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 Rejections - 35 USC § 103
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Balatzis (US 2023/0044371) in view of Leitzen (US 2017/0334659).
Regarding Claim 1, Balatzis teaches a LiDAR (Light Detection and Ranging) defect detection system [#120, #122 A of Fig 1] for use in a can manufacturing assembly, comprising: a plurality of LiDAR sensors disposed at least at outputs of one or more equipment in the can manufacturing assembly and structured to scan and create at least three-dimensional (3D) images of output cans at the outputs of the one or more equipment [#120-#124 of Fig 1; 0055-56]. Balatzis does not explicitly teach – but Leitzen does teach a controller [#200, #240 of Fig 4] communicatively coupled to the LiDAR sensors and structured to collect data including at least the 3D images and analyze the data to determine if one or more output cans are defective [0045; 0063-64]. It would have been obvious to modify the system of Balatzis for use in a can manufacturing assembly controller communicatively coupled to the Li DAR. sensors and structured to collect data including at least the 3D images and analyze the data to determine if one or more output cans are defective, as it would permit inspection of beverage container bodies as soon as possible in the container body manufacturing process [0014].
Regarding Claim 14, Balatzis teaches a LiDAR (Light Detection and Ranging) defect detection system [#120, #122 A of Fig 1] for use in a can decorator, comprising: one or more LiDAR sensors disposed adjacent to or within a component of the can decorator [#120, #122 A of Fig 1], the one or more LiDAR structured to scan and create at least three- dimensional (3D) images of cans passing through an inspection window of the one or more LiDAR sensors [#120-#124 of Fig 1; 0055-56]. Balatzis does not explicitly teach – but Leitzen does teach controller communicatively coupled to the one or more LiDAR sensors and structured to collect data including at least the 3D images and analyze the data to determine if one or more cans are defective [0045; 0063-64]. It would have been obvious to modify the system of Balatzis for use in a can manufacturing assembly controller communicatively coupled to the LiDAR. sensors and structured to collect data including at least the 3D images and analyze the data to determine if one or more output cans are defective, as it would permit inspection of beverage container bodies as soon as possible in the container body manufacturing process [0014].
Regarding Claim 19, Balatzis teaches a method of detecting a defect in cans in a can manufacturing assembly, comprising: providing a LiDAR (Light Detection and Ranging) defect detection system that comprises (i) LiDAR sensors disposed at least at outputs of one or more equipment in the can manufacturing assembly and structured to scan and create at least three-dimensional (3D) images of output cans at the outputs of the one or more equipment [Fig 1; 13, 14; 0055-56; 0098; 0110], and (ii) a controller communicatively coupled to the LiDAR sensors and structured to collect data including at least the 3D images and analyze the data to determine if one or more output cans are defective [0055-56]; scanning and creating at least the 3D images [Fig 13, 14; 0098; 0110]; transmitting the data including at least the 3D images to the controller [0120; 0127]. Balatzis does not explicitly teach – but Leitzen does teach analyzing the data received to determine if one or more output cans are defective [0045; 0063-64]. It would have been obvious to modify the method of Balatzis for in order to analyze the data to determine if one or more output cans are defective, as it would permit inspection of beverage container bodies as soon as possible in the container body manufacturing process [0014].
Regarding Claim 2, Balatzis does not explicitly teach – but Leitzen does teach wherein the one or more equipment comprise at least one of a bodymaker, a trimmer, a necker machine, a washer or a can decorator [0038-40; 0042]. It would have been obvious to modify the system of Balatzis to inspect different types of metallic containers or assemblies for flaws, cracks, or contaminants as quickly as possible [0014].
Regarding Claim 3, Balatzis does not explicitly teach – but Leitzen does teach wherein the controller is further structured to compare the data with specifications for the output cans [0072]. It would have been obvious to modify the system of Balatzis to inspect different types of cans and compare the data to most efficiently and detect flaws, cracks, or contaminants as quickly as possible [0014].
Regarding Claim 4, Balatzis does not explicitly teach – but Leitzen does teach wherein the outputs comprise one or more conveyors that carry the output cans processed by each equipment [0069]. It would have been obvious to modify the system of Balatzis to inspect different types of cans and compare the data to most efficiently and detect flaws, cracks, or contaminants as quickly as possible [0014].
