CTNF 19/225,218 CTNF 86897 DETAIILED 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. This is in response to Application 19/225218 filed June 2, 2025 in which Claims 1-20 are presented for examination. 12-151 AIA 26-51 12-51 Status of Claims Claims 1- 20 are pending, of which claims 1-13 and 16-20 are rejected under Double Patenting. No prior art has been used for this rejection. Claims 14 and 15 are objected to. Allowable Subject Matter 12-151-08 AIA 07-43 12-51-08 Claim s 14 and 15 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Double Patenting 08-33 AIA The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg , 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman , 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi , 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum , 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel , 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington , 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA/25, or PTO/AIA/26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp. Claims 1-13 and 16-20 of the instant application are rejected on the ground of obviousness-type nonstatutory double patenting as being unpatentable over Claims 1-17 and 20 of U.S. Patent No. US 12,373,271. Although the claims at issue are not identical, they are not patentably distinct from each other because the aforementioned claims of the instant application are rejected based on obviousness-type double patenting with regards to the aforementioned parent patent. The following table summarizes claim mappings associated with the obviousness-type double patenting rejections: 19/225218 12,373,271 (18/201817) 1. A computing platform comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: generate, based on telemetry data for a plurality of computing systems over a period of time and for each parameter represented in the telemetry data, a telemetry state image, wherein each telemetry state image: plots the period of time on an x axis, plots the plurality of computing systems on a y axis, and is specific to a respective parameter represented in the telemetry data; classify, using an image comparison model and using parallel processing, the telemetry state images; identify, using the parallel processing and based on the classifications of the telemetry state images, a likelihood of failure for a technology infrastructure; and send, based on the likelihood of failure for the technology infrastructure, one or more preemptive resolution commands causing modification of operations at one or more of the plurality of computing systems to prevent a predicted failure. 1. A computing platform comprising: at least one processor; a communication interface communicatively coupled to the at least one processor; and memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to: train an image comparison model to predict system failure for technology infrastructure based on telemetry state images, each telemetry state image depicting change in a respective telemetry parameter for a plurality of computing systems of the technology infrastructure over time; receive telemetry data for the plurality of computing systems over a period of time; generate, based on the telemetry data and for each parameter represented in the telemetry data, a telemetry state image, wherein each telemetry state image: plots the period of time on an x axis, plots the plurality of computing systems on a y axis, and is specific to a respective parameter represented in the telemetry data; classify, using the image comparison model and using parallel processing, the telemetry state images; identify, using the parallel processing and based on the classifications of the telemetry state images, a likelihood of failure for the technology infrastructure; and send, based on the likelihood of failure for the technology infrastructure, one or more preemptive resolution commands causing modification of operations at one or more of the plurality of computing systems to prevent a predicted failure. 2. The computing platform of claim 1, wherein training the image comparison model comprises training the image comparison model to classify input telemetry data state images as matching historical telemetry state images. 2. The computing platform of claim 1, wherein training the image comparison model comprises training the image comparison model to classify input telemetry data state images as matching historical telemetry state images. 3. The computing platform of claim 2, wherein the historical telemetry state images are labelled based on historical failures corresponding to the respective historical telemetry state images. 3. The computing platform of claim 2, wherein the historical telemetry state images are labelled based on historical failures corresponding to the respective historical telemetry state images. 4. The computing platform of claim 3, wherein training the image comparison model comprises training the image comparison model to identify the likelihood of failure of the technology infrastructure based on the labelled historical telemetry state images. 4. The computing platform of claim 3, wherein training the image comparison model comprises training the image comparison model to identify the likelihood of failure of the technology infrastructure based on the labelled historical telemetry state images. 5. The computing platform of claim 1, wherein the image comparison model comprises one or more of: a deep learning model or a structural property comparison model. 5. The computing platform of claim 1, wherein the image comparison model comprises one or more of: a deep learning model or a structural property comparison model. 