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 Arguments
The amendment filed 12/19/2025 has been entered. Claims 1, 8, 11, 13, and 16 are amended. Claims 5-7, 15 and 20 are cancelled. Claims 1-4, 8-14, and 16-19 remain pending in the application. Applicant’s amendments to the claims have overcome each and every objection and 112(b) rejection set forth in the Non-Final Office Action mailed 10/2/2025.
Applicant's arguments, see pages 13-14, with respect to the cited prior art not teaching the amended features have been fully considered but they are not persuasive. Accordingly, the amended claims are rejected in view of the same prior art.
Oridate teaches "determining, with the at least one processor, the shape of the substrate in at least two dimensions based on data received from the at least one laser system," (“At 602, a computer system may generate, based on first sensor data generated by a sensor system of a robot, a three-dimensional (3D) of a surface of a target object, the 3D representation comprising a first set of data points. For example, the sensor system may include a LiDAR sensor system that is configured to generate point cloud data that represents a target object. The point cloud data can be segmented to associate point cloud data points to different objects in the point cloud data. The computer system can extract features from the segmented objects to identify a target object, such as an aircraft panel. In embodiments, the computer system may be configured to associate point cloud data to a target object and computing a normal to and/or from the surface of the target object. The computer system can store a three-dimensional representation (e.g., a point cloud or computer-aided design (CAD) file) of a target object for use in comparison to known target objects and known CAD files for the known target objects.” See at least [0041], wherein at least the 3D dimensional representation is the determined shape of the substrate in at least two dimensions.).
Oridate does not explicitly teach, but Yoshida teaches
transmitting, with the at least one processor, a request message to at least one edge computing device, the request message comprising the shape of the substrate in the at least two dimensions and a request to determine the type of the substrate, wherein transmitting the request message to the at least one edge computing device comprises: causing the at least one edge computing device to determine the type of the substrate based on the shape of the substrate in the at least two dimensions; (“The generated image information is output to the robot controller 120 as detection information and transmitted from a transmitting part 122a of a communication control part 122 described later to the central server 200. … The central server 200 respectively accepts the image information transmitted from the robot controller 120 of each site, performs feature extraction processing (image processing) on the accepted image information, and extracts unique features (patterns) to the image information (details described later). Note that the extracted pattern of the image information links to the processed information and processed image information.” See at least [0019-0020], wherein the central server is equivalent to the edge computing device; the image information is indicative of the shape in at least two dimensions; and the transmitted image information is equivalent to a request message under broad reasonable interpretation because the central server accepts the image and processes it for the robot.)
causing the at least one edge device to access a memory comprising a plurality of patterns to look up at least one pattern of the plurality of patterns associated with the type of the substrate; and causing the at least one edge computing device to transmit the at least one pattern; (“Then, the flow proceeds to step SB30 where the control part 201 sequentially collates (matches) the input pattern extracted in the above described step SB20 and the plurality of registered patterns stored in the above described teaching information database 2030 using a suitable known pattern matching (normalized correlation) processing technique. … the control part 201 determines whether or not the plurality of teaching information stored in the teaching information database 2030 includes teaching information in which the correlation degree that indicates the degree of correlation of the related registered pattern with respect to the input pattern is greater than a predetermined value set in advance. … the acquired specific teaching information is transmitted along with correlation degree data that indicates the correlation degree corresponding to the specific teaching information to the storage device 124 of the robot controller 120 of the corresponding site by the communication control part 202 via the network cloud NW1. … the control part 121 receives the specific teaching information and correlation degree data by the receiving part 122b.” See at least [0049-0053], wherein the teaching information is equivalent to the pattern because it specifies movements of the robot according to at least [0055])
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.
Claim(s) 1, 11, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oridate (US 20240116181 A1) in view of Yoshida (US 20150019012 A1).
Regarding Claim 1,
Oridate teaches
A computer-implemented method comprising: (“A computer-implemented method, comprising:” See at least claim 1)
determining, with at least one processor, at least one optical indicium of a substrate based on data received from at least one optical detection system, (“the computer system 112 may determine the CAD file for the target object based on images captured by the image sensor 110 of the target object 102, the first surface 120, and/or markers (machine readable codes) 124. … the computer system 112 may read or scan the information included in markers 124 to identify the CAD file for the target object 102. The computer system 112 may be configured to compare the information specified in the CAD file for the target object 102 to the information detected or identified by the sensor input from sensor system 106.” See at least [0017-0018], wherein a marker is an optical indicium)
the at least one optical detection system comprising at least one laser system mounted on at least one robotic unit, (“FIG. 1 includes a robot 104 that includes a sensor system 106 … the sensor system 106 may be a laser sensor system such as a light detection and ranging (LiDAR) sensor system or other sensors configured to detect distance and rotational orientation of the target object 102 by generating point cloud data, for example.” See at least [0016] and fig. 1)
the at least one optical indicium comprising a shape of the substrate, wherein the substrate is one of a plurality of different types of substrates; determining, with the at least one processor, the shape of the substrate in at least two dimensions based on data received from the at least one laser system; … determine the type of the substrate based on the shape of the substrate in the at least two dimensions (“At 602, a computer system may generate, based on first sensor data generated by a sensor system of a robot, a three-dimensional (3D) of a surface of a target object, the 3D representation comprising a first set of data points. For example, the sensor system may include a LiDAR sensor system that is configured to generate point cloud data that represents a target object. The point cloud data can be segmented to associate point cloud data points to different objects in the point cloud data. The computer system can extract features from the segmented objects to identify a target object, such as an aircraft panel. In embodiments, the computer system may be configured to associate point cloud data to a target object and computing a normal to and/or from the surface of the target object. The computer system can store a three-dimensional representation (e.g., a point cloud or computer-aided design (CAD) file) of a target object for use in comparison to known target objects and known CAD files for the known target objects.” See at least [0041])
receiving, with the at least one processor, the at least one pattern from the at least one edge computing device; (“At 414, the computer system generates a second trajectory for traversing the second surface of the target object” See at least [0033], wherein the trajectory is a pattern.)
and controlling, with the at least one processor, the at least one robotic unit having at least one robot comprising at least one nozzle to apply at least one material to the substrate, wherein controlling the at least one robotic unit comprises: causing the at least one robotic unit to apply the at least one material to the substrate based on the at least one pattern. (“At 416, the computer system may instruct the robot to move to the starting position, again, and traverse the second surface of the target object using the second trajectory and apply a sealing to the structural property using the end effector dispensing system of the robot.” See at least [0034] and fig. 1, wherein the end effector dispensing system includes a nozzle and it applies a sealing (material) to the target object (substrate).)
