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 .
Claims 1-9 are pending regarding this application.
Priority
The present application claims foreign priority benefits from JP2023-190997 filed on
11/08/2023. The certified copies of the priority documents were electronically retrieved 11/26/2025.
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/29/2024 is considered and attached.
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, 3, 4, and 6-9, are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Analysis for claim 1 is provided in the following. Claim 1 is reproduced in the following (annotation added):
A quality inspection system comprising at least one processor, the at least one processor carrying out:
an acquisition process for acquiring an image captured of a wall material;
a division process for dividing the image into a plurality of small images;
a detection process for detecting a defect included in the wall material by referring to a combination of at least two determination results which have been obtained by inputting the small images into each of at least two machine learning models that have been trained by different training methods and that each use, as input, an image captured of a wall material and output a determination result which is obtained by determining whether or not the wall material is defective; and
an output process for outputting a detection result which is obtained in the detection process.
Step 1: Evaluating whether the claim belongs to one of the statutory categories.
Claim 1 recites at least one processor within a system. Thus, the claim is directed to a machine, which is one of the statutory categories of invention (Step 1: YES)
Step 2A Prong One: Evaluating whether the claim recites a judicial exception (an abstract idea enumerated in 2019 PEG, a law of nature, or a natural phenomenon). If no exception is recited, the claim is eligible. This concludes the eligibility analysis. If the claim recites an exception, go to Step 2A Prong Two.
Claim 1 recites an abstract idea of a mental process. At least steps b-d are recited at a high level of generality such that they could be practically performed by a human (The courts consider a mental process (thinking) that “can be performed in the human mind, or be a human using a pen and paper” to be an abstract idea.). These concepts fall into the “mental processes” group of abstract ideas, which is observation, evaluation and/or judgement. The process of dividing images and utilizing two different methods to generate a determination result regarding whether a wall is defective can ultimately be done manually. As such, determining the above detection result using computers with two distinct learning models constitutes mere automation of manual processes. MPEP, 2106.04 (a) (2) III (C): Performing a mental process on a generic computer. An example of a case identifying a mental process performed on a generic computer as an abstract idea is Voter Verified, Inc. v. Election Systems & Software, LLC, 887 F.3d 1376, 1385, 126 USPQ2d 1498, 1504 (Fed. Cir. 2018). The limitations, interpreted under their broadest reasonable interpretation and in consistence with the specification, cover performance of the limitations in the mind or by generic computer components. See MPEP 2106.04 and the 2019 PEG. (Step 2A Prong One YES)
Step 2A Prong Two: Evaluating whether the claim recites additional elements that integrate the exception into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into practical application. If the answer to (a) is YES and (b) is NO, go to Step 2B; if the answer to (a) and (b) is YES, go to PATHWAY B, i.e., the claim is not directed to a judicial exception and the claim is eligible.
The 2019 PEG defines the phrase “integration into a practical application” to require an additional element or a combination of additional elements in the claim to apple, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception.
Limitations that are indicative of integration into a practical application when recited in a claim with a judicial exception include:
Improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a);
Applying or using a judicial exception to affect a particular treatment or prophylaxis for disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05©; and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e) and the Vanda Memo issued in June 2018.
Limitations that are not indicative of integration into a practical application when recited in a claim with a judicial exception include:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f);
Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP 2106.05(g); and
Generally linking the use of the judicial exception to a particular technological environment or field of use, as discussed in MPEP 2106.05(h).
[Examiners should note that revised Step 2A excludes consideration of whether claim elements represent well-understood, routine, conventional activity. The question of whether claim elements represent only well-understood, routine, conventional activity is considered at Step 2B and is not a consideration in Step 2A.]
Step a can be regarded as an additional element recited in claim 1, along with the recited machine learning models. These additional elements, i.e., acquiring an image captured of a wall material and using trained machine learning models to detect defects, do not integrate the exception into a practical application of the exception. Note even if the specification discloses that the invention pertains to an improvement in the technology, the claim must be evaluated to ensure the claim itself reflects the improvement in technology. It is also important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. Therefore, the additional elements do not recite an improvement. (Step 2A Prong Two NO)
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim.
Step a can be regarded as an additional element recited in claim 1. This additional element, i.e., acquiring an image captured of a wall material is considered insignificant extra-solution activities which amounts to automating a manual human activity. In the instant case, the recited functional limitation in step a can be performed by a photographer (organizing human activity/ Mere automation of manual processes). Additionally, acquiring an image captured of a wall material is a well-understood, routine, conventional activity in the field.
Furthermore, regarding the machine learning models as recited in claim 1, the claim does not provide an improvement to the machine learning model that amounts to significantly more, even when considered in combination with the claim as a whole. Utilizing different machine learning models to analyze defects in images is a well-understood, routine, conventional activity in the field. Additionally, using a trained machine learning model to output a defect result is recited at a high level of generality such that it amounts to no more than automating a routine process utilizing mere generic computer system elements.
Using the broadest reasonable interpretation of the claim, the additional elements, taken individually and in combination, do not result in the claim, as a whole, amounting to significantly more than the abstract idea itself. See MPEP 2106.05. (Step 2B: NO) The claim is not eligible.
Claim 3 recites
“wherein in the detection process, the at least one processor changes, in accordance with a type of the wall material, the combination of the at least two determination results to be referred to”
Step a is directed to the abstract idea of mental processes which are mere automation of manual processes. The similar examination analysis as applied to claim 1 is applied to step a of claim 3. No additional elements are recited. Accordingly, claim 3 does not have eligible subject matter.
