\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 .
DETAILED ACTION
2. This office action is in response to the amendment filed on 07/22/2026. Claims 1-20 are pending and have been considered below.
Claim Rejections - 35 USC § 101
3. 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed invention, when the claims are taken as a whole, is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Independent Claims: For claim 1 /6/ 10, the claim recites a method / method /method, which falls into one of the statutory categories.
2A – Prong 1: Claim 1 /6 / 10, in part, recites
determine whether further measurement on the current substrate is required; configuring, based on the preferred sampling scheme and/or the determination of whether further measurement on the current substrate is required, a physical measurement process..
discriminate between the inferred optimal sampling scheme and a predetermined optimal sampling scheme…; configuring a physical measurement process based on the current sampling scheme
obtaining an initial set of measurement values corresponding to an initial sampling scheme … configuring a physical measurement process based on the decision” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. For example, sampling data covers someone mentally making a decision about the data
2A – Prong 2: This judicial exception is not integrated into a practical application. In particular, claim 1 /6/ 10 recites the additional elements: (a) obtaining a trained model configured to infer a preferred sampling scheme for a substrate based on measurement data comprising sampling locations on the substrate and corresponding measurement values; and using, by a hardware computer system, current measurement data associated with a current substrate as input for the trained model… outputting a physical signal representing, or based on, the current sampling scheme to a tool or system for enabling configuration of the physical measurement process.”
“obtaining a first model trained to infer an optimal sampling scheme based on inputting context and/or pre-exposure data associated with one or more previous substrates, wherein the first model is trained in dependency of an outcome of a second model configured to; and using, by a hardware computer system, the obtained first model to infer the current sampling scheme based on inputting context and/or pre-exposure data associated with the one or more current substrates… outputting a physical signal representing, or based on, the current sampling scheme to a tool or system for enabling configuration of the physical measurement process.”
“obtaining a model comprising: i) a first model trained to infer from a set of measurement values whether one or more requirements imposed by a process monitoring and/or process control strategy are met; and ii) a second model trained to infer from a set of measurement values that one or more further measurement values need to be acquired before meeting the requirements imposed by the process monitoring and/or process control strategy… outputting a physical signal representing, or based on, the decision to a tool or system for enabling configuration of the physical measurement process.”
mere instructions to apply the judicial exception using generic computer elements (like computer, processor, processor coupled to memory / computer-readable storage medium) (merely uses a computer as a tool to perform an abstract idea, MPEP 2106.05(f));
(b) “inputting the initial set of measurement values to the model to obtain a decision on stopping or continuing performing measurements on sampling locations on one more substrates, wherein the decision is based on balancing the output of the first and the second model.”; (insignificant extra solution activity, MPEP 2106(g))”.
For (a), these computer components are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of state transition probability calculation) such that it amounts no more than mere instructions to apply the exception using a generic computer component. For (b), these steps are insignificant extra solution activity, like mere data gathering and output, MPEP.2106.05(g). The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
(a) obtaining a trained model configured to infer a preferred sampling scheme for a substrate based on measurement data comprising sampling locations on the substrate and corresponding measurement values; and using, by a hardware computer system, current measurement data associated with a current substrate as input for the trained model… outputting a physical signal representing, or based on, the current sampling scheme to a tool or system for enabling configuration of the physical measurement process.”
“obtaining a first model trained to infer an optimal sampling scheme based on inputting context and/or pre-exposure data associated with one or more previous substrates, wherein the first model is trained in dependency of an outcome of a second model configured to; and using, by a hardware computer system, the obtained first model to infer the current sampling scheme based on inputting context and/or pre-exposure data associated with the one or more current substrates… outputting a physical signal representing, or based on, the current sampling scheme to a tool or system for enabling configuration of the physical measurement process.”
