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 .
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Objections
Claims 10 and 17 objected to because of the following informality: wherein the second technique comprise a trace analysis technique should read “wherein the second technique comprises a trace analysis technique”. Appropriate correction is required.
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 No therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more.
Regarding claim 1,
Step 1 - “Is the claim to a process, machine, manufacture or composition of matter?”
Yes, the claim is directed towards a process.
Step 2A, Prong 1 - “Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?”:
The limitation of responsive to detecting an anomaly in the trace data, … recites an evaluation of trace data to detect an anomaly, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
The limitation of and identifying, based on the trace shape, a type of issue that caused the anomaly in the trace data recites an evaluation of a trace shape and corresponding issue, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Step 2A, Prong 2 - “Does the claim recite additional elements that integrate the judicial exception into a practical application?”:
The limitation of obtaining current trace data associated with a substrate processing system; recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g).
The limitation of providing the trace data as input to a first predictive subsystem trained to detect anomalies using a first technique; recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g).
The limitation of … providing the trace data as input to a second predictive subsystem trained to detect anomalies using a second technique; recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g).
The limitation of providing output data obtained from the first predictive subsystem and the second predictive subsystem to a third predictive subsystem; recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g).
The limitation of obtaining, from the third predictive subsystem, output data reflective of a trace shape associated with the anomaly; recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g).
Step 2B - “Does the claim recite additional elements that amount to significantly more than the judicial exception?”:
The limitation of obtaining current trace data associated with a substrate processing system; recites receiving data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
The limitation of providing the trace data as input to a first predictive subsystem trained to detect anomalies using a first technique; recites transmitting data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
The limitation of … providing the trace data as input to a second predictive subsystem trained to detect anomalies using a second technique; recites transmitting data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
The limitation of providing output data obtained from the first predictive subsystem and the second predictive subsystem to a third predictive subsystem; recites transmitting data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
The limitation of obtaining, from the third predictive subsystem, output data reflective of a trace shape associated with the anomaly; recites receiving data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
Therefore, claim 1 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 2,
Claim 2 adds the additional limitations to claim 1:
wherein the first technique comprises an ensemble technique associated with using sensor statistics to establish a baseline set of values … recites an evaluation of sensor statistics to determine a baseline set of values, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
… and detecting one or more outliers that deviate by a threshold value from the baseline set of values recites a judgement of deviation from a threshold and subsequent determination of outliers, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
Therefore, claim 2 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 3,
Claim 3 adds the additional limitations to claim 1:
wherein the second technique comprises a trace analysis technique associated with using adaptive upper and lower limits around a set of target values recites mere additional details on the second technique used by the second predictive subsystem, without changing that … providing the trace data as input to a second predictive subsystem trained to detect anomalies using a second technique;, as recited in claim 1, is the mere extra-solution activity of data gathering, which does not integrate any recited abstract ideas into a practical application or amount to significantly more than any recited abstract ideas, MPEP 2106.05(d) and 2106.05(g), and also recites transmitting data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
Therefore, claim 3 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 4,
Claim 4 adds the additional limitations to claim 1:
determining a root cause of the anomaly; recites an evaluation of an anomaly to determine a root cause, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model.
and performing at least one of generating an alert or performing a corrective action recites mere instructions to apply an alert generation or a corrective action, which does not integrate the exceptions into a practical application, and is not significantly more than any recited judicial exceptions, MPEP 2106.05(d) and 2106.05(f).
Therefore, claim 4 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 5,
Claim 5 adds the additional limitations to claim 1:
wherein the type of issue is determined by performing a lookup of the trace shape in a data structure recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g), and which recites retrieving information from memory, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (iv) of WURC computer functions.
Therefore, claim 5 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 6,
Claim 6 adds the additional limitations to claim 1:
wherein the trace shape comprises at least one of an offset shape, a spike shape, an oscillation shape, a noise shape, or a shark fin shape recites mere additional details on the shape associated with the anomaly, without changing that obtaining, from the third predictive subsystem, output data reflective of a trace shape associated with the anomaly; as recited in claim 1, is the mere extra-solution activity of data gathering, which does not integrate any recited abstract ideas into a practical application or amount to significantly more than any recited abstract ideas, MPEP 2106.05(d) and 2106.05(g), and also recites receiving data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions.
