DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 2/27/2026 was in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS is being considered by the examiner.
Response to Amendment
Claims 1-2, 4-6, 8-14, 16-17, and 20-22 are amended and claim 19 is cancelled. Claims 1-18 and 20-23 are pending.
Response to Arguments
Applicant's arguments filed 2/27/2026 have been fully considered.
Regarding the objection to claim 5, and as noted by Applicant on page 7 of the response, the amendment to claim 5 overcomes the objection, which is withdrawn.
Regarding the rejections of claims 1 and 17 under 101, Examiner respectfully disagrees with Applicant’s arguments that the amendments overcome rejections for the following reasons.
On page 8 of the response, Applicant contends that the claims do not recite any mathematical relationships, formulas, equations, or calculations. In support, Applicant notes that it is irrelevant if the Applicant’s specification discloses some elements falling within the mathematical relationships sub-category of the mathematical concepts judicial exception if these are not claimed.
Regarding claim 1, Examiner notes that two of the three recited “calculate” steps are found to fall within the mathematical concepts exception relating to the “model” used. The plain meaning of calculate and the interpretation of “model” derived from Applicant’s specification ([0042]) indicates that calculating equipment health status information and calculating estimated equipment health status entail mathematical calculations/relations. Similarly, the third “calculate” step, “calculate adjusted equipment health status information”, itself entails a mathematical calculation/relation as supported by Applicant’s specification ([0094], [0125], and [0130]). Regarding claim 17, “generate a plurality of physics-based simulation values using one or more physics-based simulation models that each model a component of the semiconductor manufacturing equipment, wherein the plurality of physics-based simulation values includes an estimate of a value of a sensor at a second position based on a sensor of the plurality of sensors located at a first position” itself clearly entails mathematical calculations/relations in terms of processing physics parameters necessarily by mathematical processing as explained, for example, in Applicant’s specification ([0055]-[0056]).
Therefore, while neither claim 1 nor claim 17 recites a specific mathematical formula/equation, each recites elements that themselves constitute mathematical relations and/or calculations.
On page 9 of the response, Applicant contends that claims 1 and 17 do not recite a mental process. In support, Applicant asserts that it would not be possible for a human to perform the steps and that the Office has not provided any reasoning that it would be practical or reasonable, if even possible, to perform these steps in the human mind.
First, the Examiner submits that any mathematical calculations which are too difficult to perform as mental processes would be considered abstract idea limitations by virtue of being mathematical calculations. Second, the Examiner submits that given the considerable breadth with which each of the steps is recited in the claims, it is facially evident that these steps, individually and combination, may be performed as mental processes such as via evaluation and judgment as set forth in the grounds of rejection, particularly in simplified versions of the claim in practice. Regarding claim 1, the steps “calculate predicted equipment health status information associated with the semiconductor manufacturing equipment” [using] “the offline data as an input,” “receive real-time data that indicates current operating conditions and current manufacturing information corresponding to the semiconductor manufacturing equipment,” “calculate estimated equipment health status information associated with the semiconductor manufacturing equipment” [using] “the real-time data as an input,” and “calculate adjusted equipment health status information associated with the semiconductor manufacturing equipment by combining the predicted equipment health status information calculated based on the offline data and the estimated equipment health status information calculated based on the real-time data,” may be performed as mental processes in terms of (1) evaluation of offline data and judgement to determine predicted/estimated equipment health status; (2) evaluation of real-time data and judgment to determine an estimated equipment health; and (3) evaluation of the equipment health status determined in (2) in combination with what was previously determined from offline data in (1) and adjust the equipment health assessment accordingly.
Regarding claim 17, “generate a plurality of physics-based simulation values” “wherein the plurality of physics-based simulation values includes an estimate of a value of a sensor at a second position based on a sensor of the plurality of sensors located at a first position,” may be performed via mental processes (e.g., evaluation and judgement applied using (and potentially aided by pen-and-paper computation) physics-based relations/equations). As noted in the grounds for rejecting claim 17, the aspect of the calculation being performed via “simulation models that each model a component of the manufacturing equipment” is found to fall within the mathematical concepts exception.