“obtaining a model comprising: i) a first model trained to infer from a set of measurement values whether one or more requirements imposed by a process monitoring and/or process control strategy are met; and ii) a second model trained to infer from a set of measurement values that one or more further measurement values need to be acquired before meeting the requirements imposed by the process monitoring and/or process control strategy… outputting a physical signal representing, or based on, the decision to a tool or system for enabling configuration of the physical measurement process.”
mere instructions to apply the judicial exception using generic computer elements (like computer, processor, processor coupled to memory / computer-readable storage medium) (merely uses a computer as a tool to perform an abstract idea, MPEP 2106.05(f));
(b) “inputting the initial set of measurement values to the model to obtain a decision on stopping or continuing performing measurements on sampling locations on one more substrates, wherein the decision is based on balancing the output of the first and the second model.”; (insignificant extra solution activity, MPEP 2106(g))”.
For (a), these computer components are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of state transition probability calculation) such that it amounts no more than mere instructions to apply the exception using a generic computer component. For (b), these steps are insignificant extra solution activity, like mere data gathering and output, MPEP.2106.05(g). The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network” e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362), "electronic record keeping," and "storing and retrieving information in memory"). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, the claims do not recite any additional elements that amount to significantly more than the judicial exception.
Therefore, claims 1, 6 and 10 as well as 12 13 and 15 are not eligible.
Claim 2 recites “wherein the model is based on a neural network”. The additional element is recited so the claim does not provide a practical application and is not considered to be significant. Thus, such additional element represents insignificant extra-solution activity because it is nominal or tangential addition to the claim and does not amount to significantly more than the abstract idea.
Claim 3 recites “further comprising inputting pre-exposure data and/or context data associated with the current substrate to the trained model” merely provides specifics of the data insertion recited in claim 1. Accordingly, such additional element represents insignificant extra-solution activity because it is nominal or tangential addition to the claim and does not amount to significantly more than the abstract idea.
Claim 4 recites “wherein the pre-exposure data comprises previous measurement data associated with sampling locations and corresponding measurement values of one or more previous substrates”, this additional element does not integrate the judicial exception into a practical application, mere insignificant extra-solution activity and does not amount to significantly more.
Claim 5 recites “further comprising configuring the trained model based on the current measurement data” this additional element does not integrate the judicial exception into a practical application and does not amount to significantly more. Thus, such additional element represents insignificant extra-solution activity because it is nominal or tangential addition to the claim and does not amount to significantly more than the abstract idea.
Claim 7 recites “wherein the first model is a generative model and the second model is a discriminative model and the first and second models constitute a Generative Adversarial Network (GAN)” mere instructions to apply the judicial exception and does not amount to significantly more.
Claim 8 recites “wherein the first model is trained using input data comprising the context and/or pre-exposure data and measurement data associated with a dense sampling scheme being more dense than the inferred current sampling scheme” mere instruction to apply and provides specifics of the data insertion recited. Accordingly, the claim does not amount to significantly more than the abstract idea.
Claim 9 recites “wherein the dense sampling scheme is configured to be a sampling scheme expected to suffice for any condition of the substrate, wherein the condition is characterized by context and/or pre-exposure data associated with the substrate.” mere instruction to apply and provides specifics of the sampling scheme. such additional element represents insignificant extra-solution activity and does not amount to significantly more than the abstract idea.
The same rationale from the above claims applies to claims 11, 14 and 16-20, respectively.
Claim Rejections - 35 USC § 103
4. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-5, 12, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over CEKLI et al (US 2019/0137892 A1) in view of Sah et al. (US 2018/0321168 A1).
