DETAILED ACTION
EXAMINER’S AMENDMENT
An interview was conducted and proposed an Examiner’s amendment to the claims in a condition for allowance, but did not result in an authorization.
Response to Amendment
Amendment filed on 07 August 2026 has been entered. Claims 1 and 4-11 are now pending in the application.
Amendments to the specification and claims 1 and 11 to overcome the informalities are acceptable. Therefore, the specification and claim objections have been withdrawn.
Amendments to the claims 1 and 11 to overcome the rejections under U.S.C 112(b) have been fully considered and the rejection under 35 U.S.C. 112(b) of claims 1 and 11 has been withdrawn.
Response to Arguments
Applicant's arguments with respect to claim(s) 1-4, 6, 7 and 9-11 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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, 4, 6-7, 9 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Yong (KR 20180050945) in view of Mariyama (US 20190375112).
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Annotated Fig. 1, Yong.
Regarding claim 1, Yong teaches, a method for monitoring or controlling handling systems (substrate processing apparatus 1, Fig. 1, a camera to recognize real parts in a chamber and track a movement, Abstract), comprising:
providing a robot (index robot 144, Fig. 1), a moving element (index rail 142, the index robot (144) is installed on the index rail (142) and moves in a straight line along the index rail (142) in the second direction (14), para. [0043]), robot arm (index arm 144c), an end effector, a positioning unit (base 144a), or any combination thereof, having means for gripping (index robot (144) has a base (144a), a body (144b), and an index arm (144c), para. [0043]);
capturing, with an image acquisition unit (imaging member 310), at least one image in a digitized form depicting a location in or on the handling system or in the environment of the handling system (imaging member (310) can obtain an image by using a camera to capture the inside of the chamber, para. [0052], the process monitoring unit (340) can recognize information on the presence or absence or location of each part and chemical substance during the process using an image model
obtained from the imaging member (310) during the process, para. [0064], it is obvious that the image in digitized form);
analyzing, with an artificial neural network of a processing unit (chamber state model generation unit 320, alternatively, process chamber monitor 300, Fig. 2, generate a chamber state model including the chamber state information before the process starts, para. [0054]), the at least one image; and generating, with the artificial neural network, an information data set and/or a control command, wherein the analyzing comprises determining at least one of: a presence, an absence, an orientation, and a position of the substrate (imaging member (310) can recognize parts from an image obtained by performing image learning using a machine learning algorithm such as…Convolutional Neural Network (CNN)…imaging member (310) can recognize the…substrate, robot, arm,…from the image obtained through the above method, para. [0052-0053], the process monitoring unit (340) can recognize information on the presence or absence or location of each part…obtained from the imaging member (310) during the process, para. [0064]);
wherein the processing unit uses the information data set and/or the control command (the chamber state model generation unit (320) can generate a chamber state model by obtaining the chamber state information from the imaging member (310), para. [0054]) for:
directly controlling one of the robot, the moving element, the robot arm, the end effector, the positioning unit, to directly control at least one of: gripping, transporting, and depositing of the substrate (use a camera to recognize actual parts within a chamber and track their movement to detect abnormalities, para. [0034], the index arms (144c) are used to transport the substrate (W) from the process module (20) to the carrier (130), and other parts of them can be used to transport the substrate (W) from the carrier (130) to the process module (20),…the substrate transfer assembly (100) moves along the provided rail and transfers the substrate, para. [0043, 0047]).
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Annotated Fig. 2, Mariyama.
Yong does not explicitly teach, directly controlling one of the robot, the moving element, the robot arm. However, Mariyama teaches, in Figs. 1 and 2, a method for monitoring or controlling handling systems including, providing a robot, a moving element, robot arm (robot arm 100, Fig. 1), an end effector (gripping unit 101), a positioning unit, having means for gripping, transporting and/or depositing a substrate (para. [0062]); capturing, with an image acquisition unit (imaging unit 201, Fig. 2), at least one image in a digitized form depicting a location in or on the handling system or in the environment of the handling system; analyzing, with an artificial neural network of a processing unit, the at least one image (control parameter generation unit 202 to generate a position control amount of the robot arm 100 by using the captured images, Fig. 2, para. [0029]); and generating, with the artificial neural network (para. [0039]), an information data set and/or a control command; and directly controlling one of the robot, the moving element, the robot arm (step S104, para. [0058], a plurality of data sets of an image and a required amount of movement are collected; this is a preparation for enabling the neural network to calculate the amount of movement until successful fitting using the input image, para. [0043]).
