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 .
Election/Restrictions
Applicant’s election without traverse of Species I in the reply filed on January 20, 2026 is acknowledged.
Examiner respectfully submits that claim 2 was erroneously designated as withdrawn. Claim 2 depends from elected claim 1 and is commensurate in scope with the elected species. Accordingly, Examiner will consider claim 2 on the merits with the elected claims.
Claims 1 – 6 are presented.
Claim 6 is withdrawn.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
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) 1 – 5 are rejected under 35 U.S.C. 103 as being unpatentable over Long (US 10,593,007) in view of Sieracki (US 10,235,588).
Regarding claim 1, Long discloses a visual inspection method of a curved object executed by a visual inspection system, the visual inspection system including a camera, a fixing unit mounted under the camera, and a control unit electrically connected to the camera, when the visual inspection method of the curved object is performed by the control unit, the visual inspection method of the curved object comprising steps of: fixing a curved object which is to be inspected by the fixing unit (c.6, ll.27-36: packages on a production line); capturing the curved object which is to be inspected with a plurality of groups of preset parameters by the camera to obtain a plurality of groups of object images (fig. 6-7; c.6, ll.27-36: images of the packages are captured under different combinations of imaging parameters, as described above in connection with gradient targets, and entropy metrics are computed. After such data collection and analysis, the parameters that are found to yield the maximum metric are selected for further, ongoing operation of the production line), and then the control unit counting a quantity of pixels occupied by each grayscale value in the object images (fig. 6, 7; step 61); using the control unit to calculate a better shooting parameter according to the quantity of the pixels occupied by the grayscale values in the object images, and the better shooting parameter being used for an inspection (fig. 6-8; c.6, ll.27-36: images of the packages are captured under different combinations of imaging parameters, as described above in connection with gradient targets, and entropy metrics are computed. After such data collection and analysis, the parameters that are found to yield the maximum metric are selected for further, ongoing operation of the production line); and using the better shooting parameter for the inspection by the camera to proceed with visual inspections of the curved objects which are to be inspected in batches (fig. 6-7; c.6, ll.27-36: images of the packages are captured under different combinations of imaging parameters, as described above in connection with gradient targets, and entropy metrics are computed. After such data collection and analysis, the parameters that are found to yield the maximum metric are selected for further, ongoing operation of the production line). Long fails to explicitly disclose the camera connected to a robotic arm.
In a similar field of endeavor, Sieracki teaches system for adaptively conformed imaging of work pieces having disparate configurations, which comprises a camera-in-motion set up for the system, where the camera or other imaging unit is held and manipulated by the robot arm manipulator relative to a stationary specimen work piece (fig. 3(b); c.17, ll.40-48). In light of the teaching of Sieracki, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to use Sieracki teaching in Longs system because an artisan of ordinarily skill would recognize that this would result in flexible imaging options without needing to manipulate the imaged object.
Regarding claim 2, Long in view of Sieracki disclose the limitations of claim 1. Long also teaches wherein the step of capturing the curved object which is to be inspected with the plurality of the groups of the preset parameters further includes following steps: photograph the curved object which is to be inspected with a first exposure setting to obtain a first object image, and the control unit counts the quantity of the pixels occupied by each grayscale value in the first object image, keep the same photographing angle and position by the camera, use an exposure which is different from the first exposure setting to photograph the object which is to be inspected for several times to obtain the plurality of the object images with different exposures, and the control unit counts the quantity of the pixels occupied by each grayscale value in the object images with the different exposures (fig. 6, 7; c.3, ll.45-50; c.5, ll.54-65: probe a sensor's responses across a variety of different imaging parameters, such as exposure interval, exposure (lens) aperture, and camera gain; count grayscale values).
Regarding claim 3, Long in view of Sieracki disclose the limitations of claim 2. Long also teaches wherein in the step of using the exposure which is different from the first exposure setting to photograph the object which is to be inspected for several times, use the exposure which is lower than the first exposure setting by the camera to photograph the object which is to be inspected to obtain a lower exposure object image, then the control unit counts the quantity of the pixels occupied by each grayscale value which is in the lower exposure object image, use the exposure which is higher than the first exposure setting by the camera to photograph the object which is to be inspected to obtain a higher exposure object image, then the control unit counts the quantity of the pixels occupied by each grayscale value which is in the higher exposure object image (fig. 6, 7; c.3, ll.45-555; c.5, ll.54-65: probe a sensor's responses across a variety of different imaging parameters, such as exposure interval, exposure (lens) aperture, and camera gain; count grayscale values… Some sets of parameters will lead to over-exposure of the imagery, washing out image highlights. Some sets of parameters will lead to under-exposure, losing detail in the resulting shadows).