Claim 1. CEKLI discloses a method comprising:
obtaining a trained machine learning model configured to infer a preferred sampling scheme for a substrate (a dynamic selection function for selecting measurement locations for an individual substrate…receiving a new substrate, making a preliminary selection of measurement locations, analyzing measurements from the preliminary selection, and using the analysis to determine the measurement locations selected for that substrate…the preliminary selection may be advised by dynamic sampling software and automatically updated with experience based on the full set of measurements)([0076]–[0083])…( machine learning from past substrates to predict a fingerprint associated with a substrate and states that measurements obtained using the method can be added to the database so that the machine-learning process continues)([0092]–[0093]) based on measurement data comprising sampling locations on the substrate and corresponding measurement values (a preliminary selection of measurement locations and measurements taken at those locations. The measurements are passed to an analysis step, and the results are used to determine additional measurement locations)([0077]–[0083]);
using, by a hardware computer system, current measurement data associated with a current substrate as input for the trained model (receive a new substrate W′ and makes a preliminary selection of measurement locations. Measurements from that preliminary selection are analyzed to determine locations for the substrate)([0076]–[0080])…(a current wafer being evaluated against stored information/rules and generating an optimized alignment strategy adapted to the gradient map of the current wafer)([0090]–[0093]) to determine whether further measurement on the current substrate is required (iterative determination of additional measurement subsets. After each analysis step, a further measurement subset is determined and further measurements are made) ([0080]–[0083]);
configuring, based on the preferred sampling scheme and/or the determination of whether further measurement on the current substrate is required, a physical measurement process (a measurement apparatus and a control system that performs the selected measurements, and further states that the selected measurement locations are used by the measurement system and that the apparatus may implement the method by modifying control software of existing apparatus)([0092]–[0093], [0025]–[0026]).
CEKLI does not explicitly disclose outputting a physical signal representing, or based on, the preferred sampling scheme and/or the determination of whether further measurement on the current substrate is required to a tool or system for enabling configuration of the physical measurement process.
However, Sah discloses outputting a physical signal representing, or based on, the preferred sampling scheme and/or the determination of whether further measurement on the current substrate is required to a tool or system for enabling configuration of the physical measurement process (controller can transmit the wafer review sampling plan to the scanning electron microscope)([0015])..( transmitting a generated wafer review sampling plan to an SEM, after which inspection is performed according to the plan)([0059]–[0060]). Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify CEKLI with Sah features. One would have been motivated to do so to provide a dynamically selected sampling plan based on current and historical wafer information and communicate that plan to the measurement tool.
Claim 2. CEKLI and Sah disclose the method of claim 1, Sah further discloses wherein the model is based on a neural network (using historically classified defects on similar wafers as a training set to train a set of neural networks, with the trained model subsequently used to generate a wafer review sample plan)([0059]). One would have been motivated to do so to provide a dynamically selected sampling plan based on current and historical wafer information and communicate that plan to the measurement tool.
Claim 3. CEKLI and Sah disclose the method of claim 1, CEKLI further discloses comprising inputting pre-exposure data and/or context data associated with the current substrate to the trained model (context information associated with a substrate, including information indicating which thermal-processing tools were previously used for a particular substrate, further states that machine learning from past substrates can use such information to predict the type, orientation, and magnitude of a fingerprint)([0092]).
Claim 4. CEKLI and Sah disclose the method of claim 3, CEKLI further discloses wherein the pre-exposure data comprises previous measurement data associated with sampling locations and corresponding measurement values of one or more previous substrates ([0092]-[0093],[0112], fig. 11).
Claim 5. CEKLI and Sah disclose the method of claim 1, CEKLI further discloses comprising configuring the trained model based on the current measurement data (adding measurements from the present method to the database used in statistical analysis so that the machine-learning process continues)([0093])…( automatic updating of the dynamic sampling software with experience based on the full set of measurements)([0083],[0076]) and Fig. 2 disclose that the dynamic selection function 600 may he an added module (i.e., the claimed feature "training model") within existing control software that receives as input positional deviations (alignment measurements (i.e., the claimed feature "measurement data")) measured at a preliminary selection of measurement locations on the substrate.
Claim 12 represents the computer program product of claim 1 and is rejected along the same rationale and CEKLI further discloses a non-transitory computer-readable medium comprising computer readable instructions therein, the instructions, when executed by one or more processors are configured to cause the one or more processors to ([0026], claim 16).