Oridate does not explicitly teach, but Yoshida teaches
transmitting, with the at least one processor, a request message to at least one edge computing device, the request message comprising the shape of the substrate in the at least two dimensions and a request to determine the type of the substrate, wherein transmitting the request message to the at least one edge computing device comprises: causing the at least one edge computing device to determine the type of the substrate based on the shape of the substrate in the at least two dimensions; (“The generated image information is output to the robot controller 120 as detection information and transmitted from a transmitting part 122a of a communication control part 122 described later to the central server 200. … The central server 200 respectively accepts the image information transmitted from the robot controller 120 of each site, performs feature extraction processing (image processing) on the accepted image information, and extracts unique features (patterns) to the image information (details described later). Note that the extracted pattern of the image information links to the processed information and processed image information.” See at least [0019-0020], wherein the central server is equivalent to the edge computing device.)
causing the at least one edge device to access a memory comprising a plurality of patterns to look up at least one pattern of the plurality of patterns associated with the type of the substrate; and causing the at least one edge computing device to transmit the at least one pattern; (“Then, the flow proceeds to step SB30 where the control part 201 sequentially collates (matches) the input pattern extracted in the above described step SB20 and the plurality of registered patterns stored in the above described teaching information database 2030 using a suitable known pattern matching (normalized correlation) processing technique. … the control part 201 determines whether or not the plurality of teaching information stored in the teaching information database 2030 includes teaching information in which the correlation degree that indicates the degree of correlation of the related registered pattern with respect to the input pattern is greater than a predetermined value set in advance. … the acquired specific teaching information is transmitted along with correlation degree data that indicates the correlation degree corresponding to the specific teaching information to the storage device 124 of the robot controller 120 of the corresponding site by the communication control part 202 via the network cloud NW1. … the control part 121 receives the specific teaching information and correlation degree data by the receiving part 122b.” See at least [0049-0053], wherein the teaching information is equivalent to the pattern because it specifies movements of the robot according to at least [0055])
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of Oridate to further include the teachings of Yoshida with a reasonable expectation of success to implement a central server (edge computing device) because it facilitates control for a larger number of robots across different sites/facilities (See at least [0014] and [0075]) and improves convenience for robot teaching (See at least [0066]).
Regarding Claim 11,
Oridate teaches
A system comprising: at least one processor; at least one optical detection system comprising at least one laser system; and at least one robotic unit having at least one robot comprising at least one nozzle configured to apply at least one material to a substrate, wherein the at least one laser system is mounted to the at least one robotic unit; (“FIG. 1 is an illustration of a robot and computer system generating updates to a trajectory for traversing a surface of a target object 102, in accordance with one or more embodiments. FIG. 1 includes a robot 104 that includes a sensor system 106, an end effector dispensing system 108, and image sensor 110 … the sensor system 106 may be a laser sensor system such as a light detection and ranging (LiDAR) sensor system or other sensors configured to detect distance and rotational orientation of the target object 102 by generating point cloud data, for example.” See at least [0016] and fig. 1)
wherein the at least one processor is programmed or configured to: determine at least one optical indicium of the substrate based on data received from the at least one laser system; (“the computer system 112 may determine the CAD file for the target object based on images captured by the image sensor 110 of the target object 102, the first surface 120, and/or markers (machine readable codes) 124. … the computer system 112 may read or scan the information included in markers 124 to identify the CAD file for the target object 102. The computer system 112 may be configured to compare the information specified in the CAD file for the target object 102 to the information detected or identified by the sensor input from sensor system 106.” See at least [0017-0018], wherein a marker is an optical indicium)
the at least one optical indicum comprising a shape of the substrate, wherein the substrate is one of a plurality of different types of substrates; determine the shape of the substrate in at least two dimensions based on data received from the at least one laser system; … determine the type of the substrate based on the shape of the substrate in the at least two dimensions; (“At 602, a computer system may generate, based on first sensor data generated by a sensor system of a robot, a three-dimensional (3D) of a surface of a target object, the 3D representation comprising a first set of data points. For example, the sensor system may include a LiDAR sensor system that is configured to generate point cloud data that represents a target object. The point cloud data can be segmented to associate point cloud data points to different objects in the point cloud data. The computer system can extract features from the segmented objects to identify a target object, such as an aircraft panel. In embodiments, the computer system may be configured to associate point cloud data to a target object and computing a normal to and/or from the surface of the target object. The computer system can store a three-dimensional representation (e.g., a point cloud or computer-aided design (CAD) file) of a target object for use in comparison to known target objects and known CAD files for the known target objects.” See at least [0041])
receive the at least one pattern (“At 414, the computer system generates a second trajectory for traversing the second surface of the target object” See at least [0033], wherein the trajectory is a pattern.)
and control the at least one robotic unit to apply the at least one material to the substrate, wherein controlling the at least one robotic unit comprises: causing the at least one robotic unit to apply the at least one material to the substrate based on the at least one pattern. (“At 416, the computer system may instruct the robot to move to the starting position, again, and traverse the second surface of the target object using the second trajectory and apply a sealing to the structural property using the end effector dispensing system of the robot.” See at least [0034] and fig. 1, wherein the end effector dispensing system includes a nozzle and it applies a sealing (material) to the target object (substrate).)
