DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 1 and 18 are objected to because of the following informalities:
It is suggested to put “;” at the end each limitation instead of “,”.
Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 3, 11, 18, and 20 is/are rejected under 35 U.S.C. 102(a)(1) as anticipated by Vogelsong et al. (US10926408B1 -hereinafter Vogelsong).
Regarding Claim 1, Vogelsong teaches a computer-implemented method comprising:
training a reinforcement learning model that is installed in a controller of equipment (see Abstract; Vogelsong: “A machine learning system builds and uses control policies for controlling robotic performance of a task. Such control policies may be trained using targeted updates, for example by comparing two trials to identify which represents a greater degree of task success, using this to generate updates from a reinforcement learning system, and weighting the updates based on differences between action vectors of the trials.”), the training comprising:
inputting, into the reinforcement learning model, a desired output of a first operation to be performed via the equipment, (see column 19, lines 43-51; Vogelsong: “At step 2 of fast interactive ML process, the robotic control system 206 takes one sample action from the current policy network, and sets it as option A. This is an adaptation from general learning step 2 (block 415 of the process 400), but uses just one sample—the fast interactive ML process is trying to be as data-efficient as possible and is typically not used for training complex policies, so there might just be a dummy observation. The “A” option will be the “anchor” or “best-yet” option.”) [The option A reads on ‘the desired output’]
causing the equipment to perform a manufacturing micro-action, (see column 19, lines 52-56; Vogelsong: “At step 3 of fast interactive ML process, the robotic control system 206 takes one sample action from the current policy network, and sets it as option B. This is an adaptation from general learning step 2, but uses just one sample as the “new” or “exploration” option.”)
recording feedback from one or more sensors after the performance of the micro-action, (see column 11, lines 6-17; Vogelsong: “The feedback engine 214 can be configured in some implementations to elicit or receive feedback from a human observer on virtual or real-world performance trials, for example by outputting a suitable user interface and identifying feedback provided through the interface. This feedback may be an “AB comparison” preference where the human indicates which of two performances of the task was more successful, as depicted in block 170 of FIG. 1B.”)
comparing the feedback to the desired output to generate a score that is based on a closeness of the feedback to the desired output, (see column 13, lines 35-38; Vogelsong: “Recordings of these simulated trials are provided to the feedback engine 214, which generates success/reward scores or outputs comparison preferences indicating which of a number of performances was more successful.”)
updating a policy of the reinforcement learning model based on the score, and (see column 11, line 46-48; Vogelsong: “The reinforcement learning module 250 can be configured to optimize the policy for a particular task based on reward values output from the reward function 236.”)
iteratively repeating micro-actions, feedback recording, comparison-based score generation, and policy updating multiple times such that the reinforcement learning model becomes a trained reinforcement learning model for guiding actions of the equipment. (see column 13, lines 44-52; Vogelsong: “The robotic control system 206 can repeat this loop until the robotic control policy 236 achieves the desired performance level within the simulated environment 230. The machine learning system 218 can implement the targeted update process 100 of FIGS. 1A and 1B using recorded observations 232 of simulated trials to iteratively update the policy until it achieves satisfactory performance in the simulated environment 305, for example consistent success at the task goal.”)
Regarding Claim 3, Vogelsong teaches all the limitations of claim 1 above, Vogelsong further teaches further comprising implementing the trained reinforcement learning model in the controller to adjust one or more movements of one or more components of the equipment for manufacturing. (see column 18, lines 56-64; Vogelsong: “More precisely, the robotic control system 206 will adjust policy network parameters such that actions with high empirical returns have higher probability, and actions with low empirical returns have lower probability. Step 5 can be performed according to the update equation of the policy gradient of the machine learning system 218. This equation tells the robotic control system 206 what direction to move (the gradient) to increase expected rewards.”)