Claim 17. CEKLI and Sah disclose the computer program product of claim 12, Sah further discloses wherein the model is based on a neural network (using historically classified defects on similar wafers as a training set to train a set of neural networks, with the trained model subsequently used to generate a wafer review sample plan) ([0059]). One would have been motivated to do so to provide a dynamically selected sampling plan based on current and historical wafer information and communicate that plan to the measurement tool.
Claim 18. CEKLI and Sah disclose the computer program product of claim 12, CEKLI further discloses wherein the instructions are further configured to cause the one or more processors to input pre-exposure data and/or context data associated with the current substrate to the trained model (context information associated with the current substrate and machine learning from past substrates using that information to predict substrate fingerprints) ([0092]).
Claim 19. CEKLI and Sah disclose the computer program product of claim 18, CEKLI further discloses wherein the pre-exposure data comprises previous measurement data associated with sampling locations and corresponding measurement values of one or more previous substrates (statistical data based on previously processed substrates, variation of measurements across those substrates, and comparison at corresponding locations)([0092],[0112], fig. 11).
Claim 20. CEKLI and Sah disclose the computer program product of claim 12, CEKLI further discloses wherein the instructions are further configured to cause the one or more processors to configure the trained model based on the current measurement data (adding measurements taken from the present substrate to the database used for statistical analysis so that the machine-learning process continues)([0093],[0076]) (updating dynamic sampling software automatically with experience based on the full set of measurements)([0083]) and Fig. 2 disclose that the dynamic selection function 600 may he an added module (i.e., the claimed feature "training model") within existing control software that receives as input positional deviations (alignment measurements (i.e., the claimed feature "measurement data")) measured at a preliminary selection of measurement locations on the substrate.
5. Claims 6-9, 10-11, and 13-16 are rejected under 35 U.S.C. 103 as being unpatentable over CEKLI et al (US 20190137892 A1) in view of Sah et al. (US 2018/0321168 A1) and further in view of Zhang et al. (US 20170345140 A1).
Claim 6. CEKLI discloses a method comprising:
obtaining a first machine learning model trained to infer an optimal sampling scheme based on inputting context and/or pre-exposure data associated with one or more previous substrates (learning from past substrates and using substrate context/process history to predict substrate fingerprints and select measurement locations)([0092], fig. 6); and
using, by a hardware computer system, the obtained first model to infer a current sampling scheme for one or more current substrates based on inputting context and/or pre-exposure data associated with the one or more current substrates ([0077]-[0083], fig. 6).
configuring a physical measurement process based on the current sampling scheme (using the resulting selected measurement locations/sampling plan to control actual substrate measurement) ([0077]-[0083])
CEKLI fails to explicitly disclose wherein the first model is trained in dependency of an outcome of a second model configured to discriminate between the inferred optimal sampling scheme and a pre-determined optimal sampling scheme; outputting a physical signal representing, or based on, the current sampling scheme to a tool or system for enabling configuration of the physical measurement process.
However, Sah discloses outputting a physical signal representing, or based on, the current sampling scheme to a tool or system for enabling configuration of the physical measurement process (controller can transmit the wafer review sampling plan to the scanning electron microscope)([0015])..( transmitting a generated wafer review sampling plan to an SEM, after which inspection is performed according to the plan)([0059]–[0060]). Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify CEKLI with Sah features. One would have been motivated to do so to provide a dynamically selected sampling plan based on current and historical wafer information and communicate that plan to the measurement tool.
However, Zhang discloses wherein the first model is trained in dependency of an outcome of a second model configured to discriminate between the inferred optimal sampling scheme and a pre-determined optimal sampling scheme ([0073]). Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify CEKLI with Zhang features. One would have been motivated to do so to generate candidate sampling information and discriminate/evaluate the generated information.
Claim 7. CEKLI Sah and Zhang disclose the method of claim 6, Zhang further discloses wherein the first model is a generative model and the second model is a discriminative model and the first and second models constitute a Generative Adversarial Network (GAN) ([0073]-[0074]), (a neural network configured as a generative adversarial net)(claim 5). One would have been motivated to do so in order to generate candidate sampling information and discriminate/evaluate the generated information.
