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 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 1-13 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) mental processes – concepts performed in the human mind, as well as mathematical concepts – specifically, mathematical calculations.
Subject Matter Eligibility Analysis
Step 1: Do the Claims Specify a Statutory Category?
Claims 1-13 recite methods, therefore satisfying Step 1 of the analysis.
Step 2 Analysis
Regarding claim 8,
Step 2A – Prong 1: Is a Judicial Exception Recited?
For step 2A eligibility prong one(does the claim recite a judicial exception?), the claim(s) recite(s) “training, by a computer, the MLM based on input data and a selected training algorithm to generate a trained MLM, wherein the selected training algorithm includes a fault detection and classification algorithm”(training a MLM using a selected training algorithm can contain mathematical calculations. The broadest reasonable interpretation of training the MLM includes training a gradient-boosting model(specification par 17), which contains using mathematical functions to train the MLM(NPL “gradient boosting” – Wikipedia June 2020 pg 3-5). The process following the training algorithm instructions is also a series of mental steps); limitation “detecting, by the trained MLM, at least a first anomalous pattern of traces in the multiplicity of time-series traces at a first time-based location in a stable region or a transition region during operation of the semiconductor process; defining, by the trained MLM, an anomaly window around the first time-based location and containing the first anomalous pattern; determining, by the trained MLM, a plurality of key features for portions of the multiplicity of time-series traces within the anomaly window; comparing, by the trained MLM, the plurality of determined key features with prior key features associated with prior anomalous patterns stored in a database; based on the comparison of key features, determining, by the trained MLM, a likelihood that the first anomalous pattern matches one of the prior anomalous patterns;”, “if the likelihood exceeds a threshold”, and “taking the corrective action in the semiconductor process to correct the root cause”. Defining a plurality of windows, detecting an anomalous pattern, defining a second window within the first window, determining key features, comparing key features with prior patterns in a database, determining a match with the prior patterns, comparing a likelihood to a threshold, and taking an action to correct the root cause are mental processes of observation, evaluation, judgment, opinion [see MPEP 2106.04(a)(2) III. “mental processes”]. Reference US 20200057689 A1(Farahat) teaches that predictive maintenance has historically been done manually (par 6 “In related art implementations, one of the main objectives of predictive maintenance is to prevent failures before they happen. Related art implementations attempt to prevent failures by monitoring the equipment and searching for any pre-failure patterns. Such related art monitoring processes are conducted manually through visual inspection of equipment, or through using monitoring tools such as vibration monitoring and other devices.”). As claimed, this process can practically be performed either in the human mind or using a computer as a tool.
Even if the limitations require a computer, it can still be a mental process [see MPEP 2106.04(a)(2) III. C. "A Claim That Requires a Computer May Still Recite a Mental Process"]. Defining windows, detecting anomalous patterns, determining key features, looking up features in a database for similar situations in the past, following the instructions stored in the database on how the issue was fixed in the past, are directed to mental processes because the steps are recited at a high level of generality and merely use computers as a tool to perform the processes.
Step 2A – Prong 2: Is the Judicial Exception Integrated into a Practical Application?
For step 2A eligibility prong two(does the claim recite additional elements that integrate the judicial exception into a practical application?), This judicial exception is not integrated into a practical application because the additional limitations of “receiving,… a multiplicity of time-series traces obtained from a corresponding multiplicity of semiconductor processing equipment sensors during steps of a semiconductor process;”, and “retrieving a root cause and a corrective action associated with the matched prior anomalous pattern” are insignificant extra-solution activities of data gathering, data sending, and presentation[see MPEP 2106.05(g) Whether the limitation amounts to necessary data gathering and outputting. This is considered in Step 2A Prong Two and Step 2B.]
