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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on January 16, 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner
Status of Claims
Claims 19 and 20 have been canceled.
Claims 1, 2, 8 – 13, 18, 21 and 26 – 28 are amended
Claims 1 – 18 and 21 – 28 remain pending.
Response to Arguments
Applicant's arguments filed January 16, 2026 with respect to claims 1 – 18 and 21 – 28
have been considered but are moot because the new grounds of rejection do not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Response to Remarks
Applicant argues that Wang, Kulkarni and Matsumura is silent on the following limitations below. Examiner respectfully disagrees for the reasons provided below:
In the Remarks (p. 10) regarding claim 1, applicants assert, “Wang's disclosure is directed to repositioning and inspecting "a subset of the surface of the product" and focusing on "areas of interest." See, for example, Wang's summary stating "Inspecting the area of interest includes inspecting a subset of the surface of the product." Wang does show inspection of various sides via a rotatable base and repositioning (Fig. 4A-4C), but Wang does not teach or suggest that "all surfaces not facing a table" are inspected. The recited functionality indicates a configuration that provides complete coverage of all non-table faces. In contrast, Wang's system inspects selected regions and sides opportunistically (including only a "subset"), not "all" non-table surfaces. Therefore, Wang does not teach or suggest the claimed completeness requirement”.
Examiner respectfully disagrees because Wang in [0004] discloses, the robot can examine relevant surfaces and/or features of the product (or in some situations, each surface and/or feature of the product)”. Furthermore, Wang in [0014] discloses, “the camera at one or more additional positions, each additional position is substantially adjacent to a respective additional surface of the product, and (ii) capturing, via the camera, an additional image of each respective additional surface of the product with the camera positioned at the one or more additional positions”. The robot being able to examine each surfaces equates to inspect all exposed surfaces. Wang also discloses about covering all surfaces repositioning and through additional imaging”.
In the Remarks (p. 10 and p. 11) regarding claim 1, applicants assert, "make a determination whether the at least one detected defect is one of a large defect or a small defect," the Office Action (on page 5) relies for support on paragraph [0057] of Kulkarni. Kulkarni classifies defects by type (e.g., pin-hole (PH), resist-dot (RD), or scratch) and generates algorithm-estimated and refined size estimates for those defects. The cited paragraph [0057] refers to "defect type classifications," not a binary magnitude classification. Kulkarni does not disclose a decision node that classifies detected defects specifically as "large" versus "small." While Kulkarni estimates defect size (see e.g., Fig. 5 and related text), he uses those sizes for refining estimates and type-based processing, not for a binary size-based classification used to control downstream steps. The recited "large vs small" determination is absent from Kulkarni's disclosure and is not suggested by its type classification.
Examiner respectfully disagrees because Kulkarni in [0006] discloses, “determine one or more refined estimates of one or more defect sizes of the one or more defects based on the one or more algorithm-estimated defect sizes and the one or more defect type classifications”. Kulkarni in [0006] discloses about the determination of defect size and classifying the defect. Using a threshold and determined size Kulkarni could classify defect type if it is large or small defect.
For the reasons above, the rejections of claims 1 – 18 and 21 – 28 as established in the last Office Action (Non-Final, 10/21/2025) are proper and are hereby maintained and incorporated in this Office Action.
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, 7 – 10, 13 – 16, 18, 21 – 24, 26 – 28 are rejected under 35 U.S.C 103 as being unpatentable over Wang et al. US Patent Application Publication Document No. US-20190206045-A1 (hereinafter Wang) in view of Kulkarni US Patent Application Publication No. US-20200143528-A1 (hereinafter Kulkarni), Tee “Defect Cluster Segmentation for CMOS Fabricated Wafers” (hereinafter Tee) and Matsumura US Patent Application Publication No. US-20060078191-A1 (hereinafter Matsumura).
