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 .
Response to Amendment
This office action is in response to applicant’s amendment received on 03/03/2026.
Claims1, 9, and 10 have been amended.
Claims 12-20 have been cancelled.
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 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1:
According to the first part of the analysis, in the instant case, claims 1-20 is directed to a method. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Regarding claim 1:
A method for measuring optical properties and geometric properties of a thin film material, comprising:
a training phase, wherein
S1: a spline parameter bj is determined according to a dielectric function εj(E)=εj,1(E)+iεj,2(E) and an optical property spline model of m different thin film materials respectively; geometric parameters of a thin film sample are randomly selected within a preset range to obtain a plurality of geometric parameter sets x1, x2, . . . , xn, each εj and set xk are input to a forward optical property model corresponding to measurement conditions in the training phase to obtain a theoretical optical characterization quantity ytj,k to construct a training set; wherein j∈[1, m], k∈[1, n], and
S2: ytj,k is taken as input, and corresponding bj and xk serve as outputs, the training set is adopted to train a neural network, and an application phase,
wherein
S1′: a measured optical characterization quantity ymea of a thin film material to be tested under measurement conditions in the application phase is obtained and input into the trained neural network to obtain bpre and xpre,
S2′: bpre is input into the optical property spline model to obtain εpre,
S3′: ε pre and xpre are input into a forward optical property model corresponding to the measurement conditions in the application phase to obtain a corresponding theoretical optical characterization quantity yt; the trained neural network is optimized with a goal of minimizing a deviation between ymea and yt, wherein the measurement conditions in the application phase are the same as or different from the measurement conditions in the training phase, and
S4′: ymea is input into the optimized neural network to obtain b′pre and x′pre, and b′pre is input into the optical property spline model to obtain ε′t.
Step 2A Prong 1:
“S1: a spline parameter bj is determined according to a dielectric function εj(E)=εj,1(E)+iεj,2(E) and an optical property spline model of m different thin film materials respectively; geometric parameters of a thin film sample are randomly selected within a preset range to obtain a plurality of geometric parameter sets x1, x2, . . . , xn, each εj and set xk are input to a forward optical property model corresponding to measurement conditions in the training phase to obtain a theoretical optical characterization quantity ytj,k to construct a training set; wherein j∈[1, m], k∈[1, n],” is directed to math.
“S1′: a measured optical characterization quantity ymea of a thin film material to be tested under measurement conditions in the application phase is obtained and input into the trained neural network to obtain bpre and xpre, ” is directed to math.
“wherein a real part and an imaginary part of the dielectric function in the optical property spline model is calculated by the Kramers-Kronig consistency relation” is directed to math because the spline model relies on calculus, complex analysis, and numerical integration. The spline model ensures that the calculated the real part is consistent with the imaginary part because they are derived from the same analytic complex function.
Each limitation recites in the claim is a process that, under BRI covers performance of the limitation in the mind but for the recitation of a generic “measurement optical properties” which is a mere indication of the field of use. Nothing in the claim elements precludes the steps from practically being performed in the mind. Thus, the claim recites a mental process.
Further, the claim recites the step of “S1: a spline parameter bj is determined according to a dielectric function εj(E)=εj,1(E)+iεj,2(E) and an optical property spline model of m different thin film materials respectively; geometric parameters of a thin film sample are randomly selected within a preset range to obtain a plurality of geometric parameter sets x1, x2, . . . , xn, each εj and set xk are input to a forward optical property model corresponding to measurement conditions in the training phase to obtain a theoretical optical characterization quantity ytj,k to construct a training set; wherein j∈[1, m], k∈[1, n],” and “S1′: a measured optical characterization quantity ymea of a thin film material to be tested under measurement conditions in the application phase is obtained and input into the trained neural network to obtain bpre and xpre, wherein a real part and an imaginary part of the dielectric function in the optical property spline model is calculated by the Kramers-Kronig consistency relation” which as drafted, under BRI recites a mathematical calculation. The grouping of "mathematical concepts” in the 2019 PED includes "mathematical calculations" as an exemplar of an abstract idea. 2019 PEG Section |, 84 Fed. Reg. at 52. Thus, the recited limitation falls into the "mathematical concept" grouping of abstract ideas. This limitation also falls into the “mental process” group of abstract ideas, because the recited mathematical calculation is simple enough that it can be practically performed in the human mind, e.g., scientists and engineers have been solving the Arrhenius equation in their minds since it was first proposed in 1889.