The additional computer parts(a computer, semiconductor processing equipment sensors, database, semiconductor processing equipment, a machine learning model) are well known components recited at a high level of generality[see MPEP 2106.05(b) “If applicant amends a claim to add a generic computer or generic computer components and asserts that the claim recites significantly more because the generic computer is 'specially programmed' (as in Alappat, now considered superseded) or is a 'particular machine' (as in Bilski), the examiner should look at whether the added elements integrate the exception into a practical application or provide significantly more than the judicial exception. Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223-24, 110 USPQ2d 1976, 1983-84 (2014). See In re Alappat, 33 F.3d 1526, 1545, 31 USPQ2d 1545, 1558 (Fed. Cir. 1994); In re Bilski, 545 F.3d 943, 88 USPQ2d 1385 (Fed. Cir. 2008)”]. As a whole, the claims are directed to several abstract mental processes implemented on a generic computer, but are not integrated into a practical application[see MPEP 2106.05(f) “implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two”].
The claim’s “obtained from a corresponding multiplicity of semiconductor processing equipment sensors during steps of a semiconductor process” do not integrate the judicial exception into a practical application. The limitations are specified at a high level of generality, and does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment(“semiconductor processing equipment”). The application specification paragraph 4 describes “Typically, an FDC method starts with breaking a complex trace into logical "windows" and then computing statistics (frequently called indicators or key numbers) on the trace data in the windows. The indicators can be monitored using statistical process control ("SPC") techniques to identify anomalies, based primarily on engineering knowledge, and the indicators can be utilized as inputs for predictive models and root cause analysis. … However, the analysis of the indicators for anomaly detection is still primarily univariate in nature, with anomalies considered on a feature by feature basis, and is generally insufficient to identify equipment fail modes related to the detected anomaly.”. The claims simply apply the abstract mental process to multiple sensors. Reference US 20210042570 A1 (Iskandar) describes multivariate analysis of sensor values from multiple sensors as a conventional system(par 14 “In other conventional systems, multivariate analysis is performed to receive sensor values from multiple sensors,…”) The claims generally link the abstract idea of anomaly detection and correction, to the field of semiconductor processing equipment. The same process except for the “semiconductor processing equipment” descriptor would also work for a heart pacemaker, cloud computing systems, a car status monitoring system(check engine light), an air conditioning system. [See MPEP 2106.04(d)(1) “Evaluating Improvements in the Functioning of a Computer, or an Improvement to Any Other Technology or Technical Field in Step 2A Prong Two” and also MPEP 2106.05(h)“Field of Use and Technological Environment”]
Step 2B: Do the Claims Provide an Inventive Concept?
For step 2B eligibility (Whether a Claim Amounts to Significantly More), The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because additional elements are either gathering/storing data(“receiving, into the trained MLM, a multiplicity of time-series traces obtained from a corresponding multiplicity of semiconductor processing equipment sensors during steps of a semiconductor process;”, and “retrieving a root cause and a corrective action associated with the matched prior anomalous pattern”), or are additional computer parts that are well known components recited at a high level of generality(“receiving a multiplicity of time-series traces obtained from a corresponding multiplicity of semiconductor processing equipment sensors during steps of a semiconductor process;”, and “patterns stored in a database”).
The data gathering/storing limitations are insignificant extra-solution activity because these limitations amount to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output) [see MPEP 2106.05(g) “(1) Whether the extra-solution limitation is well known. “, “(2) Whether the limitation is significant (i.e. it imposes meaningful limits on the claim such that it is not nominally or tangentially related to the invention).”, “(3) Whether the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output).”]
The data gathering/storing limitations are also well-understood, routine, conventional computer functions, recited at a high level of generality functions as recognized by the court decisions listed in MPEP § 2106.05(d). The application specification paragraph 4 describes “Typically, an FDC method starts with breaking a complex trace into logical "windows" and then computing statistics (frequently called indicators or key numbers) on the trace data in the windows. The indicators can be monitored using statistical process control ("SPC") techniques to identify anomalies, based primarily on engineering knowledge, and the indicators can be utilized as inputs for predictive models and root cause analysis. … However, the analysis of the indicators for anomaly detection is still primarily univariate in nature, with anomalies considered on a feature by feature basis, and is generally insufficient to identify equipment fail modes related to the detected anomaly.”. The claims simply apply the abstract mental process to multiple sensors. The claims generally link the abstract idea to the field of semiconductor processing equipment. The same process except for the “semiconductor processing equipment” descriptor would also work for a pacemaker, cloud computing systems, a car warning system, an air conditioning system. [See MPEP 2106.05(h) “Field of Use and Technological Environment”] Automating a mental process and adding well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality does not qualify as “significantly more” [see MPEP 2106.05 “Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include: … ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));”].