Regarding claim 1, Wang discloses an inspection system, comprising: a plurality of robots (Wang in [0016] discloses about, “The robotic arm includes a plurality of segments, a camera at an end of the robotic arm, a plurality of rotatable joints”); one or more cameras coupled to each of respective ones of the plurality of robots to generate a plurality of captured images of a component and inspect the component for defects at various stages of fabrication (In [0037] Wang discloses about multiple edge devices, “The edge devices 102 - 1 , 102 - 2 , . . . 102 -n are used to inspect (e.g., monitor) products”. And in [0038] it discloses that the multiple edge devices 102 is a robotic arm, “an edge device 102 is a robotic arm with a camera” which equates to having plurality of robots. In [0039] Wang discloses that multiple edge devices send captured data to the server suggests having camera in different location. Furthermore, in [0055] each edge device has distinct position and orientation on production line equates to camera being located in different location corresponding to various stages in the fabrication of the component, “the graph identifies each edge device on a particular production line and connections between each edge device. The connections may include a position of the edge device, an orientation of the edge device, neighboring edge devices”. Lastly, Wang in [0014] discloses about plurality of image, “capturing, via the camera, an additional image of each respective additional surface of the product with the camera positioned at the one or more additional positions”), each of the cameras being located at a different geographical location corresponding to the various stages in the fabrication of the component (Wang in [0037] discloses about, “the edge devices 102 - 1 , 102 - 2 , . . . 102 -n monitor an operation's effect on a product (e.g., perform quality control”. Monitoring different operation effects on a product implies to monitoring various stages of fabrication. Additionally, figure 4B and 4C discloses that the camera 428 can move to different geographical location), at least some of the cameras being configured to inspect all surfaces of the component that are not facing a table upon which the component is mounted (Wang in Fig. 4A discloses that the camera inspecting the surface of the component facing a table and Fig. 4B and 4C discloses that the camera is also capable of inspecting the component in the other sides and at different angles); a data-collection station electronically coupled to each of respective ones of the plurality of robots and an associated one of the cameras (Wang in [0016] discloses, “a robotic arm is provided (e.g., robotic arm 300 , FIG. 3) ... and memory storing one or more programs, which when executed by the one or more processors cause the robotic arm to perform the method”); and a master data-collection station electronically coupled to each of the data-collection stations (Wang in [0035] discloses, “The network architecture 100 includes a number of edge devices 102 - 1 , 102 - 2 , . . . 102 -n communicably connected to a server system 104 by one or more networks 106” wherein the server is the master station), the inspection system being further configured to: set a threshold image for determining black areas of interest in one or more at least one detected defect from the one or more cameras coupled to each of respective ones of the plurality of robots (Wang in [0129] discloses about setting up a threshold. Furthermore Wang in [0129] discloses, “determining (712) whether the first image includes a defect based on the processing (i.e., based on comparing the pieces of the first image with one or more defect models)”); determine a thresholding level for setting the threshold image being based on the at least one detected defect within at least one of the plurality of captured images (Wang in [0129] discloses, “determines that the first image includes a defect when a piece (or a threshold number of pieces) of the first image either: (1) matches (or partially matches) a defect pixel in the defect model, or (2) does not match (or partially match) any non-defect pixels in the defect model. In some implementations, a partially match is deemed sufficient when a threshold percentage of the defect matches the defect pixel”);
Wang doesn’t disclose the following limitations as further recited in the claim.
Kulkarni discloses make a determination whether the at least one detected defect is one of a large defect or a small defect based on a characteristic size of the defect or a location of the defect within the component; (Kulkarni in [0057] discloses about configured to determine one or more defect type (‘determination whether a detected defect’) classifications of the one or more defects (‘one of a large defect and a small defect’) within a product image 135 with the generated machine learning classifier. Additionally, Kulkarni in [0006] discloses, “determine one or more refined estimates of one or more defect sizes of the one or more defects based on the one or more algorithm-estimated defect sizes and the one or more defect type classifications”. Kulkarni in [0006] discloses about the determination of defect size and classifying the defect. Using a threshold and determined size Kulkarni could classify defect type if it is large or small defect).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Kulkarni into the system of Wang because it would allow the system to differentiate between large and small defects ensures targeted defect handling based on size which can prevent misclassification of minor surface defects as significant defects.
Wang and Kulkarni doesn’t disclose the following limitations as further recited in the claim.
