CTNF 18/691,726 CTNF 78032 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 12-24 and 26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 1: According to the first part of the analysis, in the instant case, claims 12-23 are directed to a method, claim 24 is directed to using a system to perform at least one process step, and claim 26 is directed to a non-transitory computer-readable storage medium. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). Regarding claim 12: A method for improving a production process in a technical installation in which a process-engineering process having at least one process step is implemented, data records characterizing an iteration of a process step and containing values of process variables being captured on a time-dependent basis and stored in a data memory, the method comprising: utilizing multivariate trend data of multiple iterations of a process step to train a model to detect anomalies; selecting, for each process step, the data records of an iteration as a test phase and the data records of at least one further iteration as a reference phase; determining, for each pair of iterations, a deviation between process values of test and reference phase for each time stamp of the test phase utilizing a model for detecting anomalies and weighting the deviation with an anomaly detection tolerance; determining anomaly states from weighted deviations between the process values of test and reference phase and evaluating the determined anomaly states; and calculating a phase similarity measure of the test phase compared to a reference phase via the evaluated anomaly states and utilizing the calculated phase similarity measure to analyze and subsequently optimize the process. Step 2A Prong 1 : “utilizing multivariate trend data of multiple iterations of a process step to train a model to detect anomalies” is directed to mental step of data gathering. “selecting, for each process step, the data records of an iteration as a test phase and the data records of at least one further iteration as a reference phase” is directed to mental step of selecting data. “determining, for each pair of iterations, a deviation between process values of test and reference phase for each time stamp of the test phase utilizing a model for detecting anomalies and weighting the deviation with an anomaly detection tolerance” is directed to math because the process compare two phases (test and reference) and adjust for tolerance. That is a practical application of mathematical modeling used to define what normal looks like and how much a new data point can drift before it is flagged. At each timestamp, you are calculating the difference between a test value and a reference value. This is often represented as Δt. Weighting the deviation involves a scaling function. If the anomaly detection tolerance is represented as Ʈ , the final weighted deviation or anomaly score S(t) might look like: S(t) = Δt/Ʈ. “determining anomaly states from weighted deviations between the process values of test and reference phase and evaluating the determined anomaly states” is directed to mental step of analyzing data. “calculating a phase similarity measure of the test phase compared to a reference phase via the evaluated anomaly states” is directed to math because when analyzing anomaly states, this calculation generally involves the following mathematical concepts: Phase angle, Vector Dot Products, Circular Statistics, Complex Analysis. “utilizing the calculated phase similarity measure to analyze and subsequently optimize the process” is directed to mental step of analyzing data. The claim recites the step of “determining, for each pair of iterations, a deviation between process values of test and reference phase for each time stamp of the test phase utilizing a model for detecting anomalies and weighting the deviation with an anomaly detection tolerance” and “calculating a phase similarity measure of the test phase compared to a reference phase via the evaluated anomaly states” 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 improving a production process in a technical installation in which a process-engineering process having at least one process step is implemented, data records characterizing an iteration of a process step and containing values of process variables being captured on a time-dependent basis and stored in a data memory” 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)). “utilizing multivariate trend data of multiple iterations of a process step to train a model to detect anomalies” 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)). “selecting, for each process step, the data records of an iteration as a test phase and the data records of at least one further iteration as a reference 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)). “determining, for each pair of iterations, a deviation between process values of test and reference phase for each time stamp of the test phase utilizing a model for detecting anomalies and weighting the deviation with an anomaly detection tolerance” 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)). “determining anomaly states from weighted deviations between the process values of test and reference phase and evaluating the determined anomaly states” 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)). “calculating a phase similarity measure of the test phase compared to a reference phase via the evaluated anomaly states” 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)). “utilizing the calculated phase similarity measure to analyze and subsequently optimize the process” 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 selecting 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 12 recites the additional element(s) of using generic AI/ML technology, i.e. train a model, 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 train a model 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 train a model 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 improving a production process in a technical installation in which a process-engineering process having at least one process step is implemented, data records characterizing an iteration of a process step and containing values of process variables being captured on a time-dependent basis and stored in a data memory” 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)). “utilizing multivariate trend data of multiple iterations of a process step to train a model to detect anomalies” 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)). “selecting, for each process step, the data records of an iteration as a test phase and the data records of at least one further iteration as a reference 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)). “determining, for each pair of iterations, a deviation between process values of test and reference phase for each time stamp of the test phase utilizing a model for detecting anomalies and weighting the deviation with an anomaly detection tolerance” 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)). “determining anomaly states from weighted deviations between the process values of test and reference phase and evaluating the determined anomaly states” 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)). “calculating a phase similarity measure of the test phase compared to a reference phase via the evaluated anomaly states” 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)). “utilizing the calculated phase similarity measure to analyze and subsequently optimize the process” 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 24 is similar to claim 12 but recites a system for improving a production process of a technical installation in which a process-engineering process having at least one process step is implemented, the system comprising at least: a memory unit for at least one of (i) storing historic data records with values of process variables determined on a time-dependent basis, which characterize an iteration of a process step (phase), (ii) storing metadata which is associated with the historic data records and (iii) storing at least one of tolerances, anomaly states, phase similarities and further data; a computing unit which is connected to the at least one memory unit, a evaluation unit for analyzing current data records of an iteration of a test phase via the computing unit; and a display unit for displaying and outputting the analysis results determined via the evaluation unit. 