Note that even if most humans would use a physical aid (e.g., pen and paper, a slide rule, or a calculator) to help them complete the recited calculation, the use of such physical aid does not negate the mental nature of this limitation. See October Update at Section I(C)(i) and (iii).
Additional Elements:
Step 2A Prong 2:
“A method for measuring optical properties and geometric properties of a thin film material” recited in the preamble does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“S1: a spline parameter bj is determined according to a dielectric function εj(E)=εj,1(E)+iεj,2(E) and an optical property spline model of m different thin film materials respectively; geometric parameters of a thin film sample are randomly selected within a preset range to obtain a plurality of geometric parameter sets x1, x2, . . . , xn, each εj and set xk are input to a forward optical property model corresponding to measurement conditions in the training phase to obtain a theoretical optical characterization quantity ytj,k to construct a training set; wherein j∈[1, m], k∈[1, n]” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“S2: ytj,k is taken as input, and corresponding bj and xk serve as outputs, the training set is adopted to train a neural network, and an application phase” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“S1′: a measured optical characterization quantity ymea of a thin film material to be tested under measurement conditions in the application phase is obtained and input into the trained neural network to obtain bpre and xpre ” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“S2′: bpre is input into the optical property spline model to obtain εpre ” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“ S3′: ε pre and xpre are input into a forward optical property model corresponding to the measurement conditions in the application phase to obtain a corresponding theoretical optical characterization quantity yt; the trained neural network is optimized with a goal of minimizing a deviation between ymea and yt, wherein the measurement conditions in the application phase are the same as or different from the measurement conditions in the training phase” does not integrate the judicial exception into a practical application. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“S4′: ymea is input into the optimized neural network to obtain b′pre and x′pre, and b′pre is input into the optical property spline model to obtain ε′t , wherein a real part and an imaginary part of the dielectric function in the optical property spline model is calculated by the Kramers-Kronig consistency relation“ is directed to insignificant activity and does not integrate the judicial exception into a practical application. See MPEP 2106.05(g).
The claim is merely collecting data, manipulating or analyzing the data using math and mental process, and displaying the results.
This is similar to electric power: MPEP 2106.05(h) vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
Claim 1 recites the additional element(s) of using generic AI/ML technology, i.e. a training phase or the training set is adopted to train a neural network, to perform data evaluations or calculations, as identified under Prong 1 above. The claims do not recite any details regarding how the AI/ML algorithm or model functions or is trained. Instead, the claims are found to utilize the AI/ML algorithm as a tool that provides nothing more than mere instructions to implement the abstract idea on a general purpose computer. See MPEP 2106.05(f). Additionally, the use of the training phase or the training set is adopted to train a neural network merely indicates a field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). Therefore, the use of the training phase or the training set is adopted to train a neural network to perform steps that are otherwise abstract does not integrate the abstract idea into a practical application. See the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence; and Example 47, ineligible claim 2.
