Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Election/Restrictions
Applicant’s election without traverse of Group II, claims 10-20, in the reply filed on 7/14/26 is acknowledged.
Allowable Subject Matter
Claims 15- 18 are 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 allowance: The prior art alone or in combination does not teach or suggest the inclusion of:
when it is determined that there is a runnable processor, calculating a runnable time of the runnable processor in the digital twin.
Cheon teaches digital twin representations and visualizing the digital twin output, 0025, Figure 6B, including generating summary data e.g. “ In some embodiments, generation of summary data 162 may include use of a digital twin model of one or more components of manufacturing equipment 124. As used herein, a digital twin is a digital replica of a physical asset, such as a manufactured part, chamber component or process chamber. The digital twin includes characteristics of the physical asset, including (but not limited to) coordinate axis dimensions, weight characteristics, material characteristics (e.g., density, surface roughness), electrical characteristics (e.g., conductivity), optical characteristics (e.g., reflectivity), etc. In some embodiments, a digital twin model may be used to generate predictions of the time behavior of data, e.g., sensor data 142. Summary data 162 may include indications of how features of the measured data differ from the predictions of the digital twin model (e.g., overshoot, rise time, settle time, steady state value error, etc.). In some embodiments, summary data 162 may be generated based on part of the input data, e.g., only a steady state portion, only a transient portion, etc. of the summary data 162. Summary data 162 will be described in more detail in connection with FIG. 5B.”)
Cheon does not describe the runnable processor time in the digital twin in combination with the intervening claims.
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.”
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 10 and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) a mental process directed to determining a monitoring part affecting the first process data and a defect of the product among the plurality of parts and a first data threshold of the monitoring part and comparing the monitoring data with the first data threshold and determining whether the monitoring data and the first data threshold are different from each other. But for the inclusion of general computing components, the determination and comparison combination represents opinion, judgment, and observation.
This judicial exception is not integrated into a practical application because the following combination of limitations represent insignificant extra-solution activity, MPEP 2106.05(f)
collecting process data generated from a plurality of parts for each of a plurality of processors during manufacture of a product; inputting the collected process data to a digital twin and then comparing, among the collected process data, first process data for each of the plurality of parts collected when a defect occurs in the product with second process data for each of the plurality of parts collected when a defect does not occur in the product and, collecting monitoring data sensed from the monitoring part, claim 1.
comparing and displaying an image of the monitoring part in a state of the first data threshold and an image of the monitoring part in a state other than the state of the first data threshold, claim 19.
generating a work schedule by comparing the monitoring data with the first data threshold, claim 20.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the insignificant extra-solution activity is well understood, convention, and routine, MPEP 2106.05(d), see 11/882237, 9959515, 20090037206, 7720559 (e.g. see general maintenance schedules for equipment failure), see also graphically displaying operational states, 13/914136, 5859885, 7124368, 7729789 (e.g. displaying component states via image indicators)
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 10 and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Cheon et al. (PG/PUB 20250138522).
Claim 10.