Regarding Claim 11, Vogelsong teaches all the limitations of claim 3 above, Vogelsong teaches further comprising measuring a new load to be processed in the manufacturing, determining a deviance of the measurement from a previous measurement made of a training load (see [0135]; Vogelsong: “Training of a machine learning model and/or of a physics-based model (e.g., a digital twin) may be achieved in a supervised learning manner, which involves providing a training dataset including labeled inputs through the model, observing its outputs, defining an error (by measuring the difference between the outputs and the label values), and using techniques such as gradient descent and backpropagation to tune the weights of the model such that the error is minimized.”), and changing, based on the deviance, output of the trained reinforcement learning model for the adjustment of the one or more movements of the one or more components of the equipment for the manufacturing. (see 0138]; Vogelsong: “In the case of training a neural network, an error term or delta may be determined for each node in the artificial neural network. Based on this error, the artificial neural network adjusts one or more of its parameters for one or more of its nodes (the weights for one or more inputs of a node).” See [0124]: “As examples of use cases of such a model, a machine learning model may be trained based on updates to manufacturing parameters associated with one or more consumable components of a manufacturing system. For example, a process ring of a process chamber may be consumed as etch operations are performed in the chamber. A position of the process ring may be adjusted as the chamber is in use to account for the degradation of the process ring.”)
Regarding Claim 18, the limitations in this claim is taught by Vogelsong as discussed connection with claim 1.
Regarding Claim 20, the limitations in this claim is taught by Vogelsong as discussed connection with claim 3.
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) 2, 8, 13, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vogelsong in view of Li et al. (US20240176338A1 -hereinafter Li).
Regarding Claim 2, Vogelsong teaches all the limitations of claim 1 above; however, Vogelsong does not explicitly teach wherein the feedback comprises a measurement of an item to be manufactured by using the equipment, and wherein the desired output comprises a final-state measurement of an item that is manufactured by using the equipment.
Li from the same or similar field of endeavor teaches wherein the feedback comprises a measurement of an item to be manufactured by using the equipment (see [0187]; Li: “In some embodiments, the performed adjustment may be associated with input to the substrate manufacturing system based on one or more of user input or feedback input (e.g., via a model, algorithm, or the like) to update the equipment constant.” See [0003]: “Changes may be made to process recipes, process chambers, process procedures, or the like to improve properties of the produced products.”), and wherein the desired output comprises a final-state measurement of an item that is manufactured by using the equipment. (see [0025]; Li: “Manufacturing parameters are selected to produce substrates that meet the target property values.” See [0025]: “Described herein are technologies related to increasing performance of manufacturing equipment by updating equipment constants. Manufacturing equipment is used to produce products, such as substrates (e.g., wafers, semiconductors).”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Vogelsong to include Li’s features of the feedback comprising a measurement of an item to be manufactured by using the equipment, and the desired output comprising a final-state measurement of an item that is manufactured by using the equipment. Doing so would improve properties of the produced products. (Li, [0003])
Regarding Claim 8, Vogelsong teaches all the limitations of claim 3 above; however, Vogelsong does not explicitly teach wherein the trained reinforcement learning model controls a duration length of manufacturing that occurs via the one or more movements of the one or more components of the equipment for the manufacturing.
Li from the same or similar field of endeavor teaches wherein the trained reinforcement learning model controls a duration length of manufacturing that occurs via the one or more movements of the one or more components of the equipment for the manufacturing. (see [0124]; Li: “A machine learning model may be trained to recommend an endpoint of an etch procedure, and may be retrained based on event data indicative of additional etching operations (e.g., number of re-etchings, duration of additional etch, indications of over-etched substrates, etc.) to improve performance of the machine learning model.”)
The same motivation to combine Vogelsong and Li a set forth for Claim 2 equally applies to Claim 8.
Regarding Claim 13, Vogelsong teaches a computer program product comprising:
one or more computer-readable storage media; and (see column 3, lines 20-21; Vogelsong: “one or more non-transitory computer-readable media”)
program instructions stored on the one or more storage media to perform operations comprising: (see column 3, lines 24-26; Vogelsong: “The executable instructions may then be executed by a hardware based computer processor (e.g., a central processing unit or “CPU”) of the computing device.”)
receiving one or more measurements for manufacturing equipment; (see column 7, lines 47-50; Vogelsong: “The controller 208 can receive data from the robot's sensors”)
inputting the one or more measurements into a reinforcement learning model to obtain a next-best action to perform via the manufacturing equipment on a load (see column 19, lines 43-51; Vogelsong: “At step 2 of fast interactive ML process, the robotic control system 206 takes one sample action from the current policy network, and sets it as option A. This is an adaptation from general learning step 2 (block 415 of the process 400), but uses just one sample—the fast interactive ML process is trying to be as data-efficient as possible and is typically not used for training complex policies, so there might just be a dummy observation. The “A” option will be the “anchor” or “best-yet” option.” See column 19, lines 52-56: “At step 3 of fast interactive ML process, the robotic control system 206 takes one sample action from the current policy network, and sets it as option B. This is an adaptation from general learning step 2, but uses just one sample as the “new” or “exploration” option.” See column 20, lines 3-5: “At step 7 of fast interactive ML process, the robotic control system 206 stores the best-yet episode as the new option A.”), the next-best action comprising one or more movements of one or more components of the equipment for manufacturing; (see column 4, lines 31-33; Vogelsong: “The controller causes the robot 110 to attempt to throw the object to the desired distance, DA, using the action vector A.”)
