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 .
Response to Amendment
Claims 1, 3, 5 – 10 are currently amended. Claims 11 – 12 are new. Claims 2 and 4 are cancelled. Claims 1, 3, 5 – 12 are pending and under examination.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The listing of references in the PCT international search report is not considered to be an information disclosure statement (IDS) complying with 37 CFR 1.98. 37 CFR 1.98(a)(2) requires a legible copy of: (1) each foreign patent; (2) each publication or that portion which caused it to be listed; (3) for each cited pending U.S. application, the application specification including claims, and any drawing of the application, or that portion of the application which caused it to be listed including any claims directed to that portion, unless the cited pending U.S. application is stored in the Image File Wrapper (IFW) system; and (4) all other information, or that portion which caused it to be listed. In addition, each IDS must include a list of all patents, publications, applications, or other information submitted for consideration by the Office (see 37 CFR 1.98(a)(1) and (b)), and MPEP § 609.04(a), subsection I. states, “the list ... must be submitted on a separate paper.” Therefore, the references cited in the international search report have not been considered. Applicant is advised that the date of submission of any item of information in the international search report will be the date of submission of the IDS for purposes of determining compliance with the requirements for the IDS with 37 CFR 1.97, including all timing statement requirements of 37 CFR 1.97(e). See MPEP § 609.05(a).
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 3 and 5 – 12 are rejected under 35 U.S.C. 103 as being unpatentable over YOGO (US 2021/0150695 A1), in view of BAECK (KR 102335238 B1).
Regarding claim 1, YOGO teaches a press machine (2, FIG. 1 [0031]; [0099] “However, the present disclosure is not limited to the application for the molded article manufactured by the press-forming, and the present disclosure is applicable to molded articles manufactured by various processing methods such as injection molding, forging, casting, and extrusion molding.”) comprising:
a pressing device (2, FIG. 1 [0031]) configured to perform press-working to an article ([0031 – 0032] “The press machine 2 produces the workpiece 100 by processing a metal material by press molding.”) and drop a pressed article to a discharge region below ([0031 – 0032] “The conveying conveyor 3 conveys the workpiece came out from the press machine 2 to a next manufacturing process.”);
a conveyor (conveying conveyor 3 [0031 – 0032]) configured to convey, up to an unloading port, the pressed article (100), dropped to the discharge region below the pressing device (2) (see FIGs. 1 – 2, and [0031 – 0032] “The conveying conveyor 3 is a carrying device to convey the workpiece 100 came out from the press machine 2 to a next manufacturing process.”);
a frame (frame structure 6, FIG. 1) installed outside the pressing device (2) (see FIG. 1, and [0040] “a frame structure 6 arranged in a more front side than the first imaging unit 20. The frame structure 6 is a frame-shaped structure formed of poles and beams arranged to stride over the conveying conveyor 3 and the defective product discharging conveyor 4 arranged side by side.”);
an imaging device (e.g., camera units 21a, 21b, FIGs. 1 – 2, [0035 – 0036]) installed on the frame (6) and configured to capture an inspection region (see FIG. 1) of the conveyor (3) ([0036] “The camera units 21a, 21b may be attached to the pole 5 preferably at a position where each of the camera units 21a, 21b can image a whole first surface of the workpiece 100, which is held by the workpiece pick-up robot 10, from different directions. Alternatively, the camera units 21a, 21b may be attached so that the camera unit 21a captures one region, which is formed by dividing the whole first surface of the workpiece 100 into two regions, within the imaging range, and the camera unit 21b captures the other region within the imaging range.”);
a sensor configured to detect that the pressed article conveyed by the conveyor reaches the inspection region (e.g., [0038 – 00444] “Then, at the timing when the conveyed the workpiece 100 reaches within a given imaging range of the second imaging unit 30, the second imaging unit 30 takes an image of the second surface of the workpiece 100 based on the control of the controller 70.”, and “The camera 23 is an imaging device to take an image in an area on which the light emitted from the projector 22 is projected. The camera 23 may include a known image sensor element, such as a CCD image sensor and a CMOS image sensor.”; see FIGs. 3A – 3B);