Regarding claim 4, Long in view of Sieracki disclose the limitations of claim 2. Long also teaches wherein the step of using the control unit to calculate the better shooting parameter includes following steps, preset a target grayscale value by the control unit, compare the quantity of the pixels occupied by the target grayscale values in the first object image with the quantity of the pixels occupied by the target grayscale values in the object images with the different exposures, and set an exposure setting of the object image that has a larger quantity of the pixels occupied by the target grayscale values as the better shooting parameters for the inspection (fig. 6, 7; c.3, ll.45-555; c.5, ll.54-65: probe a sensor's responses across a variety of different imaging parameters, such as exposure interval, exposure (lens) aperture, and camera gain; count grayscale values… use parameter that resulted in best entropy).
Regarding claim 1, Long discloses a visual inspection method of a curved object executed by a visual inspection system, the visual inspection system including a camera, a fixing unit mounted under the camera, and a control unit electrically connected to the camera, when the visual inspection method of the curved object is performed by the control unit, the visual inspection method of the curved object comprising steps of: fixing a curved object which is to be inspected by the fixing unit (c.6, ll.27-36: packages on a production line); capturing the curved object which is to be inspected with a plurality of groups of preset parameters by the camera to obtain a plurality of groups of object images (fig. 6-7; c.6, ll.27-36: images of the packages are captured under different combinations of imaging parameters, as described above in connection with gradient targets, and entropy metrics are computed. After such data collection and analysis, the parameters that are found to yield the maximum metric are selected for further, ongoing operation of the production line), and then the control unit counting a quantity of pixels occupied by each grayscale value in the object images (fig. 6, 7; step 61); using the control unit to calculate a better shooting parameter according to the quantity of the pixels occupied by the grayscale values in the object images, and the better shooting parameter being used for an inspection (fig. 6-8; c.6, ll.27-36: images of the packages are captured under different combinations of imaging parameters, as described above in connection with gradient targets, and entropy metrics are computed. After such data collection and analysis, the parameters that are found to yield the maximum metric are selected for further, ongoing operation of the production line); and using the better shooting parameter for the inspection by the camera to proceed with visual inspections of the curved objects which are to be inspected in batches (fig. 6-7; c.6, ll.27-36: images of the packages are captured under different combinations of imaging parameters, as described above in connection with gradient targets, and entropy metrics are computed. After such data collection and analysis, the parameters that are found to yield the maximum metric are selected for further, ongoing operation of the production line); wherein when exposure time is shortened, the object images cause underexposure, and the overall grayscale values of the object images are reduced, relatively, direct accepting surface or light-colored region of the object images are avoided from an overexposure; and wherein when the exposure time is lengthened, the object images cause the overexposure and the overall grayscale values of the object images are increased, relatively, a backlight surface or a dark region of the object images are avoided from the underexposure (fig. 6, 7; c.3, ll.45-555; c.5, ll.54-65: probe a sensor's responses across a variety of different imaging parameters, such as exposure interval, exposure (lens) aperture, and camera gain; count grayscale values… Some sets of parameters will lead to over-exposure of the imagery, washing out image highlights. Some sets of parameters will lead to under-exposure, losing detail in the resulting shadows). Long fails to explicitly disclose the camera connected to a robotic arm.
In a similar field of endeavor, Sieracki teaches system for adaptively conformed imaging of work pieces having disparate configurations, which comprises a camera-in-motion set up for the system, where the camera or other imaging unit is held and manipulated by the robot arm manipulator relative to a stationary specimen work piece (fig. 3(b); c.17, ll.40-48). In light of the teaching of Sieracki, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to use Sieracki teaching in Longs system because an artisan of ordinarily skill would recognize that this would result in flexible imaging options without needing to manipulate the imaged object.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANTOINETTE SPINKS whose telephone number is (571)270-3749. The examiner can normally be reached M-Th 7am - 5pm EST.
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/ANTOINETTE T SPINKS/Primary Examiner, Art Unit 2639