Claim 8. CEKLI Sah and Zhang disclose the method of claim 6, CEKLI further discloses the wherein the first model is trained using input data comprising the context and/or pre-exposure data and measurement data associated with a dense sampling scheme being more dense than the inferred current sampling scheme (obtaining measurements from a preliminary selection and dynamically selecting additional measurement locations, including an example in which eight preliminary locations lead to a selected set of twenty-eight locations)([0077]–[0083], [0107],[0111]).
Claim 9. CEKLI Sah and Zhang disclose the method of claim 8, CEKLI further discloses wherein the dense sampling scheme is configured to be a sampling scheme expected to suffice for any condition of the substrate, wherein the condition is characterized by context and/or pre-exposure data associated with the substrate (selecting measurement locations from among all possible locations while adapting the selected locations to substrate-specific fingerprints and process variation… maintaining measurement relevance despite process variation between substrates while avoiding an unnecessary increase in measurement locations)([0077]-[0083],[0072]-[0075], [0088], figs 2, 6).
Claim 10. CEKLI discloses a method comprising:
obtaining an initial set of measurement values corresponding to an initial sampling scheme (a preliminary selection of measurement locations and obtaining measurements from that preliminary selection)([0077]-[0078]; Fig. 6);
obtaining a model (figs 2,6) comprising:
i) a first model trained to infer from a set of measurement values whether one or more requirements imposed by a process monitoring and/or process control strategy are met (analyzing measurements using criteria to determine whether the selected measurements sufficiently characterize the relevant substrate fingerprint and to select measurement locations accordingly) ([0077]-[0083],[0090]); and
a second model trained to infer from a set of measurement values that one or more further measurement values need to be acquired before meeting the requirements imposed by the process monitoring and/or process control strategy (following analysis, a further measurement subset is determined and further measurements are made)([0080]–[0083]);
inputting the initial set of measurement values to the model to obtain a decision on stopping or continuing performing measurements on sampling locations on one or more substrates, wherein the decision is based (iterative process determines whether another subset of measurement locations is to be selected and measured… multiple analysis criteria and iterative decisions concerning further measurements)([0080]–[0083], [0099]-[0104,[0111], fig. 6);
configuring a physical measurement process based on the decision (using the resulting selected measurement locations/sampling plan to control actual substrate measurement) ([0077]-[0083]).
CEKLI does not explicitly disclose balancing the output of the first and the second model; outputting a physical signal representing, or based on, the decision to a tool or system for enabling configuration of the physical measurement process.
However, Sah discloses outputting a physical signal representing, or based on, the decision to a tool or system for enabling configuration of the physical measurement process (controller can transmit the wafer review sampling plan to the scanning electron microscope)([0015])..( transmitting a generated wafer review sampling plan to an SEM, after which inspection is performed according to the plan)([0059]–[0060]). Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify CEKLI with Sah features. One would have been motivated to do so to provide a dynamically selected sampling plan based on current and historical wafer information and communicate that plan to the measurement tool.
However, Zhang discloses known use of multiple neural-network components, including generator/discriminator architectures, for producing and evaluating model outputs ([0058],[0079], [0087]). Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify CEKLI with Zhang features. One would have been motivated to do so to generate candidate sampling information and discriminate/evaluate the generated information.
Claim 11. CEKLI Sah and Zhang disclose the method of claim 10, Zhang discloses wherein the first model is a generative model and the second model is a discriminative model and the model is a Generative Adversarial Network (GAN) ([0073]-[0074]). One would have been motivated to do so to outputting a physical signal representing, or based on, the decision to a tool or system for enabling configuration of the physical measurement process.
Claim 13 represents the computer program product of claim 6 and is rejected along the same rationale and CEKLI further discloses a non-transitory computer-readable medium comprising computer readable instructions therein, the instructions, when executed by one or more processors are configured to cause the one or more processors to ([0026], claim 16).