On pages 9-10, Applicant contends that the elements of amended claims 1 and 17 integrate any potential judicial exception into a practical application. Specifically, Applicant cites the element “present the adjusted equipment health status information, wherein the adjusted equipment health status information includes an expected remaining useful life (RUL) of at least one component of the semiconductor manufacturing equipment, wherein presentation of the RUL of the at least one component facilitates replacement of the at least one component prior to failure of the at least one component,” as effectuating such integration of the judicial exception and cites Ex Parte Desjardins as providing analogous support in favor of finding integration into a practical application via an improvement to technology or a technical field. Applicant notes on pages 9-10 several example statements in Applicant’s specification describing various aspects of the potential utility in applying the disclosed method for monitoring manufacturing equipment.
Examiner submits that characterizing the system for which the predictive maintenance process is directed somewhat more narrowly as a “semiconductor manufacturing apparatus” and correspondingly characterizing the manufacturing equipment as “semiconductor manufacturing equipment” is not sufficient as combined with the other claim elements to integrate the judicial exception in either of claims 1 and 17 into a practical application, because this is still and extremely broad technological field and the intended utility/benefits described in Applicant’s specification are largely confined to the processing steps falling within the judicial exception, with no meaningful, functionally combined contribution being derived from the manufacturing equipment being limited to “semiconductor” manufacturing equipment. Therefore, this feature in combination with the other elements of claims 1 or 17 does not appear to result in the judicial exception being integrated into a particular practical application in terms of an improvement in a technology or technical field.
Regarding the presentation of the RUL facilitating replacement of the at least one component prior to failure of the at least one component, Examiner submits that this feature only characterizes an intended result/purpose of the overall method and in particular the result/purpose of presenting the RUL (for which maintenance facilitation is generally an inherent result), such that it does not represent an additional element that contributes to integrating the judicial exception into a practical application.
In sum, regarding integration into a practical application, claims 1 and 17 each recite a series of processing steps that fall within the mental processes and/or mathematical concepts exceptions and neither the limiting of the manufacturing context to “semiconductor” manufacturing nor the expression of purpose/intended result the RUL presentation in combination with the other claim elements appears to integrate the judicial exception into a particular practical application. Instead, the claims appear to attempt to monopolize the recited abstract idea across a very wide range of possible practical applications in a broad technological field.
Regarding the rejections of claims 17-19 and 22-23 under 103, and as noted by Applicant on page 11 of the response, claim 17 is amended to incorporate the limitations of claim 20, which as indicated in the Non-Final Office Action, in combination with the other elements of original claim 17 appear patentably distinct from the prior arts. The rejection of claim 17 and all claims depending therefrom under 103 are therefore withdrawn.
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 1-18 and 20-23 are rejected under 35 U.S.C. 101 because the claimed invention in each of these claims is directed to the abstract idea judicial exception without significantly more.
Claim 1 recites:
“[a] predictive maintenance system for a semiconductor manufacturing apparatus, comprising:
a memory; and
a processor that, when executing computer-executable instructions stored in the memory, is configured to:
receive offline data that indicates historical operating conditions and historical manufacturing information corresponding to semiconductor manufacturing equipment that conducts a semiconductor manufacturing process;
calculate predicted equipment health status information associated with the semiconductor manufacturing equipment by using a trained model that takes the offline data as an input;
receive real-time data that indicates current operating conditions and current manufacturing information corresponding to the semiconductor manufacturing equipment;
calculate estimated equipment health status information associated with the semiconductor manufacturing equipment by using the trained model that takes the real-time data as an input;
calculate adjusted equipment health status information associated with the semiconductor manufacturing equipment by combining the predicted equipment health status information calculated based on the offline data and the estimated equipment health status information calculated based on the real-time data; and
present the adjusted equipment health status information, wherein the adjusted equipment health status information includes an expected remaining useful life (RUL) of at least one component of the semiconductor manufacturing equipment, wherein presentation of the RUL of the at least one component facilitates replacement of the at least one component prior to failure of the at least one component.”