Combined and considered as a whole, the claim describes a system that gathers sensor data, analyses it to find anomalies, compares the anomalies to a database with fixes for the anomalies, and then the system implements the fixes. This system comprises only well-understood, routine, conventional mental steps recited at a high level of generality[MPEP 2106.05 “ii Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));”], and insignificant extra-solution activity[see MPEP 2106.05(g) “(1) Whether the extra-solution limitation is well known. “, “(2) Whether the limitation is significant (i.e. it imposes meaningful limits on the claim such that it is not nominally or tangentially related to the invention).”, “(3) Whether the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output).”]. Aside from the limitation that states that the sensors are “semiconductor processing equipment sensors”, and “taking the corrective action in the semiconductor process” there are no other steps or limitations that tie the claims to semiconductor manufacturing. If you replace “semiconductor process” with a different field, the claims could apply to any system that gathers sensor data for analysis and corrective actions. [see MPEP 2106.05(h) “Examples of limitations that the courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception include: … iv. Specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, FairWarning v. Iatric Sys., 839 F.3d 1089, 1094-95, 120 USPQ2d 1293, 1295 (Fed. Cir. 2016);”].
Conclusion: In light of the above, the limitations in claim 8 recite and are directed to an abstract idea and recite no additional elements that would amount to significantly more than the identified abstract idea. Claim 8 is therefore not patent eligible.
Regarding claim 9, limitations “calculating a plurality of statistical indicators for portions of the traces contained within the anomaly window” (this is a mental process of observation, evaluation, judgment, opinion [MPEP 2106.04(a)(2) III. “Mental processes”]). Calculating a plurality of statistical indicators is also a mathematical calculation type of abstract idea as well as a mental process [MPEP 2106.04(a)(2) “Mathematical concepts”]. As claimed, this process can practically be performed either in the human mind or using a computer as a tool.
Regarding claim 10, limitations “identifying a plurality of wafer attributes for the semiconductor process” (this is a mental process of observation, evaluation, judgment, opinion [MPEP 2106.04(a)(2) III. “Mental processes”]). As claimed, this process can practically be performed either in the human mind or using a computer as a tool.
The additional computer parts(wafer) are well known components recited at a high level of generality[see MPEP 2106.05(b) “If applicant amends a claim to add a generic computer or generic computer components and asserts that the claim recites significantly more because the generic computer is 'specially programmed' (as in Alappat, now considered superseded) or is a 'particular machine' (as in Bilski), the examiner should look at whether the added elements integrate the exception into a practical application or provide significantly more than the judicial exception. Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223-24, 110 USPQ2d 1976, 1983-84 (2014). See In re Alappat, 33 F.3d 1526, 1545, 31 USPQ2d 1545, 1558 (Fed. Cir. 1994); In re Bilski, 545 F.3d 943, 88 USPQ2d 1385 (Fed. Cir. 2008)”]. As a whole, the claims are directed to several abstract mental processes implemented on a generic computer, but are not integrated into a practical application [see MPEP 2106.05(f) “implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two”].
Regarding claim 11, limitation “performing feature engineering to select a subset of the key features determined to be important to detecting and identifying the first anomalous pattern.” (this is a mental process of observation, evaluation, judgment, opinion [MPEP 2106.04(a)(2) III. “Mental processes”]). As claimed, feature engineering is generic enough that it could also possibly include mathematical calculations as regression analysis algorithms are a common statistical modeling technique used for estimating the relationships between a dependent variable and one or more independent variables(Regression analysis - Wikipedia July 2020). As claimed, this process can practically be performed either in the human mind or using a computer as a tool.
Regarding claim 12, limitation “the step of detecting an anomalous pattern further comprising detecting a rapid change in values of the traces in the stable region or the transition region of operation.” (this is a mental process of observation, evaluation, judgment, opinion [MPEP 2106.04(a)(2) III. “Mental processes”]). Calculating change over time also contains mathematical calculations. As claimed, this process can practically be performed either in the human mind or using a computer as a tool.