6. The computing platform of claim 5, wherein the deep learning model comprises a convolutional neural network (CNN). 6. The computing platform of claim 5, wherein the deep learning model comprises a convolutional neural network (CNN). 7. The computing platform of claim 5, wherein the structural property comparison model is configured to compare one or more of: a number of peaks and troughs, a total area of the peaks and the troughs, a center of gravity, a moment, or a spatial frequency. 7. The computing platform of claim 5, wherein the structural property comparison model is configured to compare one or more of: a number of peaks and troughs, a total area of the peaks and the troughs, a center of gravity, a moment, or a spatial frequency. 8. The computing platform of claim 1, wherein the memory stores additional computer readable instructions that, when executed by the at least one processor, cause the computing platform to: compare the likelihood of failure to a failure threshold, wherein sending the one or more preemptive resolution commands causing modification of the operations at one or more of the plurality of computing systems to prevent the predicted failure is in response to identifying that the likelihood of failure meets or exceeds the failure threshold. 8. The computing platform of claim 1, wherein the memory stores additional computer readable instructions that, when executed by the at least one processor, cause the computing platform to: compare the likelihood of failure to a failure threshold, wherein sending the one or more preemptive resolution commands causing modification of the operations at one or more of the plurality of computing systems to prevent the predicted failure is in response to identifying that the likelihood of failure meets or exceeds the failure threshold. 9. The computing platform of claim 1, wherein sending the one or more preemptive resolution commands comprises directing a load management server associated with the one or more of the plurality of computing systems to redirect incoming requests away from the one or more of the plurality of computing systems. 9. The computing platform of claim 1, wherein sending the one or more preemptive resolution commands comprises directing a load management server associated with the one or more of the plurality of computing systems to redirect incoming requests away from the one or more of the plurality of computing systems. 10. The computing platform of claim 1, wherein sending the one or more preemptive resolution commands comprises directing a user device to display a recommended solution to avoid the predicted failure along with a prompt for whether or not the recommended solution should be executed. 10. The computing platform of claim 1, wherein sending the one or more preemptive resolution commands comprises directing a user device to display a recommended solution to avoid the predicted failure along with a prompt for whether or not the recommended solution should be executed. 11. The computing platform of claim 10, wherein the memory stores additional computer readable instructions that, when executed by the at least one processor, cause the computing platform to: receive user input accepting the recommended solution; and execute, in response to receiving the user input, the recommended solution. 11. The computing platform of claim 10, wherein the memory stores additional computer readable instructions that, when executed by the at least one processor, cause the computing platform to: receive user input accepting the recommended solution; and execute, in response to receiving the user input, the recommended solution. 12. The computing platform of claim 1, wherein the memory stores additional computer readable instructions that, when executed by the at least one processor, cause the computing platform to: receive additional telemetry data for the plurality of computing systems over a second period of time, wherein the second period of time includes a portion of the period of time and an amount of time occurring after the period of time; generate, based on the additional telemetry data and for each of the parameters, an additional telemetry state image, wherein each additional telemetry state image comprises a time series representation of the respective parameters for the plurality of computing systems over the second period of time; classify, using the image comparison model and using the parallel processing, the additional telemetry state images; and update, using the parallel processing and based on the classifications of the additional telemetry state images, the likelihood of failure for the technology infrastructure. 12. The computing platform of claim 1, wherein the memory stores additional computer readable instructions that, when executed by the at least one processor, cause the computing platform to: receive additional telemetry data for the plurality of computing systems over a second period of time, wherein the second period of time includes a portion of the period of time and an amount of time occurring after the period of time; generate, based on the additional telemetry data and for each of the parameters, an additional telemetry state image, wherein each additional telemetry state image comprises a time series representation of the respective parameters for the plurality of computing systems over the second period of time; classify, using the image comparison model and using the parallel processing, the additional telemetry state images; and update, using the parallel processing and based on the classifications of the additional telemetry state images, the likelihood of failure for the technology infrastructure. 