causing the manufacturing equipment to automatically perform the obtained next-best action; and (see column 13, lines 39-44; Vogelsong: “The evaluation from the feedback engine 214 guides the machine learning system 218 to generate and refine a robotic control policy for the task. The robotic control policy 236 is stored and then used during the next simulation of the task 101 in the simulated environment 230.”)
iteratively receiving input regarding the manufacturing, receiving another next-best action based on the input, and causing the manufacturing equipment to perform the received next best action (see column 13, lines 44-52; Vogelsong: “The robotic control system 206 can repeat this loop until the robotic control policy 236 achieves the desired performance level within the simulated environment 230. The machine learning system 218 can implement the targeted update process 100 of FIGS. 1A and 1B using recorded observations 232 of simulated trials to iteratively update the policy until it achieves satisfactory performance in the simulated environment 305, for example consistent success at the task goal.”),
However, Vogelsong does not explicitly teach wherein these iterative steps result in the manufacturing equipment manufacturing a product.
Li from the same or similar field of endeavor teaches wherein these iterative steps result in the manufacturing equipment manufacturing a product. (see [0086]; Li: “Over a series of interactions between the model and the environment (e.g., repeated provision of rewards, penalties, etc., to the model based on recommendations of the model), the model may be trained to predict recommended changes to the equipment constants”. See [0025]: “Described herein are technologies related to increasing performance of manufacturing equipment by updating equipment constants. Manufacturing equipment is used to produce products, such as substrates (e.g., wafers, semiconductors).”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Vogelsong to include Li’s features of these iterative steps result in the manufacturing equipment manufacturing a product. Doing so would improve properties of the produced products. (Li, [0003])
Regarding Claim 19, the limitations in this claim is taught by the combination of Vogelsong and Li as discussed connection with claim 2.
Claim(s) 4 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vogelsong in view of Buschman et al. (US20160224003A1 -hereinafter Buschman).
Regarding Claim 4, Vogelsong teaches all the limitations of claim 3 above; however, Vogelsong does not explicitly teach wherein the one or more movements moves the one or more components into a calibrated position to facilitate replacing a first component with a substitute component, the calibrated position being a component replacement position.
Buschman from the same or similar field of endeavor teaches wherein the one or more movements moves the one or more components into a calibrated position to facilitate replacing a first component with a substitute component, the calibrated position being a component replacement position. (see [0033]; Buschman: “The selected substitute component 13, 14 can then be installed at the same installation location 15 between the upstream system component 4 and the downstream system component 5 and is capable of accepting the product 7 at the given conveying speed 9 at the receiving position 8 and of transferring the product 10 to the downstream system component 5 at the final position 10 at the given conveying speed 11.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Vogelsong to include Buschman’s features of moving the one or more components into a calibrated position to facilitate replacing a first component with a substitute component, the calibrated position being a component replacement position. Doing so would provide an improved automation system that becomes operational again at little expense in the event of a fault in a system component. (Buschman, [0006])
Regarding Claim 14, the limitations in this claim is taught by the combination of Vogelsong and Buschman as discussed connection with claim 4.
Claim(s) 5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vogelsong in view of Kato (US5019762A -hereinafter Kato).
Regarding Claim 5, Vogelsong teaches all the limitations of claim 3 above; however, Vogelsong does not explicitly teach wherein the one or more movements moves the one or more components into a calibrated position after a first component is replaced with a substitute component, the calibrated position being a position for re-initiating operation of the equipment and the substitute component.
