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. 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. Cla im s 1 - 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites a computer-implemented method , thus a process, one of the four statutory categories of patentable subject matter. However, Claim 1 further recites steps of generating at least one reference data distribution for at least one telemetry data-related metric by processing historical telemetry data received from one or more devices using one or more artificial intelligence techniques (a mental process, for example, counting the number of data points falling within a bin to construct a histogram); generating, for at least one device associated with one or more monitoring tasks, at least one data distribution for the at least one telemetry data-related metric by processing telemetry data derived from the at least one device using the one or more artificial intelligence techniques (a mental process, for example, again, counting the number of data points falling within a bin to construct a histogram); determining one or more distributional distance values associated with the at lea s t one device w ith respect to the one or more devices by comparing at least a portion of the at least one data distribution to at least a portion of the at least one reference data distribution (a mental process of comparison); identifying one or more anomalies associated with at least a portion of the telemetry data derived from the at least one device based at least in part on the one or more distributional distance values (a mental process of judgement); performing one or more automated actions based at least in part on the one or more identified anomalies (which, interpreted in light of dependent Claim 10, must include classifying , which is a mental process of judgement). Thus, the claim recites the abstract idea of generating a reference distribution and an instant distribution and comparing them in order to declare an anomaly in the instant distribution. The claim does not recite any additional elements which could integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself, because the only additional element consists of at least one processing device comprising a processor coupled to a memory to perform the steps of the abstract idea, which by MPEP 2106.05(f)(2) can do neither. Thus, the claim is directed to the abstract idea, without significantly more and is thus subject-matter ineligible. Claims 2-10 , dependent upon Claim 1, merely recite additional mental process steps being performed by generic computing equipment (e.g. converting data into a discrete distribution of bins in recited manners) but no additional elements which could integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself. Claim 11 , dependent upon Claim 1, merely recites to generically train a model based on the feedback regarding the anomalies, which are mere instructions to train and use a model to perform a mental process, i.e. to generically apply the abstract idea (see MPEP 2106.05(f)), which cannot integrate the abstract idea into a practical application nor provide significantly more than the abstract idea itself. Claims 12-15 recite a non-transitory computer readable storage medium having stored therein program code to perform precisely the methods of Claims 1, 2, 5, and 6, respectively. As performance of an abstract idea on generic computer components cannot integrate an abstract idea into a practical application nor provide significantly more than the abstract idea itself, see MPEP 2106.05(f)(2), Claims 12-15 are rejected for reasons set forth in the rejections of Claims 1, 2, 5, and 6, respectively. Similarly, Claims 1 6 -20 recite an apparatus comprising at least one processing device configured to perform precisely the methods of Claims 1, 2, 5, 6, and 9, respectively, and are thus also rejected for reasons set forth in the rejections of those claims. Claim Rejections - 35 USC § 102 In the event the dete rmination 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. 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 – (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. Claim s 1 , 2 , 5 , 8 - 10 ; 12 , 13 , 14 ; 16 , 17 , 18 , and 20 a re rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Lambert, US Patent 10,866,006. Regarding Claim 1 , Lambert teaches a computer-implemented method (Lambert, Claim 1, “an information handing system, comprising: at least one processor … to:”) comprising: generating at least one reference data distribution for at least one telemetry data-related metric by processing historical telemetry data received from one or more devices using one or more artificial intelligence techniques (Lambert, Claim 1, “generate a first data structure representing a histogram comprising multiple bins , wherein each bin represents a respective range of tachometer frequency values for the tachometer frequency values for the tachometer signal output by the given fan during the first time period … and each bin is associated with a respective count value indicating the number of occurrences of a tachometer frequency value for the tachometer signal … save reference data representing … the respective count values” ); generating, for at least one device associated with one or more monitoring tasks, at least one data distribution for the at least one telemetry data-related metric by processing telemetry data derived from the at least one device using the one or more artificial intelligence techniques ( Lambert, Claim 1, “generate a second data structure representing a histogram comprising multiple bins, wherein each bin represents a respective range of tachometer frequency values for the tachometer signal output by the given fan during the second time period … and each bin is associated with a respective count value indicating the number of occurrences of a tachometer frequency value for the tachometer signal” ); determining one or more distributional distance values associated with the at least one device with respect to the one or more devices by comparing at least a portion of the at least one data distribution to at least a portion of the at least one reference data distribution (Lambert, Claim 1, “compare data representing … the respective count values … of the second data structure and the save reference data” ); identifying one or more anomalies associated with at least a portion of the telemetry data derived from the at least one device based at