Independent claim 17 recites:
“[a] predictive maintenance system, comprising:
a memory; and
a processor that, when executing computer-executable instructions stored in the memory, is configured to:
receive offline data that indicates historical operating conditions and historical manufacturing information corresponding to semiconductor manufacturing equipment that conducts a semiconductor manufacturing process, wherein the offline data comprises offline sensor data from a plurality of sensors associated with the semiconductor manufacturing equipment;
generate a plurality of physics-based simulation values using one or more physics-based simulation models that each model a component of the semiconductor manufacturing equipment, wherein the plurality of physics-based simulation values includes an estimate of a value of a sensor at a second position based on a sensor of the plurality of sensors located at a first position; and
train a neural network that generates a predicted equipment health status score using the offline data and the plurality of physics-based simulation values, wherein the predicted equipment health status score includes remaining useful life (RUL) information for components of the semiconductor manufacturing equipment that facilitates replacement of the components prior to failure.”
The claim limitations considered to fall within in the abstract idea are highlighted in bold font above and the remaining features are “additional elements.”
Step 1 of the subject matter eligibility analysis entails determining whether the claimed subject matter falls within one of the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. Claims 1 and 17 each recites a system, and therefore each falls within a statutory category.
Step 2A, Prong One of the analysis entails determining whether the claim recites a judicial exception such as an abstract idea. Under a broadest reasonable interpretation, the highlighted portions of claims 1 and 17 fall within the abstract idea judicial exception. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, the highlighted subject matter falls within the mental processes category (including an observation, evaluation, judgment, opinion) and the mathematical concepts category (mathematical relationships, mathematical formulas or equations, mathematical calculations). MPEP § 2106.04(a)(2).
In claim 1, the recited functions:
“receive offline data that indicates historical operating conditions and historical manufacturing information corresponding to semiconductor manufacturing equipment that conducts a semiconductor manufacturing process,” “calculate predicted equipment health status information associated with the semiconductor manufacturing equipment” [using] “the offline data as an input,” “receive real-time data that indicates current operating conditions and current manufacturing information corresponding to the semiconductor manufacturing equipment,” “calculate estimated equipment health status information associated with the semiconductor manufacturing equipment” [using] “the real-time data as an input,” and “calculate adjusted equipment health status information associated with the semiconductor manufacturing equipment by combining the predicted equipment health status information calculated based on the offline data and the estimated equipment health status information calculated based on the real-time data,”
may be performed as mental processes.
Receiving offline data that indicates historical operating conditions and historical manufacturing information corresponding to semiconductor manufacturing equipment that conducts a semiconductor manufacturing process and receiving real-time data that indicates current operating conditions and current manufacturing information corresponding to the semiconductor manufacturing equipment may be performed via mental processes (e.g., observation of computer displayed offline and real-time data). Calculating predicted equipment health status information associated with semiconductor manufacturing equipment using offline data as an input, and calculating estimated equipment health status information associated with the semiconductor manufacturing equipment using real-time data as an input may be performed via mental processes (e.g., evaluation of offline and real-time data and judgement to determine predicted equipment health and estimated equipment health from the respective evaluations). Calculating adjusted equipment health status information associated with semiconductor manufacturing equipment by combining the predicted equipment health status information calculated based on the offline data and the estimated equipment health status information calculated based on the real-time data may also be performed via mental processes (e.g., combined evaluation of predicted and estimated equipment health data and judgement to determine an equipment health status that is “adjusted” in terms of being based on the combined evaluation).
In claim 1, the recited functions:
“calculate predicted equipment health status information associated with the semiconductor manufacturing equipment by using a trained model that takes the offline data as an input,” “calculate estimated equipment health status information associated with the semiconductor manufacturing equipment by using the trained model that takes the real-time data as an input,” and “calculate adjusted equipment health status information associated with the semiconductor manufacturing equipment by combining the predicted equipment health status information calculated based on the offline data and the estimated equipment health status information calculated based on the real-time data,” are further/alternatively determined by the Examiner as falling within the mathematical relationships sub-category of mathematical concepts (MPEP 2106.04(a)(2)).
Per Applicant’s specification (e.g., [0042]) the model that calculates predicted equipment health status information and that calculates estimated equipment health status information may be a mathematical model (fundamentally characterized by mathematical relations/calculations) and therefore constitutes mathematical relationships. Per Applicant’s specification (e.g., [0094], [0125], and [0130]) the calculation of adjusted equipment health status information by combining the predicted equipment health status and estimated equipment health status information may be performed via Bayesian inference/modeling, which is fundamentally characterized by mathematical relations/calculations and therefore constitutes mathematical relationships.