a controller (controller 70, [0052]; system integrated management unit 72, [0063 – 0064]) configured to control the imaging device such that the imaging device, when the sensor detects that the pressed article reaches the inspection region, start image capturing and acquire an image of the pressed article passing through the inspection region (FIG. 5 and [0038] “at the timing when the conveyed the workpiece 100 reaches within a given imaging range of the second imaging unit 30, the second imaging unit 30 takes an image of the second surface of the workpiece 100 based on the control of the controller 70.”; [0064] “the system integrated management unit 72 performs the imaging of the workpiece 100 by controlling the first imaging unit at the timing when the workpiece 100 is placed in the imaging range of the first imaging unit 20 by controlling the workpiece pick-up robot 10.”);
an inspection circuit configured to inspect an outer appearance of the pressed article based on the image of the pressed article acquired by imaging device under control of the controller (e.g., [0053] “It is to be noted the way to realize these elements configuring the controller 70 is not limited to software, but a part or all of the element may be realized by hardware that is a combination of a logic circuit, an analog circuit and the like.”, [0031] “inspection system 1 is configured to inspect the presence or absence of a molding fault for all the workpieces 100 while the workpiece 100 came out from the press machine 2 is conveyed to a next manufacturing process.”, [0037 – 0050] “in the controller 70, a three-dimensional surface shape of the first surface of the workpiece 100 is measured from the taken image of the first surface, and then the presence or absence of the molding fault on the first surface of the workpiece 100 is inspected.”); and
a display configured to display an inspection result by the inspection circuit ([0072] “a warning information of the deterioration of the die in the press machine 2 is output to a given output destination (for example, a display device, a recording device and the like).”; [0074]),
wherein, the inspection circuit decides a posture of the pressed article based on a feature part of the pressed article, the feature part is at least one of center coordinates of a plurality of holes [0097], characteristic shape coordinates [0069 – 0070], and shape coordinates of a part end portion (e.g., [0097] “such as a distance between holes formed in the molded article and a distance from an end portion to a hole of the molded article”), and
the inspection circuit is configured to associate information of a posture with images of the pressed article in various postures captured by the imaging device based on the coordinates of the decided feature part ([0058] “measurement processor 71 specifies the position of the molding fault detected in the workpiece 100 by associating with the corresponding grid number.”; [0071] “the controller 70 determines the presence of the scratch under a condition where a point exceeding a brightness threshold serving as a criterion for the scratch is continuously distributed beyond a specified dimensional range in the portion corresponding to the steep portion.”; [0098] “the inspection system 1 is configured such that a user can arbitrarily and selectively execute three inspection modes of (1) inspecting only the molding fault, (2) inspecting only the dimensions, and (3) inspecting the molding fault and the dimensions at the same time.”),
creates an OK determination model (e.g., S112: YES, FIG. 5, [0072]) and an NG determination model (e.g., S112: NO, FIG. 5, [0072]) for each associated posture [0070 – 0072], and
performs learning (under the broadest reasonable interpretation, YOGO [0061] “the controller 70 divides the three-dimensional surface shape of the nominal contour data into two or more regions, and saves the criterion value set to each of the two or more divided regions in the memory 73”, and [0080] “the controller 70 records information including the three-dimensional point group data measured from the workpiece 100,” “the controller 70 creates a stereoscopic image in which a shading process is applied to the three-dimensional surface shape represented by the three-dimensional point group forming the molding fault, and saves the created image” – this recording information and saving of the criterion value action is analogous to the claimed “performs learning”),
wherein the inspection circuit recognizes the posture of the pressed article in the captured image of the pressed article based on the feature part, and executes quality determination of the outer appearance of the pressed article based on data obtained by learning corresponding to the recognized posture (see [0061, 0080] and [0075] “the controller 70 detects, as a defective element point, the deformed point that exceeds an allowable value for the molding quality in the created deviation value distribution diagram.”).