Oridate does not explicitly teach, but Yoshida teaches
transmit a request message to at least one edge computing device, the request message comprising the shape of the substrate in the at least two dimensions and a request to determine the type of the substrate, wherein, when transmitting the request message to the at least one edge computing device, the at least one processor is programmed or configured to: cause the at least one edge computing device to determine the type of the substrate based on the shape of the substrate in the at least two dimensions; (“The generated image information is output to the robot controller 120 as detection information and transmitted from a transmitting part 122a of a communication control part 122 described later to the central server 200. … The central server 200 respectively accepts the image information transmitted from the robot controller 120 of each site, performs feature extraction processing (image processing) on the accepted image information, and extracts unique features (patterns) to the image information (details described later). Note that the extracted pattern of the image information links to the processed information and processed image information.” See at least [0019-0020], wherein the central server is equivalent to the edge computing device.)
cause the at least one edge device to access a memory comprising a plurality of patterns to look up at least one pattern of the plurality of patterns associated with the type of the substrate; and cause the at least one edge computing device to transmit the at least one pattern; … receive at least one pattern from the at least one edge computing device; (“Then, the flow proceeds to step SB30 where the control part 201 sequentially collates (matches) the input pattern extracted in the above described step SB20 and the plurality of registered patterns stored in the above described teaching information database 2030 using a suitable known pattern matching (normalized correlation) processing technique. … the control part 201 determines whether or not the plurality of teaching information stored in the teaching information database 2030 includes teaching information in which the correlation degree that indicates the degree of correlation of the related registered pattern with respect to the input pattern is greater than a predetermined value set in advance. … the acquired specific teaching information is transmitted along with correlation degree data that indicates the correlation degree corresponding to the specific teaching information to the storage device 124 of the robot controller 120 of the corresponding site by the communication control part 202 via the network cloud NW1. … the control part 121 receives the specific teaching information and correlation degree data by the receiving part 122b.” See at least [0049-0053], wherein the teaching information is equivalent to the pattern because it specifies movements of the robot according to at least [0055])
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of Oridate to further include the teachings of Yoshida with a reasonable expectation of success to implement a central server (edge computing device) because it facilitates control for a larger number of robots across different sites/facilities (See at least [0014] and [0075]) and improves convenience for robot teaching (See at least [0066]).
Regarding Claim 16,
Oridate teaches
A computer program product comprising at least one non-transitory computer-readable medium comprising one or more instructions that, when executed by at least one processor, cause the at least one processor to: (“One ore more non-transitory computer-readable media including one or more sequences of instructions which, when executed by one or more processors, cause the one or more processors to perform operations comprising:” See at least Claim 17)
determine at least one optical indicium of a substrate based on data received from at least one optical detection system, (“the computer system 112 may determine the CAD file for the target object based on images captured by the image sensor 110 of the target object 102, the first surface 120, and/or markers (machine readable codes) 124. … the computer system 112 may read or scan the information included in markers 124 to identify the CAD file for the target object 102. The computer system 112 may be configured to compare the information specified in the CAD file for the target object 102 to the information detected or identified by the sensor input from sensor system 106.” See at least [0017-0018], wherein a marker is an optical indicium)
the at least one optical detection system comprising at least one laser system mounted on at least one robotic unit, (“FIG. 1 includes a robot 104 that includes a sensor system 106 … the sensor system 106 may be a laser sensor system such as a light detection and ranging (LiDAR) sensor system or other sensors configured to detect distance and rotational orientation of the target object 102 by generating point cloud data, for example.” See at least [0016] and fig. 1)
the at least one optical indicium comprising a shape of the substrate, wherein the substrate is one of a plurality of different types of substrates; determine the shape of the substrate in at least two dimensions based on data received from the at least one laser system; … determine the type of the substrate based on the shape of the substrate in the at least two dimensions; (“At 602, a computer system may generate, based on first sensor data generated by a sensor system of a robot, a three-dimensional (3D) of a surface of a target object, the 3D representation comprising a first set of data points. For example, the sensor system may include a LiDAR sensor system that is configured to generate point cloud data that represents a target object. The point cloud data can be segmented to associate point cloud data points to different objects in the point cloud data. The computer system can extract features from the segmented objects to identify a target object, such as an aircraft panel. In embodiments, the computer system may be configured to associate point cloud data to a target object and computing a normal to and/or from the surface of the target object. The computer system can store a three-dimensional representation (e.g., a point cloud or computer-aided design (CAD) file) of a target object for use in comparison to known target objects and known CAD files for the known target objects.” See at least [0041])
receive at least one pattern (“At 414, the computer system generates a second trajectory for traversing the second surface of the target object” See at least [0033], wherein the trajectory is a pattern.)
and control at least one robotic unit having at least one robot comprising at least one nozzle to apply at least one material to the substrate, wherein the one or more instructions that cause the at least one processor to control the at least one robotic unit, cause the at least one processor to: cause the at least one robotic unit to apply the at least one material to the substrate based on the at least one pattern. (“At 416, the computer system may instruct the robot to move to the starting position, again, and traverse the second surface of the target object using the second trajectory and apply a sealing to the structural property using the end effector dispensing system of the robot.” See at least [0034] and fig. 1, wherein the end effector dispensing system includes a nozzle and it applies a sealing (material) to the target object (substrate).)
Oridate does not explicitly teach, but Yoshida teaches
transmit the at least one optical indicium a request message to at least one edge computing device, the request message comprising the shape of the substrate in the at least two dimensions and a request to determine the type of the substrate, wherein the instructions that cause the at least one processor to transmit the request message to the at least one edge computing device, cause the at least one processor to: cause the at least one edge computing device to determine the type of the substrate based on the shape of the substrate in the at least two dimensions; (“The generated image information is output to the robot controller 120 as detection information and transmitted from a transmitting part 122a of a communication control part 122 described later to the central server 200. … The central server 200 respectively accepts the image information transmitted from the robot controller 120 of each site, performs feature extraction processing (image processing) on the accepted image information, and extracts unique features (patterns) to the image information (details described later). Note that the extracted pattern of the image information links to the processed information and processed image information.” See at least [0019-0020], wherein the central server is equivalent to the edge computing device.)