In claim 17, the recited functions:
“receive offline data that indicates historical operating conditions and historical manufacturing information corresponding to semiconductor manufacturing equipment that conducts a semiconductor manufacturing process, wherein the offline data comprises offline sensor data from a plurality of sensors associated with the semiconductor manufacturing equipment,” “generate a plurality of physics-based simulation values” “wherein the plurality of physics-based simulation values includes an estimate of a value of a sensor at a second position based on a sensor of the plurality of sensors located at a first position,” and “generates a predicted equipment health status score using the offline data and the plurality of physics-based simulation values, wherein the predicted equipment health status score includes remaining useful life (RUL) information for components of the semiconductor manufacturing equipment that facilitates replacement of the components prior to failure” may be performed as mental processes.
Receiving offline data that indicates historical operating conditions and historical manufacturing information corresponding to semiconductor manufacturing equipment that conducts a semiconductor manufacturing process, wherein the offline data comprises offline sensor data from a plurality of sensors associated with the semiconductor manufacturing equipment may be performed via mental processes such as observation of computer generated (e.g., displayed) offline data. Generating a plurality of physics-based simulation values including an estimate of a value of a sensor at a second position based on a sensor of the plurality of sensors located at a first position may be performed via mental processes (e.g., evaluation and judgement applied using (and potentially aided by pen-and-paper computation) physics-based relations/equations). Generating a predicted equipment health status score using the offline data and the plurality of physics-based simulation values in which the predicted equipment health status score includes RUL information for components of the semiconductor manufacturing equipment that facilitates replacement of the components prior to failure may also be performed via mental processes (e.g., evaluation of offline data and physics-based simulation values and judgement to determine predicted equipment health score).
In claim 17, the recited function “generate a plurality of physics-based simulation values using one or more physics-based simulation models that each model a component of the semiconductor manufacturing equipment, wherein the plurality of physics-based simulation values includes an estimate of a value of a sensor at a second position based on a sensor of the plurality of sensors located at a first position” is further/alternatively determined by the Examiner as falling within the mathematical relationships sub-category of mathematical concepts (MPEP 2106.04(a)(2)) because as disclosed in Applicant’s specification (e.g., [0055]-[0056]), the physics-based simulation models used for generating the physics-based simulation values are based on laws of physics that are fundamentally characterized by mathematical relations/calculations and therefore constitute mathematical relations.
Step 2A, Prong Two of the analysis entails determining whether the claim includes additional elements that integrate the recited judicial exception into a practical application. “A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception” (MPEP § 2106.04(d)).
MPEP § 2106.04(d) sets forth considerations to be applied in Step 2A, Prong Two for determining whether or not a claim integrates a judicial exception into a practical application. Based on the individual and collective limitations of claims 1 and 17 and applying a broadest reasonable interpretation, the most applicable of such considerations appear to include: improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)); applying the judicial exception with, or by use of, a particular machine (MPEP 2106.05(b)); and effecting a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)).
Regarding improvements to the functioning of a computer or other technology, none of the “additional elements” in claim 1 including “predictive maintenance system,” “a memory,” “a processor” for executing instruction stored in memory, “present the adjusted equipment health status information, wherein the adjusted equipment health status information includes an expected remaining useful life (RUL) of at least one component of the semiconductor manufacturing equipment, wherein presentation of the RUL of the at least one component facilitates replacement of the at least one component prior to failure of the at least one component,” in any combination appear to integrate the abstract idea in a manner that technologically improves any aspect of a device or system that may be used to implement the highlighted steps or a device for implementing the highlighted steps such as a signal processing device or a generic computer. Instead, “predictive maintenance system,” “a memory,” and “a processor” for executing instruction stored in memory represent conventional, routine data processing functions/components for implementing the elements falling within the judicial exception and therefore constitute insignificant extra solution activity. Similarly, “present the adjusted equipment health status information, wherein the adjusted equipment health status information includes an expected remaining useful life (RUL) of at least one component of the semiconductor manufacturing equipment,” represents insignificant extra solution activity (post-solution activity) that as with the data processing components, fails to integrate the judicial exception into a practical application. The feature wherein presentation of the RUL of the at least one component facilitates replacement of the at least one component prior to failure of the at least one component, conveys an intended result/purpose of the “presenting” step and does not appear to positively recite any additional functional/structural limitation of the system such that it provides no significant limiting effect.