Therefore, claim 6 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claim 7,
Claim 7 adds the additional limitations to claim 1:
wherein the third predictive subsystem is trained on a set of process runs each modified with labeled anomaly data recites mere instructions to apply training on modified process runs, which does not integrate the exceptions into a practical application, and is not significantly more than any recited judicial exceptions, MPEP 2106.05(d) and 2106.05(f).
Therefore, claim 7 is found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claims 8-14,
Claims 8-14 recite a system with a memory device and a processing device that implements the function of the method of claims 1-7, respectively, with substantially the same limitations. Therefore the same analysis and rejection applied to claims 1-7 applies to claims 8-14.
Therefore, claims 8-14 are found to be ineligible subject matter under 35 U.S.C. 101.
Regarding claims 15-20,
Claims 15-20 recite a non-transitory computer-readable storage medium with instructions that implement the function of the method of claims 1-6, respectively, with substantially the same limitations. Therefore the same analysis and rejection applied to claims 1-6 applies to claims 15-20.
Therefore, claims 15-20 are found to be ineligible subject matter under 35 U.S.C. 101.
Prior Art
The following references are used for prior art claim rejections:
Burch et al. (U.S. Patent Application Publication No. 2022/0027230)
Jeong et al. “Two-Stage Deep Anomaly Detection With Heterogeneous Time Series Data”
Li et al. “Combining Feature Extraction-Based and Full Trace Analysis Capabilities in Fault Detection: Methods and Comparative Analysis”
Hao et al. (U.S. Patent Application Publication No. 2020/0082245)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-8, 10-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Burch et al. (U.S. Patent Application Publication No. 2022/0027230), hereinafter Burch, in view of Jeong et al. “Two-Stage Deep Anomaly Detection With Heterogeneous Time Series Data”, hereinafter Jeong, further in view of Li et al. “Combining Feature Extraction-Based and Full Trace Analysis Capabilities in Fault Detection: Methods and Comparative Analysis”, hereinafter Li.
Regarding claim 1,
Burch teaches A method, comprising:
obtaining current trace data associated with a substrate processing system; ((Burch [0011]) “As used herein, the term ‘sensor trace’ refers to time-series data measuring an important physical quantity periodically during operation of a piece of semiconductor processing equipment, e.g., the sampled values of a physical sensor at each time point”)
providing the trace data as input to a first predictive subsystem trained to detect anomalies using a first technique; ((Burch [0017]) “A machine learning model is configured to detect anomalies using known methods including use of the data from window analysis. For example, a combination of wafer attributes and trace location features may be provided as inputs to a simple multi-class machine learning model, such as a gradient-boosting model, that is trained on datasets to detect anomalous behavior in the trace data”, a machine learning model is a predictive subsystem)
obtaining, from the [third] predictive subsystem, output data reflective of a trace shape associated with the anomaly; ((Burch [0014]) “between approximately 45-60 seconds, a first set of traces 112 in the top grouping of traces 110 and a second set of traces 122 in the bottom set of traces 120 both show sensor readings that suddenly spike up in value, then down, then back up, and then settle back into the gradual falling off pattern. This trace behavior is unexpected and indicates some kind of problem with the process. Thus, in order to analyze the anomalous behavior, windows 115 and 125 are defined over these Type I anomaly regions in the top group 110 and the bottom group 120, respectively, of the graph 100”, a spike is a trace shape associated with an anomaly, windows are output data reflective of a trace shape, Burch does not teach a specifically third predictive subsystem)
and identifying, based on the trace shape, a type of issue that caused the anomaly in the trace data ((Burch [0023]) “In step 402, trace data is received into a predictive model and processed. In step 404, an anomalous pattern is detected in the trace data. In step 406, features of the detected anomalous pattern are computed and in step 408 compared to features of prior anomalous patterns stored in a database of past trace data. In step 410, if a features match is determined, then in step 412, information regarding the anomalous pattern from past trace data is retrieved from the database, including one or more root causes for the anomaly”, a detected anomalous pattern in trace data is a particular shape in the trace data, a root cause of an anomaly is a type of issue that caused the anomaly)
