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 .
Claim Interpretation
Claims 1-9 are not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because these claims are method claims.
Claims 10-18 are is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because each of these claims is an article of manufacture claim.
Claims 19-20 are not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the recitations of “memory” and “processor” provide sufficient structure to perform all claimed limitations.
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 1-2, 4, 9-11, 13 and 18-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1 as a presentative claim, the 101 analysis is presented below.
Step 1: It is noted that claim 1 recites a method and thus is directed to one of statutory categories of invention.
Step 2A Prong 1: Limitations “identify whether one or more defects are present in the products”, “labeling the image with the detected type of defect”, “identifying duplicate defects using at least one of data describing context of the detected defects or data describing context of cameras that captured images containing the detected defects”, “grouping duplicate defects together”, and “initiating a corrective action based on the grouped defects” are interpreted to be performed in the human mind. Claim does not specifically define anything particular to such limitations that could not be performed in human mind. Thus, these limitations fall into the “mental process” grouping of abstract idea. Therefore, claim 1 recites an abstract idea.
Step 2A Prong 2: It is noted that claim 1 recites addition elements (I)“receiving a plurality of images of products captured from a plurality of perspective” and (ii)“machine-learning model”. First, the additional element (I) is nothing more than data gathering and thus is insignificant extrasolution activity. Lastly, the additional elements (II) is recited at a high level of generality such that they amount to no more than mere instructions to implement the abstract idea on a conventional computer and do not point to a specific improvement in computer itself. Thus, all of these additional elements do not amount to an integration of the judicial exception into a practical application.
Step 2B: The additional elements, as pointed out in Step 2A prong 2 above, are insignificant extrasolution activity and recited at a high level of generality such that they amount to no more than mere instructions to implement the abstract idea on a conventional computer and do not point to a specific improvement in computer itself. These additional elements, taken individually and in combination, do not contribute to an inventive concept and do not amount to significantly more than the judicial exception. Therefore, claim is not a patent eligible.
The advanced statements as applied to claim 1 above are incorporated hereinafter.
Regarding claim 2, it is noted that claim does recite additional elements (i)“identifying an area in an image that corresponds to a detected defect”, (ii)“annotating the image by generating a bounding box around the identified area”, and (iii)“providing the annotated image for display at a client device”. These additional elements do not make it to be statutory because the additional elements (i)-(ii) are interpreted to be performed in the human mind and additional element (iii) is nothing more than insignificant post-solution activity. Therefore, claim is not a patent eligible.
Regarding claim 4, it is noted that claim does recite additional element “retraining the machine-learning model based on the labeled images” is interpreted to be performed in the human mind. The additional element “machine-learning model” is recited at a high level of generality such that they amount to no more than mere instructions to implement the abstract idea on a conventional computer. The claim does not point to a specific improvement in computer itself. The additional elements, taken individually and in combination, do not contribute to an inventive concept. Therefore, claim 4 is also directed to an abstract idea without significantly more and is not a patent eligible.
Regarding claim 9, it is noted that claim does recite additional element “providing images containing unique or duplicate defects for display at a client device”. Such additional element is nothing more than insignificant post-solution activity. The additional elements, taken individually and in combination, do not contribute to an inventive concept. Therefore, claim 9 is also directed to an abstract idea without significantly more and is not a patent eligible.
Claim 10 recites a manufacture and claim 19 recites an apparatus. so each of these claims falls within one of the statutory categories of invention. It is noted that each of these claims recites similar claim limitations called for in the counterpart claim 1. Thus, the advanced statements as applied to claim 1 above are incorporated herein. It is also noted that claim 10 recites additional elements “medium” and “computer” and claim 19 recites addition elements “memory” and “processor”. These additional elements “memory”, “processor”, “medium” and “computer” are recited at a high level of generality such that they amount to no more than mere instructions to implement the abstract idea on a conventional computer. The claims do not point to a specific improvement in computer itself. The additional elements, taken individually and in combination, do not contribute to an inventive concept. Therefore, claims 10 and 19 are also directed to an abstract idea without significantly more.
Regarding claim 11, it is noted that it recites similar claim limitations called for in the counterpart claim 2 above and thus is rejected for the same reasons as applied to claim 2 above.
Regarding claim 13, it is noted that it recites similar claim limitations called for in the counterpart claim 4 above and thus is rejected for the same reasons as applied to claim 4 above.
Regarding claim 18, it is noted that it recites similar claim limitations called for in the counterpart claim 9 above and thus is rejected for the same reasons as applied to claim 9 above.
Regarding claim 20, it is noted that it recites similar claim limitations called for in the counterpart claims 2 and 11 above and thus is rejected for the same reasons as applied to claims 2 and 11 above.
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.
Claim(s) 1, 10 and 19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kano et al. (U.S. Pat, App. Pub. No. 2019/0360942 A1, referred as Kano hereinafter).