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 24 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 24. Claim 26 cites a non-transitory computer-readable storage medium encoded with a computer program which, when executed by a processor of a computer, causes a production process in a technical installation in which a process-engineering process having at least one process step is implemented to be improved, data records characterizing an iteration of a process step and containing values of process variables being captured on a time-dependent basis and stored in a data memory, the computer program comprising: program codes to perform the steps as in claim 12. 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 26 for the same reason discussed above with regard to the rejection of claim 12. Regarding claim 13 , “ wherein metadata of the reference phases is taken into account when optimizing the process; and wherein a correlation is created between the phase similarity measure of the test and reference phase with the metadata of the reference phases and, based on this correlation, at least one of statements about the test phase are determined and a cause analysis occurs using the metadata of the reference phases” is directed to mental step of analyzing data. Regarding claim 14, “ wherein the anomaly states are calculated; and wherein for each process step for the same process variables of the test and reference phase time stamp by time stamp, at least one of (i) a size of the differences or mathematical distances of values of the process variables, (ii) their tolerances and (iii) a difference in runtimes of the phases is determined” is directed to math. Regarding claim 15 , “ wherein the anomaly states are calculated; and wherein for each process step for the same process variables of the test and reference phase time stamp by time stamp, at least one of (i) a size of the differences or mathematical distances of values of the process variables, (ii) their tolerances and (iii) a difference in runtimes of the phases is determined” is directed to math. Regarding claim 16 , “ wherein the anomaly states are evaluated via at least one of weightings, averaging and categories” is directed to mental step of analyzing data. Regarding claim 17, “ wherein the anomaly states are evaluated via at least one of weightings, averaging and categories” is directed to mental step of analyzing data. Regarding claim 18, “ wherein the anomaly states are evaluated via at least one of weightings, averaging and categories” is directed to mental step of analyzing data. Regarding claim 19 , “ wherein the evaluation of the anomaly states follows a previously defined hierarchy” is directed to insignificant activity and does not integrate the judicial exception into a practical application. It 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). Regarding claim 20 , “ wherein the evaluation of the anomaly states follows a previously defined hierarchy” is directed to insignificant activity and does not integrate the judicial exception into a practical application. It 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). Regarding claim 21 , “ wherein the evaluation of the anomaly states follows a previously defined hierarchy” is directed to insignificant activity and does not integrate the judicial exception into a practical application. It 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). Regarding claim 22 , “ wherein similar phases are grouped based on the calculated phase similarity measure and a cause analysis is performed for the grouping via the metadata” is directed to math. Regarding claim 23, “wherein a ranking of the phase similarity measure is performed and phases with the greatest match between test and reference phases are displayed” is directed to insignificant activity and does not integrate the judicial exception into a practical application. It 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). Hence the claims 12-24 and 26 are treated as ineligible subject matter under 35 U.S.C. § 101. Claim 25 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claims are directed to ineligible software per se . The claim recites a computer program comprising a software application including program code instructions without any positive recitation of hardware structure within the scope of the claims, i.e. under the broadest reasonable interpretation any processor, memory, etc. for executing and storing the computer program/code is outside of the scope of the claimed system, e.g. the computer program is [intended] to cause a processor to perform functions. Therefore, under the broadest reasonable interpretation, claim 25 found to be directed to ineligible software per se (see MPEP 2106.03(I): “Non-limiting examples of claims that are not directed to any of the statutory categories include: Products that do not have a physical or tangible form, such as information (often referred to as "data per se") or a computer program per se (often referred to as "software per se") when claimed as a product without any structural recitations”). Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 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 – 07-08-aia AIA (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. 07-15-aia AIA Claim(s) 24 is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Frikha et al. (US 2022/0147871 A1) . Regarding claim 24, Frikha et al. disclose a system for improving a production process of a technical installation in which a process-engineering process having at least one process step is implemented (para. [0007]: … automated defect detection during the production/manufacture of a workpiece and/or product is the first step toward fully automatic, data-controlled quality control in factories and production installations), the system comprising at least: a memory unit for at least one of (iii) storing at least one of tolerances, anomaly states, phase similarities and further data (e.g. para. [0037]: 7. Rate the performance of the anomaly detection of the learning model on the basis of these examples. 8. Update the learning model parameters θ according to θ′ using a gradient method. 9. End for 10. Take n x k training examples for the detection of anomalies from the taken set of n processes. 11. Update the learning model parameters θ with reference to the error that arose from the model parameterized by θ′ on account of the gradient method. This feature is seen to be an inherent teaching a memory unit for at least one of (iii) storing at least one of anomaly states); a computing unit which is connected to the at least one memory unit, a evaluation unit for analyzing current data records of an iteration of a test phase via the computing unit (e.g. para. [0017]: the learning model is configured to be calibrated using normalized data of the at least one production process for the at least one workpiece and/or product. One or more sensors are configured to generate current data of a production process for a currently produced workpiece and to forward the data to the learning model. The learning model is configured to compare the currently generated data with the normalized data, to find deviations, and to scale the deviations between the currently generated data and the normalized data. The learning model is configured to communicate the presence of an anomaly for the currently produced workpiece/product. As discussed above, the learning model can be present for and executed on a computing unit which is connected to the at least one memory unit, a evaluation unit for analyzing current data records. In para. [0032]: the learning model 100 achieves the required adaptation to a new manufacturing process cycle simply by virtue of the data generated for the test runs during the calibration phase. This feature is seen to be an inherent teaching a computing unit which is connected to the at least one memory unit, a evaluation unit for analyzing current data records of an iteration of a test phase via the computing unit); and a display unit for displaying and outputting the analysis results determined via the evaluation unit (Fig.2, para. [0049]: In act S70, the learning model communicates the presence of an anomaly for the currently produced workpiece/product). 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 Eastern Time. 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. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /JOHN H LE/Primary Examiner, Art Unit 2857 Application/Control Number: 18/691,726 Page 2 Art Unit: 2857 Application/Control Number: 18/691,726 Page 3 Art Unit: 2857 Application/Control Number: 18/691,726 Page 4 Art Unit: 2857
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