Similarly, none of the “additional elements” in claim 17 including “predictive maintenance system,” “a memory,” “a processor” for executing instructions stored in the memory, and “train a neural network,” in any combination appear to integrate the abstract idea in a manner that technologically improves any aspect of a device or system that may be used to implement the highlighted steps or a device for implementing the highlighted steps such as a signal processing device or a generic computer. Instead, “predictive maintenance system,” “a memory,” and “a processor” for executing instruction stored in memory represent conventional, routine data processing functions/components for implementing the elements falling within the judicial exception and therefore constitute insignificant extra solution activity. Similarly, “train a neural network,” represents using known data processing functions (modeling training) to provide instructions for implementing the judicial exception and therefore constitutes insignificant extra solution activity that fails to integrate the judicial exception into a practical application.
Regarding application of the judicial exception with, or by use of, a particular machine, the additional elements in claims 1 and 17 are configured and implemented in a conventional rather than a particularized manner of implementing health monitoring of equipment/machinery such as semiconductor manufacturing equipment.
Regarding a transformation or reduction of a particular article to a different state or thing, neither of claims 1 and 17 includes any such transformation or reduction. Instead, each of claims 1 and 17 as a whole entails receiving input information (offline and real-time data for claim 1 and offline data for claim 17), applying standard processing techniques (processor execution of instructions stored in memory) to the information to generate equipment health information (including adapting instructions per the training step in claim 17), with the additional elements failing to provide a meaningful integration of the abstract idea in an application that transforms an article to a different state. Instead, the additional elements represent extra solution activity that does not integrate the judicial exception into a practical application. In view of the various considerations encompassed by the Step 2A, Prong Two analysis, neither of claims 1 and 17 include additional elements that integrate the recited abstract idea into a practical application.
Therefore, each of claims 1 and 17 are directed to a judicial exception and require further analysis under Step 2B.
Regarding Step 2B, and as explained in the Step 2A Prong Two analysis, the additional elements in each of claims 1 and 17 constitute insignificant extra solution activity. Therefore, the additional elements do not result in either of claims 1 or 17 as a whole amounting to significantly more than the judicial exception. Furthermore, the additional elements in each of claims 1 and 17 appear to be generic and well understood as evidenced by the disclosures of Paul (US 2021/0116895 A1) and Kurian (US 2018/0308002 A1), each of which teach substantially similar data collection and processing functionality.
Regarding claims 1 (and similarly for claim 17), Paul teaches “predictive maintenance system (Abstract describing a system for implementing predicting monitoring for potential corrective actions (maintenance); FIG. 1 system 100, [0021]),” “a memory (FIG. 1 data store 104; [0058]; FIG. 6 main memory 604),” “a processor (FIG. 6 processor 602)” for executing instruction stored in memory (FIG. 6 processor 602 configured to access instructions in main memory 604), and “present” “equipment health status information ([0043] display corrective action indication associated with equipment; [0125] display component prediction data; FIG. 1 predictive component 114 configured to output (present) predictive data 168), wherein the” “equipment health status information includes an expected remaining useful life (RUL) of at least one component of the manufacturing equipment ([0044]),” as does Kurian (predictive maintenance system (FIGS. 1A and 1B computing environment including computing platform 110 configured to monitor systems 120, 130, and 140; [0025]-[0027] computing platform 110 for monitoring (e.g., identify potential issues including predicting likelihood of issues for) systems 120, 130, and 140); processor (FIG. 1B computing platform 110 includes processor 111) and memory (FIG. 1B computing platform 110 includes memory 112; [0033]) for executing instructions (FIG. 1B computing platform 110 includes processor 111 configured in association with memory 112 for execution; [0033]); output for presenting equipment health information FIG. 1B block 112i; FIG. 3 step 316; FIG. 4).
For claim 17, Paul teaches “train a neural network” using offline data for generating predicted equipment health information (FIG. 3 block 312; [0063] predictive modeling may be implemented generating/training any of a variety of types of machine learning models including a neural network; [0066], [0083], [0102]); FIG. 3 predictive data 368; [0021] and [0025] predictive data may predict failure date (evolving condition) and/or product defect; [0044]; FIG. 3 training set 302 obtained from historical data; [0057] model trained using historical data; FIG. 4B blocks 410, 412, 414, and 416 and corresponding descriptions), and similarly Kurian teaches training the machine learning model that may be a neural network model based on data including historical data ([0041] and [0063]).