13. The computing platform of claim 1, wherein the parallel processing comprises: classifying, in parallel and at substantially a same time, the telemetry state images for each of the parameters; and identifying, in parallel and at substantially the same time, the likelihood of failure based on each of the classifications. 13. The computing platform of claim 1, wherein the parallel processing comprises: classifying, in parallel and at substantially a same time, the telemetry state images for each of the parameters; and identifying, in parallel and at substantially the same time, the likelihood of failure based on each of the classifications. 16. A method comprising: at a computing platform comprising at least one processor, a communication interface, and memory: generating, based on telemetry data for a plurality of computing systems over a period of time and for each parameter represented in the telemetry data, a telemetry state image, wherein each telemetry state image: plots the period of time on an x axis, plots the plurality of computing systems on a y axis, and is specific to a respective parameter represented in the telemetry data; classifying, using an image comparison model and using parallel processing, the telemetry state images; identifying, using the parallel processing and based on the classifications of the telemetry state images, a likelihood of failure for a technology infrastructure; and sending, based on the likelihood of failure for the technology infrastructure, one or more preemptive resolution commands causing modification of operations at one or more of the plurality of computing systems to prevent a predicted failure. 14. A method comprising: at a computing platform comprising at least one processor, a communication interface, and memory: training an image comparison model to predict system failure for technology infrastructure based on telemetry state images, each telemetry state image depicting change in a respective telemetry parameter for a plurality of computing systems of the technology infrastructure over time; receiving telemetry data for the plurality of computing systems over a period of time; generating, based on the telemetry data and for each parameter represented in the telemetry data, a telemetry state image, wherein each telemetry state image: plots the period of time on an x axis, plots the plurality of computing systems on a y axis, and is specific to a respective parameter represented in the telemetry data; classifying, using the image comparison model and using parallel processing, the telemetry state images; identifying, using the parallel processing and based on the classifications of the telemetry state images, a likelihood of failure for the technology infrastructure; and sending, based on the likelihood of failure for the technology infrastructure, one or more preemptive resolution commands causing modification of operations at one or more of the plurality of computing systems to prevent a predicted failure. 17. The method of claim 16, wherein training the image comparison model comprises training the image comparison model to classify input telemetry data state images as matching historical telemetry state images. 15. The method of claim 14, wherein training the image comparison model comprises training the image comparison model to classify input telemetry data state images as matching historical telemetry state images. 18. The method of claim 17, wherein the historical telemetry state images are labelled based on historical failures corresponding to the respective historical telemetry state images. 16. The method of claim 15, wherein the historical telemetry state images are labelled based on historical failures corresponding to the respective historical telemetry state images. 19. The method of claim 18, wherein training the image comparison model comprises training the image comparison model to identify the likelihood of failure of the technology infrastructure based on the labelled historical telemetry state images. 17. The method of claim 16, wherein training the image comparison model comprises training the image comparison model to identify the likelihood of failure of the technology infrastructure based on the labelled historical telemetry state images. 20. One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, a communication interface, and memory, cause the computing platform to: generate, based on telemetry data for a plurality of computing systems over a period of time and for each parameter represented in the telemetry data, a telemetry state image, wherein each telemetry state image: plots the period of time on an x axis, plots the plurality of computing systems on a y axis, and is specific to a respective parameter represented in the telemetry data; classify, using an image comparison model and using parallel processing, the telemetry state images; identify, using the parallel processing and based on the classifications of the telemetry state images, a likelihood of failure for a technology infrastructure; and send, based on the likelihood of failure for the technology infrastructure, one or more preemptive resolution commands causing modification of operations at one or more of the plurality of computing systems to prevent a predicted failure. 