YOGO does not explicitly disclose, wherein the quality determination of the outer appearance of the pressed article is executed by the inspection circuit is based on machine learning data obtained by machine learning corresponding to the recognized posture.
YOGO, however, discloses that “the controller collates” the measured 3D workpiece’s surface shape and a nominal contour data representing a non-defective product 3D surface shape, then detects, as a possible molding fault, a portion recognized to show a shape different from the 3D surface shape of the non-defective product in the workpiece, and specifies, “as a molding fault, among the detected possible molding faults, only a possible molding fault in which a dimension of the shape is equal to or more than a criterion value representing a criterion for the molding fault.” (Abstract, [0007 – 0008], [0016 – 0017] “the determiner collates the three-dimensional surface shape of the standard measured by the measuring unit and a given calibration data representing a normal three-dimensional surface shape corresponding the standard, then the determiner determines an appropriateness of an inspection result related to the inspection target depending on whether the three-dimensional surface shape of the standard conforms to the calibration data. With this configuration, it is possible to improve the reliability of the inspection result since the validity of the inspection result can be guaranteed for every inspection on each molded article.”; [0073] “the controller 70 evaluates the accuracy of the measurement based on a matching degree between the three-dimensional surface shape of the standard gauges 40a, 40b and the three-dimensional surface shape of the calibration data. The calibration data used for the accuracy evaluation in S115 is data representing a reference three-dimensional surface shape of the standard gauges 40a, 40b when the standard gauges 40a, 40b are appropriately imaged. In this embodiment, the configuration data is prestored in the memory 73 and the like of the controller 70.”).
Like YOGO, BAECK teaches a metal product forming apparatus 10 and method, comprising, inter alia, a robot driving unit 11, a positioner 12, a bending unit 13, a cutting unit 14, a control unit, a forming method recommendation unit, (Abstract, lines 89 – 111),
a learning model (defect detection model) (lines 326 – 375),
the metal product forming apparatus 10 may include a press apparatus 100 (lines 444 – 460),
a determination unit 20 capable of applying the molding request information and the measurement information measuring the molded product from the metal product molding apparatus 10 to an AI-based learning model to determine whether the molded product is good or defective (lines 802 – 884), the press apparatus 100 senses a position where a molded article is fixed to an upper fixing device 111 and a position where an adhesive member 300 is fixed to the lower fixing device 121 and may include a check/confirmation unit 140 that determines whether or not it is within a preset error range using various devices such as an image capturing device, an ultrasonic sensor, a laser sensor, and a pressure sensor for sensing the position of the molded article and the adhesive member 300, (lines 565 – 607).
(lines 359 – 396) “The learning model may be generated through artificial intelligence-based learning such as machine learning and deep learning.”
(lines 1005 – 1049) “the determination unit 20” may store a deep-learning algorithm for learning the defective rate reduction model. “However, the present invention is not limited thereto, and it is obvious to those skilled in the art that a conventionally known machine learning algorithm or a machine learning algorithm developed in the future, including unsupervised learning, reinforcement learning, and supervised learning, may be applied,” and “may input the above described molding request information, cutting information and bending information, and defect information about a molded product based on the information to a deep-learning algorithm,” and “may build big data by storing the cutting information and bending information in correspondence with the defective information of the molded product based thereon.”
(lines 1066 – 1116) “the metal product forming apparatus 10 according to an embodiment of the present application, in order to form the metal in a direction to reduce the defect rate according to the above sequence method, big data on the above-described forming method and defect information on the formed product can be input into artificial intelligence machine learning to train a shaping method auxiliary model.”
(lines 1245 – 1253) “the determination unit 20 according to another exemplary embodiment of the present application may input big data into an artificial intelligence algorithm to learn a defective rate reduction model. In addition, the determination unit 20 may learn the failure determination model by inputting the process data generated by the metal product forming apparatus 10 into the artificial intelligence algorithm. However, the present invention is not limited thereto. In another embodiment, the determination unit 20 may learn a molding method auxiliary model based on big data on the molding method and defect information of the molding request product.”
