CTNF 17/557,754 CTNF 100516 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 02/17/2026 has been entered. Response to Amendment The following is in response to the amendment filed on October 6, 2025. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. 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 s 1, 4-6, and 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al, “Deep representation clustering-based fault diagnosis method with unsupervised data applied to rotating machinery” in view of Nikitaki et al, (Pub No: WO 2017067615), Hadad et al (US PG Pub 2020/0202997) and Tomar et al “A Survey on Pre-processing and Post-processing Techniques in Data Mining” . With respect to claim 1: Li teaches: A processor-implemented method for measuring data of an artificial intelligence (AI) system, the method comprising: Starting data measurement by one or more processors based on a predetermined data configuration of data required for development of an AI model when design of the AI model starts on the AI system; (Page 3 Paragraph 1-3, discloses measuring collected data, i.e. classifying and quantifying data based on completeness and accuracy) Storing raw data measured through a data measurement and generating data processed for AI model learning or machine learning (ML) by pre-processing the raw data; (Section 2.2, discloses a k means clustering method taking the high level data (raw data) and processing it to create a labeled data set (processed data)) Completing the development of the AI model by learning and validating the AI model designed based on the raw data and/or the pre-processed raw data. (Section 2.2, discloses training a discriminative model using the pre-processed data) Li does not appear to explicitly disclose: wherein the predetermined data configuration includes a measurement profile of the data required for the development of the Al model, wherein the starting data measurement is performed in a network according to the measurement profile, Nikitaki teaches: wherein the predetermined data configuration includes a measurement profile of the data required for the development of the Al model, wherein the starting data measurement is performed in a network according to the measurement profile, (Nikitaki, [0051], “The raw data may include metrics data. In some cases, the data intake and query system can receive structured metrics data including, for example, a time series of metrics generated for a computing resource.” This teaches that the system is measuring data or having metric data that measurements of data.). Li in view of Nikitaki, are analogous because they are from the same field of endeavor and their disclosure generally relates to the field of collecting data and collecting data for development of AI model. Accordingly, it would have been obvious to a person having ordinary skill in the art, before the effective fining data of a claimed invention, having the combination of Li and Nikitaki before them, to incorporate Li’s and Nikitaki’s teachings. One would have been motivated to make such combination in order to not only to collect and store raw data but also to collect metrics data for the development AI model. The combination of Li and Nikitaki does not appear to explicitly disclose: wherein the measuring data in the network according to the measurement profile includes determining raw data to be measured according to the measurement profile and determining a measurement location and a measurement target for the raw data, Hadad teaches: wherein the measuring data in the network according to the measurement profile includes determining raw data to be measured according to the measurement profile and determining a measurement location and a measurement target for the raw data, (Hadad, [0090], “The retrieving module 230 may also retrieve data from applications that push data into the platform. Examples of services and devices that can push data into the platform may include Abbott FreeStyle Libre, Garmin, FitBit, and the like. These devices can send notifications to the platform 150 , which can be received by the retrieving module 230 . In response, the retrieving module 230 can pull data from these applications and consolidate/store the data into the stream or a plurality of streams. In some instances, the data may be sent to the platform concurrently with the notifications, thus eliminating the need for the retrieving module 230 to pull data in response. In some embodiments, the retrieving module 230 can also store tokens created by the token module 210 . Tokens may be stored in a serverless database provided with the retrieving module.” This teaches that the retrieving module will store the raw data to a predefine location and will also determine which raw data set belongs to which source.) It would have been obvious to a person having ordinary skill in the art, before the effective fining data of a claimed invention, having the combination of Li, Nikitaki and Hadad before them, to incorporate Li’s, Nikitaki’s and Hadad’s teachings. One would have been motivated to make such combination in order as to have know what data needs to be measured and what the source for that data is. The combination of Li, Nikitaki and Hadad does not