CTNF 17/957,592 CTNF 88484 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. Continued Examination Under 37 CFR 1.114 07-42-04 AIA A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/19/2026 has been entered. Response to Arguments 07-37 AIA Applicant's arguments filed 3/19/2026 have been fully considered but they are not persuasive. Applicant argues, “Kim does not disclose or suggest the subject matter of claim 1 including ‘providing a plurality of data templates defining expected data for a production process, wherein the data points comprise information about input and output of the specific production process; providing plant data of the industrial plant, comprising data points of the specific production process.’” Kim teaches this in fig. 12 where the manufacturing data such as spalling and flaking are provided, see below. PNG media_image1.png 728 938 media_image1.png Greyscale Applicant argues, “There is no indication that the generic data input to the source network includes information about the inputs and outputs of a production process or that the specific features resulting from the source network includes or reflects inputs and output of the specific production process.” The source network is focused on the same industrial process as the later-trained networks. Fig. 13 above. Applicant argues, “The input signal traces themselves are unstructured data and do not indicate any identified combinations. To the extent the input signal traces are analogous to any claim element, that element is the unstructured plant data.” The claim recites “identified combinations”, this is not a term of art and it is not defined in the specification. The groupings of data in the feature space in fig. 13 and the input traces in Kim fig. 1 teach the claimed “identified combinations”. There is no structure to applicant’s claimed combinations, not in the claim and not in the specification. Kim teaches what is claimed. Applicant argues, “The Office Action alleges the source domain in Figure 2 is the process instance. The source domain appears to include the source neural network shown in Figure 1 and reflects the training of the source neural network on the source data and labels. The parameters of the source network are then transferred to the target network. However, Kim does not disclose that the parameters define a mapping between the plant data to the expected data of a specified industrial process.” Kim fig. 2 teaches that the source data and labels are mapped to each other by training parameters of a neural network, “obtain source parameters”, see below. The process instance is the data about the process in the source domain. PNG media_image2.png 590 344 media_image2.png Greyscale Applicant argues, “There is no disclosure or suggestion in Kim that the source data is obtained using the specific process instance…” Kim teaches this in fig. 13 above and tables 1-4 where process data is shown. Applicant argues, “his is different from what the claim requires, which is 'training a new machine learning model using the provided pre-trained model … This is different from what Kim discloses which is the transfer of parameters between two preexisting models.” Kim teaches transferring parameters in Fig. 2 and then initializing a new model with source parameters and retraining on the target domain to create a new model with target parameters. The new model in Kim is applicant’s claimed new model. Applicant argues, “Applicant respectfully submits that the recitation of ‘signal combinations are identified in the expected data’ and ‘data signal combinations as identified are grouped together in the training data’ precludes the various claimed operations of a computer- implemented method from being performed in the mind.” Claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include: a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016); a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011). Applicant’s claim recite collecting and analyzing information, MPEP 2106.04(a)(2) says that is a mental concept. Applicant argues, “The specification describes that identifying and grouping signals improves the performance of the new machine leaning model and facilitates learning.” Identifying groups of signals does not necessarily improve training. The specification doesn’t say that grouping signals improves training. Because the claims don’t improve training, they don’t integrate the abstract idea into a practical application. Applicant argues “the step of training the new machine learning model as recited in claim 1 is not a mental concept.” 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). Using the computer to do general training is a mere instruction to apply an exception. MPEP 2106.05(f); also July 2024 Subject Matter Eligibility Examples p. 8. Applicant argues “As amended, claim 1 is tied into the practical application of providing status data relating to the specific production process of the industrial plant using the new trained machine learning model.” MPEP 2106.05(g) states, “Below are examples of activities that the courts have found to be insignificant extra-solution activity:…Selecting a particular data source or type of data to be manipulated:… iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)…” Using industrial data in a machine learning model is insignificant extra solution activity, according to MPEP 2106.05(g). Therefore, the claims are patent ineligible . 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 1-7 and 10-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mental concept abstract idea without significantly more. The claims recite providing data, determining a process, determining historical data, determining training data and providing a pretrained model. This judicial exception is not integrated into a practical application because the additional elements of training don’t integrate the abstract idea into a practical application because they only generally link the abstract idea to machine learning. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the sensor data and industrial plant only generally link the abstract idea to the broad field of industry. 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 Claims 1-3 and 7-11 are r ejected under 35 U.S.C. 102(a )(1) as being d escribed by A New Parameter Repurposing Method for Parameter Transfer With Small Dataset and Its Application in Fault Diagnosis of Rolling Element Bearings b y K im et al. K im teaches claim 1. A computer-implemented method of transfer learning for a specific production process of an industrial plant, comprising: (Kim title “A New Parameter Repurposing Method for Parameter Transfer With Small Dataset and Its Application in Fault Diagnosis of Rolling Element Bearings”) providing a plurality of data templates defining expected data for a production process; (Kim fig. 2 “target domain, “source labels”, the production process is “bearing operations”. Sec. IV(A)1 and tables 1 and 2 show data templates.) providing plant data of the industrial plant, comprising data points of the specific production process, (Kim sec. IV(A) tables 1-4.) wherein the data points comprise information about input and output of the specific production process; (Kim fig. 2 “source domain” “Source data & labels”.) wherein the data template defines a grouping for the expected data according to their relation in the industrial plant, (Kim sec. II(A) Fig. 1d.) wherein the data templates comprise a list of the data points and measurements that are available from an asset of the specific production process of the industrial plant and wherein, (Kim sec. II(A) Fig. 1 shows groups of source data, target data and labels. These are the data templates. The template and their connection is shown in Kim sec. IV(A) tables 1-4.) when determining the data templates, signal combinations are identified in the expected data, and (The individual signal traces shown in Fig. 1 of Kim sec. II(A) are the signal combinations.) determining a process instance of the specific production process, defining a mapping between the plant data to the expected data of the specific production process by selecting a data template corresponding to the industrial plant data of the industrial plant; (Kim sec. III fig. 2 “source domain”, Kim fig. 13 shows the source network with data like severe flake and severe spall.) determining historic process data, being historic sensor data relating to the specific production process using the determined process instance; (Kim sec. IV(A) tables 1-4) determining training data using the determined process instance and the determined historic process data, (Kim sec. IV(A) fig. 2 source domain) wherein the training data comprises a structured data matrix, wherein columns of the data matrix represent the sensor data that are grouped in accordance with the data template, and wherein rows of the data matrix represent timestamps of obtaining the sensor data, wherein data signal combination as identified are grouped together in the training data; (Kim sec. III fig. 1, tables 1-4 and sec. IV figs. 4-8. The signal traces are kept together as one input trace for training, see Kim fig. 1.) providing a pre-trained machine learning model using the determined process instance; and (Kim fig. 2 “Source domain”, Kim abs “training the target network using the parameters of the source network…”) training a new machine learning model using the provided pre-trained model and the determined training data. (Kim fig. 2 “Target domain”, Kim abs “training the target network using the parameters of the source network…”) and providing, via the trained new machine learning model, status data for the specific production process of the industrial plant. (Kim Fig. 13 shows a trained network in (b) (c) and (d) which provides the status data for the rolling bearing production, e.g. sever spall.) Kim teaches claim 2. The method of claim 1, wherein determining the training data comprises preprocessing the historic process data, thereby standardizing a format of the training data. (Kim sec. IV(A) fig. 2 source domain, sec. IV(B) “Data augmentation is a preprocess performed to obtain better performance from a limited amount of training data.”) Kim teaches claim 3. The method of claim 2, wherein preprocessing the historic process data comprises adapting a sampling frequency to a standardized data matrix format. (Kim’s data shown in fig. 6 has a sampling frequency. This data is in the training data. As far as examiner can tell, this is what Applicant is claiming.) Kim teaches claim 7. The method of claim 1, wherein the pre-trained model comprises trained weights, and wherein training the new machine learning model comprises adjusting the trained weights. (Kim fig. 2 “Target domain”, Kim abs “training the target network using the parameters of the source network…”) Kim teaches claim 8. The method of claim 1, wherein the pre-trained machine learning model is an artificial neural network and comprises at least one layer, and wherein training the new machine learning model comprises: categorizing each layer using the determined process instance a froze category or a non-frozen category; and (Kim sec. III(A), “In CNN case, the average sensitivity within a kernel determines whether the kernel should be frozen or not.” The Kernel is the frozen layer here.) reusing the frozen category layers of the pre-trained machine learning model and retraining the non-frozen category layers of the pre-trained machine learning model. (Kim sec. III(A) “If the ξi . Is higher than α, the parameter θi is set to be untrainable in the training.”) Kim teaches claim 9. The method of claim 1, wherein the pre-trained machine learning model is an artificial neural network comprises at least one layer, and wherein training the new machine learning model comprises: categorizing each layer using the determined process instance in one of the categories frozen or non-frozen; and (Kim sec. III(A) “If the ξi . Is higher