Regarding claim 13, limitation “the step of detecting the first anomalous pattern further comprising detecting a rapid change in the rate of change for values of the traces in the stable region or the transition region of operation.” (this is a mental process of observation, evaluation, judgment, opinion [MPEP 2106.04(a)(2) III. “Mental processes”]). Calculating rate of change and differences in rates of change over time are also mathematical calculations. As claimed, this process can practically be performed either in the human mind or using a computer as a tool.
Regarding claim 1,
Step 2A – Prong 1: Is a Judicial Exception Recited?
For step 2A eligibility prong one(does the claim recite a judicial exception?), the claim(s) recite(s) “training, by a computer, a machine learning model (MLM) based on input data and a selected training algorithm to generate a trained MLM, wherein the selected training algorithm includes a fault detection and classification algorithm”(training a MLM using a selected training algorithm can contain mathematical calculations. The broadest reasonable interpretation of training the MLM includes training a gradient-boosting model(specification par 17), which contains using mathematical functions to train the MLM(NPL “gradient boosting” – Wikipedia June 2020 pg 3-5). The process of following the training algorithm instructions is also a series of mental steps); limitation “detecting, by the trained MLM, an anomalous pattern of traces in a stable region or a transition region of process operation at a first time-based location in the multiplicity of time-series traces; defining, by the trained MLM, an anomaly window around the first time-based location and containing the anomalous pattern; determining, by the trained MLM, a plurality of key features for the anomalous pattern of traces contained within the anomaly window; comparing, by the trained MLM, the plurality of determined key features with prior key features associated with prior anomalous patterns; determining that the anomalous pattern matches one of the prior anomalous patterns;”, and “taking the corrective action in the semiconductor process to correct the root cause for the anomalous pattern”. Defining a plurality of windows, detecting an anomalous pattern, defining a second window within the first window, determining key features, comparing key features with prior patterns, determining a match with the prior patterns, and taking an action to correct the root cause are mental processes of observation, evaluation, judgment, opinion [see MPEP 2106.04(a)(2) III. “mental processes”]. Reference US 20200057689 A1(Farahat) teaches that predictive maintenance has historically been done manually (par 6 “In related art implementations, one of the main objectives of predictive maintenance is to prevent failures before they happen. Related art implementations attempt to prevent failures by monitoring the equipment and searching for any pre-failure patterns. Such related art monitoring processes are conducted manually through visual inspection of equipment, or through using monitoring tools such as vibration monitoring and other devices.”). As claimed, this process can practically be performed either in the human mind or using a computer as a tool.
Even if the limitations require a computer, it can still be a mental process [see MPEP 2106.04(a)(2) III. C. "A Claim That Requires a Computer May Still Recite a Mental Process"]. Defining windows, detecting anomalous patterns, determining key features, looking up features in a database for similar situations in the past, following the instructions stored in the database on how the issue was fixed in the past, are directed to mental processes because the steps are recited at a high level of generality and merely use computers as a tool to perform the processes.
Step 2A – Prong 2: Is the Judicial Exception Integrated into a Practical Application?
For step 2A eligibility prong two(does the claim recite additional elements that integrate the judicial exception into a practical application?), This judicial exception is not integrated into a practical application because the additional limitations of “receiving,… a multiplicity of time-series traces obtained from a corresponding multiplicity of equipment sensors during steps of a semiconductor process;”, and “retrieving a root cause and a corrective action associated with the matched prior anomalous pattern” are insignificant extra-solution activities of data gathering, data sending, and presentation[see MPEP 2106.05(g) Whether the limitation amounts to necessary data gathering and outputting. This is considered in Step 2A Prong Two and Step 2B.]
