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 Arguments
Applicant's arguments filed 01/22/26 have been fully considered but they are not persuasive.
With respect to the 35 U.S.C. 101 rejection, the applicant has amended the claims, in order to try to overcome the 101 rejection. Details are given in the 101 rejection below, as to why the newly amended limitations are not considered to overcome the 101 rejection.
The applicant’s Prong One (1) rejections are moot, as the majority of the amended limitations were evaluated under step 2A, prong two.
For step 2A, prong two, the applicant argued, “MPEP 2106.04(II)(A)(2) emphasizes “whether the claim as a whole integrates the exception into a practical application of that exception.” (emphasis mine).
The examiner did consider the claims, as a whole. As stated in the rejection below, as a whole, claim 1 is directed to collecting data through a general and generic spectral measurement device, processing that data through mathematical computer processing techniques, and then outputting the result, where the output data stays on the computer. The examiner suggests that if there is some affirmative action, where the data comes off the computer and is used for some sort of real-world transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), that such a transformation be affirmatively and positively recited. For example, is there a real-world change to the optical measurement system or measuring object as a result of the data processing? If so, the examiner suggests that the applicant affirmatively and positively recite this in the claims and also show support for where such a transformation is supported in the applicant’s disclosure.
The applicant also argues that because no art rejection was made, the 101 rejection should also be overcome. However, the analysis for 101 is different than the analysis for 102 and 103. Detailed explanation for the 101 analysis is given in the rejection below.
The rejection is maintained.
Examiner’s Note - Information Disclosure Statement
The examiner noted that no IDS was filed, but there was an International Search Report filed. Even though no IDS was filed, the examiner did consider all of the documents considered to be relevant by the International Search Report.
Drawings
The amended drawings of 01/22/26 are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the amended description of 01/22/26: 122. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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 and 13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
With respect to step 1 of the patent subject matter eligibility analysis, the claims are directed to a process, machine, manufacture, or composition of matter. Independent claim 1 is directed to a computer-implemented method for an optical measurement system, which is a process. Independent claim 11 is directed to a computing device, which is a machine. Independent claim 13 is directed to a measuring system, which is a machine. Claims 2-10 depend on claim 1. As such, claims 1-11 and 13 are directed to a statutory category.
With respect to step 2A, prong one, the claims recite an abstract idea, law of nature, or natural phenomenon. Specifically, the following limitations recite mathematical concepts and/or mental processes.
Claim 1
generating input data for inputting to a neural network model based on coordinate data of dispersion curve data and preset values of geometric parameters with respect to a reference model (This limitation recites abstract mathematical concepts in the form of mathematical relationships between mathematical data (i.e. coordinate data of dispersion curve data and preset values of geometric parameters) and a reference model. Please also note that paragraph 0062 of the applicant’s original specification explicitly defines the input data in the form of a mathematical equation. Please see image below.)
PNG
media_image1.png
262
910
media_image1.png
Greyscale
extracting features of the input data based on the neural network model trained via a plurality of samples, so as to generate simulated dispersion curve data associated with the preset values of the geometric parameters, the simulated dispersion curve data indicating a plurality of optical responses corresponding to a plurality of coordinate data of the dispersion curve data (This limitation recites abstract mathematical concepts in the form of mathematical relationships between the simulated dispersion curve data and the optical responses corresponding to a plurality of coordinate data of the dispersion curve data.)
calculating a distance of the measured dispersion curve data from the simulated dispersion curve data, so as to determine whether the distance meets a predetermined condition (This limitation recites abstract mathematical concepts in the form of an explicit mathematical calculation.)
in response to determining the distance does not meet the predetermined condition, determining a gradient for updating the preset values of the geometric parameters with respect to the reference model based on the distance, so as to regenerate simulated dispersion curve data via the neural network model based on the updated preset values to recalculate the distance (This limitation recites abstract mathematical concepts. It discloses determining a gradient, which defines a mathematical relationship, as one of ordinary skill in the art recognizes that in a machine learning context, a gradient is a vector. The limitation also discloses recalculating the distance, which recites abstract mathematical calculations.)
in response to determining the distance meets the predetermined condition, determining and outputting the geometric parameters of the measuring object based on preset values corresponding to the input data for generating the current simulated dispersion curve data (This limitation recites abstract mathematical concepts in the form of mathematical relationships. As noted in paragraph 0062 of the applicant’s original specification, the input data, which was represented by a specific equation (as shown above), includes four geometric parameters, such as upper base of the trapezoid, lower base of the trapezoid, period of the grating, and etching depth. These serve as variables in the listed mathematical equation and form mathematical relationships, in relation to each other and also the coordinate data.)