cause the at least one edge device to access a memory comprising a plurality of patterns to look up at least one pattern of the plurality of patterns associated with the type of the substrate; and cause the at least one edge computing device to transmit the at least one pattern; … receive at least one pattern from the at least one edge computing device; (“Then, the flow proceeds to step SB30 where the control part 201 sequentially collates (matches) the input pattern extracted in the above described step SB20 and the plurality of registered patterns stored in the above described teaching information database 2030 using a suitable known pattern matching (normalized correlation) processing technique. … the control part 201 determines whether or not the plurality of teaching information stored in the teaching information database 2030 includes teaching information in which the correlation degree that indicates the degree of correlation of the related registered pattern with respect to the input pattern is greater than a predetermined value set in advance. … the acquired specific teaching information is transmitted along with correlation degree data that indicates the correlation degree corresponding to the specific teaching information to the storage device 124 of the robot controller 120 of the corresponding site by the communication control part 202 via the network cloud NW1. … the control part 121 receives the specific teaching information and correlation degree data by the receiving part 122b.” See at least [0049-0053], wherein the teaching information is equivalent to the pattern because it specifies movements of the robot according to at least [0055])
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of Oridate to further include the teachings of Yoshida with a reasonable expectation of success to implement a central server (edge computing device) because it facilitates control for a larger number of robots across different sites/facilities (See at least [0014] and [0075]) and improves convenience for robot teaching (See at least [0066]).
Claim(s) 2-3, 8-9, 12-13, and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oridate (US 20240116181 A1) in view of Yoshida (US 20150019012 A1) and Atherton (US 20180307207 A1).
Regarding Claim 2,
Oridate does not explicitly teach, but Atherton teaches
wherein causing the at least one robotic unit to apply the at least one material to the substrate based on the at least one pattern comprises: determining at least one application parameter using the at least one optical detection system while the at least one material is being applied to the substrate, wherein the at least one application parameter comprises at least one of: an amount of the at least one material that has been applied to the substrate; a thickness of the at least one material that has been applied to the substrate; a width of the at least one material that has been applied to the substrate; a position of the at least one material that has been applied to the substrate; or any combination thereof; and continually modifying movement of the at least one robotic unit based on the at least one application parameter and the at least one pattern. (“At step 808, robot system 100 causes optical device 114 to gather optical data representing real-time fabrication of 3D object 120. … In embodiments where optical device 114 is a laser scanner, code generator 210 fits point cloud data generated via that laser scanner to a model of 3D object 120 using an iterative closest-point algorithm, and then evaluates deviations based on differences between the point cloud and the model. In evaluating these deviations, code generator 210 may train an artificial neural network to correlate system-level parameters, such as material feedrate and voltage load, to particular types of deviations. Then, code generator 210 may anticipate deviations and preemptively compensate accordingly.” See at least [0067]; “Robot system 100 may rely on computer vision processor 220 to determine the previous deposition locations, as described above. Then, robot system 100 corrects a subsequent target location based on the difference between the previous deposition location and the corresponding target location.” See at least [0051]; “As shown in FIG. 26B, robot system 100 adjusts deposition rate 2610 to increase proximate to dimple 2602 and decrease in other places. For example, deposition rate 2610(2) is greater than deposition rates 2610(1) and 2610(3) and much greater than deposition rates 2610(0) and 2610(4), as indicated by arrow thickness. Thus, progressing from left to right, deposition rate 2610 increases when approaching dimple 2602 and then decreases afterward. Robot system 100 determines deposition rates 2610 based on frames of video data gathered by optical device 114 and processed by computer vision processor 220.” See at least [0108]; Also see at least [0075-0077] and [0116-0117])
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of modified Oridate to further include the teachings of Atherton with a reasonable expectation of success such “that the robot is capable of tolerating and compensating for a wide variety of potential faults, thereby increasing the likelihood of fabricating a 3D object that meets design specifications.” (See at least [0009])
Regarding Claim 3,
Oridate further teaches
wherein the at least one optical detection system comprises a first optical detection system and a second optical detection system, (“FIG. 1 includes a robot 104 that includes a sensor system 106, an end effector dispensing system 108, and image sensor 110. … the sensor system 106 may be a laser sensor system such as a light detection and ranging (LiDAR) sensor system or other sensors configured to detect distance and rotational orientation of the target object 102 by generating point cloud data.” See at least [0016] and fig. 1 (provided below))
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wherein the second optical detection system has an optical view of the at least one robotic unit while the at least one material is being applied to the substrate, (“The sensor system 106 can continue to obtain sensor input of the target object 102 as the robot 104 uses the end effector dispensing system 108 to apply a sealant to the seam 118 using the updated trajectory.” See at least [0018])
wherein determining the at least one optical indicium of the substrate based on the data received from the at least one optical detection system comprises determining the at least one optical indicium of the substrate based on the data received from the first optical detection system, (“the computer system 112 may determine the CAD file for the target object based on images captured by the image sensor 110 of the target object 102, the first surface 120, and/or markers (machine readable codes) 124. … the computer system 112 may read or scan the information included in markers 124 to identify the CAD file for the target object 102.” See at least [0017-0018], wherein a marker is an optical indicium)
Oridate does not explicitly teach, but Yoshida teaches
wherein the first optical detection system has an optical view of a conveyor that conveys the substrate to the at least one robotic unit, … wherein determining the at least one optical indicium of the substrate based on the data received from the at least one optical detection system comprises determining the at least one optical indicium of the substrate based on the data received from the first optical detection system (“when an image of the work W fed to an area inside the angle of view of the lens 131 by the conveyor 101 is taken by the camera 130, the image information of the work W is generated and the image information is output to the robot controller 120 by the input/output part 133. … Subsequently, in step SB20, the control part 201 performs suitable known feature extraction processing on the image information received in the above described step SB10 based on the common image processing algorithm configured in the above described step SB4. With this arrangement, the pattern of the image information is extracted. Hereinafter, the extracted pattern is suitably referred to as the "input pattern."” See at least [0047-0048] and fig. 3 (provided below))
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It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of modified Oridate to further include the teachings of Yoshida with a reasonable expectation of success to facilitate identification of irregular work objects conveyed to the robot (See at least [0015-16] and [0047-0048]) and to improve convenience for robot teaching (See at least [0065-0066]).