Tee discloses based on a determination that the at least one detected defect is a large defect; perform at least one operation selected from operations including an erosion operation (Tee in [Section – IV, Paragraph - 2] discloses, “Erosion is performed on the data before dilation so that the random defects are eliminated in the first pass. Since defect clusters must be of a considerable size, they would not be eliminated at the erosion stage. This stage is akin to a low pass filter, whereby tiny groups of high defect density will be removed (speck removal) through erosion, while large groups will still remain”).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Tee into the system of Wang and Kulkarni because it would allow the system to determine more accurate defect measurement.
Wang, Kulkarni and Tee in the combination doesn’t disclose the following limitations as further recited in the claim.
Matsumura discloses a dilation operation to make a determination as to whether black regions from the at least one detected defect are included as at least a portion of a larger defect (Matsumura in [0045] discloses, “performs a dilation on the binarized probability product image so that a plurality of adjacent defects should be included in one defect inclusion area ... if a plurality of adjacent defects constituting one defect inclusion area includes a false defect, the shape of a genuine defect is largely different from that of the defect inclusion area. And the defect inclusion area image is generated by using two error probability value images”).
It would have been obvious to one with one having an ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Matsumura into the system of Wang in view of Kulkarni and Tee because it would allow the system to ensure accurate identification of the true size and shape of large size defects.
Summary of Citations (Tee)
[Section – IV, Paragraph - 2]; “Erosion is performed on the data before dilation so that the random defects are eliminated in the first pass. Since defect clusters must be of a considerable size, they would not be eliminated at the erosion stage. This stage is akin to a low pass filter, whereby tiny groups of high defect density will be removed (speck removal) through erosion, while large groups will still remain”
Summary of Citations (Kulkarni)
Paragraph [0006]; “determine one or more refined estimates of one or more defect sizes of the one or more defects based on the one or more algorithm-estimated defect sizes and the one or more defect type classifications”.
Paragraph [0057]; “In some embodiments, the controller 104 is configured to determine one or more defect type classifications of the one or more defects within a product image 135 with the generated machine learning classifier”.
Paragraph [0067]; “The one or more morphological image processing operations may include any morphological image processing operations known in the art including, but not limited to, a morphological closing operation (e.g., morphological binary image closing operation), a morphological erosion operation, a morphological dilation operation, a morphological opening operation, or a morphological closing operation, and the like”.
Summary of Citations (Matsumura)
Paragraph [0045]; “performs a dilation on the binarized probability product image so that a plurality of adjacent defects should be included in one defect inclusion area ... if a plurality of adjacent defects constituting one defect inclusion area includes a false defect, the shape of a genuine defect is largely different from that of the defect inclusion area. And the defect inclusion area image is generated by using two error probability value images”.
Summary of Citations (Wang)
Paragraph [0016]; “a robotic arm is provided (e.g., robotic arm 300 , FIG. 3). The robotic arm includes a plurality of segments, a camera at an end of the robotic arm, a plurality of rotatable joints, each rotatable joint connecting two segments of the plurality of segments or the camera with one segment of the plurality of segments, one or more processors, and memory storing one or more programs, which when executed by the one or more processors cause the robotic arm to perform the method described in any one of A1-A11”.
Paragraph [0035]; “The network architecture 100 includes a number of edge devices 102 - 1 , 102 - 2 , . . . 102 -n communicably connected to a server system 104 by one or more networks 106”.
Paragraph [0037]; “The edge devices 102 - 1 , 102 - 2 , . . . 102 -n are used to inspect (e.g., monitor) products. In some implementations, the edge devices 102 - 1 , 102 - 2 , . . . 102 -n monitor an operation's effect on a product (e.g., perform quality control). To do this, each of the edge devices 102 - 1 , 102 - 2 , . . . 102 -n includes one or more capture devices, such as a camera, an infrared camera, an X-ray camera, a depth camera, etc.”.
Paragraph [0129]; “the robotic arm determines that the first image includes a defect when a piece (or a threshold number of pieces) of the first image either: (1) matches (or partially matches) a defect pixel in the defect model, or (2) does not match (or partially match) any non-defect pixels in the defect model. In some implementations, a partially match is deemed sufficient when a threshold percentage of the defect matches the defect pixel”.