Kato from the same or similar field of endeavor teaches wherein the one or more movements moves the one or more components into a calibrated position after a first component is replaced with a substitute component, the calibrated position being a position for re-initiating operation of the equipment and the substitute component. (see column 1, lines 52-55; Kato: “Before a normal robot performs an operation in place of a malfunctioning robot, the normal robot is controlled to replace its hand with a different hand corresponding to that of the malfunctioning robot”. See column 5, lines 40-47: “Therefore, in Step 395, the hand replacement controller 25 selects a suitable hand 4a to be used by the first robot 3, and in Step 396, the hand 3a of the first robot 3 is replaced by one of the hands 4a for the second robot 4 at the hand storage area 9. In Step 397, the first robot 3 then performs the operation that was selected in Step 37 in place of the malfunctioning second robot 4.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Vogelsong to include Kato’s features of moving the one or more components into a calibrated position after a first component is replaced with a substitute component, the calibrated position being a position for re-initiating operation of the equipment and the substitute component. Doing so would smoothly continue manufacturing operations. (Kato, column 6, lines 52-54)
Regarding Claim 15, the limitations in this claim is taught by the combination of Vogelsong and Kato as discussed connection with claim 5.
Claim(s) 6-7 and 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vogelsong in view of Li in view of Wang et al. (US20060119592A1 -hereinafter Wang).
Regarding Claim 6, Vogelsong teaches all the limitations of claim 3 above; however, Vogelsong does not explicitly teach wherein the one or more movements moves the one or more components into a calibrated position in response to sensing material degradation of a first component, the calibrated position being a position for re-initiating operation of the equipment and the first component to compensate for the material degradation.
Li from the same or similar field of endeavor teaches wherein the one or more movements moves the one or more components into a calibrated position in response to sensing …degradation of a first component (see [0124]; Li: “For example, a process ring of a process chamber may be consumed as etch operations are performed in the chamber. A position of the process ring may be adjusted as the chamber is in use to account for the degradation of the process ring.”), the calibrated position being a position for re-initiating operation of the equipment and the first component to compensate for the …degradation. (see [0124]; Li: “A machine learning model may be configured to recommend and/or enact updates to the process ring position, and may be retrained based on a data set including indications of manual adjustments to the process ring position.”)
The same motivation to combine Vogelsong and Li a set forth for Claim 2 equally applies to Claim 6.
However, it does not explicitly teach …sensing material degradation of a first component, …compensate for the material degradation.
Wang from the same or similar field of endeavor teaches
…sensing material degradation of a first component, (see [0125]; Wang: “In normal (sensing) operation, adjustment factors can be used by sense amplifiers or other circuits to compensate for the degradation or aging of the electronic components.)
…compensate for the material degradation. (see [0103]; Wang: “FIG. 12 illustrates how the values generated during the calibration can be used to adjust signals going to the display to compensate for degradation or aging of the electronic components within the display.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Vogelsong and Li to include Wang’s features of sensing material degradation of a first component and compensating for the material degradation. Doing so would decrease the complexity or cost of the design. (Wang, [0098])
Regarding Claim 7, the combination of Vogelsong, Li, and Wang teaches all the limitations of claim 6 above, Li further teaches wherein the sensing of the …degradation of the first component occurs via comparing actual results against expected results for iterations of use of the equipment. (see [0072]; Li: “Monitoring the performance over time of components, e.g. manufacturing equipment 124, sensors 126, metrology equipment 128, and the like, may provide indications of degrading components. Monitoring equipment constants 152 over time may provide indications of degrading components, e.g., if recommended equipment constants fall outside a control limit, outside a statistical limit, outside a guardband, or the like.”)
The same motivation to combine Vogelsong and Li a set forth for Claim 2 equally applies to Claim 7.
However, it does not explicitly teach …the sensing material degradation of a first component…
Wang from the same or similar field of endeavor teaches
…the sensing material degradation of a first component… (see [0125]; Wang: “In normal (sensing) operation, adjustment factors can be used by sense amplifiers or other circuits to compensate for the degradation or aging of the electronic components.)
The same motivation to combine Vogelsong, Li, and Wang a set forth for Claim 6 equally applies to Claim 7.
Regarding Claim 16, the limitations in this claim is taught by the combination of Vogelsong, Li, and Wang as discussed connection with claim 6.
Regarding Claim 17, the limitations in this claim is taught by the combination of Vogelsong, Li, and Wang as discussed connection with claim 7.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vogelsong in view of Oroojlooyjadid et al. (US20220374732A1 -hereinafter Oroojlooyjadid).
Regarding Claim 9, Vogelsong teaches all the limitations of claim 3 above; however, Vogelsong does not explicitly teach wherein the trained reinforcement learning model controls a number of repeated manufacturing cycles which include the one or more movements of the one or more components of the equipment for the manufacturing.