Yoshida also does not explicitly teach, but Atherton teaches
wherein the at least one optical detection system comprises … a second optical detection system, … wherein the second optical detection system has an optical view of the at least one robotic unit while the at least one material is being applied to the substrate, … and wherein determining the at least one application parameter using the at least one optical detection system comprises determining the at least one application parameter using the second optical detection system. (“At step 808, robot system 100 causes optical device 114 to gather optical data representing real-time fabrication of 3D object 120.” See at least [0067] and fig. 1 (provided below); “Robot system 100 may rely on computer vision processor 220 to determine the previous deposition locations, as described above.” See at least [0051])
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It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of modified Oridate and Yoshida to further include the teachings of Atherton with a reasonable expectation of success such “that the robot is capable of tolerating and compensating for a wide variety of potential faults, thereby increasing the likelihood of fabricating a 3D object that meets design specifications.” (See at least [0009])
Regarding Claim 8,
Oridate does not explicitly teach, but Atherton teaches
wherein causing the at least one robotic unit to apply the at least one material to the substrate based on the at least one pattern comprises: continually modifying movement of the at least one robotic unit based on the at least one pattern and based on ongoing data received from the at least one laser system. (“At step 808, robot system 100 causes optical device 114 to gather optical data representing real-time fabrication of 3D object 120. That optical data may include frames 206 of video data and/or point cloud data gathered via laser scanner. … In embodiments where optical device 114 is a laser scanner, code generator 210 fits point cloud data generated via that laser scanner to a model of 3D object 120 using an iterative closest-point algorithm, and then evaluates deviations based on differences between the point cloud and the model. In evaluating these deviations, code generator 210 may train an artificial neural network to correlate system-level parameters, such as material feedrate and voltage load, to particular types of deviations. Then, code generator 210 may anticipate deviations and preemptively compensate accordingly.” See at least [0067]; “Robot system 100 may rely on computer vision processor 220 to determine the previous deposition locations, as described above. Then, robot system 100 corrects a subsequent target location based on the difference between the previous deposition location and the corresponding target location.” See at least [0051]; Also see at least [0075-0077] and [0116-0117])
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of modified Oridate to further include the teachings of Atherton with a reasonable expectation of success such “that the robot is capable of tolerating and compensating for a wide variety of potential faults, thereby increasing the likelihood of fabricating a 3D object that meets design specifications.” (See at least [0009])
Regarding Claim 9,
Oridate does not explicitly teach, but Atherton teaches
wherein the at least one laser system functions as a locator sensor and the ongoing data comprises position data of the at least one material that has been applied to the substrate, (“At step 810, computer vision processor 220 within closed-loop control process 200 of robot system 100 processes the optical data gathered at step 808 to determine one or more locations where robot 110 deposited material onto 3D object 120. Code generator 210 processes these locations to identify deviation of real-time fabrication from the fabrication procedure defined in fabrication data 202. In embodiments where optical device 114 is a laser scanner, code generator 210 fits point cloud data generated via that laser scanner to a model of 3D object 120 using an iterative closest-point algorithm, and then evaluates deviations based on differences between the point cloud and the model.” See at least [0067]; “Robot system 100 may rely on computer vision processor 220 to determine the previous deposition locations, as described above.” See at least [0051])
and wherein continually modifying the movement of the at least one robotic unit based on the at least one pattern and based on the ongoing data comprises: generating an application trajectory based on the ongoing data; comparing the application trajectory to a predetermined trajectory of the at least one pattern; and modifying the movement of the at least one robotic unit to adjust the application trajectory to be closer to the predetermined trajectory. (“Code generator 210 processes tracking data 208 and compares the specific locations where robot 110 deposited material to the set of target locations where robot 110 was instructed to deposit material. Code generator 210 analyzes the difference between each deposition location and the corresponding target location, and then updates control code 204 to compensate for those differences.” See at least [0048]; “Plot 300 includes exemplary fabrication data 310 that includes a guide curve 312. Guide curve 312 indicates a fabrication pathway that robot 110 should follow during material deposition. Vertices V0, V1, V2 and V3 are disposed at intervals along guide curve 312. Each vertex is a target location where robot 110 should deposit material. Tangent vectors T0, T1, T2, and T3 are also disposed along guide curve 312 and associated with vertices V0, V1, V2, and V3, respectively. Each tangent vector is tangent to guide curve 312 at the corresponding vertex. Robot system 110 is configured to generate the set of vertices and tangent vectors based on fabrication data 310 and/or update those vertices and vectors based on real-time feedback. Robot system 110 may then generate or update control code 204 for robot 110 by processing these vertices and tangent vectors to compensate for deviations from guide curve 312. … To generate a given vertex, robot system compares a previous deposition location to the target location associated with that deposition location. Robot system 100 may rely on computer vision processor 220 to determine the previous deposition locations, as described above. Then, robot system 100 corrects a subsequent target location based on the difference between the previous deposition location and the corresponding target location.” See at least [0050-0051] and figs. 3-4; Examiner Interpretation: Acquiring the locations of deposited material from real time feedback generates an application trajectory based on the ongoing data. This is compared to target locations or a guide curve which is equivalent to comparing the application trajectory to a predetermined trajectory of the at least one pattern. Compensating/correcting for deviations is equivalent to modifying the movement of the at least one robotic unit to adjust the application trajectory to be closer to the predetermined trajectory.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of modified Oridate to further include the teachings of Atherton with a reasonable expectation of success such “that the robot is capable of tolerating and compensating for a wide variety of potential faults, thereby increasing the likelihood of fabricating a 3D object that meets design specifications.” (See at least [0009])