Jeong teaches the following further limitations that Burch does not teach:
responsive to detecting an anomaly in the trace data, providing the trace data as input to a second predictive subsystem trained to detect anomalies using a second technique; (Jeong Pg. 4, Fig. 2 shows that, in response to anomaly candidates being detected in trace data, the anomaly candidates of the trace data are filtered at a second predictive subsystem with additional trace data as input)
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At the time of filing, one of ordinary skill in the art would have motivation to combine Burch and Jeong by taking the method for using a predictive subsystem to detect anomalies in substrate processing trace data, with output reflecting an anomalous trace shape that is used to identify an issue, taught by Burch, and including a second predictive subsystem that receives trace data when an anomaly is detected and detects anomalies using a separate technique, taught by Jeong, as Jeong teaches: (Jeong Pg. 2) “A distinguishable feature of our T-DAD framework is that signal-specific detection tasks are carried out in such a way that, along with the two empirically optimized thresholds, operation cycle signals are exploited first to find likely anomalous points whereas sensor signals are leveraged as a secondary tool to filter out unlikely anomalous points afterward. In other words, our T-DAD framework fully takes advantage of the characteristics of our heterogeneous time series data in detecting anomalies”, that is, that such an approach allows for a second model to confirm anomalies based on a different subset of features, enabling optimal use of all data available. Such a combination would be obvious.
Li teaches the following further limitations that neither Burch nor Jeong teaches:
providing output data obtained from the first predictive subsystem and the second predictive subsystem to a third predictive subsystem; (Li Pg. 2, Fig. 2 shows that the output of a segmentation feature extraction subsystem and the output of a full trace analysis subsystem are provided to a fault detection model)
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obtaining, from the third predictive subsystem, output data … ((Li Pg. 6) “Fig. 13 shows another case if additional SFE parameters are added into FTA parameters. In this case performance of the FD model is improved by introducing SFE parameters, and the solution can detect both the yellow and red anomaly feature”, detection of anomaly features is output data)
At the time of filing, one of ordinary skill in the art would have motivation to combine Burch, Jeong, and Li by taking the method for using a first predictive subsystem to detect anomalies in substrate processing trace data, and when an anomaly is detected a second predictive subsystem that uses a different technique to confirm the anomaly, with output reflecting an anomalous trace shape that is used to identify an issue, jointly taught by Burch and Jeong, and including a third predictive subsystem that uses the outputs of the first and second predictive subsystems, taught by Li, as Li teaches: (Li Abstract) “a multivariate fault detection model is developed that combines SFE and FTA capabilities…Experimental results indicate that FTA can better capture the anomaly features with complex/atypical patterns, and the combined capability of SFE + FTA exhibits better performance than each method applied individually”, that is, a third predictive subsystem that uses the outputs of a first and second predictive subsystem can provide improved predictions by combining the benefits of both other predictive subsystems. Such a combination would be obvious.
Regarding claim 3,
Burch, Jeong, and Li jointly teach The method of claim 1,
Li further teaches:
wherein the second technique comprises a trace analysis technique associated with using adaptive upper and lower limits around a set of target values ((Li Pg. 4) “In this study, the team proposes an adaptive guardband approach to address these problems to enhance FTA capability. First, a static improvement, illustrated in Fig. 5 (a), is proposed to generate robust guardband that considers (1) variance dependency, (2) feature extraction on violation segment, and (3) the upper/lower limit separately. Second, a dynamic improvement is also proposed to automatically adjust the guardband to the system dynamics, such as trace-to-trace drift, transient, and trace data misalignment, making violation detection more robust”)
At the time of filing, one of ordinary skill in the art would have motivation to combine the method jointly taught by Burch, Jeong, and Li for the parent claim of claim 3, claim 1. No new embodiments are introduced, so the reason to combine is the same as for the parent claim.