Claim 4 recites “wherein in the detection process, the at least one processor detects the defect included in the wall material by performing logical operations on the at least two determination results”. This contains steps that are directed to the abstract idea of mental processes which are mere automation of manual processes. The similar examination analysis as applied to claim 1 is applied to the steps of claim 4. No additional elements are recited. Accordingly, claim 4 does not have eligible subject matter.
Claim 6 recites “wherein in the output process, the at least one processor outputs an image in which a defective part is shown”. This contains steps that are directed to the abstract idea of mental processes which are mere automation of manual processes. The similar examination analysis as applied to claim 1 is applied to the steps of claim 6. No additional elements are recited. Accordingly, claim 6 does not have eligible subject matter.
Claim 7 recites “wherein the detection result includes at least one selected from the group consisting of information indicating whether or not an outer dimension of the wall material falls within a threshold range and information indicating whether or not a value of a pixel value of the wall material in an image acquired in the acquisition process falls within a threshold range”. This contains steps that are directed to the abstract idea of mental processes which are mere automation of manual processes. The similar examination analysis as applied to claim 1 is applied to the steps of claim 7. No additional elements are recited. Accordingly, claim 7 does not have eligible subject matter.
Independent claim 8 is directed to a process, which is a statutory category of invention. Similar analysis is applicable as applied above to the method of claim 1. Accordingly claim 8 does not have eligible subject matter.
Independent claim 9 is directed to a machine, which is a statutory category of invention. Similar analysis is applicable as applied above to the method of claim 1. Claim 12 further recites an additional element of “computer-readable non-transitory storage medium storing a quality inspection program for causing a computer to function”. This element is recited at a high level of generality such that they amount to no more than mere generic computer system elements. The remainder of the claim is identical to claim 1. The similar examination analysis as applied to claim 1 is applied to the remaining steps of claim 9. Accordingly claim 9 does not have eligible subject matter.
Claim Rejections - 35 USC § 112(a), written description
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 5 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Background
35 U.S.C. § 112(a) requires that the “specification shall contain a written description of the invention”. To satisfy the written description requirement, a patent specification must describe the claimed invention in sufficient detail that one skilled in the art can reasonably conclude that the inventor had possession of the claimed invention. See, e.g., Moba, B.V. v. Diamond Automation, Inc., 325 F.3d 1306, 1319, 66 USPQ2d 1429, 1438 (Fed. Cir. 2003); Vas-Cath, Inc. v. Mahurkar, 935 F.2d at 1563, 19 USPQ2d at 1116. An applicant shows possession of the claimed invention by describing the claimed invention with all of its limitations using such descriptive means as words, structures, figures, diagrams, and formulas that fully set forth the claimed invention. Lockwood v. Amer. Airlines, Inc., 107 F.3d 1565, 1572, 41 USPQ2d 1961, 1966 (Fed. Cir. 1997). Possession may be shown in a variety of ways including description of an actual reduction to practice, or by showing that the invention was “ready for patenting” such as by the disclosure of drawings or structural chemical formulas that show that the invention was complete, or by describing distinguishing identifying characteristics sufficient to show that the applicant was in possession of the claimed invention. See, e.g., Pfaff v. Wells Elecs., Inc., 525 U.S. 55, 68, 119 S.Ct. 304, 312, 48 USPQ2d 1641, 1647 (1998); Eli Lilly, 119 F.3d at 1568, 43 USPQ2d at 1406; Amgen, Inc. v. Chugai Pharm., 927 F.2d 1200, 1206, 18 USPQ2d 1016, 1021 (Fed. Cir. 1991).
There is a presumption that an adequate written description of the claimed invention is present when the application is filed. In re Wertheim, 541 F.2d 257, 263, 191 USPQ 90, 97 (CCPA 1976) (“we are of the opinion that the PTO has the initial burden of presenting evidence or reasons why persons skilled in the art would not recognize in the disclosure a description of the invention defined by the claims”). However, as discussed in subsection I., supra, the issue of a lack of adequate written description may arise even for an original claim when an aspect of the claimed invention has not been described with sufficient particularity such that one skilled in the art would recognize that the applicant had possession of the claimed invention. The claimed invention as a whole may not be adequately described if the claims require an essential or critical feature which is not adequately described in the specification and which is not conventional in the art or known to one of ordinary skill in the art. While it is not necessary for the examiner to present factual evidence, to make a prima facie case it is necessary to point out the claim limitations that are not adequately supported and explain any other reasons that the claim is not fully supported by the disclosure to show that the inventor had possession of the invention. See for example, Hyatt v. Dudas, 492 F.3d 1365, 1371, 83 USPQ2d 1373, 1376-1377 (Fed. Cir. 2007).
