DETAILED ACTION
Claims 1-5 are pending in this application and have been examined with the priority date of 12/01/2023 in accordance with the applicant’s claimed foreign priority.
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 statements (IDS) submitted on 03/26/2025 and 05/07/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
Image generator preparation process of claim 1
image generator in claims 1, 2, and 3
inspection image preparation process of claim 1
missing-image generation process of claim 1
complemented-image generation process of claim 1
difference acquisition process of claim 1
determination process of claim 1
image-generator storage section of claim 2
image storage section of claim 2
missing-image generator of claims 2 and 4
complemented-image generator of claims 2 and 5
difference acquirer of claims 2 and 5
determiner of claim 2
Because these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-5 rejected under 35 U.S.C. 101 as being drawn to an abstract idea or mental process without significantly more.
Regarding Claims 1 and 2, the claims are drawn to an abstract idea, mental process or step of mere data gathering.
The claims recite the following limitations;
“An inspection device, comprising: (Additional element)
an image-generator storage section in which an image generator built by machine learning is stored, (Additional element)
the image generator configured to generate, from an image having a partially missing region, an image in which the missing region is filled with a complementary image; (Step of mere data gathering/generation which could be performed manually)
an image storage section in which an inspection target image is stored; (Additional element)
a missing-image generator configured to generate, from the inspection target image, a missing image in which a region is missing, a window having a previously specified shape and size being applied on the region; (Step of mere data gathering/generation which could be performed manually)
a complemented-image generator configured to generate a complemented image in which the region is filled with a complementary image by inputting the missing image into the image generator; (Step of mere data gathering/generation which could be performed manually)
a difference acquirer configured to determine a difference between the inspection target image and the complemented image; (Mental process of discerning visually the differences between two images)
and a determiner configured to determine whether the region is normal or abnormal by comparing the difference with a previously determined criterion. (Mental process of discerning visually the differences between two images)
Under step 2A prong 1, the limitations are drawn to abstract ideas, mental processes or steps of mere data gathering as noted above by the examiner. Further, under step 2A prong 2, the claims recite the additional elements of an inspection device, a storage section, an image generator, a missing-image generator, a complemented-image generator, a difference acquirer and a determiner, which neither constitute judicial exception nor integrate the claim into practical application. Further, under step 2B, the claim does not include any additional elements which translate the claim into practical application or amount to significantly more than an abstract idea. (See MPEP section 2106)
Dependent claims 3-5, which depend from claim 2, do not add limitations which meaningfully translate the abstract ideas above into practical application, or add significantly more.
Regarding claim 3, the claim recites “The inspection device according to claim 2, wherein the image generator consists of a generator used with a discriminator in adversarial learning. (additional elements recited with a high level of generality)”
The limitations are drawn to an abstract idea without significantly more. Further, the additional elements of an image generator, a generator and a discriminator are recited with a high level of generality and do not translate the claim into practical application or amount to significantly more.
Regarding claim 4, the claim recites “The inspection device according to claim 2, wherein the missing-image generator is configured to sequentially set the window at a plurality of different positions in the inspection target image so that the new window partially overlaps the previous window. (Step of mere data gathering/generation which a human could manually perform)”
The limitations are drawn to an abstract idea or step of mere data gathering without significantly more. Further, the additional element of a missing- image generator is recited with a high level of generality and does not translate the claim into practical application or amount to significantly more.
Regarding claim 5, the claim recites “The inspection device according to claim 2, wherein: the complemented-image generator is configured to generate a plurality of complemented images for one image which is the missing image; (Step of mere data gathering/generation which a human could manually perform)
and the difference acquirer is configured to determine a difference between the inspection target image and each of the plurality of complemented images. (Mental process of discerning between multiple images to assess differences”
The limitations are drawn to an abstract idea without significantly more. Further, the additional elements of a complemented-image generator and a difference acquirer are recited with a high level of generality and do not translate the claim into practical application or amount to significantly more.