Regarding claim 4,
Burch, Jeong, and Li jointly teach The method of claim 1, further comprising:
Burch further teaches:
determining a root cause of the anomaly; ((Burch [0012]) “If the same or similar anomalies are found in the past trace data, a likelihood can be determined as to whether or not the current anomaly can be accurately classified in accordance those past anomalies; e.g., the current anomaly is most like a prior anomaly in the past trace data. If so, then the type of anomaly, its root cause, and action steps to correct can likely be retrieved from the database of past trace data”)
and performing at least one of generating an alert or performing a corrective action ((Burch [0022]) “in step 318, the type of anomaly, its root cause, and action steps to correct can be retrieved from database for the same or similar the past occurrences, and appropriate corrective action taken in step 320”)
At the time of filing, one of ordinary skill in the art would have motivation to combine the method jointly taught by Burch, Jeong, and Li for the parent claim of claim 4, claim 1. No new embodiments are introduced, so the reason to combine is the same as for the parent claim.
Regarding claim 5,
Burch, Jeong, and Li jointly teach The method of claim 1,
Burch further teaches:
wherein the type of issue is determined by performing a lookup of the trace shape in a data structure ((Burch [0012]) “Disclosed herein is a predictive model for equipment fail modes. The model detects and identifies a current anomaly in trace data, calculates key features associated with the current anomaly, and searches for anomalies having those key features in a database of past trace data”)
At the time of filing, one of ordinary skill in the art would have motivation to combine the method jointly taught by Burch, Jeong, and Li for the parent claim of claim 5, claim 1. No new embodiments are introduced, so the reason to combine is the same as for the parent claim.
Regarding claim 6,
Burch, Jeong, and Li jointly teach The method of claim 1,
Burch further teaches:
wherein the trace shape comprises at least one of an offset shape, a spike shape, an oscillation shape, a noise shape, or a shark fin shape ((Burch [0014]) “between approximately 45-60 seconds, a first set of traces 112 in the top grouping of traces 110 and a second set of traces 122 in the bottom set of traces 120 both show sensor readings that suddenly spike up in value, then down, then back up, and then settle back into the gradual falling off pattern. This trace behavior is unexpected and indicates some kind of problem with the process”)
At the time of filing, one of ordinary skill in the art would have motivation to combine the method jointly taught by Burch, Jeong, and Li for the parent claim of claim 6, claim 1. No new embodiments are introduced, so the reason to combine is the same as for the parent claim.
Regarding claim 7,
Burch, Jeong, and Li jointly teach The method of claim 1,
Li further teaches:
wherein the third predictive subsystem is trained on a set of process runs each modified with labeled anomaly data ((Li Pg. 2) “Providing a multivariate Data Generation extension to the existing univariate data generator [10] to evaluate the SFE and FTA solutions exhaustively and to convert data from semi-supervise to supervise for better feature ranking and limits optimization”, (Li Pg. 4) “The data generator takes a set of existing real trace data (normal) as seeds and then generates as many traces as needed. It inserts univariate and multivariate anomalies including drift, noise, and user defined anomalies”, Li Pg. 2, Fig. 2 shows that real process data is used initially and modified with generated data, supervised data is labeled data, building a model as shown in Fig. 2 inherently involves training the model)
At the time of filing, one of ordinary skill in the art would have motivation to combine the method jointly taught by Burch, Jeong, and Li for the parent claim of claim 7, claim 1. No new embodiments are introduced, so the reason to combine is the same as for the parent claim.
Regarding claims 8 and 10-14,
Claims 8 and 10-14 recite a system comprising a memory device and a processing device for performing the function of the method of claims 1 and 3-7, respectively. Specifically, claim 8 recites A system, comprising: a memory device; and a processing device, operatively coupled to the memory device, to perform operations comprising: [the method of claim 1]. Burch recites: (Burch [0025]) “The creation and use of processor-based models for trace analysis can be desktop-based, i.e., standalone, or part of a networked system; but given the heavy loads of information to be processed and displayed with some interactivity, processor capabilities (CPU, RAM, etc.) should be current state-of-the-art to maximize effectiveness”, with a CPU being a processing device and RAM being a memory device.
All other limitations in claims 8 and 10-14 are substantially the same as those in claims 1 and 3-7, respectively, therefore the same rationale for rejection applies.