Regarding Claim 12,
Oridate does not explicitly teach, but Atherton teaches
wherein, when causing the at least one robotic unit to apply the at least one material to the substrate based on the at least one pattern, the at least one processor is programmed or configured to: determine at least one application parameter using the at least one optical detection system while the at least one material is being applied to the substrate, wherein the at least one application parameter comprises at least one of: an amount of the at least one material that has been applied to the substrate; a thickness of the at least one material that has been applied to the substrate; a width of the at least one material that has been applied to the substrate; a position of the at least one material that has been applied to the substrate; or any combination thereof; and continually modify movement of the at least one robotic unit based on the at least one application parameter and the at least one pattern. (“At step 808, robot system 100 causes optical device 114 to gather optical data representing real-time fabrication of 3D object 120. … In embodiments where optical device 114 is a laser scanner, code generator 210 fits point cloud data generated via that laser scanner to a model of 3D object 120 using an iterative closest-point algorithm, and then evaluates deviations based on differences between the point cloud and the model. In evaluating these deviations, code generator 210 may train an artificial neural network to correlate system-level parameters, such as material feedrate and voltage load, to particular types of deviations. Then, code generator 210 may anticipate deviations and preemptively compensate accordingly.” See at least [0067]; “Robot system 100 may rely on computer vision processor 220 to determine the previous deposition locations, as described above. Then, robot system 100 corrects a subsequent target location based on the difference between the previous deposition location and the corresponding target location.” See at least [0051]; “As shown in FIG. 26B, robot system 100 adjusts deposition rate 2610 to increase proximate to dimple 2602 and decrease in other places. For example, deposition rate 2610(2) is greater than deposition rates 2610(1) and 2610(3) and much greater than deposition rates 2610(0) and 2610(4), as indicated by arrow thickness. Thus, progressing from left to right, deposition rate 2610 increases when approaching dimple 2602 and then decreases afterward. Robot system 100 determines deposition rates 2610 based on frames of video data gathered by optical device 114 and processed by computer vision processor 220.” See at least [0108]; Also see at least [0075-0077] and [0116-0117])
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of modified Oridate to further include the teachings of Atherton with a reasonable expectation of success such “that the robot is capable of tolerating and compensating for a wide variety of potential faults, thereby increasing the likelihood of fabricating a 3D object that meets design specifications.” (See at least [0009])
Regarding Claim 13,
Oridate further teaches
wherein the at least one optical detection system comprises a first optical detection system and a second optical detection system, (“FIG. 1 includes a robot 104 that includes a sensor system 106, an end effector dispensing system 108, and image sensor 110. … the sensor system 106 may be a laser sensor system such as a light detection and ranging (LiDAR) sensor system or other sensors configured to detect distance and rotational orientation of the target object 102 by generating point cloud data.” See at least [0016] and fig. 1)
wherein the second optical detection system has an optical view of the at least one robotic unit while the at least one material is being applied to the substrate, (“The sensor system 106 can continue to obtain sensor input of the target object 102 as the robot 104 uses the end effector dispensing system 108 to apply a sealant to the seam 118 using the updated trajectory.” See at least [0018])
wherein while determining the at least one optical indicium of the substrate based on the data received from the at least one optical detection system, the at least one processor is programmed or configured to determine the at least one optical indicium of the substrate based on the data received from the first optical detection system, (“the computer system 112 may determine the CAD file for the target object based on images captured by the image sensor 110 of the target object 102, the first surface 120, and/or markers (machine readable codes) 124. … the computer system 112 may read or scan the information included in markers 124 to identify the CAD file for the target object 102.” See at least [0017-0018], wherein a marker is an optical indicium)
Oridate does not explicitly teach, but Yoshida teaches
wherein the first optical detection system has an optical view of a conveyor that conveys the substrate to the at least one robotic unit, … wherein while determining the at least one optical indicium of the substrate based on the data received from the at least one optical detection system, the at least one processor is programmed or configured to determine the at least one optical indicium of the substrate based on the data received from the first optical detection system, (“when an image of the work W fed to an area inside the angle of view of the lens 131 by the conveyor 101 is taken by the camera 130, the image information of the work W is generated and the image information is output to the robot controller 120 by the input/output part 133. … Subsequently, in step SB20, the control part 201 performs suitable known feature extraction processing on the image information received in the above described step SB10 based on the common image processing algorithm configured in the above described step SB4. With this arrangement, the pattern of the image information is extracted. Hereinafter, the extracted pattern is suitably referred to as the "input pattern."” See at least [0047-0048] and fig. 3)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of modified Oridate to further include the teachings of Yoshida with a reasonable expectation of success to facilitate identification of irregular work objects conveyed to the robot (See at least [0015-16] and [0047-0048]) and to improve convenience for robot teaching (See at least [0065-0066]).