Claim Rejections - 35 USC § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3 and 5 are rejected under 35 U.S.C. 102(a) as being anticipated by Lin (US 20190355102 A1).
Regarding claim 1 Lin discloses; An inspection method, comprising: an image generator preparation process for preparing an image generator by machine learning (Lin, [0034] an image completer is part of machine learning network to generate completed image from images with holes in them, given that paragraph [0017] of applicant’s spec defines this as a part of a machine learning model to generate completed images, this would be analogous to what is taught in Lin.),
the image generator configured to generate, from an image having a partially missing region, an image in which the missing region is filled with a complementary image (Lin, [0034] the image completer generates a patch for an image which has a missing region and outputs a completed image which has had the generated complementary image data patch in place of the missing region);
an inspection-image preparation process for preparing an inspection target image (Lin, [0050] a training image (target image) is collected by the training manager module);
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(Lin, [0050])
a missing-image generation process for specifying a window having a previously determined shape and size (Lin, [0051] the training manager module introduces a hole into the training image, the hold being a rectangular region (set shape), [0050] the digital images of the training set have set dimensions and size criteria for the holes being added to the images),
and for generating, from the inspection target image, a missing image in which a region is missing, the window being applied on the region (Lin, [0051] the images (target images) have the rectangular region (set window) applied to them to generate a training image/incomplete image with a missing region (missing image));
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326
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(Lin, [0051])
a complemented-image generation process for generating a complemented image in which the region is filled with a complementary image by inputting the missing image into the image generator (Lin, [0052] the holey image (missing image) is then input into a coarse image network, which is a part of the image completer generation system (image generator), which generate a course patch prediction (complementary image portion) which then fills in the hole in the holey/missing image to generate a filled image, which is output as an intermediate image (complementary image) shown as 310 in Figure 3);
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(Lin, [0052])
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(Lin figure 3)
a difference acquisition process for determining a difference between the inspection target image and the complemented image (Lin, [0052] the model compares the initial training image (inspection target image) with the intermediate image (complemented image) to generate a reconstruction loss (difference));
and a determination process for determining whether the region is normal or abnormal by comparing the difference with a previously determined criterion (Lin,[0059] the intermediate (complemented) and training images (inspection images) are compared to determine if the model can tell the difference between the generated image portion and the original image, [0061] the intermediate/filled image (complemented image) is compared with the training image (inspection target image) using metrics of pixel distance, where [0063]-[0064] the distances/gradients for the pixels are determined between the intermediate image (complemented) and the training image (inspection image), applicant defined in specification paragraphs [0010]-[0011] that a region is normal when there is a high level of accuracy and less difference detected between the reconstruction and the original, therefore comparing the generated filled image to the original to assess whether the model can tell the difference is functionally equivalent to the determination of normal or abnormal).