Therefore, it would have been prima facie obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modify the controller in the press machine of YOGO with a learning model and determination unit (e.g., BAECK 20), by known methods and with good expectations of obtaining predictable results, so that the quality determination of the outer appearance of the pressed article is executed by the inspection circuit based on machine learning data (e.g., YOGO’s “collated” data) obtained by machine learning corresponding to the recognized posture, and one of ordinary skill would have been motivated to pursue the modification for the purpose of, as suggested by the prior art, training a defective rate reduction model and/or a shaping method auxiliary model to determine whether the molded product is good or defective and/or to reduce the defect rate, as taught by BAECK (lines 802 – 824, lines 1066 – 1116, lines 1245 – 1253). See MPEP 2143 (I) (Rationale G).
Regarding claim 3, YOGO/BAECK teaches the press machine according to claim 1, wherein the inspection circuit decides the posture of the pressed article based on coordinate information of the feature part in an image capturing range of the imaging device (e.g., YOGO [0034] “while holding the workpiece 100, the workpiece pick-up robot 10 performs an operation of carrying the workpiece 100 within a given imaging range of the first imaging unit 20 provided in the vicinity thereof, and directing the first surface of the workpiece 100 towards the first imaging unit 20 in a predetermined posture.”).
Regarding claim 5, YOGO/BAECK teaches the press machine according to claim 1, wherein the inspection circuit compares the image captured by the imaging device and one of an OK determination model and an NG determination model with a matching posture by image analysis software (YOGO [0053]), thereby determining that the captured pressed article is non-defective or defective (YOGO [0072 – 0079]).
Regarding claim 6, YOGO/BAECK teaches the press machine according to claim 1, wherein the NG determination model includes an NG model by the feature part having a different shape (e.g., YOGO [0007] “The comparing and detecting unit is configured to detect a portion recognized to show a shape different from the three-dimensional surface shape of the non-defective product in the three-dimensional surface shape of the inspection target, as a possible molding fault, together with a dimension of the shape.”), an NG model by the feature part with a position deviation (YOGO [0097]), and an NG model including a crack (YOGO [0031] “molding fault that is an inspection target for the inspection system 1 is a part corresponding to a molding defect including a flaw, dent, missing part, burr, crack, dross, and a peeling of plating generated during the molding process.”; BAECK lines 286 – 298 “the output of the learning model (defect detection model) is poor bead appearance, spatter, crack, PIT and blow hole, slag mixing, crater, under cut, lack of penetration, fish eye, overlap, lack of throat, lack of leg length, lamellar tearing may include”).
Regarding claim 7, YOGO/BAECK teaches the press machine according to claim 1, wherein the imaging device comprises a plurality of cameras and captures the inspection region from above, from sides, from an upstream side, and from a downstream side (see YOGO FIG. 1, [0035 – 0046, 0067 – 0070]), and the inspection circuit inspects the outer appearance of the pressed article using a plurality of captured images (YOGO [0034 – 0048]).
Regarding claim 8, YOGO/BAECK teaches the press machine according to claim 7, wherein at least one of the plurality of cameras (YOGO second imaging unit 30) is installed (YOGO [0040] “The camera unit 31a may be attached to the frame structure 6”) to enter a side of the discharge region of the pressing device (see YOGO FIG. 1, and [0038 – 0050]), except for specifically disclosing, using a bracket.
YOGO [0040] discloses that the attachment position for the cameras could be rearranged depending on the desired region that wants to be capture.
Therefore, it would have been prima facie obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to use attachment means that allows for rearrangement, e.g., a bracket, to install the plurality of cameras in the press machine of YOGO/BAECK, since it have held to be within the ordinary skill of worker in the art to select a known material on the basis of its suitability for the intended use. See MPEP § 2144.07.
Sinclair & Carroll Co. v. Interchemical Corp., 325 U.S. 327, 65 USPQ 297 (1945). The selection of a known material based on its suitability for its intended use supports a prima facie obviousness determination.