appear to explicitly disclose: And wherein the predetermined data configuration includes a post data processing profile defining a post-processing policy. Tomar teaches: And wherein the predetermined data configuration includes a post data processing profile defining a post-processing policy. (Tomar, page 117 – 124, section 3, teaches post-processing techniques for use to train machine learning models and to process the data that is produced from machine learning algorithms.) Li in view of Tomar, are analogous because they are from the same field of endeavor and their disclosure generally relates to the field of collecting data and collecting data for development of AI model. Accordingly, it would have been obvious to a person having ordinary skill in the art, before the effective fining data of a claimed invention, having the combination of Li, Nikitaki, Hadad, and Tomar before them, to incorporate of Li, Nikitaki, Hadad, and Tomar’s teachings. One would have been motivated to make such combination in order as to process the data that is output from machine learning for further training or use in another field. Claims 6 and 11 are rejected on the same grounds as Claim 1. With respect to claim 4: Hadad teaches: The predetermined data configuration further includes a pre-processing profile of the data required for the development of the AI model, and the generation data processed for ML by pre-processing the raw data includes pre-processing the raw data according to the pre-processing profile. (Hadad, [0111], “FIG. 7 shows the components of the storage module 290 in accordance with some embodiments. The storage module 290 may include a data monitoring module 720 , a data classification module 750 , and a data storage module 780 .”, Hadad, [0113], “The data classification module 750 may contain one or more lambda functions that perform pre-processing on these files before the collected data can be classified. Pre-processing activities may include unzipping and decrypting the files.” This teaches that the storage module has a predefined action to pre-process the raw data that will be used for developing a machine learning model later on.) With respect to claim 5: The predetermined data configuration further includes a data storing process profile of the data required for the development of the AI model, and the method further comprising storing the raw data and the pre-processed data according to the data storing process profile after the generating data processed for ML by pre-processing the raw data. (Hadad, [0111], “FIG. 7 shows the components of the storage module 290 in accordance with some embodiments. The storage module 290 may include a data monitoring module 720, a data classification module 750, and a data storage module 780. The storage module's components may operate on passively collected data. The passive data may be collected from applications on a user mobile device.”, Hadad, [0113], “The data classification module 750 may receive encrypted files that contain passively collected data. The data classification module 750 may contain one or more lambda functions that perform pre-processing on these files before the collected data can be classified. Pre-processing activities may include unzipping and decrypting the files. The data classification module may use lambda functions to classify the collected data. Classification may involve using machine learning or deep learning techniques, such as convolutional or recurrent neural networks … After classification has been completed, classified data may be stored in a debug bucket in order to troubleshoot the classifier.” Hadad, [0114], “The data storage module 780 may store passive data that has been classified. Stored data can be analyzed by third party applications, and can provide users with analytics (such as geolocation, file resolution, and camera module data) to improve data analysis models.” These paragraphs teaches that the storage module has a predetermined function to store the raw data and preprocessed data. Then preprocess the data based on the predetermined lambda functions for the AI or machine learning model.) Claims 9-10 are rejected on the same grounds as Claims 4-5. Response to Arguments Claim Rejections - 35 USC § 102 Applicant’s arguments are moot in view of the new ground(s) of rejection. Claim Rejections - 35 USC § 103 Applicant’s arguments are moot in view of the new ground(s) of rejection. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS B LANE whose telephone number is (571)272-1872. The examiner can normally be reached M-Th: 6:40am-4:40pm; F: Out of Office. 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. /THOMAS BERNARD LANE/Examiner, Art Unit 2142 /HAIMEI JIANG/Primary Examiner, Art Unit 2142 Application/Control Number: 17/557,754 Page 2 Art Unit: 2142 Application/Control Number: 17/557,754 Page 3 Art Unit: 2142 Application/Control Number: 17/557,754 Page 4 Art Unit: 2142 Application/Control Number: 17/557,754 Page 5 Art Unit: 2142 Application/Control Number: 17/557,754 Page 6 Art Unit: 2142 Application/Control Number: 17/557,754 Page 7 Art Unit: 2142 Application/Control Number: 17/557,754 Page 8 Art Unit: 2142
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