Regarding Claim 2 Lin discloses; An inspection device, comprising: an image-generator storage section in which an image generator built by machine learning is stored (Lin, [0034] an image completer (image generator) is part of machine learning network to generate completed image from images with holes in them, [0106] the machine learning systems are stored and executed on computing system (storage section), given that paragraph [0017] of applicant’s spec defines this as a part of a machine learning model to generate completed images, this would be analogous to what is taught in Lin.),
the image generator configured to generate, from an image having a partially missing region, an image in which the missing region is filled with a complementary image (Lin, [0034] the image completer generates a patch for an image which has a missing region and outputs a completed image which has had the generated complementary image data patch in place of the missing region);
an image storage section in which an inspection target image is stored (Lin, [0035] the system stores and transmits the image data to the model on a computing device which includes a storage section;
a missing-image generator configured to generate, from the inspection target image, a missing image in which a region is missing, a window having a previously specified shape and size being applied on the region (Lin, [0051] the training manager module(missing-image generator) introduces a hole into the training image, the hold being a rectangular region (set shape), [0050] the digital images of the training set have set dimensions and size criteria for the holes being added to the images);
a complemented-image generator configured to generate a complemented image in which the region is filled with a complementary image by inputting the missing image into the image generator (Lin, [0052] the holey image (missing image) is then input into a coarse image network (complemented image generator), which is a part of the image completer generation system (image generator), which generate a course patch prediction (complementary image portion) which then fills in the hole in the holey/missing image to generate a filled image, which is output as an intermediate image (complementary image) shown as 310 in Figure 3);
a difference acquirer configured to determine a difference between the inspection target image and the complemented image (Lin, [0052] the model (difference acquirer) compares the initial training image (inspection target image) with the intermediate image (complemented image) to generate a reconstruction loss (difference));
and a determiner configured to determine whether the region is normal or abnormal by comparing the difference with a previously determined criterion (Lin,[0059] the intermediate (complemented) and training images (inspection images) are compared to determine if the model (determiner) can tell the difference between the generated image portion and the original image, [0061] the intermediate/filled image (complemented image) is compared with the training image (inspection target image) using metrics of pixel distance, where [0063]-[0064] the distances/gradients for the pixels are determined between the intermediate image (complemented) and the training image (inspection image), applicant defined in specification paragraphs [0010]-[0011] that a region is normal when there is a high level of accuracy and less difference detected between the reconstruction and the original, therefore comparing the generated filled image to the original to assess whether the model can tell the difference is functionally equivalent to the determination of normal or abnormal).
Regarding Claim 3 Lin discloses; The inspection device according to claim 2, wherein the image generator consists of a generator used with a discriminator in adversarial learning (Lin, [0039] the image completer generation system (generator) has two connected networks, a coarse image network (generator) and an image refinement network (discriminator), [0052] where the coarse image network generates content to fill the hole in the image (functionally equivalent to a generator) and [0057] the refinement network determines the loss and compares the output generated filled image with the ground truth image (functionally equivalent to a discriminator), [0052] both us generative adversarial network loss computations).
Regarding Claim 5 Lin discloses; The inspection device according to claim 2, wherein: the complemented-image generator is configured to generate a plurality of complemented images for one image which is the missing image (Lin, [0057]-[0058] the training manager module (complemented image generator) creates a refined filled image (additional intermediate/complemented image) to refined the initial intermediate image (first complemented image) further from the training image (missing image));
and the difference acquirer is configured to determine a difference between the inspection target image and each of the plurality of complemented images (Lin, [[0052] the training image (initial inspection image) is compared with the intermediate image (first complemented image), [0058] the global critic (difference acquirer) compares the generated filled image (second complemented image) to the initial training image (target inspection image), therefore each of the plurality of generated complemented image is compared to the initial training image (target inspection image)).
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.
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.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Lin (US 20190355102 A1) in view of Iizawa (US 20210019878 A1).
Regarding Claim 4 Lin fails to teach; The inspection device according to claim 2, wherein the missing-image generator is configured to sequentially set the window at a plurality of different positions in the inspection target image so that the new window partially overlaps the previous window.
However, in the same field of endeavor, Iizawa teaches; wherein the missing-image generator is configured to sequentially set the window at a plurality of different positions in the inspection target image so that the new window partially overlaps the previous window (Iizawa, [0061] the image pair generation unit (missing-image generator) may generate multiple patch images (windows) that overlap one another).
The combination of Lin and Iizawa would have been obvious to one of ordinary skill in the art before the effective filing date of the presently claimed invention. The motivation for the addition of the overlapping window/patch feature of Iizawa is that this allows the system to generate multiple windows/patches that assure the area of the image which is being patched is effectively covered. (Iizawa, [0058]-[0062])
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. For a listing of analogous art as determined by the examiner, please see the attached PTO-892 Notice of References Cited form.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN M ELLIOTT whose telephone number is (703)756-5463. The examiner can normally be reached M-F 8AM-5PM ET.
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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.
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/J.M.E./Examiner, Art Unit 2666
/EMILY C TERRELL/Supervisory Patent Examiner, Art Unit 2666
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