Yoshida also does not explicitly teach, but Atherton teaches
wherein the at least one optical detection system comprises … a second optical detection system, … wherein the second optical detection system has an optical view of the at least one robotic unit while the at least one material is being applied to the substrate, … and wherein while determining the at least one application parameter using the at least one optical detection system, the at least one processor is programmed or configured to determine the at least one application parameter using the second optical detection system. (“At step 808, robot system 100 causes optical device 114 to gather optical data representing real-time fabrication of 3D object 120.” See at least [0067] and fig. 1; “Robot system 100 may rely on computer vision processor 220 to determine the previous deposition locations, as described above.” See at least [0051])
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of modified Oridate and Yoshida to further include the teachings of Atherton with a reasonable expectation of success such “that the robot is capable of tolerating and compensating for a wide variety of potential faults, thereby increasing the likelihood of fabricating a 3D object that meets design specifications.” (See at least [0009])
Regarding Claim 17,
Oridate does not explicitly teach, but Atherton teaches
wherein the one or more instructions that cause the at least one processor to cause the at least one robotic unit to apply the at least one material to the substrate based on the at least one pattern, cause the at least one processor to: determine at least one application parameter using the at least one optical detection system while the at least one material is being applied to the substrate, wherein the at least one application parameter comprises at least one of: an amount of the at least one material that has been applied to the substrate; a thickness of the at least one material that has been applied to the substrate; a width of the at least one material that has been applied to the substrate; a position of the at least one material that has been applied to the substrate; or any combination thereof; and continually modify movement of the at least one robotic unit based on the at least one application parameter and the at least one pattern. (“At step 808, robot system 100 causes optical device 114 to gather optical data representing real-time fabrication of 3D object 120. … In embodiments where optical device 114 is a laser scanner, code generator 210 fits point cloud data generated via that laser scanner to a model of 3D object 120 using an iterative closest-point algorithm, and then evaluates deviations based on differences between the point cloud and the model. In evaluating these deviations, code generator 210 may train an artificial neural network to correlate system-level parameters, such as material feedrate and voltage load, to particular types of deviations. Then, code generator 210 may anticipate deviations and preemptively compensate accordingly.” See at least [0067]; “Robot system 100 may rely on computer vision processor 220 to determine the previous deposition locations, as described above. Then, robot system 100 corrects a subsequent target location based on the difference between the previous deposition location and the corresponding target location.” See at least [0051]; “As shown in FIG. 26B, robot system 100 adjusts deposition rate 2610 to increase proximate to dimple 2602 and decrease in other places. For example, deposition rate 2610(2) is greater than deposition rates 2610(1) and 2610(3) and much greater than deposition rates 2610(0) and 2610(4), as indicated by arrow thickness. Thus, progressing from left to right, deposition rate 2610 increases when approaching dimple 2602 and then decreases afterward. Robot system 100 determines deposition rates 2610 based on frames of video data gathered by optical device 114 and processed by computer vision processor 220.” See at least [0108]; Also see at least [0075-0077] and [0116-0117])
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of modified Oridate to further include the teachings of Atherton with a reasonable expectation of success such “that the robot is capable of tolerating and compensating for a wide variety of potential faults, thereby increasing the likelihood of fabricating a 3D object that meets design specifications.” (See at least [0009])
Regarding Claim 18,
Oridate further teaches
wherein the at least one optical detection system comprises a first optical detection system and a second optical detection system, (“FIG. 1 includes a robot 104 that includes a sensor system 106, an end effector dispensing system 108, and image sensor 110. … the sensor system 106 may be a laser sensor system such as a light detection and ranging (LiDAR) sensor system or other sensors configured to detect distance and rotational orientation of the target object 102 by generating point cloud data.” See at least [0016] and fig. 1)
wherein the second optical detection system has an optical view of the at least one robotic unit while the at least one material is being applied to the substrate, (“The sensor system 106 can continue to obtain sensor input of the target object 102 as the robot 104 uses the end effector dispensing system 108 to apply a sealant to the seam 118 using the updated trajectory.” See at least [0018])
wherein the one or more instructions that cause the at least one processor to determine the at least one optical indicium of the substrate based on the data received from the at least one optical detection system cause the at least one processor to determine the at least one optical indicium of the substrate based on the data received from the first optical detection system, (“the computer system 112 may determine the CAD file for the target object based on images captured by the image sensor 110 of the target object 102, the first surface 120, and/or markers (machine readable codes) 124. … the computer system 112 may read or scan the information included in markers 124 to identify the CAD file for the target object 102.” See at least [0017-0018], wherein a marker is an optical indicium)
Oridate does not explicitly teach, but Yoshida teaches
wherein the first optical detection system has an optical view of a conveyor that conveys the substrate to the at least one robotic unit, … wherein the one or more instructions that cause the at least one processor to determine the at least one optical indicium of the substrate based on the data received from the at least one optical detection system cause the at least one processor to determine the at least one optical indicium of the substrate based on the data received from the first optical detection system, (“when an image of the work W fed to an area inside the angle of view of the lens 131 by the conveyor 101 is taken by the camera 130, the image information of the work W is generated and the image information is output to the robot controller 120 by the input/output part 133. … Subsequently, in step SB20, the control part 201 performs suitable known feature extraction processing on the image information received in the above described step SB10 based on the common image processing algorithm configured in the above described step SB4. With this arrangement, the pattern of the image information is extracted. Hereinafter, the extracted pattern is suitably referred to as the "input pattern."” See at least [0047-0048] and fig. 3)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of modified Oridate to further include the teachings of Yoshida with a reasonable expectation of success to facilitate identification of irregular work objects conveyed to the robot (See at least [0015-16] and [0047-0048]) and to improve convenience for robot teaching (See at least [0065-0066]).
Yoshida also does not explicitly teach, but Atherton teaches
wherein the at least one optical detection system comprises … a second optical detection system, … wherein the second optical detection system has an optical view of the at least one robotic unit while the at least one material is being applied to the substrate, … and wherein the one or more instructions that cause the at least one processor to determine the at least one application parameter using the at least one optical detection system cause the at least one processor to determine the at least one application parameter using the second optical detection system. (“At step 808, robot system 100 causes optical device 114 to gather optical data representing real-time fabrication of 3D object 120.” See at least [0067] and fig. 1; “Robot system 100 may rely on computer vision processor 220 to determine the previous deposition locations, as described above.” See at least [0051])
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of modified Oridate and Yoshida to further include the teachings of Atherton with a reasonable expectation of success such “that the robot is capable of tolerating and compensating for a wide variety of potential faults, thereby increasing the likelihood of fabricating a 3D object that meets design specifications.” (See at least [0009])
Claim(s) 4, 14, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oridate (US 20240116181 A1) in view of Yoshida (US 20150019012 A1) and Heldt (US 20090304940 A1).
Regarding Claim 4,
Oridate further teaches
wherein the at least one robotic unit comprises a first robotic unit causing the first robotic unit to apply the first material to the substrate based on a first pattern of the at least one pattern; (“At 416, the computer system may instruct the robot to move to the starting position, again, and traverse the second surface of the target object using the second trajectory and apply a sealing to the structural property using the end effector dispensing system of the robot.” See at least [0034] and fig. 1, wherein the end effector dispensing system includes a nozzle and it applies a sealing (material) to the target object (substrate).)