Regarding Claim 5, Balatzis also teaches wherein the LiDAR sensors are disposed above the output cans [#122, #126 of Fig 1; 0056].
Regarding Claim 6, Balatzis also teaches wherein the LiDAR sensors further comprise one or more LiDAR sensors disposed below the output cans or on the one or more conveyors [0061].
Regarding Claim 7, Balatzis also teaches wherein the LiDAR sensors further comprise one or more LiDAR sensors disposed in the one or more equipment [0059].
Regarding Claim 8, Balatzis does not explicitly teach – but Leitzen does teach wherein the one or more LiDAR sensors are disposed in a pin chain, a transfer wheel, or a curing oven of a can decorator [0042]. It would have been obvious to modify the system of Balatzis to dispose of the sensors on a pin chain to most efficiently detect flaws, cracks, or contaminants [0014].
Regarding Claim 9, Balatzis does not explicitly teach – but Leitzen does teach wherein for analyzing the data, the controller is further structured to compare the data with specifications for the output cans, the specifications comprising at least reference sizes and reference images for the output cans [0072]. It would have been obvious to modify the system of Balatzis to inspect and compare the data to reference data in order to detect flaws, cracks, or contaminants as quickly as possible [0014].
Regarding Claim 10, Balatzis does not explicitly teach – but Leitzen does teach wherein the controller determines if a defect has been detected in one or more output cans based on the comparison and determine if the detected defect is an actual defect based at least in part on the specifications [0072; 0092]. It would have been obvious to modify the system of Balatzis to inspect and compare the data to reference data in order to detect flaws, cracks, or contaminants as quickly as possible [0014].
Regarding Claim 11, Balatzis does not explicitly teach – but Leitzen does teach wherein an actual defect exceeds an acceptable threshold for respective specification using respective equipment [0061]. It would have been obvious to modify the system of Balatzis to inspect and compare the data to reference data in order to detect flaws, cracks, or contaminants as quickly as possible, and identify them for removal to prevent damage.
Regarding Claims 12, 16, and 17, Balatzis does not explicitly teach – but Leitzen does teach wherein the one or more conveyors are operably coupled to respective removal devices that are communicatively coupled to the controller, the removal devices being structured to remove the one or more defective cans from the one or more conveyors and wherein the controller is further structured to cause the removal device to remove the one or more defective cans from the one or more conveyors based on the determination that the defect is an actual defect [0054; 0065; 0093]. It would have been obvious to modify the system of Balatzis to inspect and compare the data to reference data in order to detect flaws, cracks, or contaminants as quickly as possible, and identify them for removal to prevent damage.
Regarding Claim 13, Balatzis also teaches wherein the data from the LiDAR sensors further comprise at least distance information associated with the output cans [0083; 0093]. .
Regarding Claim 15, Balatzis also teaches wherein the controller is further structured to compare the data including at least the 3D images to at least reference images for the cans in determining if one or more cans include an image defect, the image defect comprising an image registration error [0131].
Regarding Claim 18, Balatzis does not explicitly teach – but Leitzen does teach wherein the cans are carried by rotating can pads through the inspection window such that the one or more LiDAR sensors are able to scan and create images of all sides of each can passing through the inspection window [0060]. It would have been obvious to modify the system of Balatzis to inspect and compare the data and scan through an inspection to detect flaws, cracks, or contaminants as quickly as possible, and to prevent damage to the cans or the inspection system.
20. The method of claim 19, wherein the determining if one or more output cans are defective comprises: comparing the data to specifications for the output cans, the specifications including at least reference sizes and reference images for the output cans; detecting a defect in one or more output cans based on the comparison; determining if the detected defect is an actual defect; and in response to determining that the detected defect is an actual defect, causing a removal device to remove the one or more defective output cans from can manufacturing assembly line [0051; 0054; 0065; 0072; 0093] . It would have been obvious to modify the method of Balatzis to inspect and compare the data to reference data in order to detect flaws, cracks, or contaminants as quickly as possible, and identify them for removal to prevent damage [0054; 0065; 0093].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES R HULKA whose telephone number is (571)270-7553. The examiner can normally be reached M-R: 9am-6pm, F: 10am-2pm.
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JAMES R. HULKA
Primary Examiner
Art Unit 3645
/JAMES R HULKA/Primary Examiner, Art Unit 3645
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