The claim as a whole does not meet any of the following criteria to integrate the judicial exception into a practical application:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Step 2B:
“A method for measuring optical properties and geometric properties of a thin film material” recited in the preamble does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“S1: a spline parameter bj is determined according to a dielectric function εj(E)=εj,1(E)+iεj,2(E) and an optical property spline model of m different thin film materials respectively; geometric parameters of a thin film sample are randomly selected within a preset range to obtain a plurality of geometric parameter sets x1, x2, . . . , xn, each εj and set xk are input to a forward optical property model corresponding to measurement conditions in the training phase to obtain a theoretical optical characterization quantity ytj,k to construct a training set; wherein j∈[1, m], k∈[1, n]” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“S2: ytj,k is taken as input, and corresponding bj and xk serve as outputs, the training set is adopted to train a neural network, and an application phase” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“S1′: a measured optical characterization quantity ymea of a thin film material to be tested under measurement conditions in the application phase is obtained and input into the trained neural network to obtain bpre and xpre ” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“S2′: bpre is input into the optical property spline model to obtain εpre ” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“ S3′: ε pre and xpre are input into a forward optical property model corresponding to the measurement conditions in the application phase to obtain a corresponding theoretical optical characterization quantity yt; the trained neural network is optimized with a goal of minimizing a deviation between ymea and yt, wherein the measurement conditions in the application phase are the same as or different from the measurement conditions in the training phase” does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
“S4′: ymea is input into the optimized neural network to obtain b′pre and x′pre, and b′pre is input into the optical property spline model to obtain ε′t, wherein a real part and an imaginary part of the dielectric function in the optical property spline model is calculated by the Kramers-Kronig consistency relation “ is directed to insignificant activity and does not amount to significantly more than the judicial exception in the claim. See MPEP 2106.05(g) and 2106.05(d)(ii), third list, (iv).
The claim is therefore ineligible under 35 USC 101.
Claim 9 is similar to claim 1 but recites “A system for determining optical properties and geometric properties of a thin film material, comprising: a computer-readable storage medium and a processor; wherein the computer-readable storage medium is configured to store executable instructions; the processor is configured to read executable instructions stored in the computer-readable storage medium, and execute a method”. These additional elements fail to integrate the abstract idea into a practical application. These limitations are recited at a high level of generality and do not add significantly more to the judicial exception. These elements are generic computing devices that perform generic functions. Using generic computer elements to perform an abstract idea does not integrate an abstract idea into a practical application. See 2019 Guidance, 84 Fed. Reg. at 55. Moreover, “the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.” Alice, 573 U.S. at 223; see also FairWarninglP, LLCv. latric SysInc., 839 F.3d 1089, 1096 (Fed. Cir. 2016) (citation omitted) (“[T]he use of generic computer elements like a microprocessor or user interface do not alone transform an otherwise abstract idea into patent-eligible subject matter”). On the record before us, we are not persuaded that the hardware of claim 9 integrates the abstract idea into a practical application. Nor are we persuaded that the additional elements are anything more than well-understood, routine, and conventional so as to impart subject matter eligibility to claim 9.
Claim 9 is drawn to a "computer readable medium". The broadest reasonable interpretation of a claim drawn to a computer readable medium covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent (see MPEP 2111.01). Because the broadest reasonable interpretation covers a signal per se, a rejection under 35 USC 101 is appropriate as covering non-statutory subject matter. See 351 OG 212, Feb 23 2010.
Claim 10 cites a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions for causing, when the computer instructions read by a processor, the processor is configured to execute a method as in claim 1. This amounts to nothing more than instructions to implement the abstract idea on a computer, which fails to integrate the abstract idea into a practical application. See 2019 Guidance, 84 Fed. Reg. at 55. Additionally, using instructions to implement an abstract idea on a generic computer “is not ‘enough’ to transform an abstract idea into a patent-eligible invention.” Alice, 573 U.S. at 226. Therefore, the rejection of claim 10 for the same reason discussed above with regard to the rejection of claim 1.
Claim 10 is drawn to a "computer readable medium". The broadest reasonable interpretation of a claim drawn to a computer readable medium covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent (see MPEP 2111.01). Because the broadest reasonable interpretation covers a signal per se, a rejection under 35 USC 101 is appropriate as covering non-statutory subject matter. See 351 OG 212, Feb 23 2010.