Regarding claims 1 and 17, the Examiner notes that even if the elements “receive offline data that indicates historical operating conditions and historical manufacturing information corresponding to manufacturing equipment that conducts a manufacturing process,” “receive real-time data that indicates current operating conditions and current manufacturing information corresponding to the manufacturing equipment,” “using a trained model” to calculate predicted equipment health status information associated with the manufacturing equipment and calculate estimated equipment health status information associated with the manufacturing equipment, and for claim 17, “receive offline data that indicates historical operating conditions and historical manufacturing information corresponding to semiconductor manufacturing equipment that conducts a manufacturing process, wherein the offline data comprises offline sensor data from a plurality of sensors associated with the semiconductor manufacturing equipment,” are interpreted to fall outside the mental processes and/or mathematical concepts excepts, these element constitute extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception. Receiving offline data that indicates historical operating conditions and historical manufacturing information corresponding to semiconductor manufacturing equipment that conducts a semiconductor manufacturing process, receiving real-time data that indicates current operating conditions and current manufacturing information corresponding to the manufacturing equipment, and for claim 17, receiving offline data that indicates historical operating conditions and historical manufacturing information corresponding to semiconductor manufacturing equipment that conducts a semiconductor manufacturing process, wherein the offline data comprises offline sensor data from a plurality of sensors associated with the manufacturing equipment, represents routine, conventional data processing activity (data input for processing) and further represents high-level data collection. Using a trained model to calculate predicted equipment health status information associated with the manufacturing equipment and calculate estimated equipment health status information associated with the manufacturing equipment represents routine, conventional data processing activity in the form of computer instructions for implementing an underlying function that falls within the judicial exception.
Therefore, the additional elements are insufficient to amount to significantly more than the judicial exception.
Independent claims 1 and 17 are therefore not patent eligible.
Claims 2-16 depending from claim 1, and claims 18 and 20-23 depending from claim 17 provide additional features/steps which are part of an expanded algorithm that includes the abstract idea of claim 13 (Step 2A, Prong One). None of dependent claims 2-16, 18, and 20-23 recite additional elements that integrate the abstract idea into practical application (Step 2A, Prong Two), and all fail the “significantly more” test under the step 2B for substantially similar reasons as discussed with regards to the independent claims.
For example, claim 2 further characterizes the data (offline data) in terms of source (sensors) that is used in the operations falling within the judicial exception and therefore includes no further additional elements (the sensors are not positively recited as part of the system) such that claim 2 itself falls within the judicial exception.
Claim 3, substantially representative also of claims 6 and 18, further recites training the model using particular available data and therefore represents routine, conventional computer processing for preparing/generating data processing instructions for implementing the underlying function that falls within the judicial exception, which constitutes extra-solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Similarly, claim 4 further characterizes the available data used for training the model (sensors not recited as part of the system in claim 4) and therefore includes no further additional elements (the sensors are not positively recited as part of the system) such that these claims also fall within the judicial exception.
Claim 5 further characterizes the available estimated data (interpolation not positively recited as a function of the system) and therefore also falls within the judicial exception. The lack of interpolation as a function of the system notwithstanding, interpolation itself would fall within the mathematical concepts exception because interpolation is fundamentally characterized by mathematical relations/calculations.
Claim 7 further recites the function “extract features of the offline data that indicates historical operating conditions and of the real-time data that indicates current operating conditions,” which may be performed via mental processes (e.g., evaluation of offline and real-time data and judgement in determining/identifying significant features). As discussed with respect to claim 1 use of a processor for implementing the function constitutes insignificant extra solution activity, and “wherein the trained model takes the extracted features as inputs” represents routine, conventional data processing activity (inputting the data for processing) that also constitutes extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Claims 8-11 include elements that fall within the mental processes exception because each of the elements, individually or in combination, may be performed via mental processes (e.g., evaluation and judgement).