least in part on the one or more distributional distance values (Lambert, Claim 1, “respective to detecting a discrepancy between … the respective count values … of the second data structure and the saved reference data, provide an indication of an anomaly” ); performing one or more automated actions based at least in part on the one or more identified anomalies (Lambert, Claim 1, “provide an indication of an anomaly” ); wherein the method is performed by at least one processing device comprising a processor coupled to a memory (Lambert, Claim 1, “at least one processor; and a memory medium coupled to the at least one processor”) . Regarding Claim 2 , Lambert teaches the computer-implemented method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Lamber t further teaches wherein generating the at least one reference data distribution for the at least one telemetry data-related metric comprises converting at least one continuous historical time series data stream derived from the one or more devices (Lambert, Claim 1, “the tachometer signal comprising a pair of pulses for each rotation of the first fan”) into at least one discrete data distribution by defining a set of two or more bin boundaries, wherein the two or more bin boundaries are mutually exclusive and cover at least one range of input variable values related to the at least one telemetry data-related metric (Lambert, Claim 1, “generate a first data structure representing a histogram comprising multiple bins, wherein each bin represents a respective range of tachometer frequency values for the tachometer signal output by the given fan during the first time period” where “histogram” denotes mutually exclusive ). Regarding Claim 5 , Lambert teaches the computer-implemented method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Lambert further teaches wherein generating the at least one data distribution for the at least one telemetry data-related metric comprises converting at least one continuous historical time series data stream derived from the one or more devices (Lambert, Claim 1, “the tachometer signal comprising a pair of pulses for each rotation of the first fan”) into at least one discrete data distribution by defining a set of two or more bin boundaries, wherein the two or more bin boundaries are mutually exclusive and cover at least one range of input variable values related to the at least one telemetry data-related metric (Lambert, Claim 1, “generate a second data structure representing a histogram comprising multiple bins, wherein each bin represents a respective range of tachometer frequency values for the tachometer signal output by the given fan during the second time period” where “histogram” denotes mutually exclusive ). Regarding Claim 8 , Lambert teaches the computer-implemented method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Lamber t further teaches wherein generating the at least one reference data distribution comprises processing historical telemetry data derived from one or more devices (Lambert, Claim 1, “the tachometer signal comprising a pair of pulses for each rotation of the first fan”) using one or more machine learning-based data discretization techniques (Lambert, Claim 1, where creating a histogram is a machine learning-based data discretization technique ). Regarding Claim 9 , Lambert teaches the computer-implemented method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Lamber t further teaches wherein performing one or more automated actions comprises, initiating, in connection with one or more systems, one or more automated actions responsive to at least one of the one or more identified anomalies (Lambert, Claim 1, “responsive to detecting a discrepancy … provide an indication of an anomaly”). Regarding Claim 10 , Lambert teaches the computer-implemented method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Lamber t further teaches wherein performing one or more automated actions comprises classifying the one or more identified anomalies using one or more classification techniques (Lambert, Claim 3, “determining … that the given fan is not a fan of the first fan time … determining, based on the second comparison, that the given fan is a fan of the second fan type”). Claims 12-1 4 recite a non-transitory computer readable storage medium having stored therein program code to perform precisely the methods of Claims 1, 2, and 5, respectively. As Lambert teaches such a medium (Lambert, Claim 1, “a memory medium coupled to the at least one processor and storing program instructions”), Claims 12-15 are rejected for reasons set forth in the rejections of Claims 1, 2, and 5, respectively. Similarly, Claims 1 6 - 18 and 20 recite an apparatus comprising at least one processing device configured to perform precisely the methods of Claims 1, 2, 5, and 9, respectively, and are thus also rejected for reasons set forth in the rejections of those claims (Lambert, Claim 1, “at least one processor; and a memory medium coupled to the at least one processor”). Claim Rejections - 35 USC § 103 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. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. Clai m s 3 , 6 , 11 , 15 , and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Lambert, US Patent 10,866,006, in view of Gasthaus, US PG Pub 2021/0406671. Regarding Claim 3 , Lamber t teaches the computer-implemented method of Claim 2 (and thus the rejection of Claim 2 is incorporated). Lambert is silent regarding whether the two or more bin boundaries cover at least one range of input variable values equal to a total number of observations in the at least one historical time series data stream, but Gasthaus teaches this limitation (Gasthaus, [0060], “the domain space is divided into bins” spanning from y min to y max , thus spanning the range of the total number of observations ). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have the histogram bins of Lambert span the entire range of data, as does Gasthaus. The motivation to do so is to be able to count every data point in the histogram counts, and not miss any. Regarding Claim 6 , Lambert teaches the computer-implemented method of Claim 5 (and thus the rejection of Claim 5 is incorporated). Lambert Claim 1 is silent regarding whether defining a set of two or more bin boundaries which are identical to the set of two or more bin boundaries defined in connection occurs, but Gasthaus teaches this limitation (Gasthaus, [0060], “the domain space is divided into bins” and clearly uses the same det of bins for every histogram for each time period, see [0063]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use identical bin boundaries for reference and instant distributions. The motivation to do so is to be able to compare the distributions equivalently, to detect “discrepancies” (Lambert, Claim 1). Regarding Claim 11 , Lambert teaches the computer-implemented method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Lambert does not teach, but Gasthaus does teach, wherein performing one or more automated actions comprises automatically training the one or more artificial intelligence techniques using feedback related to one or more identified anomalies (Gasthaus, Claim 14, “wherein models to be used in the analyzing time-series data from the one or more data sources are stored in a model repository and the models are adjustable based on user feedback” with [0045], “feedback from a user about an alert or recommendation is used to change the anomaly detection/prediction component/service”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to update the model of Lambert based on user feedback, as does Gasthaus. The motivation to do so is so that the “ML model learns what should be marked as a finding (what is an anomaly … etc.) and what should not be” (Gasthaus, [0045]), that is, feedback improves the prediction model. Claims 15 and 19 recite a computer-readable medium and apparatus comprising at least one processing device to perform the method of Claim 6, and as Lambert teaches such embodiments (Lambert, Claim 1, “at least one processor, and a memory medium coupled to the at least one processor and storing program instructions”), Claims 15 and 19 are rejected for reasons set forth in the rejection of Claim 6. Claim 4 i s rejected under 35 U.S.C. 103 as being unpatentable over Lambert, in view of Kasioumis, US PG Pub 2022/0026228. Regarding Claim 4 , Lambert teaches the computer-implemented method of Claim 2 (and thus the rejection of Claim 2 is incorporated). Lambert is silent regarding whether generating the at least one reference data distribution for the at least one telemetry data-related metric comprises incorporating one or more user-provided expectations for each of the two or more bins boundaries (because Lambert Claim 1 does not explain how the bins are determined) but Kasioumis teaches incorporating one or more user-provided expectations for each of the two or more bins boundaries (Kas ioumis, [0151], “The total number of bins is a hyperparameter that needs to be tuned by the user taking into account the number of data available” where bin boundaries must depend on the total number of bins ). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to allow a user to select the number of bins, as does Kasioumis, in the invention of Lambert. The motivation to do so is that Kasioumis teaches it is “a hyperparameter that needs to be tuned by the user ” (Kasioumis, [0151]). Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Lambert, in view of Nara yanam, US PG Pub 2024/0320538 (with a filing date of 3/20/2023). Regarding Claim 7 , Lambert teaches the computer-implemented method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Lambert does not teach, but Narayanam does teach, generating at least one list of instances of deviation of the at least one data distribution from the at least one reference data distribution ranked in accordance with an amount by which a corresponding portion of the telemetry data derived from the at least one device deviates from one or more expectations associated with the historical telemetry data derived from the one or more devices (Narayanam, [0025], “The system determines anomaly scores for attributes and records using the evidence sets … the anomalous data subsets are ranked based on the relative extent of their anomalies”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to rank the list of anomalies, as does Narayanam, in the invention of Lambert. The motivation to do so is to indicate the most anomalous anomalies to the user, i.e. for prioritization (Narayanam, [0026]). Double Patenting The nonstatutor y 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 § 2146 et seq. 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 filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual 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/apply/applying-online/eterminal-disclaimer . Claims 1 , 2 , 5 , 8 , 9 ; 12 , 13 , 14 ; 16 , 17 , 18 , and 20 are rejected on the ground of nonstatutory double patenting a s being unpatentable over Claim 1 of U.S. Patent No. 10,866,006. Although the claims at issue are not identical, they are not patentably distinct from each other because Claim 1 of the reference patent anticipates the instant claims. See the 35 U.S.C. 102(a)(1) rejections of the claims for details. Claim 10 is rejected on the ground of nonstatutory double patenting a s being unpatentable over Claim 3 of U.S. Patent No. 10,866,006. Although the claims at issue are not identical, they are not patentably distinct from each other because Claim 3 of the reference patent anticipates the instant claim. See the 35 U.S.C. 102(a)(1) rejection of Claim 10 for details. Claim s 3 , 6 , 11 , 15 , and 19 are rejected on the ground of nonstatutory double patenting a s being unpatentable over Claim 1 of U.S. Patent No. 10,866,006 , in view of Gasthaus, US PG Pub 2021/0406671. The claims are obvious over Claim 1 of the reference patent, as described in the 35 U.S.C. 101 rejections. Claim 4 i s rejected on the ground of nonstatutory double patenting a s being unpatentable over Claim 1 of U.S. Patent No. 10,866,006, in view of Kasioumis, US PG Pub 2022/0026228. The claim is obvious over Claim 1 of the reference patent, as described in the 35 U.S.C. 101 rejection. Claim s 7 is rejected on the ground of nonstatutory double patenting a s being unpatentable over Claim 1 of U.S. Patent No. 10,866,006, in view of Narayanam, US PG Pub 2024/0320538 (with a filing date of 3/20/2023). The claim is obvious over Claim 1 of the reference patent, as described in the 35 U.S.C. 101 rejection. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT BRIAN M SMITH whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (469)295-9104 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Monday - Friday, 8:00am - 4pm Pacific . 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. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /BRIAN M SMITH/ Primary Examiner, Art Unit 2122