20. One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, a communication interface, and memory, cause the computing platform to: train an image comparison model to predict system failure for technology infrastructure based on telemetry state images, each telemetry state image depicting change in a respective telemetry parameter for a plurality of computing systems of the technology infrastructure over time; receive telemetry data for the plurality of computing systems over a period of time; generate, based on the telemetry data and for each parameter represented in the telemetry data, a telemetry state image, wherein each telemetry state image: plots the period of time on an x axis, plots the plurality of computing systems on a y axis, and is specific to a respective parameter represented in the telemetry data; classify, using the image comparison model and using parallel processing, the telemetry state images; identify, using the parallel processing and based on the classifications of the telemetry state images, a likelihood of failure for the technology infrastructure; and send, based on the likelihood of failure for the technology infrastructure, one or more preemptive resolution commands causing modification of operations at one or more of the plurality of computing systems to prevent a predicted failure. The claims of US Patent No. 12,373,271 do not explicitly teach determining an outlier storage drive based at least on the calculated distance between the subset of each operational coefficient of the plurality of operational coefficients of the cluster plot, wherein the outlier storage drive is the furthest away from the cluster plot; and initiating a remedial action on the outlier storage drive, wherein the remedial action of the outlier storage drive comprises maintenance, removal, or any combination thereof. It would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to combine the claims of US Patent No. 12,373,271 with determining an outlier storage drive based at least on the calculated distance between the subset of each operational coefficient of the plurality of operational coefficients of the cluster plot, wherein the outlier storage drive is the furthest away from the cluster plot; and initiating a remedial action on the outlier storage drive, wherein the remedial action of the outlier storage drive comprises maintenance, removal, or any combination thereof for the purpose of remediating an outlier storage drive based on the calculated distance between the subset of each operational coefficient furthest away from a cluster plot for the purpose of preventing a failure. Prior Art Made of Record Rathinasabapathy et al. (2020/0295986) teaches generating a graph. Ratkovic et al. (11,625,293) teaches graphing telemetry data. Zhang et al. (2019/0199602) teaches the telemetry data is collected during the first phase is irrelevant and filtered out. Allin et al. (2019/0179723) teaches a degree of parallelism employed for performing the data processing operation. Spencer et al. (2014/0278496) teaches two processing applications (associated with different modalities) each need to perform filtering, image processing, and scan conversion processes on incoming sensing data. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to SARAI E BUTLER whose telephone number is (571)270-3823. The examiner can normally be reached 8 am to 4 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, Ashish Thomas can be reached at 571-272-0631. 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. /SARAI E BUTLER/Primary Examiner, Art Unit 2114 Application/Control Number: 19/225,218 Page 2 Art Unit: 2114 Application/Control Number: 19/225,218 Page 3 Art Unit: 2114 Application/Control Number: 19/225,218 Page 4 Art Unit: 2114 Application/Control Number: 19/225,218 Page 5 Art Unit: 2114 Application/Control Number: 19/225,218 Page 6 Art Unit: 2114 Application/Control Number: 19/225,218 Page 7 Art Unit: 2114 Application/Control Number: 19/225,218 Page 8 Art Unit: 2114 Application/Control Number: 19/225,218 Page 9 Art Unit: 2114 Application/Control Number: 19/225,218 Page 10 Art Unit: 2114 Application/Control Number: 19/225,218 Page 11 Art Unit: 2114 Application/Control Number: 19/225,218 Page 12 Art Unit: 2114 Application/Control Number: 19/225,218 Page 13 Art Unit: 2114 Application/Control Number: 19/225,218 Page 14 Art Unit: 2114 Application/Control Number: 19/225,218 Page 15 Art Unit: 2114 Application/Control Number: 19/225,218 Page 16 Art Unit: 2114 Application/Control Number: 19/225,218 Page 17 Art Unit: 2114 Application/Control Number: 19/225,218 Page 18 Art Unit: 2114 Application/Control Number: 19/225,218 Page 19 Art Unit: 2114 Application/Control Number: 19/225,218 Page 20 Art Unit: 2114 Application/Control Number: 19/225,218 Page 21 Art Unit: 2114 Application/Control Number: 19/225,218 Page 22 Art Unit: 2114 Application/Control Number: 19/225,218 Page 23 Art Unit: 2114 Application/Control Number: 19/225,218 Page 24 Art Unit: 2114 Application/Control Number: 19/225,218 Page 25 Art Unit: 2114 Application/Control Number: 19/225,218 Page 26 Art Unit: 2114 Application/Control Number: 19/225,218 Page 27 Art Unit: 2114 Application/Control Number: 19/225,218 Page 28 Art Unit: 2114 Application/Control Number: 19/225,218 Page 29 Art Unit: 2114 Application/Control Number: 19/225,218 Page 30 Art Unit: 2114 Application/Control Number: 19/225,218 Page 31 Art Unit: 2114 Application/Control Number: 19/225,218 Page 32 Art Unit: 2114 Application/Control Number: 19/225,218 Page 33 Art Unit: 2114 Application/Control Number: 19/225,218 Page 34 Art Unit: 2114 Application/Control Number: 19/225,218 Page 35 Art Unit: 2114 Application/Control Number: 19/225,218 Page 36 Art Unit: 2114 Application/Control Number: 19/225,218 Page 37 Art Unit: 2114 Application/Control Number: 19/225,218 Page 38 Art Unit: 2114 Application/Control Number: 19/225,218 Page 39 Art Unit: 2114 Application/Control Number: 19/225,218 Page 40 Art Unit: 2114 Application/Control Number: 19/225,218 Page 41 Art Unit: 2114 Application/Control Number: 19/225,218 Page 42 Art Unit: 2114 Application/Control Number: 19/225,218 Page 43 Art Unit: 2114
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