the quality of the camera) may be classified as a `gaussian`, `pillbox` or `boxcar` function …), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the references as they are analogous because they are solving similar problem of measure of the quality. The teaching of Fitzgibbon can be incorporated into Brauer as suggested (see paragraph 0050 defect classification), for suggestion, and modifying the system yields improve model (see Fitzgibbon paragraph 0001), for motivation.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Brauer et al (US 2018/0157933) in view of Wu (CN 112733853) as applied to claim 10 above, and further in view of Wang et al (US Pub. 2024/0202528).
With respect to claim 11, Brauer and Wu discloses all the limitation as claimed and rejected in claim 10 above. However, Brauer and Wu fail to explicitly disclose wherein the training apparatus further comprises a feedback providing unit adapted to provide feedback of a user on an assessed quality of a foam product determined by the trained assessment model, wherein the training unit is adapted to train the assessment model further based on the feedback, as claimed.
Wang teaches wherein the training apparatus further comprises a feedback providing unit adapted to provide feedback of a user on an assessed quality of a foam product determined by the trained assessment model, wherein the training unit is adapted to train the assessment model further based on the feedback, (see paragraph 0029, wherein …user-supplied subjective quality metrics (that is, subjective feedback based on user input) as the data stream is transmitted and processed, and the managing component utilizes 136 this feedback to further refine or modify the neural network architectural configuration of one or more neural networks…), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the references as they are analogous because they are solving similar problem of measure of the quality. Teaching of Wang, user feedback into CNN, can be incorporated into Brauer as suggested (see Brauer paragraph 0008, machine learning algorithm), for suggestion, and modifying the system yields a user friendly system that improves the throughput of the system, for motivation.
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/VIKKRAM BALI/Primary Examiner, Art Unit 2663
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