Regarding claim 2, 7 – 10, the same grounds of rejection based on Wang in view of Kulkarni, Tee and Matsumura from the last Office Action (Non-Final, 10/20/2025) applies in here.
Regarding claim 13, Wang discloses about the inspection system of claim 1, further comprising a process- monitoring database that is electronically coupled to the master data-collection station, wherein the process-monitoring database contains metrics based on image quality for how an idealized sample of the component should appear at each step in the various stages of the fabrication of the component (In [0051] Wang discloses about defect model 220 which include previously identified defects and/or desired results and [0052] discloses, “a threshold difference (or threshold similarity) between a piece of an image captured and a defect pixel included in a defect model 220” (containing metrics based on image quality for how an idealized sample of the component). Additionally, Figure 1 and 2 discloses about database 218 that stores the defect information).
Summary of Citations (Wang)
Paragraph [0051]; “In some implementations, the one more defect models 220 include previously identified defects and/or desired results (i.e., non-defect pixels)”.
Paragraph [0052]; “The one or more criteria and thresholds 222 can include thresholds for identifying a defect. For example, a threshold difference (or threshold similarity) between a piece of an image captured and a defect pixel included in a defect model 220 . Additionally, in some implementations, an image is not deemed to include a defect until a threshold number of pieces of the image are deemed to include defects”.
Regarding claim 14 – 16, the same grounds of rejection based on Wang in view of Kulkarni, Tee and Matsumura from the last Office Action (Non-Final, 10/20/2025) applies in here.
Regarding claim 18, claim 18 is claim 1 except for a method of operating an automated visual-inspection (AVI) system for detecting defects on a component, the method comprising: calibrating the AVI system; capturing a plurality of images from the component; and loading each of the plurality of images into a program to analyze for a presence of defects within the plurality of images, thus the rejection of claim 1 is incorporated herein. With respect to the addition limitation, reference Wang discloses a method of operating an automated visual-inspection (AVI) system for detecting defects on a component, the method comprising: calibrating the AVI system (Wang in [0008] discloses about determining the size of the product and positioning the camera implies to calibrating the AVI system (“the method further includes determining a size of the product from the first image of the surface of the product and the first position of the camera”); capturing a plurality of images from the component (Wang in [0014] discloses, “capturing, via the camera, an additional image of each respective additional surface of the product with the camera positioned at the one or more additional positions”); and loading each of the plurality of images into a program to analyze for a presence of defects within the plurality of images (Wang in [0039] discloses, “...send the captured data to the server system 104 . The server system 104 can then use the received data to evaluate a product for product defects”).
Summary of Citations (Wang)
Paragraph [0008]; “the method further includes determining a size of the product from the first image of the surface of the product and the first position of the camera, and based on the determined size, assigning a working space for the robotic arm. The size may include one or more of the product's height, width, length, and volume”.
Paragraph [0014]; “capturing, via the camera, an additional image of each respective additional surface of the product with the camera positioned at the one or more additional positions”.
Paragraph [0039]; “In some implementations, the edge devices 102 - 1 , 102 - 2 , . . . 102 -n send the captured data to the server system 104 . The server system 104 can then use the received data to evaluate a product for product defects. Alternatively, in some implementations, the edge devices 102 - 1 , 102 - 2 , . . . 102 -n evaluate a product for product defects locally (e.g., in those implementations where the server system 104 is a computer at the edges devices 102 - 1 , 102 - 2 , . . . 102 -n )”.
Regarding claim 21, apparatus claim 21 corresponds to apparatus claim 1 and 18. Therefore, the rejection analysis of claims 1 and 18 is applicable to claim 21.
Regarding claim 22 – 24, the same grounds of rejection based on Wang in view of Kulkarni, Tee and Matsumura from the last Office Action (Non-Final, 10/20/2025) applies in here.
Regarding claim 26, claim 26, which is similar in scope to claim 9, thus rejected under the same rationale.
Regarding claim 27, claim 27, which is similar in scope to claim 10, thus rejected under the same rationale.
Regarding claim 28, claim 28, which is similar in scope to claim 15, thus rejected under the same rationale.