Cheon teaches a method of manufacturing a display apparatus (e.g. as interpreted, an intended use. The display apparatus is not accorded patentable weight because the claim body stands on its own, 2111.02), the method comprising:
collecting process data generated from a plurality of parts (e.g. machine components) for each of a plurality of processors (e.g. processing chambers) during manufacture of a product (e.g. substrate) (0034 e.g. “In some cases, sensor data is used as a measure of processing conditions. In conventional systems, utilizing sensor data for diagnosis and corrective actions may be inconvenient. In some systems, sensor data may be used to identify a root cause of sub-optimal performance of a tool. Sub-optimal performance may be detected by performing measurements on completed products, e.g., via random quality control, sparse sampling, etc. Responsive to measurement of sub-optimal performance (e.g., products outside manufacturing tolerances), sensor data of a tool may be investigated. Sensor data may be utilized to identify a subsystem, component, etc., of the tool that is aging, drifting, failing, malfunctioning, etc., contributing to sub-optimal performance of the tool. Identifying sensor data of interest may include comparing trace data from many different processing operations and many different sensors, and may include comparisons with historical data as well as current data. It can be cumbersome to isolate sensor data indicative of a corrective action when such a large amount of sensor data is available. This system of analysis can be wasteful in terms of time expended, computer processing time and associated energy costs, and the danger of finding some information but missing other indicative sensor data due to stopping cumbersome analysis after finding a number of components to perform maintenance on, replace, or the like.,”
inputting the collected process data to a digital twin and then comparing, among the collected process data, first process data for each of the plurality of parts collected when a defect occurs in the product with second process data for each of the plurality of parts collected when a defect does not occur in the product (0066-67 e.g. see golden run comparisons based on part on digital twin analysis e.g. “ some embodiments, sensor data 142 may include data collected from sensors 126 during a manufacturing run that produced an acceptable product (e.g., as measured by metrology equipment 128). A manufacturing run that produces an acceptable product may be referred to as a golden run. Sensor data associated with such a manufacturing run may be stored in data store 140 as a part of historical sensor data 144. Predictive component 114 of predictive server 112 may compare golden run sensor data, current sensor data 146, and expected sensor data (e.g., as output by the trained physics-based model) to determine if component failure, drift, or the like has occurred. In some embodiments, some or all of these operations may instead be performed by a different device, e.g., client device 120, server machine 170, server machine 180, etc. In some embodiments, predictive component 114 may determine which components contribute to differences between expected sensor data (e.g., historical sensor data, sensor data from a different tool, sensor data reflecting manufacturing parameters, etc.), current sensor data 146, and/or golden run sensor data. This may be performed, for example, by an examination (e.g., by a machine learning model, by a statistical model, etc.) of historical data of systems performing as-intended and systems exhibiting sub-optimal performance. Labeling sensor trace data with an indication of the cause of a fault (e.g., a subsystem responsible for sub-optimal performance) may enable a processing device to connect sensor differences (e.g., a particular pattern or feature in data from a particular sensor) to faults. Processing logic may be configured to connect certain features or patterns in sensor data to certain types of faults, certain components aging, drifting, or failing, etc. Sensors may be assigned a metric value indicating performance, e.g., a metric value aggregating the likelihood that a sensor indicates a fault over a number of operations or runs. Sensor data may be grouped into subsystems. Sensor metric values or indices may be aggregated into subsystem metric values or indices. Aggregated subsystem values (e.g., metric values, indices, etc.) may be used in identifying subsystem performance, etc,” see digital twin analysis, 0184 e.g. “In some embodiments, summary data is generated for each portion (e.g., each steady state portion, each transient portion, etc.) separately. Summary data of a steady state portion may include statistical descriptions of the data, e.g., mean, maximum, minimum, range, standard deviation, kurtosis, etc. Summary data of a transient portion may include statistical descriptions. Summary data of a transient portion may further include metrics generated in view of the output of a digital twin model. A digital twin model of one or more components of a manufacturing chamber associated with one or more sensors may be generated (e.g., a sensor may measure power supplied to a component, and a digital twin model of the component and the power supply for the component may be built). The digital twin model may receive as input information indicative of a processing recipe (e.g., set points, proximate environment property values, etc.) and produce as output time trace data predicting sensor readings. In some embodiments, output of the digital twin model (e.g., simulated sensor data) may be compared to measured sensor data. In some embodiments, differences between features in simulated and measured sensor data (e.g., differences larger than a threshold) may be included in summary data of a transient portion. Features of the transient data may include metrics such as overshoot over a target or final value, rise time, settle time, parameters of detected oscillation, a difference between a set point and a final value, etc.”, see