Yong teaches, in para. [0051], imaging member (310) can capture the inside of the chamber to obtain chamber state information, and in para. [0062], process monitoring unit (340) can compare the chamber state information obtained during the process according to the process recipe with the process reference model to determine whether the current chamber state information deviates from the process reference model to a certain extent within a reference range, and from para. [0064], the process monitoring unit (340) can recognize information on the presence or absence or location of each part and chemical substance during the process using an image model obtained from the imaging member (310) during the process. Mariyama teaches in Fig. 2, step S104, directly controlling one of the robot, the moving element, the robot arm. Therefore, in view of the teachings of Mariyama, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the method for monitoring or controlling the handling system of Yong and to directly control the index robot 144 of Yong to transport the substrate as Mariyama disclosed in Fig. 1 so that it enables minimizing the errors by calculating the positional relationship as Mariyam disclosed in para. [0057-0058].
Regarding claim 4, Yong in view of Mariyama teaches the recited limitations with respect to claim 1. Yong further teaches, the method according to claim 1, wherein the information data set contains information about a type of object in the at least one image, including a presence of: trays, cassettes, parts, markings, stickers, labels, reference marks, or any combination thereof (imaging member (310) can recognize parts from an image obtained by performing image learning using a machine learning algorithm such as…Convolutional Neural Network (CNN)…imaging member (310) can recognize the…substrate, robot, arm,…from the image obtained through the above method, para. [0052-0053]).
Regarding claim 6, Yong in view of Mariyama teaches the recited limitations with respect to claim 1. Yong further teaches, the method according to claim 1, wherein the information data set contains information about a presence of processing stations (imaging member (310) can recognize the nozzle, substrate, robot, arm, chemical discharge, etc. inside the chamber from the image obtained through the above method and obtain location information and presence information of each part, para. [0053]).
Regarding claim 7, Yong in view of Mariyama teaches the recited limitations with respect to claim 1. Yong further teaches, the method according to claim 1, wherein the information data set contains information about spacing between an at least one object in the at least one image to a reference point of the handling system (process reference model generating unit that generates a process reference model based on a plurality of chamber state information repeatedly obtained from the imaging member…and determines whether the degree of deviation from the process reference model is within a reference range, para. [0009]).
Regarding claim 9, Yong in view of Mariyama teaches the recited limitations with respect to claim 1. Yong further teaches, the method according to claim 1, wherein the capturing of the at least one image includes storing the at least one image for use in at least one initial learning process or at least one new learning process, to improve a result of the artificial neural network (capturing the inside of a chamber to obtain chamber state information including location information of a component inside the chamber; generating a process reference model based on a plurality of chamber state information obtained by repeatedly capturing the inside of the chamber when the process is repeated according to an input process recipe, para. [0083]).
Regarding claim 11, Yong teaches, a handling system or machine or processing substrates including semiconductor wafers, masks, flat panel displays, or any combination thereof, the handling system or, or machine (substrate processing apparatus 1, Fig. 1, a camera to recognize real parts in a chamber and track a movement, Abstract) comprising:
a semi-conductor industry machine (substrate processing apparatus 1, para. [0029]), a robot (index robot 144, Fig. 1), a moving element (index rail 142, the index robot (144) is installed on the index rail (142) and moves in a straight line along the index rail (142) in the second direction (14), para. [0043]), a robot arm, (index arm 144c) an end effector, a positioning unit (base 144a), or any combination thereof;
a processing unit (chamber state model generation unit 320, alternatively, process chamber monitor 300, Fig. 2) including at least one trained artificial neural network (Convolutional Neural Network (CNN), para. [0052]); and an acquisition unit comprising an image acquisition unit (imaging member 310, imaging member (310) can obtain an image by using a camera to capture the inside of the chamber, para. [0052], the process monitoring unit (340) can recognize information on the presence or absence or location of each part and chemical substance during the process using an image model obtained from the imaging member (310) during the process, para. [0064]) for capturing at least one image in a digitized form depicting a location in or on the handling system or in an environment of the handling system;
wherein the artificial neural network of the processing unit: captures at least one image; analyzes the at least one image to determine at least one of a presence, an absence, an orientation, and a position of a substrate (imaging member (310) can recognize parts from an image obtained by performing image learning using a machine learning algorithm such as…Convolutional Neural Network (CNN)…imaging member (310) can recognize the…substrate, robot, arm,…from the image obtained through the above method, para. [0052-0053], the process monitoring unit (340) can recognize information on the presence or absence or location of each part…obtained from the imaging member (310) during the process, para. [0064]); and
generates an information data set and/or a control command; and wherein the processing unit uses the information data set and/or the control command to directly control one of the robot, the moving element, the robot arm, the end effector, and the positioning unit, so as to directly control at least one of gripping, transporting, and depositing of the substrate (use a camera to recognize actual parts within a chamber and track their movement to detect abnormalities, para. [0034], the index arms (144c) are used to transport the substrate (W) from the process module (20) to the carrier (130), and other parts of them can be used to transport the substrate (W) from the carrier (130) to the process module (20),…the substrate transfer assembly (100) moves along the provided rail and transfers the substrate, para. [0043, 0047]).