than α, the parameter θi is set to be untrainable in the training.”) applying different learning rates on the at least one layer depending on the determination if the layer is a frozen layer or a non-frozen layer. ((Kim sec. III(A) “If the ξi . Is higher than α, the parameter θi is set to be untrainable in the training.” Untrainable means a learning rate of 0. PNG media_image3.png 70 318 media_image3.png Greyscale .) Kim teaches claim 10. The method of claim 1, wherein the data points comprise input/output names of the specific production process, and wherein the historic process data is determined using the input/output names. (Kim sec. IV table 2 PNG media_image4.png 154 286 media_image4.png Greyscale . Item names are the IO names.) Kim teaches claim 11. The method of claim 1, wherein training the new machine learning model comprises using the data matrix as input for the new machine learning model to obtain a prediction as output from the new machine learning model. (Kim abs “Freezing only sensitive parameters while training is to reduce the amount of trainable parameter and protect informative parameters from overfitting to a small number of target data. Using two sets of the source–target domain, artificial faults with different fault size and artificial faults—natural faults of rolling element bearing, the proposed SPF allows adaptation to the target domain by choosing the best degree of freezing with various amounts of target data and size of networks compared to conventional approaches.”) Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-21-aia AIA Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over A New Parameter Repurposing Method for Parameter Transfer With Small Dataset and Its Application in Fault Diagnosis of Rolling Element Bearings by Kim et al and https://stats.stackexchange.com/questions/70801/how-to-normalize-data-to-0-1-range (Stack) 07-21-aia AIA Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over A New Parameter Repurposing Method for Parameter Transfer With Small Dataset and Its Application in Fault Diagnosis of Rolling Element Bearings by Kim et al and US 20210174580 A1 Mundy et al . 07-21-aia AIA Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over A New Parameter Repurposing Method for Parameter Transfer With Small Dataset and Its Application in Fault Diagnosis of Rolling Element Bearings by Kim et al and US 20170132538 A1 to Ohara et al . Kim teaches claim 4. The method of claim 2, wherein preprocessing the historic process data comprises scaling the historic process data to a 0-1 domain. (Kim fig. 7 shows enveloped data, instead of 0-1 the data goes from 0 to 2000 or 1000.) Kim doesn’t teach 0-1 domain. Stack teaches a 0-1 domain. (Stack PNG media_image5.png 84 224 media_image5.png Greyscale ) Stack, Kim and the claims all normalize data. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to normalize from 0-1 to capture the meaning of the data points inside the context of the whole dataset and minimize impact of large numbers that should have an outsize effect on results. Kim teaches claim 5. The method of claim 2, wherein preprocessing the historic process data comprises fusing missing data points of the historic process data from available data points of the historic process data. Kim doesn’t teach fusing data for missing data points. However, Mundy teaches preprocessing the historic process data comprises fusing missing data points of the historic process data from available data points of the historic process data. (Mundy para 60 “if a plurality of stereoscopic image pairs are available at different times of the data and from different viewpoints, then the missing data values can be filled in by fusing multiple point clouds.”) Kim, Mundy and the claims preprocess data. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to fuse data to fill in for missing data in order to “automatically and efficiently process” data. Kim teaches claim 6. The method of claim 2, wherein preprocessing the historic process data comprises removing outliers from the historic process data. Kim doesn’t teach fusing data for missing data points. Ohara teaches preprocessing the historic process data comprises removing outliers from the historic process data. (Ohara para 9 “A plant model creating device may include an outlier remover configured to remove outliers from operating data of a plant…”) Ohara, Kim and the claims all preprocess industrial data. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to remove outliers ”in order to create the model of the plant more precisely.” Ohara para 7. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to Austin Hicks whose telephone number is (571)270-3377. The examiner can normally be reached Monday - Thursday 8-4 PST. 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, Mariela Reyes can be reached at (571) 270-1006. 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. /AUSTIN HICKS/Primary Examiner, Art Unit 2142 Application/Control Number: 17/957,592 Page 2 Art Unit: 2142 Application/Control Number: 17/957,592 Page 3 Art Unit: 2142 Application/Control Number: 17/957,592 Page 4 Art Unit: 2142 Application/Control Number: 17/957,592 Page 5 Art Unit: 2142 Application/Control Number: 17/957,592 Page 6 Art Unit: 2142 Application/Control Number: 17/957,592 Page 7 Art Unit: 2142 Application/Control Number: 17/957,592 Page 8 Art Unit: 2142 Application/Control Number: 17/957,592 Page 9 Art Unit: 2142 Application/Control Number: 17/957,592 Page 10 Art Unit: 2142 Application/Control Number: 17/957,592 Page 11 Art Unit: 2142 Application/Control Number: 17/957,592 Page 12 Art Unit: 2142 Application/Control Number: 17/957,592 Page 13 Art Unit: 2142 Application/Control Number: 17/957,592 Page 14 Art Unit: 2142 Application/Control Number: 17/957,592 Page 15 Art Unit: 2142 Application/Control Number: 17/957,592 Page 16 Art Unit: 2142 Application/Control Number: 17/957,592 Page 17 Art Unit: 2142
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