`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 original filing of 04/17/2023. Claim 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;
discriminate between the inferred optimal sampling scheme and a predetermined optimal sampling scheme…
obtaining an initial set of measurement values corresponding to an initial sampling scheme …” 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…”
“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.”
“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”
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 the decision, 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…”
“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.”
“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”
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 the decision, 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 § 102
4. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3-5, 12, and 18-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by CEKLI et al (US 2019/0137892 A1).
Claim 1. CEKLI discloses a method comprising:
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 ([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, and that the dynamic selection function quickly processes these measurements and determines a dynamic selection 604 to be used for completion of the alignment measurements step 202; paragraph [0077] and Fig. 6 disclose that one or more further subsets of the final selection (i.e., the claimed feature "preferred sampling scheme") can be added in response to the measurement results obtained ·with the preliminary selection), the measurement data comprising sampling locations on the substrate and corresponding measurement values (paragraphs [0077] and [0078]; Fig. 6) and
using, by a hardware computer system, current measurement data associated with a current substrate as input for the trained model to determine whether further measurement on the current substrate is required (abstract, [0079]-[0083] and [0090] and Fig. 6 disclose: the selection of a set of measurement locations ( 606, 606', 606") from all possible measurement locations (302); dynamically selecting at least one subset (202c) of the selected measurement locations responsive to the measurements obtained from using the preliminary measurement; the selection (610) of the measurement locations; the applicability of the preliminary height measurement to the selection of aligned measurement locations: and the inspection of abnormal measurements (i e., the claimed feature "further measurement") by supplementary data such as height measurement and historical data (i.e., the claimed feature "current measurement data")).
Claim 3. CEKLI discloses the method of claim 1, further comprising inputting pre-exposure data and/or context data associated with the current substrate to the trained model ([0092]).
Claim 4. CEKLI discloses the method of claim 3, wherein the pre-exposure data comprises previous measurement data associated with sampling locations and corresponding measurement values of one or more previous substrates ([0092],[0112], fig. 11).
Claim 5. CEKLI discloses the method of claim 1, further comprising configuring the trained model based on the current measurement data ([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 18. CEKLI discloses the computer program product of claim 12, 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 ([0092]).
Claim 19. CEKLI discloses the computer program product of claim 18, wherein the pre-exposure data comprises previous measurement data associated with sampling locations and corresponding measurement values of one or more previous substrates ([0092],[0112], fig. 11).
Claim 20. CEKLI discloses the computer program product of claim 12, wherein the instructions are further configured to cause the one or more processors to configure the trained model based on the current measurement data ([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 Rejections - 35 USC § 103
5. 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 2, 6-9, 10-11, 13-15 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over CEKLI et al (US 20190137892 A1) in view of Zhang et al. (US 20170345140 A1)
Claim 2. CEKLI discloses the method of claim 1, but fails to explicitly disclose wherein the model is based on a neural network.
However, Zhang discloses wherein the model is based on a neural network ([0060]-[0061],[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 in order to optimize the system through qualitative and quantitative evaluation of the generated samples.
Claim 6. CEKLI discloses a method for inferring a current sampling scheme for one or more current substrates, the method comprising:
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 ([0092], fig. 6); 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 ([0077]-[0083], fig. 6).
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.
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 in order to optimize the system through qualitative and quantitative evaluation of the generated samples.
Claim 7. CEKLI 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]). One would have been motivated to do so in order to optimize the system through qualitative and quantitative evaluation of the generated samples.
Claim 8. CEKLI 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 ([0107],[0111]).
Claim 9. CEKLI 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 ([0077]-[0083], [0088], figs 2, 6).
Claim 10. CEKLI discloses a method for providing a decision on stopping or continuing performing measurements on sampling locations on one more substrates, the method comprising:
obtaining an initial set of measurement values corresponding to an initial sampling scheme ([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 ([0090]); and
CEKLI does not explicitly disclose 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.
However, Zhang discloses a discriminative model D that estimates the probability that a sample came from the training data rather than G ([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 in order to optimize the system through qualitative and quantitative evaluation of the generated samples
CEKLI further discloses inputting the initial set of measurement values to the model to obtain the decision ([0099]-[0104,[0111], fig. 6), but fails to explicitly disclose wherein the decision is based on balancing the output of the first and the second model .
However, Zhang discloses wherein the decision is based on balancing the output of the first and the second model (generative models in which two models are simultaneously trained: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G..) ([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 in order to reduce the amounts of computation at runtime.
Claim 11. CEKLI 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]). One would have been motivated to do so in order to optimize the system through qualitative and quantitative evaluation of the generated samples.
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 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]). One would have been motivated to do so in order to optimize the system through qualitative and quantitative evaluation of the generated samples.
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 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 in order to optimize the system through qualitative and quantitative evaluation of the generated samples.
Claim 17. CEKLI discloses the computer program product of claim 12, but fails to explicitly disclose wherein the model is based on a neural network.
However, Zhang discloses wherein the model is based on a neural network ([0060]-[0061],[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 in order to optimize the system through qualitative and quantitative evaluation of the generated samples.
Conclusion
6. 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