Regarding claims 15 and 17-20,
Claims 15 and 17-20 recite a non-transitory computer-readable storage medium comprising instructions for performing the function of the method of claims 1 and 3-6, respectively. Specifically, claim 15 recites A non-transitory computer-readable storage medium comprising instructions that, when executed by a processing device operatively coupled to a memory, performs operations comprising: [the method of claim 1]. Burch recites: (Burch [0025]) “The creation and use of processor-based models for trace analysis can be desktop-based, i.e., standalone, or part of a networked system; but given the heavy loads of information to be processed and displayed with some interactivity, processor capabilities (CPU, RAM, etc.) should be current state-of-the-art to maximize effectiveness”, use of a desktop or networked system with RAM for use of models is use of a non-transitory computer-readable medium, RAM, inherently comprising instructions for model operations.
All other limitations in claims 15 and 17-20 are substantially the same as those in claims 1 and 3-6, respectively, therefore the same rationale for rejection applies.
Claims 2, 9 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Burch, in view of Jeong, further in view of Li, further in view of Hao et al. (U.S. Patent Application Publication No. 2020/0082245), hereinafter Hao.
Regarding claim 2,
Burch, Jeong, and Li jointly teach The method of claim 1,
Burch further teaches:
wherein the first technique comprises an ensemble technique … ((Burch [0017]) “a combination of wafer attributes and trace location features may be provided as inputs to a simple multi-class machine learning model, such as a gradient-boosting model, that is trained on datasets to detect anomalous behavior in the trace data”, gradient-boosting is an ensemble technique)
Hao teaches the following further limitation that neither Burch, nor Jeong, nor Li teaches:
wherein the first technique comprises an [ensemble] technique associated with using sensor statistics to establish a baseline set of values and detecting one or more outliers that deviate by a threshold value from the baseline set of values ((Hao [0007]) “The server extracts a set of input time-series traces from one or more sensors associated with one or more manufacturing tools configured to produce a silicon substrate…The server calculates a mean square error between a first input time series trace of the set of input time series traces and a corresponding first output time series trace of the set of output time-series traces. The server declares the sensor corresponding to the first input time-series trace as having an anomaly when the mean square error exceeds a pre-determined value”, ((Hao [0022]) “the pre-determined value may be set to the mean plus three sigma of the training reconstruction mean squared error as the cutoff”, a mean square error involving input traces from sensors corresponds to a sensor statistic, a training reconstruction mean squared error is a baseline set of values, Hao does not teach a specifically ensemble technique
At the time of filing, one of ordinary skill in the art would have motivation to combine Burch, Jeong, Li, and Hao by taking the method of claim 1, including a first ensemble technique for anomaly detection by a predictive subsystem, jointly taught by Burch, Jeong, and Li, and including having the first technique involve using sensor statistics to establish a baseline and detecting outliers that deviate by a threshold from that baseline, taught by Hao, as outliers from an otherwise consistent trend in trace data are well-known in the art as potentially signaling anomalies, and a threshold difference from baseline normal values is a simple and computationally inexpensive method for detecting potentially anomalous outliers. Such a combination would be obvious.
Regarding claim 9,
Claim 9 recites a system for performing the function of the method of claim 2. All other limitations in claim 9 are substantially the same as those in claim 2, therefore the same rationale for rejection applies.
Regarding claim 16,
Claim 16 recites a non-transitory computer-readable storage medium for performing the function of the method of claim 2. All other limitations in claim 16 are substantially the same as those in claim 2, therefore the same rationale for rejection applies.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Furnari et al. “An Ensembled Anomaly Detector for Wafer Fault Detection” teaches the use of an ensemble of machine learning models, including several on a variety of sensor statistics, for anomaly detection in semiconductor wafer trace data.
Mellah et al. “Semiconductor Multivariate Time-Series Anomaly Classification based on Machine Learning Ensemble Techniques” teaches the use of bagging and boosting ensemble machine learning models for detecting various fault types in semiconductor wafer trace data.
Lee at al. “Generative Pre-Training of Time-Series Data for Unsupervised Fault Detection in Semiconductor Manufacturing” teaches the use of a generative pre-trained transformer model to detect anomalies of various fault types in semiconductor wafer trace data.
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/V.A.N./Examiner, Art Unit 2124
/Kevin W Figueroa/Primary Examiner, Art Unit 2124
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