Claim 14. CEKLI Sah and Zhang disclose the computer program product of claim 13, Zhang further discloses wherein the first model is a generative model and the second model is a discriminative model and the first and second models constitute a Generative Adversarial Network (GAN) ([0073]-[0074]). One would have been motivated to do so to outputting a physical signal representing, or based on, the decision to a tool or system for enabling configuration of the physical measurement process.
Claim 15 represents the computer program product of claim 10 and is rejected along the same rationale and CEKLI further discloses a non-transitory computer-readable medium comprising computer readable instructions therein, the instructions, when executed by one or more processors are configured to cause the one or more processors to ([0026], claim 16)
Claim 16. CEKLI Sah and Zhang disclose the computer program product of claim 15, Zhang further discloses wherein the first model is a generative model and the second model is a discriminative model and the model is a Generative Adversarial Network (GAN) ([0073]). One would have been motivated to do so to optimize the system through qualitative and quantitative evaluation of the generated samples.
Response to Arguments
6. Applicant’s arguments and amendments filed on 7/22/2026 have been fully considered but are not persuasive.
A. Applicants argue that the presently claimed subject matter is analogous to the patent-eligible rubber-molding process in Diehr because both apply a calculated result to a physical industrial process.
In response, the Examiner respectfully disagrees and submits that applying a mathematical concept to an existing industrial process may constitute a patent-eligible practical application. However, Diehr does not establish that every claim reciting a mathematical determination and an intended industrial use is patent-eligible.
The claims in Diehr required an integrated rubber-curing process involving actual temperature measurements, repeated calculations of cure time, comparison with elapsed time, and automatic opening of the molding press when the calculated condition was satisfied. These operations worked together to control the curing process.
The presently rejected claims are materially broader.
For example, claim 1 recites inferring a preferred sampling scheme, determining whether further substrate measurements are required, and subsequently configuring a physical measurement process and/or outputting a physical signal representing the sampling scheme or determination to a tool or system.
Under the broadest reasonable interpretation consistent with the specification, the expressly recited signal-output alternative does not require the receiving tool to implement the sampling scheme, acquire additional measurements, terminate an ongoing measurement operation, or otherwise change its physical operation.
Accordingly, claim 1 encompasses implementations in which the claimed machine-learning analysis produces a result that is merely communicated for possible subsequent use.
In contrast, the eligible claims in Diehr required the calculated result to be used in controlling the rubber-molding operation. The fact that both inventions concern industrial equipment does not establish that their claimed operations impose equivalent technological limitations.
The Examiner has considered the configuring alternative as well. Nevertheless, claim 1 does not require that alternative in every implementation. Nor does the claim specify a particular configuration procedure, measurement-tool architecture, or physical feedback sequence.
Applicant’s analogy therefore does not establish that the full scope of claim 1 integrates the recited mathematical processing into a practical application under Step 2A, Prong Two.
The same reasoning applies to independent claims 6 and 10, which likewise permit outputting a signal without requiring execution of the inferred sampling scheme or implementation of the stop-or-continue decision.
B. Applicants argue that configuring a physical measurement process or outputting a physical signal to enable configuration is sufficient to establish patent eligibility.
In response, the Examiner respectfully disagrees and submits that these alternatives necessarily have the same significance under § 101.
A claim requiring actual control of a particular industrial apparatus may integrate an otherwise abstract mathematical concept into a practical application. However, transmitting information representing the result of mathematical processing does not necessarily constitute such integration.
Claim 1 expressly permits outputting a signal representing or based on the inferred sampling scheme or measurement determination. The claim does not require that the signal contain executable measurement instructions, actuate a measurement component, select particular physical sampling locations within the apparatus, or cause the apparatus to acquire additional measurements.
The term “physical signal” does not, by itself, establish that the underlying mathematical concept has been meaningfully applied. The relevant inquiry concerns what the claim requires the signal to accomplish, rather than whether information is communicated through a physical medium.