Yong does not explicitly teach, directly controlling one of the robot, the moving element, the robot arm. However, Mariyama teaches, in Figs. 1 and 2, a method for monitoring or controlling handling systems including, providing a robot, a moving element, robot arm (robot arm 100, Fig. 1), an end effector (gripping unit 101), a positioning unit, having means for gripping, transporting and/or depositing a substrate (para. [0062]); capturing, with an image acquisition unit (imaging unit 201, Fig. 2), at least one image in a digitized form depicting a location in or on the handling system or in the environment of the handling system; analyzing, with an artificial neural network of a processing unit, the at least one image (control parameter generation unit 202 to generate a position control amount of the robot arm 100 by using the captured images, Fig. 2, para. [0029]); and generating, with the artificial neural network (para. [0039]), an information data set and/or a control command; and directly controlling one of the robot, the moving element, the robot arm (step S104, para. [0058], a plurality of data sets of an image and a required amount of movement are collected; this is a preparation for enabling the neural network to calculate the amount of movement until successful fitting using the input image, para. [0043]). Therefore, in view of the teachings of Mariyama, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the method for monitoring or controlling the handling system of Yong and to directly control the index robot 144 of Yong to transport the substrate as Mariyama disclosed in Fig. 1 so that it enables minimizing the errors by calculating the positional relationship as Mariyam disclosed in para. [0057-0058].
Claim(s) 5 is rejected under 35 U.S.C. 103 as being unpatentable over Yong in view of Mariama as applied to claim 1 above, and further in view of Fukutome (US 20210150241).
Regarding claim 5, modified Yong does not teach, information data set contains information about possible obstacles. However, Fukutome teaches, a semiconductor processing machine comprising an imaging device and a plurality of neural network processing units, in which, the method according to claim 1, wherein the information data set contains information about possible obstacles in a movement area of the handling system, including doors or load locks (an obstacle exists or not in the direction where the robot 2100 advances with the moving mechanism 2108, para. [0433]). Therefore, in view of the teachings of Fukutome, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the method for monitoring or controlling handling system of Yong and to include an obstacle detection system so that it enables the robot handling system to move safely by recognizing the surrounding environment using the processed image.
Claim(s) 8 is rejected under 35 U.S.C. 103 as being unpatentable over Yong in view of Mariama as applied to claim 1 above, and further in view of Koopman (WO 2019162204, see US 20210374936 for English Translation).
Regarding claim 8, modified Yong does not teach, the information data set contains information about dimensions of an object. However, Koopman teaches, a method for monitoring or controlling handlings systems of a semiconductor industry machine, including providing a robot, capturing image and processing with artificial neural network, in which, the method according to claim 1, wherein the information data set contains information about dimensions of an object in the at least one image, wherein the object includes substrates and/or parts of substrates (one or more physical sizes, one or more physical dimensions, etc., para. [0117]). Therefore, in view of the teachings of Koopman, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to modify the method for monitoring or controlling handling system of Yong and to include a physical size detection so that it enables to select desirable processing steps while manufacturing.
Allowable Subject Matter
Claim 10 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is an examiner’s statement of reasons for indicating allowable subject matter:
Claim 10 would be allowable for disclosing a method for monitoring or controlling handling systems, wherein the trained artificial neural network uses geometric methods including triangulation, to determine the position and/or orientation and/or spacing and/or dimensions of the object.
Though, prior art of records Yong and Mariyama teach, a method for monitoring or controlling handling systems, including analyzing with an artificial neural network of a processing unit, the at least one image, Yong and Mariyama fail to teach, the trained artificial neural network uses geometric methods including triangulation, to determine the position and/or orientation and/or spacing and/or dimensions of the object.
Prior art of record Fukutome or Kooperman does not teach, geometric methods including triangulation to determine the position and/or orientation and/or spacing and/or dimensions of the object.
Therefore, claim 10 would be allowable.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSE K. ABRAHAM whose telephone number is (571)270-1087. The examiner can normally be reached Monday-Friday 8:30-4:30 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, THOMAS J. HONG can be reached at (571) 272-0993. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOSE K ABRAHAM/Examiner, Art Unit 3729