The additional computer parts(a computer, semiconductor processing equipment sensors, semiconductor processing equipment, a machine learning model) are well known components recited at a high level of generality[see MPEP 2106.05(b) “If applicant amends a claim to add a generic computer or generic computer components and asserts that the claim recites significantly more because the generic computer is 'specially programmed' (as in Alappat, now considered superseded) or is a 'particular machine' (as in Bilski), the examiner should look at whether the added elements integrate the exception into a practical application or provide significantly more than the judicial exception. Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223-24, 110 USPQ2d 1976, 1983-84 (2014). See In re Alappat, 33 F.3d 1526, 1545, 31 USPQ2d 1545, 1558 (Fed. Cir. 1994); In re Bilski, 545 F.3d 943, 88 USPQ2d 1385 (Fed. Cir. 2008)”]. As a whole, the claims are directed to several abstract mental processes implemented on a generic computer, but are not integrated into a practical application[see MPEP 2106.05(f) “implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two”].
The claim’s “obtained from a corresponding multiplicity of semiconductor processing equipment sensors during steps of a semiconductor process” do not integrate the judicial exception into a practical application. The limitations are specified at a high level of generality, and does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment(“semiconductor processing equipment”). The application specification paragraph 4 describes “Typically, an FDC method starts with breaking a complex trace into logical "windows" and then computing statistics (frequently called indicators or key numbers) on the trace data in the windows. The indicators can be monitored using statistical process control ("SPC") techniques to identify anomalies, based primarily on engineering knowledge, and the indicators can be utilized as inputs for predictive models and root cause analysis. … However, the analysis of the indicators for anomaly detection is still primarily univariate in nature, with anomalies considered on a feature by feature basis, and is generally insufficient to identify equipment fail modes related to the detected anomaly.”. The claims simply apply the abstract mental process to multiple sensors. Reference US 20210042570 A1 (Iskandar) describes multivariate analysis of sensor values from multiple sensors as a conventional system(par 14 “In other conventional systems, multivariate analysis is performed to receive sensor values from multiple sensors,…”) The claims generally link the abstract idea of anomaly detection and correction, to the field of semiconductor processing equipment. The same process except for the “semiconductor processing equipment” descriptor would also work for a heart pacemaker, cloud computing systems, a car status monitoring system(check engine light), an air conditioning system. [See MPEP 2106.04(d)(1) “Evaluating Improvements in the Functioning of a Computer, or an Improvement to Any Other Technology or Technical Field in Step 2A Prong Two” and also MPEP 2106.05(h)“Field of Use and Technological Environment”]
Step 2B: Do the Claims Provide an Inventive Concept?
For step 2B eligibility (Whether a Claim Amounts to Significantly More), The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because additional elements are either gathering/storing data(“receiving, into the trained MLM, a multiplicity of time-series traces obtained from a corresponding multiplicity of semiconductor processing equipment sensors during steps of a semiconductor process;”, and “retrieving a root cause and a corrective action associated with the matched prior anomalous pattern”), or are additional computer parts that are well known components recited at a high level of generality(“receiving a multiplicity of time-series traces obtained from a corresponding multiplicity of semiconductor processing equipment sensors during steps of a semiconductor process;”, and “training, by a computer, a machine learning model (MLM)”).
The data gathering/storing limitations are insignificant extra-solution activity because these limitations amount to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output) [see MPEP 2106.05(g) “(1) Whether the extra-solution limitation is well known. “, “(2) Whether the limitation is significant (i.e. it imposes meaningful limits on the claim such that it is not nominally or tangentially related to the invention).”, “(3) Whether the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output).”]
The data gathering/storing limitations are also well-understood, routine, conventional computer functions, recited at a high level of generality functions as recognized by the court decisions listed in MPEP § 2106.05(d). The application specification paragraph 4 describes “Typically, an FDC method starts with breaking a complex trace into logical "windows" and then computing statistics (frequently called indicators or key numbers) on the trace data in the windows. The indicators can be monitored using statistical process control ("SPC") techniques to identify anomalies, based primarily on engineering knowledge, and the indicators can be utilized as inputs for predictive models and root cause analysis. … However, the analysis of the indicators for anomaly detection is still primarily univariate in nature, with anomalies considered on a feature by feature basis, and is generally insufficient to identify equipment fail modes related to the detected anomaly.”. The claims simply apply the abstract mental process to multiple sensors. The claims generally link the abstract idea to the field of semiconductor processing equipment. The same process except for the “semiconductor processing equipment” descriptor would also work for a pacemaker, cloud computing systems, a car warning system, an air conditioning system. [See MPEP 2106.05(h) “Field of Use and Technological Environment”] Automating a mental process and adding well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality does not qualify as “significantly more” [see MPEP 2106.05 “Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include: … ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));”].