determining a monitoring part (e.g. component/sensor) affecting the first process data and a defect of the product among the plurality of parts and a first data threshold of the monitoring part (0188 -see root cause analysis, e.g. “[0188] In some embodiments, a quality index score may be generated as an aggregation of calculated metrics. For example, a TTTM index score may be generated by aggregating data associated with many sensors (e.g., via a weighted sum) to calculate a quality index score indicative of the quality of one operation of one processing run (e.g., one product). Other index aggregations may also be applicable for a TTTM process, e.g., aggregating many runs to indicate quality of a process over time, aggregating over operations and sensors to provide a general quality value of a processing run, etc. Processing logic may generate many indices, and provide information (e.g., to a user, to further processing logic, etc.) associated with index values that may be indicative of a problem with manufacturing equipment (e.g., all index values that meet a threshold, a certain percentage or number of index values most likely to be associated with a problem or fault, etc.). Index values indicative of a problem or fault may be used to initiate further analysis, e.g., drill down analysis to determine root cause, a corrective action to be performed, etc., 0191-0192 , see also multiple thresholds, 0038, 0042, 0075, 0109-0111, 0118-0122, 0145, 0150-151, 0166-167, 0183, 0186
collecting monitoring data sensed from the monitoring part (0034 e.g. “ensuring conditions are consistent, e.g., from run to run in a single chamber, consistent between chambers, etc., may increase consistency, performance, quality, etc., of produced products. Tool to tool matching (TTTM) is a process directed at ensuring consistency. In some cases, sensor data is used as a measure of processing conditions. In conventional systems, utilizing sensor data for diagnosis and corrective actions may be inconvenient. In some systems, sensor data may be used to identify a root cause of sub-optimal performance of a tool. Sub-optimal performance may be detected by performing measurements on completed products, e.g., via random quality control, sparse sampling, etc. Responsive to measurement of sub-optimal performance (e.g., products outside manufacturing tolerances), sensor data of a tool may be investigated. Sensor data may be utilized to identify a subsystem, component, etc., of the tool that is aging, drifting, failing, malfunctioning, etc., contributing to sub-optimal performance of the tool. Identifying sensor data of interest may include comparing trace data from many different processing operations and many different sensors, and may include comparisons with historical data as well as current data. It can be cumbersome to isolate sensor data indicative of a corrective action when such a large amount of sensor data is available. This system of analysis can be wasteful in terms of time expended, computer processing time and associated energy costs, and the danger of finding some information but missing other indicative sensor data due to stopping cumbersome analysis after finding a number of components to perform maintenance on, replace, or the like.
comparing the monitoring data with the first data threshold and determining whether the monitoring data and the first data threshold are different from each other (e.g. as interpreted, whether sensed conditions are equal or not equal to thresholds, 0144-0146, 0150-151, 0166-167, 0183-0186)
Claim 20.
The method of claim 10, further comprising generating a work schedule by comparing the monitoring data with the first data threshold (0036 e.g. see scheduled maintenance based on threshold comparisons indicative of component/sensor failure, see also 0041-42, 0144, 0166)
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) 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Cheon et al. (PG/PUB 20250138522) in view over Freese et al. (PG/PUB 20230400508).
Claim 11.
The method of claim 12 but does not teach the stopping limitations described below. Freese teaches the stopping limitations described below,
further comprising stopping an operation of a processor comprising the monitoring part when the monitoring data exceeds a certain percentage of the first data threshold (0021, 0076, 0118-0120 e.g. see stopping operation of the processing system based on health index percentages/thresholds exceeded, supra claim 10 for controlling operation of processing chambers/processors)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Freese, namely stopping operation of a processing device upon a threshold percentage, to the teachings of Cheon, namely controlling substrate processing devices/processes and quantifying the monitoring data using thresholds, would achieve an expected and predictable result of stopping processing chamber operations when the monitoring data exceeds a threshold by a percentage. Freese is reasonably pertinent to a problem of granulating threshold levels an invoking responsive actions (e.g. alarms vs. stopping).
Claim 12. The method of claim 11 but does not expressly teach the 90 percent limitation. Freese teaches the 90 percent limitations
wherein the certain percentage is about 90%. (0021, 0087 e.g. see also setting limits)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Freese, namely applying a 90 percent threshold for purposes of implementing responsive actions including stopping the equipment, as a matter of design choice given the finite and quantifiable percentages, would achieve an expected and predictable result of stopping machine processors upon exceeding about 90 percent.