Regarding claim 9, YOGO/BAECK teaches the press machine according to claim 7, wherein the inspection circuit associates information of a posture with results of image capturing of the pressed article in various posture states by the plurality of cameras based on the coordinates of the decided feature part (YOGO [0034, 0047]), creates an OK determination model and an NG determination model for each associated posture (YOGO [0058, 0072, 0079]), performs learning (see the discussion of the combination of YOGO/BAECK in claim 1 above), and compares the image and one of the OK determination model and the NG determination model with a matching posture by image analysis software (YOGO [0053 – 0054, 0075, 0093], and BAECK lines 831 – 867), thereby determining that the pressed article is non-defective or defective (e.g., see the discussion of YOGO/BAECK in claim 1 above and e.g., BAECK lines 831 – 867 discloses the determination unit 20 can determine whether a molded product is defective or not by analyzing “the image taken using the image capturing device, and when it is determined that there are cracks, burrs, wrinkles, bends, necks, etc. in the photographed molded product image”).
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Regarding claim 10, YOGO/BAECK teaches the press machine according to claim 7, wherein the frame (6, [0040]) is installed outside a pressing device foundation on which the pressing device is installed (see YOGO FIG. 1), and comprises legs that are installed on a floor on both sides of the conveyor (3, 4, see the annotated portion of YOGO FIG. 1 below):
In the above figure, the annotated were added by the examiner to facilitate the discussion of YOGO. In the above figure, a beam (beams) is(are) shown that is laid between the legs while straddling the conveyor (3, 4), and the imaging device includes:
a first camera (e.g., second imaging unit 30) attached to the beam and configured to capture the inspection region from above (see YOGO FIG. 1, [0039 – 0040] “second imaging unit 30 is a device that images the second surface side of the workpiece 100, which is conveyed by the conveying conveyor 3, from above.”);
a second camera (first imaging unit 20) attached to a first bracket that is supported by the beam ([0035 – 0037] “first imaging unit 20 is attached to a pole 5 standing beside the conveying conveyor 3. The camera units 21a, 21b may be attached to the pole 5 preferably at a position where each of the camera units 21a, 21b can image a whole first surface of the workpiece 100”) and enters the discharge region (see YOGO FIG. 1 and [0035 – 0038]), and configured to capture the inspection region from the upstream side of the conveyor (see YOGO FIG. 1, [0035 – 0038]);
except for specifically disclosing a third camera attached to a second bracket that is supported by the beam and extends in a direction of the unloading port, and configured to capture the inspection region from the downstream side of the conveyor; and a fourth camera and a fifth camera, which are attached to the legs and configured to capture the inspection region from both sides with respect to a conveyance direction of the conveyor.
YOGO, however, discloses that the configuration for the cameras is not limited to the above configuration i.e., [0035] “a case where the first imaging unit 20 is configured by two camera units 21a, 21b. The configuration is not limited to this, and the first imaging unit 20 may be configured by one camera unit, or the first imaging unit 20 may be configured by three or more camera units.”; [0036] “Alternatively, the camera units 21a, 21b may be attached so that the camera unit 21a captures one region…, within the imaging range, and the camera unit 21b captures the other region within the imaging range.”; [0039] “the second imaging unit 30 may be configured by one camera unit, or and the second imaging unit 30 may be configured by three or more camera units.”; [0040] “the camera units 31a, 31b may be attached so that the camera unit 31a captures a first region…, within an imaging range, and the camera unit 31b captures a second region within the imaging range.”; [0044 – 0046] “FIG. 3A shows an example of a configuration of a small-sized monocular camera unit including one projector 22 and one camera 23.”, “FIG. 3B shows an example of a configuration of a large-sized binocular camera unit including one projector 22 and two cameras 23.”
Therefore, it would have been prima facie obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the press machine by duplicating the cameras in the press machine of YOGO/BAECK, with a third camera and a fourth camera and a fifth camera, since it have been held that a mere duplication of working parts of a device involves only routine skill in the art. See MPEP § 2144.04 (VI) (B).