Combined and considered as a whole, the claim describes a system that gathers sensor data, analyses it to find anomalies, compares the anomalies to a database with fixes for the anomalies, and then the system implements the fixes. This system comprises only well-understood, routine, conventional mental steps recited at a high level of generality[MPEP 2106.05 “ii Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));”], and insignificant extra-solution activity[see MPEP 2106.05(g) “(1) Whether the extra-solution limitation is well known. “, “(2) Whether the limitation is significant (i.e. it imposes meaningful limits on the claim such that it is not nominally or tangentially related to the invention).”, “(3) Whether the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output).”]. Aside from the limitation that states that the sensors are “semiconductor processing equipment sensors”, and “taking the corrective action in the semiconductor process” there are no other steps or limitations that tie the claims to semiconductor manufacturing. If you replace “semiconductor process” with a different field, the claims could apply to any system that gathers sensor data for analysis and corrective actions. [see MPEP 2106.05(h) “Examples of limitations that the courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception include: … iv. Specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, FairWarning v. Iatric Sys., 839 F.3d 1089, 1094-95, 120 USPQ2d 1293, 1295 (Fed. Cir. 2016);”].
Conclusion: In light of the above, the limitations in claim 1 recite and are directed to an abstract idea and recite no additional elements that would amount to significantly more than the identified abstract idea. Claim 1 is therefore not patent eligible.
Regarding claims 2-6 they are the same as claims 9-13 and are rejected for the same reasons.
Regarding claim 20,
Step 2A – Prong 1: Is a Judicial Exception Recited?
For step 2A eligibility prong one(does the claim recite a judicial exception?), the claim(s) recite(s) “training, by a computer, the MLM based on input data and a selected training algorithm to generate a trained MLM, wherein the selected training algorithm includes a fault detection and classification algorithm”(training a MLM using a selected training algorithm can contain mathematical calculations. The broadest reasonable interpretation of training the MLM includes training a gradient-boosting model(specification par 17), which contains using mathematical functions to train the MLM(NPL “gradient boosting” – Wikipedia June 2020 pg 3-5). The process following the training algorithm instructions is also a series of mental steps); limitation “detecting one or more anomalous patterns of time-series traces in the multiplicity of timeseries traces at a time-based location in a stable region of process operation using the trained MLM; defining a window around the first time-based location and containing at least a first anomalous pattern of the one or more anomalous patterns using the trained MLM; calculating a plurality of statistical indicators for the first anomalous pattern of traces contained within the window; determining that the first anomalous pattern is a likely match with at least one prior anomalous pattern using the MLM; based on the comparison of key features,”, and “taking the corrective action in the semiconductor process”. Defining a plurality of windows, detecting an anomalous pattern, defining a second window within the first window, determining key statistical indicators, comparing key statistical indicators with prior patterns in a database, determining a match with the prior patterns, and taking an action to correct the root cause are mental processes of observation, evaluation, judgment, opinion [see MPEP 2106.04(a)(2) III. “mental processes”]. Reference US 20200057689 A1(Farahat) teaches that predictive maintenance has historically been done manually (par 6 “In related art implementations, one of the main objectives of predictive maintenance is to prevent failures before they happen. Related art implementations attempt to prevent failures by monitoring the equipment and searching for any pre-failure patterns. Such related art monitoring processes are conducted manually through visual inspection of equipment, or through using monitoring tools such as vibration monitoring and other devices.”). As claimed, this process can practically be performed either in the human mind or using a computer as a tool. Under broadest reasonable interpretation, the process of “calculating a plurality of statistical indicators for the first anomalous pattern”, also contains statistical functions which is also considered a mathematical calculation [see MPEP 2106.04(a)(2) 1. “Mathematical concepts”])
Even if the limitations require a computer, it can still be a mental process [see MPEP 2106.04(a)(2) III. C. "A Claim That Requires a Computer May Still Recite a Mental Process"]. Defining windows, detecting anomalous patterns, determining key features, looking up features in a database for similar situations in the past, following the instructions stored in the database on how the issue was fixed in the past, are directed to mental processes because the steps are recited at a high level of generality and merely use computers as a tool to perform the processes.