Independent claims 11 and 13 disclose similar limitations that recite abstract mathematical concepts.
Dependent claims 2-10 depend on independent claim 1 and also recite its abstract limitations by virtue of their dependence. In addition, the dependent claims also recite their own abstract mathematical concepts and/or mental processes.
Claim 2 states that the coordinate data comprises: angle and wavelength, or frequency and wave vector. This recites mathematical relationships in the form of defining the variables of the input data equation that was discussed above.
Claim 3 further describes what determining the gradient entails, and as discussed above, the gradient, as a vector, defines mathematical relationships.
Claim 4 further describes what generating the simulated dispersion curve entails, and it describes abstract mathematical relationships between geometric parameters and coordinate data.
Claim 5 discloses, “wherein the simulated dispersion curve data comprises a thin film interference portion indicating a smooth change and a grating energy band portion indicating an abrupt-sharp change.” This discloses an abstract mathematical relationship between the various portions.
Claim 6 discloses training the neural network model based on a rigorous coupled wave analysis algorithm or a finite different time domain algorithm. This recites abstract mathematical concepts, in the form of specific formulas or equations.
Claim 7 recites further abstract mathematical concepts, such as the mathematical relationships between geometric parameters and coordinate data.
Claim 8 discloses further abstract mathematical concepts, such as the mathematical relationships between the simulated candidate simulated dispersion curve data and the measured dispersion curve data.
Claim 9 discloses Euclidean distance between measured data and simulated data, which defines an abstract mathematical relationship. Euclidean distance is also defined by a specific mathematical equation. The claim therefore recites abstract mathematical concepts.
Claim 10 discloses a dispersion curve graph, which is a graphical representation of various abstract mathematical relationships. The claim therefore recites abstract mathematical concepts.
With respect to step 2A, prong two, the claims do not recite additional elements that integrate the judicial exception into a practical application. The following limitations are considered “additional elements” and explanation will be given as to why these “additional elements” do not integrate the judicial exception into a practical application.
Claim 1
A computer-implemented method for an optical measurement system configured for measuring a structure of a measuring object, the optical measurement system comprising a spectral measurement device and a computing device (This limitation is not indicative of integration into a practical application for a number of reasons. First, the inclusion of “computer-implemented” merely implements an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). This is not indicative of integration into a practical application. Next, the generic mention of an optical measurement system and a spectral measurement device merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). This is not indicative of integration into a practical application.)
measuring, by the spectral measurement device, dispersion curve data in relation to the measuring object, the measured dispersion curve data being based on measurement of the measuring object in momentum space under irradiation of incident light (This limitation is not indicative of integration into a practical application because it merely serves to collect the data that will be processed by the mathematical data processing limitations. A general obtaining of data to be data processed merely adds insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)). The context of the data, such as dispersion curve data based on measurement of the measuring object in momentum space under irradiation of incident light, merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).)
receiving, by the computing device from the spectral measurement device, the measured dispersion curve data, the measured dispersion curve data being converted at a momentum-wavelength coordinate or an angle-wavelength coordinate (This limitation is not indicative of integration into a practical application for similar reasons as those given for the preceding limitations. Receiving data to be processed merely adds insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)). Using a computing device merely implements an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)). Giving technological context to the type of data that is processed by the computer merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).)
by the computing device (As discussed above, mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, is not considered to be indicative of integration into a practical application.)
the determined geometric parameters being indicative of the structure of the measuring object (This limitation is not indicative of integration into a practical application because it merely serves to generally link the use of the judicial exception to a particular technological environment or field of use. There is a distinction between a positive recitation of structure versus disclosing data that is about structure. Here, all that is claimed is the data. There is no positive recitation of structure.)
As a whole, claim 1 above is directed to collecting data through a general and generic spectral measurement device, processing that data through mathematical computer processing techniques, and then outputting the result, where the output data stays on the computer. The examiner suggests that if there is some affirmative action, where the data comes off the computer and is used for some sort of real-world transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), that such a transformation be affirmatively and positively recited. For example, is there a real-world change to the optical measurement system or measuring object as a result of the data processing? If so, the examiner suggests that the applicant affirmatively and positively recite this in the claims and also show support for where such a transformation is supported in the applicant’s disclosure.
Dependent claims 2-10 depend on independent claim 1 and also recite its limitations that are not indicative of integration into a practical application by virtue of their dependence. In addition, some of the other claims also recite their own limitations that are not indicative of integration into a practical application.