The courts have described the essential question to be addressed in a description requirement issue in a variety of ways. An objective standard for determining compliance with the written description requirement is, “does the description clearly allow persons of ordinary skill in the art to recognize that he or she invented what is claimed.” In re Gosteli, 872 F.2d 1008, 1012, 10 USPQ2d 1614, 1618 (Fed. Cir. 1989). Under Vas-Cath, Inc.v. Mahurkar, 935 F.2d 1555, 1563-64, 19 USPQ2d 1111, 1117 (Fed. Cir. 1991), to satisfy the written description requirement, an applicant must convey with reasonable clarity to those skilled in the art that, as of the filing date sought, he or she was in possession of the invention, and that the invention, in that context, is whatever is now claimed. The test for sufficiency of support in a parent application is whether the disclosure of the application relied upon “reasonably conveys to the artisan that the inventor had possession at that time of the later claimed subject matter.” Ralston Purina Co.v.Far-Mar-Co., Inc., 772 F.2d 1570, 1575, 227 USPQ 177, 179 (Fed. Cir. 1985) (quoting In reKaslow, 707 F.2d 1366, 1375, 217 USPQ 1089, 1096 (Fed. Cir. 1983)). See MPEP§ 2163 - https://www.uspto.gov/web/offices/pac/mpep/s2163.html
Regarding claim 5, applicant claims “connection between images acquired in the acquisition process” and “alignment of the wall material in an image acquired in the acquisition process”. However, applicant’s specification never describes what the “connection” or “alignment” entails. Furthermore, it is unclear how an “alignment of the wall material in an image acquired in the acquisition process” can occur, as the process of alignment involves utilizing relative positions of two or more different entities, and the aforementioned subject matter only recites aligning the wall material in an acquired image. Said differently, it is unclear as to with what the wall material in an acquired image is aligned. The only portions of the specification that discusses the above limitations are in para. [0082], [0178], [0190], [0200], and [0210]. These sections merely recite language similar to the claim language, and do not provide any insight regarding what the “connection” or “alignment” as claimed in claim 5 entails. In addition, the sections fail to specify any details regarding any other element or respect with which the wall material is aligned. Furthermore, one of ordinary skill in the art would not understand how, based on applicant’s disclosure, a connection between images acquired during an acquisition process is derived, as the term “connection” does not have a clear or specific meaning in the field of image analysis. Therefore, one of ordinary skill in the art would not have recognized that the inventor was in possession of the invention as claimed in view of the disclosure of the application as filed.
Claim Rejections - 35 USC § 112(a), enablement
Claim 5 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
Regarding claim 5, applicant claims “connection between images acquired in the acquisition process” and “alignment of the wall material in an image acquired in the acquisition process”. It is the Examiner’s position that the subject matter described above is not described in the specification in such a way as to enable one skilled in the art to which it pertains, to make and use the invention, without undue experimentation.
In accordance with MPEP § 2164, the examiner has the initial burden of establishing a prima facie case of lack of enablement. The question posed when making a lack of enablement rejection is: Is the experimentation needed to practice the invention undue or unreasonable? See Mineral Separation v. Hyde, 242 U.S. 261, 270 (1916). The test for lack of enablement was established in In re Wands, 858 F.2d 731, 737, 8 USPQ2d 1400, 1404 (Fed. Cir. 1988) and set forth several factors which must be considered by the examiner when making a determination of lack of enablement. These factors can be found in MPEP § 2164.01(a). Furthermore, the examiner need not discuss every factor. The examiner need only to focus on those factors, reasons, and evidence that lead the examiner to conclude that the specification fails to teach how to make and use the claimed invention without undue experimentation.
In Re Wands Factors
B) The nature of the invention
The invention of the aforementioned claim is directed towards a method of detecting defects in wall material using machine learning models. The limitations in question are drawn towards “connection between images acquired in the acquisition process” and “alignment of the wall material in an image acquired in the acquisition process”. Applicant’s disclosure generally recites similar language to the claim language recited above.
C) The state of the prior art
After a thorough prior art search, regarding the claims, a determination has been made that it is known in the art to detect connections between images and align images. However, no prior art has been found that is capable of “carrying out connection between images”, wherein the term “connection” is not defined. Additionally, no prior art has been found that is capable of “aligning wall material in an image” specifically in a situation where the entity with which the imaged wall material is aligned is not specified.
D) The level of one of ordinary skill in the art
The examiner is of the opinion that it is well known in the art to detect a distance between a camera and an object. However, the Examiner's position on the claims is that it is not well known in the art to “connection between images acquired in the acquisition process” and “alignment of the wall material in an image acquired in the acquisition process”, wherein the term “connection” is not further described, and no entity with respect to with what the wall material in an image is aligned is specified anywhere in the specification. Applicant has not described the invention is sufficient detail for one of ordinary skill in the art to ascertain how applicant’s invention is carried out; and one of ordinary skill in the art would have trouble understanding applicant’s invention based on their disclosure.
F) The amount of direction provided by the inventor
As discussed above applicant has not provided any details on the aforementioned claim limitations. Regarding the claims, applicant has not described how the “connection between images acquired in the acquisition process” and “alignment of the wall material in an image acquired in the acquisition process” – is carried out. Applicant appears to only generally refer to the above subject matter using language identical to the claims. See para. [0082], [0178], [0190], [0200], and [0210] of the specification. These paragraphs merely recite language similar to the claim language. Applicant’s specification does not describe any further information regarding what the “connection” entails, nor does the specification clarify with respect to what the wall material is aligned. One of ordinary skill in the art would recognize that much more information would be needed in order to carry out a preprocessing process consisting of connection between images acquired in the acquisition process or alignment of the wall material in an image acquired in the acquisition process.
G) The existence of working examples
There is neither mention of a working example, nor any example in any of the prior art.