Claims 3 and 4 are rejected under 35 U.S.C 103 as being unpatentable over Wang in view of Kulkarni, Tee and Matsumura and further in view of Hyatt US Patent Application Publication No. US-20220148152-A1 (hereinafter Hyatt).
Regarding claim 3 and 4, the same grounds of rejection based on Wang in view of Kulkarni, Tee, Matsumura and Hyatt from the last Office Action (Non-Final, 10/20/2025) applies in here.
Claims 5, 6 and 25 are rejected under 35 U.S.C 103 as being unpatentable over Wang in view of Kulkarni, Tee and Matsumura and further in view of Kuno Patent Application Publication No. JP-2017062160-A (hereinafter Kuno).
Regarding claim 5, 6 and 25, the same grounds of rejection based on Wang in view of Kulkarni, Tee, Matsumura and Kuno from the last Office Action (Non-Final, 10/20/2025) applies in here.
Claim 11 is rejected under 35 U.S.C 103 as being unpatentable over Wang in view of Kulkarni, Tee and Matsumura and further in view of Flitsch US Patent Application Publication No. US-20200056336-A1 (hereinafter Flitsch).
Regarding claim 11, Wang discloses about the inspection system of claim 1, wherein the inspection system is further configured to perform further comprising at least one additional inspection of the component selected from inspection techniques (Wang in [0004] discloses, “the robot is capable of performing a more exacting inspection of the product (e.g., reposition itself to focus on the abnormality)”) comprising microscopy (Wang in [0080] discloses about zooming which implies to microscopy) , optical profilometry (Wang in [0056] discloses about using depth camera and three dimensional camera capable of capturing detailed surface data which implies to optical profilometry).
Wang, Kulkarni, Tee and Matsumura in the combination doesn’t disclose the following limitations as further recited in the claim.
Flitsch discloses about stylus-based profilometry (Flitsch in [0070] discloses, “An array of deflecting needles or stylus may be dragged over the surface”).
It would have been obvious to one of ordinary skill in art before the effective filling date of the claimed invention to integrate the technique of Flitsch into the system of Wang in view of Kulkarni, Tee and Matsumura because it would allow the system to accurately measure the roughness on the surface and topography at a microscopic level.
Summary of Citations (Wang)
Paragraph [0004]; “Moreover, if the robot identifies an abnormality during an initial inspection of the product (e.g., an image captured by the robot includes an abnormality, such as a product defect), the robot is capable of performing a more exacting inspection of the product (e.g., reposition itself to focus on the abnormality)”.
Paragraph [0056]; The robotic arm 300 also includes one or more capture devices 312 , such as a camera, an infrared camera, an X-ray camera, a depth camera, a three-dimensional camera, and the like.
Paragraph [0080]; “the robotic arm 402 controls other features/functions of the camera 408 , such as zoom (e.g., the robotic arm 402 adjusts a focal length of the camera 408 ) and video based features/functions”.
Summary of Citations (Flitsch)
Paragraph [0070]; “An array of deflecting needles or stylus may be dragged over the surface”.
Claim 12 is rejected under 35 U.S.C 103 as being unpatentable over Wang in view of Kulkarni, Tee and Matsumura and further in view of Statham Patent Application Publication No. JP-5264061-B2 (hereinafter Statham).
Regarding claim 12, the same grounds of rejection based on Wang in view of Kulkarni, Tee, Matsumura and Kuno from the last Office Action (Non-Final, 10/20/2025) applies in here.
Claim 17 is rejected under 35 U.S.C 103 as being unpatentable over Wang in view of Kulkarni, Tee and Matsumura and further in view of Park US Patent Publication No. US-10181185-B2 (hereinafter Park).
Regarding claim 17, the same grounds of rejection based on Wang in view of Kulkarni, Tee, Matsumura and Park from the last Office Action (Non-Final, 10/20/2025) applies in here.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZAID MUHAMMAD SALEH whose telephone number is (703)756-1684. The examiner can normally be reached M-F 8 am - 5 pm ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached on (571)272-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ZAID MUHAMMAD SALEH/
Examiner, Art Unit 2668
2/10/2026
/VU LE/Supervisory Patent Examiner, Art Unit 2668