Moreover, the recitation that the signal is provided to a tool or system “for enabling configuration” identifies the intended purpose of the communication. It does not expressly require the receiving apparatus to perform the configuration.
The Examiner therefore determines that the signal-output alternative, as presently claimed, constitutes an insufficient limitation on the use of the mathematical inference and decision-making operations.
This determination does not rest on disregarding the physical measurement language. Rather, the Examiner has evaluated both expressly recited alternatives and finds that the claim encompasses an implementation in which the mathematical result is generated and communicated without a required physical measurement operation.
C. Applicant further relies on USPTO Subject Matter Eligibility Example 25, concerning rubber manufacturing.
In response, the Examiner respectfully disagrees and submits that Example 25 does not establish eligibility based merely on the inclusion of a computer-generated signal or a reference to an industrial apparatus.
The example’s eligible method claim requires a coordinated sequence of temperature measurement, repeated cure-time calculation and automatic opening of a rubber-molding press. Its eligible computer-readable-medium claim likewise requires initiating a signal that controls the press to open when the specified condition is satisfied.
The presently rejected claims do not impose equivalent requirements.
In particular, claim 1 permits outputting a signal representing a preferred sampling scheme or measurement determination without requiring that the signal actually control the measurement apparatus. Claim 6 similarly permits outputting the inferred current sampling scheme, while claim 10 permits outputting a stop-or-continue decision.
Unlike the control signals in Example 25, these claimed signals need not cause a specified physical operation.
Furthermore, Example 25 describes how its claimed measurement, calculation and control steps cooperate to improve the rubber-curing process. Applicant has not identified corresponding limitations in the presently rejected claims that necessarily establish an integrated physical measurement sequence across their full scope.
Accordingly, Example 25 is distinguishable and does not warrant withdrawal of the rejection.
D. Applicant also cites USPTO Example 35 as support for the proposition that similarly worded claims are patent-eligible.
In response, the Examiner respectfully disagrees and submits that Example 35 concerns verifying a bank customer’s identity in connection with an automated teller machine. Importantly, the example does not find every claim involving an ATM or the communication of a decision patent-eligible. Example 35 identifies a broadly recited verification method as ineligible. Its eligible claims contain additional, specifically recited technological operations, including a particular authentication sequence involving communication devices or a control signal that governs access to an ATM keypad.
The relevant distinction is therefore not the presence of language referring to a physical signal or a machine. Rather, it is the specific technological implementation required by the claim considered as a whole. The presently rejected claims do not require an analogous specifically defined implementation of the inferred sampling scheme or measurement decision.
Further, the USPTO’s subject matter eligibility examples are illustrative and must be applied according to their particular claim language and factual circumstances. Similar terminology does not establish that materially different claims must receive identical eligibility determinations.
Applicant’s reliance on Example 35 is therefore unpersuasive.
Applicant’s arguments have been fully considered. However, Diehr and Examples 25 and 35 involve claim limitations requiring particular integrated technological operations that are not necessarily required by the presently rejected claims.
The Examiner has considered the claimed physical measurement process and signal-output limitations, both individually and in combination with the machine-learning operations. Nevertheless, the expressly recited alternatives encompass mathematical inference or decision-making followed by communication of the result without requiring its implementation in a physical measurement operation.
Accordingly, Applicant’s arguments do not overcome the rejection, and the rejection of claims 1–20 under 35 U.S.C. § 101 is maintained.
Conclusion
7. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (See PTO-892).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Phenuel S. Salomon whose telephone number is (571) 270-1699. The examiner can normally be reached on Mon-Fri 7:00 A.M. to 4:00 P.M. (Alternate Friday Off) EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Usmaan Saeed can be reached on (571) 272-4046. The fax phone number for the organization where this application or proceeding is assigned is 571-273-3800.
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/PHENUEL S SALOMON/Primary Examiner, Art Unit 2146