H) The quantity of experimentation needed based on the disclosure
Since the invention as claimed is not described in detail in the specification, the amount of experimentation would be great in order to make/use the invention. As mentioned previously, applicant has not provided any direction on the above-mentioned claim limitation. Applicant merely discloses these limitations in passing in para. [0082], [0178], [0190], [0200], and [0210] of the specification and has not provided any disclosure on these limitations. In particular, applicant has not described anything capable of performing “connection between images acquired in the acquisition process” and “alignment of the wall material in an image acquired in the acquisition process”, wherein the term “connection” is not further described, and no entity with respect to with what the wall material in an image is aligned is specified. Thus, one of ordinary skill in the art would have to engage in undue experimentation in order to figure out how to create the claimed invention. Therefore, since the specification provides no detail on how these claim limitations are made and used, the disclosure is non-enabling. See MPEP § 2164.06.
When considering all of the pertinent In re Wands factors, 858 F.2d 731, 737, 8 USPQ2d 1400, 1404 (Fed. Cir. 1988), the Examiner has reached the conclusion that one of ordinary skill in the art would not be enabled to make and/or use the claimed invention without undue experimentation, particularly since the amount of direction provided by the applicant is minimal.
Claim Rejections - 35 USC § 112(b)
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 1-9 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.
Regarding claim 1, claim 1 recites “an image captured of a wall material” in lines 3-4 and “an image captured of a wall material” in line 13. As such, it is unclear whether the image captured of the wall material as recited in lines 3-4 is equivalent to or distinct from the image captured of the wall material as recited in line 13. Applicant discusses this image throughout the specification. However, none of these sections clarify whether the image captured of the wall material as recited in lines 3-4 is equivalent to or distinct from the image captured of the wall material as recited in line 13. Therefore, claim 1 is rejected for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention.
Corresponding claims 8 and 9 are similarly rejected.
Claims 2-7 are rejected due to their dependence upon claim 1.
Regarding claim 5, claim 5 recites “an image process acquired in the acquisition process” in lines 7-8 and “a plurality of small images” in line 14. However, claim 1, upon which claim 5 depends, already recites “acquiring an image captured of a wall material” in line 3 and “a plurality of small images” in lines 5-6. As such, it is unclear whether the acquired image and small images as recited in claim 5 are equivalent to or distinct from the acquired image and small images as recited in claim 1. Applicant discusses the subject matter of claim 5 in para. [0082] of the specification. However, nowhere in this section, nor any other section of the specification, does the applicant clarify whether the acquired image and small images as recited in claim 5 are equivalent to or distinct from the acquired image and small images as recited in claim 1.
Claim 5 additionally recites “connection between images acquired in the acquisition process” in lines 5-6. However, even when considering the context of the claim language and the field of art, it is unclear what the term “connection” entails. Applicant’s specification discusses the above subject matter in para. [0082], [0178], [0190], [0200], and [0210]. However, these sections merely recite language similar to the claim language, and do not provide any insight regarding what the “connection” process involves. As such, claim 5 is rejected for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention.
Regarding claim 7, claim 7 recites “an image process acquired in the acquisition process” in lines 8-9. However, claim 1, upon which claim 7 depends, already recites “acquiring an image captured of a wall material” in line 3. As such, it is unclear whether the acquired image as recited in claim 7 are equivalent to or distinct from the acquired image as recited in claim 1. Applicant discusses the subject matter of claim 7 in para. [0062]-[0064] of the specification. However, nowhere in this section, nor any other section of the specification, does the applicant clarify whether the acquired image as recited in claim 7 are equivalent to or distinct from the acquired image as recited in claim 1. As such, claim 7 is rejected for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention.
Additionally regarding claim 7, claim 7 recites “a value of a pixel value” in line 7. However, it is unclear whether the “value” is equivalent to or distinct from the “pixel value”. Applicant discusses this subject matter in para. [0062]-[0064]. However, nowhere in this section, nor any other section of the specification, does the applicant define the “value” or clarify whether it is equivalent to the claimed “pixel value”. If applicant intends for the “value” to be equivalent to the pixel value, it is suggested that the applicant delete the recitation of “a value of” as recited in line 7 of claim 7.
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.
Claims 1, 2, and 6-9 are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (KR 10-2270768 B1, see attached English translation for citations), hereinafter Kim, in view of Tanaka et al. (U.S. Publication No. 2018/0211374A1), hereinafter Tanaka.
Regarding claim 1, Kim teaches a quality inspection system comprising at least one processor (Kim teaches “system and method for detecting cracks in the surface of the tunnel according to the present invention collect image data by photographing the surface of the tunnel” in para. [0012] wherein the system includes an image analysis device 200 which inherently includes at least one processor), the at least one processor carrying out:
an acquisition process for acquiring an image captured of a wall material (Kim teaches “the camera driver 120 may receive a control signal from the image analysis device 200 located at a remote location to take a picture of a specific area of the inner wall of the tunnel with interest” in para. [0031]);
a division process for dividing the image into a plurality of small images (Kim teaches a process of dividing the large-capacity tunnel inner wall image of high pixels into small patches as shown in para. [0039]-[0040] and [0052]-[0055]);
a detection process for detecting a defect included in the wall material (Kim teaches inputting the patch images (small images) into a deep learning model (machine learning model) in order to determine whether the wall material of the input image includes a crack (defect)); and
an output process for outputting a detection result which is obtained in the detection process (Kim teaches “the main control unit 240 marks the position of the patch image including the crack found by the deep learning unit 230 as a red rectangle in the original image” which is interpreted as equivalent to the claimed output process/detection result based on the determination result).