Oridate does not explicitly teach, but Heldt teaches
wherein the at least one robotic unit comprises a first robotic unit and a second robotic unit, wherein the first robotic unit comprises a first robot comprising a first nozzle for applying a first material of the at least one material to the substrate, wherein the second robotic unit comprises a second robot comprising a second nozzle for applying a second material of the at least one material to the substrate, wherein the second material is a different material from the first material, and wherein controlling the at least one robotic unit to apply the at least one material to the substrate comprises: causing the first robotic unit to apply the first material to the substrate based on a first pattern of the at least one pattern; and causing the second robotic unit to apply the second material to the substrate based on a second pattern of the at least one pattern. (“The robots may be generally grouped for application of various different coating materials, such as paint. In another example, robots 102a, 102d, and 102i may cooperate to apply a first primer coat to both the interior and exterior portion of the body 200. Further, robots 102b 102g and 102j may be grouped together to apply a primer coat of paint to both underhood (interior) and deck areas (exterior) of the body 200. Each of robots 102c, 102e, 102f, and 102h may be grouped for application of an exterior base coat layer to the body 200, e.g., to interior and exterior surfaces of a motor vehicle.” See at least [0025]; Also see at least [0034] and fig. 2 (provided below))
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It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of modified Oridate to further include the teachings of Heldt with a reasonable expectation of success to implement a plurality of robots operating simultaneously to improve operational efficiency and reduce costs. (See at least [0002-0004], [0014], and [0034])
Regarding Claim 14,
Oridate further teaches
wherein the at least one robotic unit comprises a first robotic unit when controlling the at least one robotic unit to apply the at least one material to the substrate, the at least one processor is programmed or configured to: cause the first robotic unit to apply the first material to the substrate based on a first pattern of the at least one pattern; (“At 416, the computer system may instruct the robot to move to the starting position, again, and traverse the second surface of the target object using the second trajectory and apply a sealing to the structural property using the end effector dispensing system of the robot.” See at least [0034] and fig. 1, wherein the end effector dispensing system includes a nozzle and it applies a sealing (material) to the target object (substrate).)
Oridate does not explicitly teach, but Heldt teaches
wherein the at least one robotic unit comprises a first robotic unit and a second robotic unit, wherein the first robotic unit comprises a first robot comprising a first nozzle for applying a first material of the at least one material to the substrate, wherein the second robotic unit comprises a second robot comprising a second nozzle for applying a second material of the at least one material to the substrate, wherein the second material is a different material from the first material, and wherein, when controlling the at least one robotic unit to apply the at least one material to the substrate, the at least one processor is programmed or configured to: cause the first robotic unit to apply the first material to the substrate based on a first pattern of the at least one pattern; and cause the second robotic unit to apply the second material to the substrate based on a second pattern of the at least one pattern. (“The robots may be generally grouped for application of various different coating materials, such as paint. In another example, robots 102a, 102d, and 102i may cooperate to apply a first primer coat to both the interior and exterior portion of the body 200. Further, robots 102b 102g and 102j may be grouped together to apply a primer coat of paint to both underhood (interior) and deck areas (exterior) of the body 200. Each of robots 102c, 102e, 102f, and 102h may be grouped for application of an exterior base coat layer to the body 200, e.g., to interior and exterior surfaces of a motor vehicle.” See at least [0025]; Also see at least [0034] and fig. 2)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of modified Oridate to further include the teachings of Heldt with a reasonable expectation of success to implement a plurality of robots operating simultaneously to improve operational efficiency and reduce costs. (See at least [0002-0004], [0014], and [0034])
Regarding Claim 19,
Oridate further teaches
wherein the at least one robotic unit comprises a first robotic unit (“At 416, the computer system may instruct the robot to move to the starting position, again, and traverse the second surface of the target object using the second trajectory and apply a sealing to the structural property using the end effector dispensing system of the robot.” See at least [0034] and fig. 1, wherein the end effector dispensing system includes a nozzle and it applies a sealing (material) to the target object (substrate).)
Oridate does not explicitly teach, but Heldt teaches
wherein the at least one robotic unit comprises a first robotic unit and a second robotic unit, wherein the first robotic unit comprises a first robot comprising a first nozzle for applying a first material of the at least one material to the substrate, wherein the second robotic unit comprises a second robot comprising a second nozzle for applying a second material of the at least one material to the substrate, wherein the second material is a different material from the first material, and wherein the one or more instructions that cause the at least one processor to control the at least one robotic unit to apply the at least one material to the substrate cause the at least one processor to: cause the first robotic unit to apply the first material to the substrate based on a first pattern of the at least one pattern; and cause the second robotic unit to apply the second material to the substrate based on a second pattern of the at least one pattern. (“The robots may be generally grouped for application of various different coating materials, such as paint. In another example, robots 102a, 102d, and 102i may cooperate to apply a first primer coat to both the interior and exterior portion of the body 200. Further, robots 102b 102g and 102j may be grouped together to apply a primer coat of paint to both underhood (interior) and deck areas (exterior) of the body 200. Each of robots 102c, 102e, 102f, and 102h may be grouped for application of an exterior base coat layer to the body 200, e.g., to interior and exterior surfaces of a motor vehicle.” See at least [0025]; Also see at least [0034] and fig. 2)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of modified Oridate to further include the teachings of Heldt with a reasonable expectation of success to implement a plurality of robots operating simultaneously to improve operational efficiency and reduce costs. (See at least [0002-0004], [0014], and [0034])
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oridate (US 20240116181 A1) in view of Yoshida (US 20150019012 A1) and Ooe (US 20200368770 A1).
Regarding Claim 10,
Oridate does not explicitly teach, but Ooe teaches
wherein the substrate is an automotive glass substrate and the at least one material comprises a primer. (“applying glass primer coatings onto the marginal edge of the windshield or other glass elements of the vehicle.” See at least col. 1, lines 9-11)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify the teachings of modified Oridate to further include the teachings of Ooe to implement the robotic system/method to apply primer to automotive glass with a reasonable expectation of success to automate and improve quality of assembling glass elements of a vehicle. (See at least the Background, col. 1, line 15 through col. 3, line 16)
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/K.G.E./Examiner, Art Unit 3657 /ABBY LIN/Supervisory Patent Examiner, Art Unit 3657