Claim 13. The method of claim 11, further comprising calculating an action time for stopping the operation of the processor comprising the monitoring part in order to replace the monitoring part (Freese, 0087, 0118, 0169 e.g. see maintenance schedule for stopping operation, see 0054 for “end of life” of a sensor, metrology equipment, or component of equipment)
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Cheon et al. (PG/PUB 20250138522) in view over Freese et al. (PG/PUB 20230400508) in view over Koparal (PG/PUB 20090304907)
Claim 14. The method of claim 13 but does not expressly teach the runnable processor limitations described below. Koparal teaches the runnable processor limitations described below
further comprising determining whether there is a runnable processor among the plurality of processors during the action time (Koparal, 0012, 0059 e.g. see determining available processing chambers concurrently with maintenance/repair of a primary chamber/processor, supra claim 10 for action time/maintenance/repair)
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Koparal, namely determining runnable processors/chambers during an action time/maintenance, to the teachings of Cheon, as modified, namely scheduling/performing maintenance on a processor/chamber, would achieve an expected and predictable result of determining available chambers to minimize downtime. Koptaral is in the same field of endeavor and pertinent to a problem of ascertaining processor[s] states concurrently with action times, as described, 0012, 0059, summary of invention.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Cheon et al. (PG/PUB 20250138522) in view over Hongo et al. (PG/PUB 20180120827).
Claim 19.
The method of claim 10 but does not expressly teach the image limitations described below. Hongo teaches the image limitations described below while Cheon teaches the threshold limitations described below (e.g. normal vs abnormal component states via threshold analysis) further comprising comparing and displaying an image of the monitoring part in a state of the first data threshold and an image of the monitoring part in a state other than the state of the first data threshold (Hongo, ABSTRACT, Figure 3 (e.g. image representations/comparisons of a machine tool), 0002, 0019-0022 e.g. see abnormal, color based state representation in light of Cheon for determining normal vs abnormal states, 0054-56
One of ordinary skill in the art before the effective filing date of the claimed invention applying the teachings of Hongo, namely using image representations of normal vs. abnormal machine tool states, to the teachings of Cheon, namely determining normal vs. abnormal monitoring part states (e.g. components/sensors/metrology equipment) based on threshold analysis/traces, would achieve an expected and predictable result of visually apprising a user of abnormal states via color representations. Hongo is reasonably pertinent to a problem of representing tool states and would commend itself to indicating abnormal vs. normal monitoring part states of Cheon.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
20230213930 – see AI based digital twin for abnormal production tasks e.g. “An approach for providing solutions to ad in the process recovery in manufacturing systems to resume manufacturing activity is disclosed. The outcome of the approach can include the following advantages, minimizing waste, reducing cost of recovery, and increasing efficiency in a manufacturing process. The approach includes collecting initial factory data; identifying and categorizing one or more shop floor activities; determining initial recovery process; creating a digital twin copy of the factory; performing one or more initial simulation scenarios; generating factory improvement plan; and executing factory improvement plan”
20210209189 – see monitoring technical equipment e.g. “A computer-implemented method for determining an abnormal technical status of a technical system includes: receiving, from the technical system, a plurality of signals, each signal being sampled over time and reflecting the technical status of at least one system component; computing, for each signal with associated high and low alarm thresholds obtained from an alarm management system, at every sampling time point, a univariate distance to its associated alarm thresholds as a maximum of the distances between a value of the respective signal and its associated alarm thresholds to quantify a degree of abnormality for the respective at least one system component; computing, at every sampling time point, based on the univariate distances at the respective sampling time points, an aggregate abnormality indicator reflecting the technical status of the technical system; and providing, to an operator, a comparison of the aggregate abnormality indicator with a predetermined abnormality threshold.”
See miscellaneous digital twin analysis”
12147212 20230195074 20210209189 2025025312 12412010 20230213930
20230259112 20250138522 20240273443 20240273443 20200322703 20260140498 20100168892 20220214662 20100168892 20090304907
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/DARRIN D DUNN/Patent Examiner, Art Unit 2117
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