Claim 12 recites “identify a modification of the current operating conditions of the manufacturing equipment and a likelihood that the modification in the current operating conditions will change the expected remaining useful life of the at least one component of the manufacturing equipment,” which falls within the mental processes exception because it can be performed via mental processes (e.g., evaluation and judgement), and further recites “present the identified modification of the current operating conditions,” which represents routine, conventional data processing activity (outputting of processing results) and therefore constitutes extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Claim 13 falls within the mental processes exception because identifying current manufacturing equipment operating conditions based on physics-based simulation data may be performed via mental processes (e.g., evaluation and judgement).
Claim 14 recites “calculate second adjusted equipment health status information associated with second manufacturing equipment that conducts the manufacturing process, wherein the second adjusted equipment health status information is based on the second manufacturing equipment having the at least one component of the manufacturing equipment,” which for substantially similar reasons as for the “calculate adjusted equipment health status information” step in claim 1 falls within the mental processes exception. Claim 14 further recites “presenting a recommendation to remove the at least one component from the manufacturing equipment to use in the second manufacturing equipment based on the second adjusted equipment health status information,” which partially falls within the mental processes exception because the determination of whether/how to present “based on the second adjusted equipment health status information” may be performed via mental processes, with the “presenting” itself representing routine, conventional data processing activity (outputting processing results) and therefore constituting extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Claim 15 falls within the mental processes exception because both calculating the second adjusted equipment health status information and determining the condition for such calculation (determining level of RUL) may be performed via mental processes (e.g., evaluation and judgement).
Claim 16 partially falls within the mental processes exception because the determination of whether/how to present the recommendation - “in response to determining that a second RUL corresponding to the at least one component when used in the second manufacturing equipment exceeds the RUL of the at least one component when used in the manufacturing equipment” may be performed via mental processes (e.g., evaluation and judgement), with “the recommendation is presented” itself representing routine, conventional data processing activity (outputting processing results) and therefore constituting extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Claim 19 recites that the plurality of sensors “provide in situ sensor data obtained during fabrication of a substrate using the semiconductor manufacturing equipment.” Examiner notes that the “plurality of sensors” are not positively recited as being part of the recited system, such that the recited system itself does not perform any in situ sensing. The recited system only processes the data that, external to the structure/function of the system itself, it obtained via in situ sensing. Furthermore, the in situ sensing characterizes a relatively broad context of conventional sensing and does not appear to indicate, any particularity that may convey aspects of semiconductor equipment monitoring, that as combined with the steps falling within the judicial exception, may represent an improvement in the field of semiconductor manufacturing or monitoring of semiconductor manufacturing, such that in situ sensing constitutes insignificant extra solution activity.
Claims 21-23 further characterize the source/type of the available data comprising the “historical manufacturing data” that is processed by the functions falling within the judicial exception and therefore include no further additional elements and themselves fall within the judicial exception.
Subject Matter Patentably Distinguishable Over the Prior Arts
Claims 1-18 and 20-23 appear to be patentably distinct over the prior arts.
The most pertinent prior arts are represented by Paul (US 2021/0116895 A1) and Kurian (US 2018/0308002 A1).
Regarding claim 1, and as set forth in the Non-Final Office Action dated 12/4/2025, the prior arts do not fairly teach or suggest “calculate adjusted equipment health status information associated with the manufacturing equipment by combining the predicted equipment health status information calculated based on the offline data and the estimated equipment health status information calculated based on the real-time data,” taken in combination with the other elements of claim 1.
Claims 2-16 depend from claim 1 and are likewise patentably distinct over the prior arts for the same reasons.
Regarding claim 17, and as set forth in the Non-Final Office Action dated 12/4/2025, the most pertinent prior arts as represented by Paul (US 2021/0116895 A1) and Chan (US 2020/0387818 A1), do not fairly teach or suggest “wherein the plurality of physics-based simulation values includes an estimate of a value of a sensor at a second position based on a sensor of the plurality of sensors located at a first position,” taken in combination with the other elements of claim 17.
Claims 18 and 20-23 depend from claim 17 and are likewise patentably distinct over the prior arts for the same reasons.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW W BACA whose telephone number is (571)272-2507. The examiner can normally be reached Monday - Friday 8:00 am - 5:30 pm.
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/MATTHEW W. BACA/Examiner, Art Unit 2857
/ANDREW SCHECHTER/Supervisory Patent Examiner, Art Unit 2857
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