Claim 5
the measuring object being a grating (This merely serves to generally link the use of the judicial exception to a particular technological environment or field of use. Please also note that the limitation is directed to processing data about the grating and does not positively recite the grating itself.)
Claim 7
configured on a plurality of GPUs (This element is not indicative of integration into a practical application because it merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).)
Claim 11
A computing device (This limitation is not indicative of integration into a practical application because it merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).)
associated with an optical measurement system (This limitation is not indicative of integration into a practical application because it merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).)
a memory configured to store one or more computer programs (This limitation is not indicative of integration into a practical application because it merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).)
a processor coupled to the memory and configured to execute the one or more programs (This limitation is not indicative of integration into a practical application because it merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).)
to cause the optical measurement system to: receive, by the computing device from a spectral measurement device of the optical measurement system, measured dispersion curve data in relation to a measuring object, the measured dispersion curve data being based on measurement of the measuring object in momentum space under irradiation of incident light, the measured dispersion curve data being converted at a momentum-wavelength coordinate or an angle-wavelength coordinate (This limitation is not indicative of integration into a practical application for similar reasons as given with respect to claim 1 above.)
by the computing device (This limitation is not indicative of integration into a practical application for similar reasons as given with respect to claim 1 above.)
the determined geometric parameters being indicative of a structure of the measuring object (This limitation is not indicative of integration into a practical application for similar reasons as given with respect to claim 1 above.)
Claim 13
A measuring system (This limitation is not indicative of integration into a practical application because the general mention of “measuring” here merely serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)).)
an angle-resolved spectrometer configured to measure a measuring object based on incident light so as to generate an optical energy band with respect to the measuring object (This limitation is not indicative of integration into a practical application because the disclosure of a general and generic spectrometer, that performs the claimed operations, merely serves to generally link the use of the judicial exception to a particular technological environment or field of use. Also, measuring data merely adds insignificant extra-solution activity to the judicial exception.)
a computing device (This limitation is not indicative of integration into a practical application because it merely uses a computer as a tool to perform an abstract idea (see MPEP 2106.05(f)).)
receive, from the angle-resolved spectrometer, measured dispersion curve data in relation to the measuring object, the measured dispersion curve data being converted at a momentum-wavelength coordinate or an angle-wavelength coordinate (This limitation is not indicative of integration into a practical application for similar reasons as given with respect to claim 1 above.)
the determined geometric parameters being indicative of a structure of the measuring object (This limitation is not indicative of integration into a practical application for similar reasons as given with respect to claim 1 above.)
With respect to step 2B, the claims do not recite additional elements that amount to significantly more than the judicial exception. The claimed invention does not add significantly more because, as discussed above in step 2A, prong two, the claims do nothing more than merely use a computer as a tool to perform an abstract idea; add insignificant extra-solution activity to the judicial exception; and/or generally link the use of the judicial exception to a particular technological environment or field of use. The claims are directed to receiving and processing data. This is well-understood, routine, and conventional. Simply appending well-understood, routine, and conventional activities previously known to the industry, and specified at a high level of generality, to the judicial exception is not indicative of an inventive concept (aka “significantly more”) (see MPEP 2106.05(d) and Berkheimer Memo).
Examiner’s Note - Allowable Subject Matter
With respect to claim 1, the following limitations, when considered as a whole, were not found, taught, disclosed, or suggested in the prior art. Please note that the claims cannot be allowed until the above 101 rejection is overcome.
receiving, by the computing device from the spectral measurement device, the measured dispersion curve data, the measured dispersion curve data being converted at a momentum-wavelength coordinate or an angle-wavelength coordinate
generating, by the computing device, input data for inputting to a neural network model based on coordinate data of dispersion curve data and preset values of geometric parameters with respect to a reference model
extracting, by the computing device, features of the input data based on the neural network model trained via a plurality of samples, so as to generate simulated dispersion curve data associated with the preset values of the geometric parameters, the simulated dispersion curve data indicating a plurality of optical responses corresponding to a plurality of coordinate data of the dispersion curve data
in response to determining the distance does not meet the predetermined condition, determining, by the computing device, a gradient for updating the preset values of the geometric parameters with respect to the reference model based on the distance, so as to regenerate simulated dispersion curve data via the neural network model based on the updated preset values to recalculate the distance
in response to determining the distance meets the predetermined condition, determining and outputting, by the computing device, the geometric parameters of the measuring object based on preset values corresponding to the input data for generating the current simulated dispersion curve data, the determined geometric parameters being indicative of the structure of the measuring device
Claims 2-10 depend on claim 1 and also include subject matter that was not found, taught, disclosed, or suggested in the prior art, as a result of their dependence.