Kim fails to teach detecting a defect by referring to a combination of at least two determination results which have been obtained by inputting the small images into each of at least two machine learning models that have been trained by different training methods and that each use, as input, an image captured of a wall material.
However, Tanaka (in view of Kim) teaches detecting a defect by referring to a combination of at least two determination results which have been obtained by inputting the small images into each of at least two machine learning models that have been trained by different training methods and that each use, as input, an image captured of a wall material (Tanaka teaches a first learning model wherein “the first learning device 201 uses a set of non-defective images (examples of normal data) each indicating a captured image obtained by capturing an object with no defect out of captured images obtained by capturing respective objects, to learn the first model for determining the non-defective image” in para. [0029]. Tanaka additionally teaches a second learning model, wherein “the abnormality candidate areas selected by a user as correct data and the abnormality candidate areas not selected by the user as incorrect data [are used] to learn a second model for identifying the correct data and the incorrect data” as shown in para. [0032]. Here, the “learning” methods as taught by Tanaka are interpreted as equivalent to the claimed “different training methods”. Furthermore, Tanaka teaches that the first model takes the captured image as input, and the second model takes the defect candidate area(s) within the captured image as input in para. [0038]-[0039]. The teachings of Tanaka as described above can be combined with Kim’s teaching of specifically inputting the small images into a machine learning model, wherein the small images are images captured of a wall material. See also that Tanaka teaches that this method can apply to a defect within a material as shown in para. [0004]).
Kim and Tanaka are both considered to be analogous to the claimed invention because they are in the same field of analyzing defects through image analysis and machine learning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Kim to incorporate the teachings of Tanaka and include “detecting a defect by referring to a combination of at least two determination results which have been obtained by inputting the small images into each of at least two machine learning models that have been trained by different training methods and that each use, as input, an image captured of a wall material”. The motivation for doing so would have been to “suppress erroneous detection of a portion that is statistically abnormal but is not a defect to be detected. Accordingly, sufficient inspection accuracy can be obtained”, as suggested by Tanaka in para. [0044]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Kim with Tanaka to obtain the invention specified in claim 1.
Regarding claim 2, Kim and Tanaka teach the quality inspection system according to claim 1, wherein the at least two machine learning models include:
at least one first machine learning model that has been trained by machine learning with use of, as training data, a set of an image of a normal wall material which includes no defect and a determination result indicating that the normal wall material is normal (Tanaka teaches a first learning model wherein “the first learning device 201 uses a set of non-defective images (examples of normal data) each indicating a captured image obtained by capturing an object with no defect out of captured images obtained by capturing respective objects, to learn the first model for determining the non-defective image” in para. [0029] wherein the first learning model can use semi-supervised learning wherein model learning is performed “assuming that the input data belongs to one specific class” (the class here being normal data) in para. [0031] and “the first learning device 201 accepts input of a plurality of non-defective images, and for each corresponding pixel, obtains the pixel mean value and the pixel variance value and stores the values as parameters (constructs (learns) the first model)” as shown in para. [0029]. Here, this learning data is interpreted as equivalent to the claimed training data. Kim further teaches identifying storing a set of wall image patches without cracks (normal wall material) as shown in para. [0062]. As such, the first machine learning model as taught by Tanaka can be combined with Kim’s teaching of the determination result indicating that the normal wall material is normal to teach the above claim limitation); and
at least one second machine learning model that has been trained by machine learning with use of, as training data, a set of an image of a normal wall material and a determination result indicating that the normal wall material is normal and a set of an image of a defective wall material and a determination result indicating that the defective wall material is defective (Tanaka teaches a second learning model, wherein “the abnormality candidate areas selected by a user as correct data and the abnormality candidate areas not selected by the user as incorrect data [are used] to learn a second model for identifying the correct data and the incorrect data” as shown in para. [0032]. Here, the data used for “learning” as taught by Tanaka is interpreted as equivalent to the claimed “training data” which includes both defective and non-defective areas. Furthermore, “the second learning device 202 can calculate a Z-score indicating a value depending on difference from the parameters of the first model described above for each pixel of the plurality of captured images prepared in advance, to detect an aggregation area of pixels each indicating a Z-score equal to or greater than a predetermined threshold value, as the defect candidate area” as shown in para. [0032] The teachings of Tanaka as described above can be combined with Kim’s teaching of specifically inputting the small images into a machine learning model, wherein the images are images captured of a wall material. See also that Tanaka teaches that this method can apply to a defect within a material as shown in para. [0004]).
Similar motivations as applied to claim 1 can be applied here to claim 2.
Regarding claim 6, Kim and Tanaka teach the quality inspection system according to claim 1,
wherein in the output process, the at least one processor outputs an image in which a defective part is shown (Kim teaches “the main control unit 240 [displays the patch image with cracks and] marks the position of the patch image including the crack found by the deep learning unit 230 as a red rectangle in the original image” in para. [0046] and [0064]-[0065]).