Regarding claim 1 as a representative claim, Kano teaches a method comprising:
receiving a plurality of images of products captured from a plurality of perspectives (see figure 1 (camera 8) and para. [0017] (capturing an image of each product with a camera 8 so as to conduct a visual inspection; apparatus 10 receiving data of images captured with the camera 8));
for each of the plurality of images, applying a machine-learning model to the image to identify whether one or more defects are present in the products (see para. [0018] (apparatus 10 determines (i)whether the product is non-defective or defective and (ii)a defect type/category for each defective product); paras. [0020] (apparatus 10 employs a machine learning for identifying defects), [0021] (apparatus 10 includes a learner 12) and [0022] (learner 12 conducts machine learning so as to generate a non-defective product learning model and a defective product learning models);
responsive to detecting a defect of a type, labeling the image with the detected type of defect (see paras. [0023] (defective product labels) and [0024] (defective type); figure 6 (there are three type of defects depicted as defect 1, defect 2 and defect 3));
identifying duplicate defects using at least one of data describing context of the detected defects or data describing context of cameras that captured images containing the detected defects (see para. [0027] (The likelihood is an indicator of a probability. The likelihood of the defect 1, for example, is an indicator of the “probability of being the defect 1”); also see figure 6 and para. [0005]);
grouping duplicate defects together (see para. [0027]; and also see figure 6 and para. [0005]);
initiating a corrective action based on the grouped defects (see para. [0039] (repeating procedures).
Regarding claims 10 and 19, it is noted that these claims recite similar claim limitations called for in the counterpart claim 1 and thus the advanced statements as applied to claim 1 above are incorporated hereinafter. Kano further teaches a medium, instructions and a computer (see para. [0019]).
Regarding claim 19, it is noted that it recites similar claim limitations called for in the counterpart claim 1 and thus the advanced statements as applied to claim 1 above are incorporated hereinafter. Kano further teaches a memory, instructions and a computer (see para. [0019]).
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(s) 2, 4, 9, 11, 13, 18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kano.
The advanced statements as applied to claims 1, 10 and 19 above are incorporated hereinafter.
Regarding claim 2, Kano further teaches identifying an area in an image that corresponds to a detected defect (see para. [0027] (The likelihood is an indicator of a probability. The likelihood of the defect 1, for example, is an indicator of the “probability of being the defect 1”); also see figure 6 and para. [0005] (classifying defects to defect type 1/2/3)).
Kano does not specifically teach claim limitations “annotating the image by generating a bounding box around the identified area” and “providing the annotated image for display at a client device”.
However, such claim limitations are well known in the art (Official Notice).
The motivation for doing so is to improve the visualization of the defects.
Therefore, before the effective filing date of the instant claim invention, it would have been obvious to one of ordinary skill in the art to incorporate such claim limitations in combination with Kano for that reasons.
Regarding claim 4, Kano does not specifically teach claim limitations “retraining the machine-learning model based on the labeled images”.
However, such claim limitations are well known in the art (Official Notice).
The motivation for doing so is to improve the inspection.
Therefore, before the effective filing date of the instant claim invention, it would have been obvious to one of ordinary skill in the art to incorporate such claim limitations in combination with Kano for that reasons.
Regarding claim 9, Kano does not specifically teach claim limitations “wherein the corrective action comprises providing images containing unique or duplicate defects for display at a client device”.
However, such claim limitations are well known in the art (Official Notice).
The motivation for doing so is to improve the visualization of the defects.
Therefore, before the effective filing date of the instant claim invention, it would have been obvious to one of ordinary skill in the art to incorporate such claim limitations in combination with Kano for that reasons.
Regarding claims 11 and 20, each of these claims recites similar claim limitations called for in the counterpart claim 2 and thus is rejected for the same reasons as above.
Regarding claim 13, this claim recites similar claim limitations called for in the counterpart claim 4 and thus is rejected for the same reasons as above.
Regarding claim 18, this claim recites similar claim limitations called for in the counterpart claim 9 and thus is rejected for the same reasons as above.
Allowable Subject Matter
Claims 3, 5-8, 12 and 14-17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claims 3 and 13, the cited prior art does not teach or suggest claim limitations “generating a heatmap that indicates confidence of the machine-learning model in identifying defects; overlaying the heatmap onto the image; and providing the image overlayed with the heatmap for display on a client device”.
Regarding claims 5 and 14, the cited prior art does not teach or suggest claim limitations “generating one or more synthetic images based on one or more original images identified as containing a particular type of defect; and training the machine-learning model based on the one or more original images and the generated one or more synthetic images”.
Claims 6-8 depend on claim 5 and thus are allowable for the same reasons.
Claims 15-17 depend on claim 14 and thus are allowable for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Li (U.S. Pat. App. Pub. No. 2020/0126210 A1) teaches defect detection method and apparatus for identifying defect in drug products comprising machine learning model (see abstract; figure 6) and camera for capturing multiple images and proving these images to model (para. [0013]).
Rothmund et al. (U.S. Pat. App. Pub. No. 2023/0022631 A1) teaches defect detection method and apparatus for identifying defect in drug products comprising machine learning model (see abstract) and camera for capturing images of medical product which is then inspected for defect (para. [0091]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DUY M DANG whose telephone number is (571)272-7389. The examiner can normally be reached Monday to Friday from 7:00AM to 3:00PM.
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DMD
3/2026
/DUY M DANG/Primary Examiner, Art Unit 2662