Specifically, the language "non-transitory" must be added to at least claim 9 and 10.
Regarding claim 2, “wherein in step S1, the optical property spline model is established based on a B-spline model, or the optical property spline model is established based on the B-spline model and a pole oscillator model” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 3, “wherein when a cubic B-spline model is adopted, the optical property spline model based on the B-spline model and the pole oscillator model is established as follows:
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“ is directed to math.
Regarding claim 4, “wherein the forward optical property model is established based on a thin film transfer matrix method, a rigorous coupled wave analysis, a boundary element method or a finite-difference time-domain method, and corresponding measurement conditions; the measurement conditions are the measurement conditions in the application phase or the measurement conditions in the training phase” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 5, “wherein in step S1′, the measured optical characterization quantity of the thin film material to be tested under preset measurement conditions is obtained through an ellipsometer; in step S2′, the deviation is a mean square error or a mean absolute error” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 6, “wherein the optical characterization quantity is at least one of a reflectance, a transmittance, ellipsometric parameters, and a Mueller matrix; the geometric parameter is at least one of a thickness, a roughness and a non-uniformity” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 7, “wherein the preset measurement conditions comprise: an incident angle, a measurement wavelength, a material and a thickness of a substrate” is directed to math.
Regarding claim 8, “wherein the neural network is a fully connected neural network or a convolutional neural network” does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Regarding claim 11, “wherein the preset measurement conditions comprise: an incident angle, a measurement wavelength, a material and a thickness of a substrate” is directed to math.
Hence the claims 1-11 are treated as ineligible subject matter under 35 U.S.C. § 101.
Response to Arguments
Applicant's arguments filed 03/03/2026 have been fully considered but they are not persuasive.
-Applicant argues that the steps recited in training phase and the application phase cannot be performed by human mind and does not recite a mental process. For example, in "S2: ytj,k is taken as input, and corresponding bj and xk serve as outputs, the training set is adopted to train a neural network", which involves "to train a neural network", this cannot be performed by human mind and does not recite a mental process.
Response: The examiner respectfully disagrees.
Whether the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015). In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
Claim 1 recites the additional element(s) of using generic AI/ML technology, i.e. a training phase or the training set is adopted to train a neural network, to perform data evaluations or calculations, as identified under Prong 1 above. The claims do not recite any details regarding how the AI/ML algorithm or model functions or is trained. Instead, the claims are found to utilize the AI/ML algorithm as a tool that provides nothing more than mere instructions to implement the abstract idea on a general purpose computer. See MPEP 2106.05(f). Additionally, the use of the training phase or the training set is adopted to train a neural network merely indicates a field of use or technological environment in which the judicial exception is performed. See MPEP 2106.05(h). Therefore, the use of the training phase or the training set is adopted to train a neural network to perform steps that are otherwise abstract does not integrate the abstract idea into a practical application. See the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence; and Example 47, ineligible claim 2.
The claim does not provide any details about how the trained machine model operates, and the plain meaning of “inputting” and “obtaining” encompasses mental observations or evaluations, e.g., a computer programmer’s mental inputting and obtaining the parameters.
The additional element: “wherein a real part and an imaginary part of the dielectric function in the optical property spline model is calculated by the Kramers-Kronig consistency relation” is directed to math because the spline model relies on calculus, complex analysis, and numerical integration. The spline model ensures that the calculated the real part is consistent with the imaginary part because they are derived from the same analytic complex function. It does not integrate the judicial exception into a practical application. It does not amount to significantly more than the judicial exception in the claim. This additional element is merely using a computer as a tool to perform an abstract idea (see MPEP 2106.05(h)).
Conclusion
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 extension fee 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 date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN H LE whose telephone number is (571)272-2275. The examiner can normally be reached on Monday-Friday from 7:00am – 3:30pm ET.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shelby A. Turner can be reached on (571) 272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN H LE/Primary Examiner, Art Unit 2857
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