Regarding claim 7, Kim and Tanaka teach the quality inspection system according to claim 6, wherein the detection result includes at least one selected from the group consisting of
information indicating whether or not an outer dimension of the wall material falls within a threshold range and
information indicating whether or not a value of a pixel value of the wall material in an image acquired in the acquisition process falls within a threshold range (Tanaka teaches “the second learning device 202 can calculate a Z-score indicating a value depending on difference from the parameters of the first model described above for each pixel of the plurality of captured images prepared in advance, to detect an aggregation area of pixels each indicating a Z-score equal to or greater than a predetermined threshold value, as the defect candidate area” in para. [0032], wherein this aggregation is used to generate the detection result as shown in para. [0034] and [0039]).
Similar motivations as applied to claim 1 can be applied here to claim 7.
Regarding claim 8, Kim teaches a quality inspection method (Kim teaches “system and method for detecting cracks in the surface of the tunnel according to the present invention collect image data by photographing the surface of the tunnel” in para. [0012] wherein the system includes an image analysis device 200 which inherently includes at least one processor)comprising:
an acquisition process for at least one processor acquiring an image captured of a wall material (Kim teaches “the camera driver 120 may receive a control signal from the image analysis device 200 located at a remote location to take a picture of a specific area of the inner wall of the tunnel with interest” in para. [0031]);
a division process for the at least one processor for dividing the image into a plurality of small images (Kim teaches a process of dividing the large-capacity tunnel inner wall image of high pixels into small patches as shown in para. [0039]-[0040] and [0052]-[0055]);
a detection process for the at least one processor detecting a defect included in the wall material output a determination result which is obtained by determining whether or not the wall material is defective (Kim teaches inputting the patch images (small images) into a deep learning model (machine learning model) in order to determine whether the wall material of the input image includes a crack (defect)); and
an output process for the at least one processor outputting a detection result which is obtained in the detection process (Kim teaches “the main control unit 240 marks the position of the patch image including the crack found by the deep learning unit 230 as a red rectangle in the original image” which is interpreted as equivalent to the claimed output process/detection result based on the determination result).
Kim fails to teach detecting a defect by referring to a combination of at least two determination results which have been obtained by inputting the small images into each of at least two machine learning models that have been trained by different training methods and that each use, as input, an image captured of a wall material.
However, Tanaka (in view of Kim) teaches detecting a defect by referring to a combination of at least two determination results which have been obtained by inputting the small images into each of at least two machine learning models that have been trained by different training methods and that each use, as input, an image captured of a wall material (Tanaka teaches a first learning model wherein “the first learning device 201 uses a set of non-defective images (examples of normal data) each indicating a captured image obtained by capturing an object with no defect out of captured images obtained by capturing respective objects, to learn the first model for determining the non-defective image” in para. [0029]. Tanaka additionally teaches a second learning model, wherein “the abnormality candidate areas selected by a user as correct data and the abnormality candidate areas not selected by the user as incorrect data [are used] to learn a second model for identifying the correct data and the incorrect data” as shown in para. [0032]. Here, the “learning” methods as taught by Tanaka are interpreted as equivalent to the claimed “different training methods”. Furthermore, Tanaka teaches that the first model takes the captured image as input, and the second model takes the defect candidate area(s) within the captured image as input in para. [0038]-[0039]. The teachings of Tanaka as described above can be combined with Kim’s teaching of specifically inputting the small images into a machine learning model, wherein the small images are images captured of a wall material. See also that Tanaka teaches that this method can apply to a defect within a material as shown in para. [0004]).
Kim and Tanaka are both considered to be analogous to the claimed invention because they are in the same field of analyzing defects through image analysis and machine learning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Kim to incorporate the teachings of Tanaka and include “detecting a defect by referring to a combination of at least two determination results which have been obtained by inputting the small images into each of at least two machine learning models that have been trained by different training methods and that each use, as input, an image captured of a wall material”. The motivation for doing so would have been to “suppress erroneous detection of a portion that is statistically abnormal but is not a defect to be detected. Accordingly, sufficient inspection accuracy can be obtained”, as suggested by Tanaka in para. [0044]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Kim with Tanaka to obtain the invention specified in claim 8.
Regarding claim 9, Kim teaches:
an acquisition process for acquiring an image captured of a wall material (Kim teaches “the camera driver 120 may receive a control signal from the image analysis device 200 located at a remote location to take a picture of a specific area of the inner wall of the tunnel with interest” in para. [0031]);
a division process for dividing the image into a plurality of small images (Kim teaches a process of dividing the large-capacity tunnel inner wall image of high pixels into small patches as shown in para. [0039]-[0040] and [0052]-[0055]);
a detection process for detecting a defect included in the wall material (Kim teaches inputting the patch images (small images) into a deep learning model (machine learning model) in order to determine whether the wall material of the input image includes a crack (defect)); and
an output process for outputting a detection result which is obtained in the detection process (Kim teaches “the main control unit 240 marks the position of the patch image including the crack found by the deep learning unit 230 as a red rectangle in the original image” which is interpreted as equivalent to the claimed output process/detection result based on the determination result).
Kim fails to teach a computer-readable non-transitory storage medium storing a quality inspection program for causing a computer to function as a quality inspection system, the quality inspection program causing the computer to carry out: (…) and detecting a defect by referring to a combination of at least two determination results which have been obtained by inputting the small images into each of at least two machine learning models that have been trained by different training methods and that each use, as input, an image captured of a wall material.