Oroojlooyjadid from the same or similar field of endeavor teaches wherein the trained reinforcement learning model controls a number of repeated manufacturing cycles which include the one or more movements of the one or more components of the equipment for the manufacturing. (see [0039]; “The trained simulator is used with a reinforcement learning (RL) algorithm to train a specialized policy to control each of the machines, or any number of them together.” See [0043]: “The MTL model receives input encoding the type and thickness of the raw glass, the product being manufactured, the desired curvature of the glass, the number of cycles of relevant equipment that undergoes regular replacement”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Vogelsong to include Oroojlooyjadid’s features of controlling a number of repeated manufacturing cycles which include the one or more movements of the one or more components of the equipment for the manufacturing. Doing so would control the machine(s) in an efficient way. (Oroojlooyjadid, [0039])
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vogelsong in view of Weisgerber et al. (US4045660A -hereinafter Weisgerber).
Regarding Claim 10, Vogelsong teaches all the limitations of claim 3 above; however, Vogelsong does not explicitly teach wherein the one or more movements moves the one or more components into a calibrated position in response to sensing displacement of one or more components of the equipment, the calibrated position being a realignment position for re-initiating operation of the equipment and a first component.
Weisgerber from the same or similar field of endeavor teaches wherein the one or more movements moves the one or more components into a calibrated position in response to sensing displacement of one or more components of the equipment (see column 5, lines 41-44; Weisgerber: “The realignment signal represents the magnitude and direction of a realignment displacement which will place the machine element in the position it had when power to the machine was interrupted.” See column 4, lines 23-26: “A second routine 42 calculates the realignment distance to be moved after power is restored to the machine, and the routine 44 controls the execution of the realignment move by the machine element.”), the calibrated position being a realignment position for re-initiating operation of the equipment and a first component. (see Abstract; Weisgerber: “A method and apparatus are disclosed for automatically realigning a machine element to a predetermined position after an interruption of power to a drive mechanism coupled to the machine element”.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teaching of Vogelsong to include Weisgerber’s features of moving the one or more components into a calibrated position in response to sensing displacement of one or more components of the equipment, the calibrated position being a realignment position for re-initiating operation of the equipment and a first component. Doing so would restore the most complex situation the machine element to its original position. (Weisgerber, column 2, lines 24-26)
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vogelsong in view of Buschman in view of Tabata et al. (US 20210146936 A1 -hereinafter Tabata).
Regarding Claim 12, Vogelsong teaches all the limitations of claim 1 above; however, Vogelsong does not explicitly teach further comprising loading a first component into the equipment in order to replace a degraded component of the equipment, wherein the loading occurs before the performance of the micro-action.
Buschman from the same or similar field of endeavor teaches further comprising loading a first component into the equipment in order to replace a degraded component of the equipment, (see [0033]; Buschman: “The selected substitute component 13, 14 can then be installed at the same installation location 15 between the upstream system component 4 and the downstream system component 5 and is capable of accepting the product 7 at the given conveying speed 9 at the receiving position 8 and of transferring the product 10 to the downstream system component 5 at the final position 10 at the given conveying speed 11.”)
The same motivation to combine Vogelsong and Buschman a set forth for Claim 4 equally applies to Claim 12.
However, it does not explicitly teach wherein the loading occurs before the performance of the micro-action.
Tabata from the same or similar field of endeavor teaches wherein the loading occurs before the performance of the micro-action. (see [0005]; Tabata: “provide a vehicle control device that automatically determines that a part has been replaced and appropriately executes learning upon replacement of the part to quickly mitigate degradation of the controllability of the vehicle after replacement of the part.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the teachings of Vogelsong and Buschman to include Tabata’s features of loading a first component into the equipment in order to replace a degraded component of the equipment, wherein the loading occurs before the performance of the micro-action. Doing so would quickly reduce the likelihood of failure and mitigate degradation of the controllability. (Tabata, [0114]-[0115])
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Noone (US20200166909A1) discloses machine learning-based methods and systems for automated object defect classification and adaptive, real-time control of manufacturing processes are described.
Seo (US12579634B2) discloses detecting a defect or a non-defect by comparing the work data with the instruction data using machine learning model.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VI N TRAN whose telephone number is (571)272-1108. The examiner can normally be reached Mon-Fri 9:00-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ROBERT FENNEMA can be reached at (571) 272-2748. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/V.N.T./ Examiner, Art Unit 2117
/ROBERT E FENNEMA/ Supervisory Patent Examiner, Art Unit 2117
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