With respect to claim 11, the following limitations, when considered as a whole, were not found, taught, disclosed, or suggested in the prior art. Please note that the claims cannot be allowed until the above 101 rejection is overcome.
receive, by the computing device from a spectral measurement device of the optical measurement system, measured dispersion curve data in relation to a measuring object, the measured dispersion curve data being based on measurement of the measuring object in momentum space under irradiation of incident light, the measured dispersion curve data being converted at a momentum-wavelength coordinate or an angle-wavelength coordinate
generate, by the computing device, input data for inputting to a neural network model based on coordinate data of dispersion curve data and preset values of geometric parameters with respect to a reference model
extract, by the computing device, features of the input data based on the neural network model trained via a plurality of samples, so as to generate simulated dispersion curve data associated with the preset values of the geometric parameters, the simulated dispersion curve data indicating a plurality of optical responses corresponding to a plurality of coordinate data of the dispersion curve data
in response to determining the distance does not meet the predetermined condition, determine and output, by the computing device, a gradient for updating the preset values of the geometric parameters with respect to the reference model based on the distance, so as to regenerate simulated dispersion curve data via the neural network model based on the updated preset values to recalculate the distance, the determined geometric parameters being indicative of a structure of the measuring object
With respect to claim 13, the following limitations, when considered as a whole, were not found, taught, disclosed, or suggested in the prior art. Please note that the claims cannot be allowed until the above 101 rejection is overcome.
receive, from the angle-resolved spectrometer, measured dispersion curve data in relation to the measuring object, the measured dispersion curve data being converted at a momentum-wavelength coordinate or an angle-wavelength coordinate
generate input data for inputting to a neural network model based on coordinate data of dispersion curve data and preset values of geometric parameters with respect to a reference model
extract features of the input data based on the neural network model trained via a plurality of samples, so as to generate simulated dispersion curve data associated with the preset values of the geometric parameters, the simulated dispersion curve data indicating a plurality of optical responses corresponding to a plurality of coordinate data of the dispersion curve data
in response to determining the distance does not meet the predetermined condition, determine and output a gradient for updating the preset values of the geometric parameters with respect to the reference model based on the distance, so as to regenerate simulated dispersion curve data via the neural network model based on the updated preset values to recalculate the distance, the determined geometric parameters being indicative of a structure of the measuring object.
The closest piece of art found was Wang et al (US PgPub 20190041266). Wang et al discloses a spectroscopic metrology system that includes a spectroscopic metrology tool and a controller. The controller generates a model of a multilayer grating. Wang et al also discloses using dispersion curves (paragraphs 0019, 0029, 0073, 0077, 0090, and 0092-0096) and neural networks (paragraphs 0082 and 0084). However, Wang et al does not disclose the specific details of the claimed limitations above. It is also silent about determining a gradient, in response to determining the distance does not meet the predetermined condition.
Another relevant piece of art found was Chouaib et al (US PgPub 20200200525). Chouaib et al discloses methods and systems for measuring optical properties of transistor channel structures. Chouaib et al discloses using dispersion curves (paragraph 0095) and neural networks (paragraphs 106 and 121). Chouaib et al also discloses comparing simulated optical response signals with measured data (paragraph 0090). However, Chouaib et al does not disclose the specific details of the claimed limitations above. It is also silent about determining a gradient, in response to determining the distance does not meet the predetermined condition.
Another relevant piece of art found was Chuang et al (US PgPub 20190285407). Chuang et al discloses an overlay metrology system and method. Chuang et al discloses using dispersion curves (paragraphs 0012) and neural networks (paragraphs 0036, 0089, and 0091). However, Chuang et al does not disclose the specific details of the claimed limitations above. It is also silent about determining a gradient, in response to determining the distance does not meet the predetermined condition.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Fukshima et al (US PgPub 20050030611) discloses an optical device using photonic crystal and light beam deflection method using the same.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEONARD S LIANG whose telephone number is (571)272-2148. The examiner can normally be reached M-F 10:00 AM - 7 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ARLEEN M VAZQUEZ can be reached at (571)272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/LEONARD S LIANG/ Examiner, Art Unit 2857 05/22/26
/ARLEEN M VAZQUEZ/Supervisory Patent Examiner, Art Unit 2857
Prosecution Insights