However, Tanaka (in view of Kim) teaches a computer-readable non-transitory storage medium storing a quality inspection program for causing a computer to function as a quality inspection system (Tanaka teaches “The CPU 131 controls entire operation of the information processing apparatus 130. The CPU 131 uses a predetermined area of the RAM 133 as a work area to execute a program stored in the ROM 132 , the memory 134 , or the like, and implements various functions of the information processing apparatus 130” wherein the ROM 132 is non-volatile memory as shown in para. [0023]-[0024]), and
detecting a defect by referring to a combination of at least two determination results which have been obtained by inputting the small images into each of at least two machine learning models that have been trained by different training methods and that each use, as input, an image captured of a wall material (Tanaka teaches a first learning model wherein “the first learning device 201 uses a set of non-defective images (examples of normal data) each indicating a captured image obtained by capturing an object with no defect out of captured images obtained by capturing respective objects, to learn the first model for determining the non-defective image” in para. [0029]. Tanaka additionally teaches a second learning model, wherein “the abnormality candidate areas selected by a user as correct data and the abnormality candidate areas not selected by the user as incorrect data [are used] to learn a second model for identifying the correct data and the incorrect data” as shown in para. [0032]. Here, the “learning” methods as taught by Tanaka are interpreted as equivalent to the claimed “different training methods”. Furthermore, Tanaka teaches that the first model takes the captured image as input, and the second model takes the defect candidate area(s) within the captured image as input in para. [0038]-[0039]. The teachings of Tanaka as described above can be combined with Kim’s teaching of specifically inputting the small images into a machine learning model, wherein the small images are images captured of a wall material. See also that Tanaka teaches that this method can apply to a defect within a material as shown in para. [0004]).
Kim and Tanaka are both considered to be analogous to the claimed invention because they are in the same field of analyzing defects through image analysis and machine learning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Kim to incorporate the teachings of Tanaka and include “a computer-readable non-transitory storage medium storing a quality inspection program for causing a computer to function as a quality inspection system” and “detecting a defect by referring to a combination of at least two determination results which have been obtained by inputting the small images into each of at least two machine learning models that have been trained by different training methods and that each use, as input, an image captured of a wall material”. The motivation for doing so would have been to “suppress erroneous detection of a portion that is statistically abnormal but is not a defect to be detected. Accordingly, sufficient inspection accuracy can be obtained”, as suggested by Tanaka in para. [0044]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Kim with Tanaka to obtain the invention specified in claim 9.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (KR 10-2270768 B1, see attached English translation for citations), hereinafter Kim, in view of Tanaka et al. (U.S. Publication No. 2018/0211374 A1), hereinafter Tanaka and Tsai et al. (U.S. Publication No. 2020/0364850 A1) hereinafter Tsai.
Regarding claim 3, Kim and Tanaka teach the quality inspection system according to claim 1.
Kim and Tanaka fail to teach wherein in the detection process, the at least one processor changes, in accordance with a type of the wall material, the combination of the at least two determination results to be referred to.
However, Tsai teaches wherein in the detection process, the at least one processor changes, in accordance with a type of the wall material, the combination of the at least two determination results to be referred to (Tsai teaches “the output of each neural network system 33 can be divided into a normal group and an abnormal group” wherein “If the object images IM are categorized to the first-stage normal group, the object images IM outputted to the first-stage normal group by the first-stage sub neural network system 33 are subsequently fed to the second-stage sub neural network system 33, such that the second-stage sub neural network system 33 continues to execute the predictive model on the object images IM so as to categorize the object images IM to a second-stage normal group or a second-stage abnormal group. Conversely, if the object images IM are categorized to the first-stage abnormal group, the object images IM outputted to the first-stage normal group by the first-stage sub neural network system 33 are not fed to the second-stage sub neural network system 33” as shown in para. [0044]. Subsequently, “the output unit 35 receives the abnormal groups outputted by all the sub neural network systems 33 and accordingly outputs an abnormal result, and the output unit 35 further receives the normal group outputted by the last-stage sub neural network system 33 and accordingly outputs a normal result” as shown in para. [0045]. Here, the sub neural networks perform surface type identification in order to categorize the images into the abnormal/normal groups. As such, the combination of sub neural networks used to produce the determination results (see para. [0045]) is determined based on the surface type identification as shown in para. [0049]-[0050] and FIG. 3).
Kim, Tanaka, and Tsai are all considered to be analogous to the claimed invention because they are in the same field of analyzing defects through image analysis and machine learning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Kim (as modified by Tanaka) to incorporate the teachings of Tsai and include “wherein in the detection process, the at least one processor changes, in accordance with a type of the wall material, the combination of the at least two determination results to be referred to”. The motivation for doing so would have been that “the artificial neural network system 30 is capable of rapidly performing categorization prediction on a large number of objects under detection, while satisfying an improved miss rate” and “continually perform[ing] surface type identification on an object image by using multiple cascaded neural networks of different training conditions so as to accurately and rapidly categorize the image object, and efficiently selects an object corresponding to the object image based on the categorization result of the object image, thereby achieving a lower miss rate”, as suggested by Tsai in para. [0055] and [0006], respectively. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Kim and Tanaka with Tsai to obtain the invention specified in claim 3.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (KR 10-2270768 B1, see attached English translation for citations), hereinafter Kim, in view of Tanaka et al. (U.S. Publication No. 2018/0211374A1), hereinafter Tanaka and Kim et al. (WO 2022/250253 A1, see attached English translation for citations) hereinafter Kim ‘253.