In re Harza, 274 F.2d 669, 124 USPQ 378 (CCPA 1960). The court held that mere duplication of parts has no patentable significance unless a new and unexpected result is produced.
One of ordinary skill in the art would have been motivated to pursue the duplication for the purpose of, as suggested by the prior art, configured the first imaging unit and/or the second imaging unit by three or more camera units, that may be attached to the frame so that the camera units captures a first region within an imaging range, different from a second region within the imaging range e.g., in a more front side than the first imaging unit (YOGO [0035 – 0046]).
Therefore, it would have been prima facie obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the press machine of YOGO/BAECK by rearranging the a third camera attached to a second bracket that is supported by the beam and extends in a direction of the unloading port, and configured to capture the inspection region from the downstream side of the conveyor; and a fourth camera and a fifth camera, which are attached to the legs and configured to capture the inspection region from both sides with respect to a conveyance direction of the conveyor, since it have been held that a mere rearrangement of element without modification of the operation of the device involves only routine skill in the art. MPEP § 2144.04 (VI) (C).
It has generally been recognized that to shift location of parts when the operation of the device is not otherwise changed is within the level of ordinary skill in the art. In re Japikse, 86 USPQ 70; In re Gazda, 104 USPQ 400.
One of ordinary skill in the art would have been motivated to pursue the rearrangement for the purpose of, as suggested by the prior art, configured the plurality of cameras e.g., a third, fourth, and fifth, that may be attached to the frame so that the camera units captures a first region within an imaging range, different from a second region within the imaging range e.g., in a more front side than the first imaging unit (YOGO [0035 – 0046]).
Regarding claim 11, YOGO/BAECK teaches the press machine according to claim 1, wherein the posture of the pressed article is the posture of the pressed article that is press-worked (YOGO [0034, 0047]), extracted by a robot arm (workpiece pick-up robot 10), dropped onto the conveyance unit (3) in the discharge region, and discharged (see YOGO FIG. 1, [0032 – 0038, 0047]).
Regarding claim 12, YOGO/BAECK teaches the press machine according to claim 5, wherein the NG (e.g., S112: NO; a molding fault [0079]) determination model includes an NG model (YOGO [0071 – 0072, 0076 – 0080], FIG. 5) by the feature part having a different shape (e.g., YOGO [0007] “The comparing and detecting unit is configured to detect a portion recognized to show a shape different from the three-dimensional surface shape of the non-defective product in the three-dimensional surface shape of the inspection target, as a possible molding fault, together with a dimension of the shape.”), an NG model by the feature part with a position deviation (YOGO [0097]), and an NG model including a crack (YOGO [0031] “molding fault that is an inspection target for the inspection system 1 is a part corresponding to a molding defect including a flaw, dent, missing part, burr, crack, dross, and a peeling of plating generated during the molding process.”; BAECK lines 286 – 298 “the output of the learning model (defect detection model) is poor bead appearance, spatter, crack, PIT and blow hole, slag mixing, crater, under cut, lack of penetration, fish eye, overlap, lack of throat, lack of leg length, lamellar tearing may include”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Haven et al. (US Pat. No. 6,462,813 B1): teaches a surface defect inspection system and method, “the system is capable of characterizing surface defects in a number of ways, including by size of the defect and by surface elevation of the defect (that is, whether it is a crater or a piece of foreign matter). The system is also capable of "learning" to recognize known features on the workpiece, such as, for example, a cutout for a door handle on a side auto body panel.” (Abstract, Col. 2 lines 60 – 68, cont. Col. 3 lines 1 – 4, FIGs. 1 – 8).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDGAREDMANUEL TROCHE whose telephone number is (571)272-9766. The examiner can normally be reached M-F 7:30-5:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sam Zhao can be reached at 571-270-5343. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/EDGAREDMANUEL TROCHE/Examiner, Art Unit 1744
/JEFFREY M WOLLSCHLAGER/Primary Examiner, Art Unit 1742
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