Step 2A – Prong 2: Is the Judicial Exception Integrated into a Practical Application?
For step 2A eligibility prong two(does the claim recite additional elements that integrate the judicial exception into a practical application?), This judicial exception is not integrated into a practical application because the additional limitations of “receiving,… a multiplicity of time-series traces obtained from a corresponding multiplicity of semiconductor processing equipment sensors during steps of a semiconductor process;”, and “retrieving a root cause and a corrective action associated with the prior anomalous pattern” are insignificant extra-solution activities of data gathering, data sending, and presentation[see MPEP 2106.05(g) Whether the limitation amounts to necessary data gathering and outputting. This is considered in Step 2A Prong Two and Step 2B.]
The additional computer parts(a computer, semiconductor processing equipment sensors, semiconductor processing equipment, a machine learning model) are well known components recited at a high level of generality[see MPEP 2106.05(b) “If applicant amends a claim to add a generic computer or generic computer components and asserts that the claim recites significantly more because the generic computer is 'specially programmed' (as in Alappat, now considered superseded) or is a 'particular machine' (as in Bilski), the examiner should look at whether the added elements integrate the exception into a practical application or provide significantly more than the judicial exception. Merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 223-24, 110 USPQ2d 1976, 1983-84 (2014). See In re Alappat, 33 F.3d 1526, 1545, 31 USPQ2d 1545, 1558 (Fed. Cir. 1994); In re Bilski, 545 F.3d 943, 88 USPQ2d 1385 (Fed. Cir. 2008)”]. As a whole, the claims are directed to several abstract mental processes implemented on a generic computer, but are not integrated into a practical application[see MPEP 2106.05(f) “implementing an abstract idea on a generic computer, does not integrate the abstract idea into a practical application in Step 2A Prong Two”].
The claim’s “obtained from a corresponding multiplicity of semiconductor processing equipment sensors during steps of a semiconductor process” do not integrate the judicial exception into a practical application. The limitations are specified at a high level of generality, and does not meaningfully limit the claim by going beyond generally linking the use of the judicial exception to a particular technological environment(“semiconductor processing equipment”). The application specification paragraph 4 describes “Typically, an FDC method starts with breaking a complex trace into logical "windows" and then computing statistics (frequently called indicators or key numbers) on the trace data in the windows. The indicators can be monitored using statistical process control ("SPC") techniques to identify anomalies, based primarily on engineering knowledge, and the indicators can be utilized as inputs for predictive models and root cause analysis. … However, the analysis of the indicators for anomaly detection is still primarily univariate in nature, with anomalies considered on a feature by feature basis, and is generally insufficient to identify equipment fail modes related to the detected anomaly.”. The claims simply apply the abstract mental process to multiple sensors. Reference US 20210042570 A1 (Iskandar) describes multivariate analysis of sensor values from multiple sensors as a conventional system(par 14 “In other conventional systems, multivariate analysis is performed to receive sensor values from multiple sensors,…”) The claims generally link the abstract idea of anomaly detection and correction, to the field of semiconductor processing equipment. The same process except for the “semiconductor processing equipment” descriptor would also work for a heart pacemaker, cloud computing systems, a car status monitoring system(check engine light), an air conditioning system. [See MPEP 2106.04(d)(1) “Evaluating Improvements in the Functioning of a Computer, or an Improvement to Any Other Technology or Technical Field in Step 2A Prong Two” and also MPEP 2106.05(h)“Field of Use and Technological Environment”]
Step 2B: Do the Claims Provide an Inventive Concept?
For step 2B eligibility (Whether a Claim Amounts to Significantly More), The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because additional elements are either gathering/storing data(“receiving, into the trained MLM, a multiplicity of time-series traces obtained from a corresponding multiplicity of semiconductor processing equipment sensors during steps of a semiconductor process;”, and “retrieving a root cause and a corrective action associated with the prior anomalous pattern”), or are additional computer parts that are well known components recited at a high level of generality(“receiving, into the trained MLM, a multiplicity of time-series traces obtained from a corresponding multiplicity of semiconductor processing equipment sensors during steps of a semiconductor process;”).