Regarding claim 4, Kim and Tanaka teach the quality inspection system according to claim 1.
Kim and Tanaka fail to teach wherein in the detection process, the at least one processor detects the defect included in the wall material by performing logical operations on the at least two determination results.
However, Kim ‘253 teaches wherein in the detection process, the at least one processor detects the defect included in the wall material by performing logical operations on the at least two determination results (Kim ‘253 teaches performing a first logic operation on first image data VD1 and second image data VD2, wherein the second image data is a result of “apply[ing] the learned first anomaly detection model to first image data VD1” to generate third image data VD3 in para. [0056]-[0057] and [0105]-[0106]. Furthermore, teaches “and the learned second anomaly detection model may be applied to the first image data VD1, as the inspection object, and the second image data VD2, to output image mask data VMD having feature information between the first image data VD1 and the second image data VD2” in para. [0107], wherein “a second logic operation may be performed on the third image data VD3 and the image mask data VMD, to generate fourth image data VD4 having anomaly information” as shown in para. [0108]. Here, logical operations are performed using results of the first learning model and the second learning model in order to derive defect detection results (VD4)).
Kim, Tanaka, and Kim ‘253 are all considered to be analogous to the claimed invention because they are in the same field of analyzing defects through image analysis and machine learning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Kim (as modified by Tanaka) to incorporate the teachings of Kim ‘253 and include “wherein in the detection process, the at least one processor detects the defect included in the wall material by performing logical operations on the at least two determination results”. The motivation for doing so would have been “to reduce a difference between feature vectors at the same location and increase a difference between feature vectors at different locations, to detect quickly defects of the manufacturing image”, as suggested by Kim ‘253 in para. [0030]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Kim and Tanaka with Kim ‘253 to obtain the invention specified in claim 4.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (KR 10-2270768 B1, see attached English translation for citations), hereinafter Kim, in view of Tanaka et al. (U.S. Publication No. 2018/0211374A1), hereinafter Tanaka and Tsujimoto (JP 2023060798 A, see attached English translation for citations).
Regarding claim 5, Kim and Tanaka teach the quality inspection system according to claim 1.
While Kim further teaches wherein in the division process, the at least one processor divides, into a plurality of small images, the image (Kim teaches a process of dividing the large-capacity tunnel inner wall image of high pixels into small patches as shown in para. [0039]-[0040] and [0052]-[0055]), Kim and Tanaka fail to teach wherein the at least one processor further carries out a preprocessing process for carrying out at least one selected from the group consisting of connection between images acquired in the acquisition process, inclination correction of the wall material in an image acquired in the acquisition process, alignment of the wall material in an image acquired in the acquisition process, color processing on an image acquired in the acquisition process, and edge processing on an image acquired in the acquisition process; and in the division process, the at least one processor divides, into a plurality of small images, the image that has been processed in the preprocessing process.
However, Tsujimoto teaches wherein the at least one processor further carries out a preprocessing process for carrying out at least one selected from the group consisting of
(path 1) connection between images acquired in the acquisition process,
(path 2) inclination correction of the wall material in an image acquired in the acquisition process,
(path 3) alignment of the wall material in an image acquired in the acquisition process,
(path 4) color processing on an image acquired in the acquisition process (Tsujimoto teaches “in the defect detection system according to the present embodiment, an appropriate coloring tool is used to label (paint) the defect portion of the original image data including the defect” in para. [0054], wherein ), and
(path 5) edge processing on an image acquired in the acquisition process; and
in the division process, the at least one processor divides, into a plurality of small images, the image that has been processed in the preprocessing process (Tsujimoto teaches “the image data processed by the machine learning model (eg, original image data and marked image data) is cut to match the size of, for example, 256 (pixels)×256 (pixels). That is, "tiling processing" is performed on the image data processed by the machine learning model” in para. [0070], wherein “a machine learning model 44 is learned using the cut teacher data (cut image data 42)” as shown in para. [0071]).
**Note: only one pathway above need be found in the prior art due to the “carrying out at least one selected from the group consisting of…” language as recited in the claim above.
Kim, Tanaka, and Tsujimoto are all considered to be analogous to the claimed invention because they are in the same field of analyzing defects through image analysis and machine learning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Kim (as modified by Tanaka) to incorporate the teachings of Tsujimoto and include “wherein the at least one processor further carries out a preprocessing process for carrying out at least one selected from the group consisting of connection between images acquired in the acquisition process, inclination correction of the wall material in an image acquired in the acquisition process, alignment of the wall material in an image acquired in the acquisition process, color processing on an image acquired in the acquisition process, and edge processing on an image acquired in the acquisition process; and in the division process, the at least one processor divides, into a plurality of small images, the image that has been processed in the preprocessing process”. The motivation for doing so would have been “that preparing two machine learning models corresponding to the contour labeling data and the area labeling data tends to improve the accuracy of defect prediction/detection”, as suggested by Tsujimoto in para. [0066]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Kim and Tanaka with Tsujimoto to obtain the invention specified in claim 5.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bogomolny et al. (US 20220051391 A1) teaches training a machine learning model with images of tires without an anomaly, and training a separate machine learning model with both anomalous images of tires and images of tires without an anomaly.
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/Kyla Guan-Ping Tiao Allen/
Examiner, Art Unit 2661
/JOHN VILLECCO/Supervisory Patent Examiner, Art Unit 2661
Prosecution Insights