The data gathering/storing limitations are insignificant extra-solution activity because these limitations amount to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output) [see MPEP 2106.05(g) “(1) Whether the extra-solution limitation is well known. “, “(2) Whether the limitation is significant (i.e. it imposes meaningful limits on the claim such that it is not nominally or tangentially related to the invention).”, “(3) Whether the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output).”]
The data gathering/storing limitations are also well-understood, routine, conventional computer functions, recited at a high level of generality functions as recognized by the court decisions listed in MPEP § 2106.05(d). The application specification paragraph 4 describes “Typically, an FDC method starts with breaking a complex trace into logical "windows" and then computing statistics (frequently called indicators or key numbers) on the trace data in the windows. The indicators can be monitored using statistical process control ("SPC") techniques to identify anomalies, based primarily on engineering knowledge, and the indicators can be utilized as inputs for predictive models and root cause analysis. … However, the analysis of the indicators for anomaly detection is still primarily univariate in nature, with anomalies considered on a feature by feature basis, and is generally insufficient to identify equipment fail modes related to the detected anomaly.”. The claims simply apply the abstract mental process to multiple sensors. The claims generally link the abstract idea to the field of semiconductor processing equipment. The same process except for the “semiconductor processing equipment” descriptor would also work for a pacemaker, cloud computing systems, a car warning system, an air conditioning system. [See MPEP 2106.05(h) “Field of Use and Technological Environment”] Automating a mental process and adding well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality does not qualify as “significantly more” [see MPEP 2106.05 “Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include: … ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));”].
Combined and considered as a whole, the claim describes a system that gathers sensor data, analyses it to find anomalies, compares the anomalies to a database with fixes for the anomalies, and then the system implements the fixes. This system comprises only well-understood, routine, conventional mental steps recited at a high level of generality[MPEP 2106.05 “ii Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));”], and insignificant extra-solution activity[see MPEP 2106.05(g) “(1) Whether the extra-solution limitation is well known. “, “(2) Whether the limitation is significant (i.e. it imposes meaningful limits on the claim such that it is not nominally or tangentially related to the invention).”, “(3) Whether the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output).”]. Aside from the limitation that states that the sensors are “semiconductor processing equipment sensors”, and “taking the corrective action in the semiconductor process” there are no other steps or limitations that tie the claims to semiconductor manufacturing. If you replace “semiconductor process” with a different field, the claims could apply to any system that gathers sensor data for analysis and corrective actions. [see MPEP 2106.05(h) “Examples of limitations that the courts have described as merely indicating a field of use or technological environment in which to apply a judicial exception include: … iv. Specifying that the abstract idea of monitoring audit log data relates to transactions or activities that are executed in a computer environment, because this requirement merely limits the claims to the computer field, i.e., to execution on a generic computer, FairWarning v. Iatric Sys., 839 F.3d 1089, 1094-95, 120 USPQ2d 1293, 1295 (Fed. Cir. 2016);”].
Conclusion: In light of the above, the limitations in claim 20 recite and are directed to an abstract idea and recite no additional elements that would amount to significantly more than the identified abstract idea. Claim 20 is therefore not patent eligible.
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) 1-6,8-13,20 is/are rejected under 35 U.S.C. 103 as being unpatentable over by US 20210042570 A1 (Iskandar) in view of US 20200057689 A1 (Farahat).
Regarding claim 8, Iskandar teaches
A method of using a machine learning model (MLM) for predicting semiconductor processing equipment failure and taking corrective action,(par 4 “The method further includes processing the current trace data to identify a plurality of features of the current trace data and providing the plurality of features of the current trace data as input to a trained machine learning model that uses a hyperplane limit for product classification. The method further includes obtaining, from the trained machine learning model, one or more outputs indicative of predictive data associated with the hyperplane limit. A corrective action associated with the manufacturing equipment is to be performed based on the predictive data.”) comprising:
training, by a computer, the MLM based on input da