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 .
Amendments
This action is in response to amendments filed May 26th, 2026, in which Claims 1, 3, 4, 7, 8, 12, and 16 are amended. Claims 2, 5, 6, 13-15, and 17-19 are cancelled. Claims 21-29 are added. The amendments have been entered, and Claims 1, 3-4, 7-12, 16, and 20-29 are currently pending.
Duplicate Claims Warning
Applicant is advised that should Claim 22 be found allowable, Claim 23 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. Similarly, should Claim 27 be found allowable, Claim 28 will be objected to for the same reason. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim Rejections - 35 USC § 103
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.
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.
Claims 1, 8-10; 12, 25, 27, 28; 16, 20, 22, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Lambert, US Patent 10,866,006, in view of Callegari et al., “Histogram Cloning and CuSum: An Experimental Comparison Between Different Approaches to Anomaly Detection.”
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 discrete 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, wherein generating the at least one discrete reference data distribution comprises converting at least one continuous historical time series data stream derived from one or more devices into the at least one discrete reference data distribution, utilizing a first set of two or more bins that cover respective ranges of 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 frequency values for the tachometer signal output by the given fan during the first time period … the tachometer signal comprising a pair of pulses for each rotation of the given fan … 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 from the one or more devices, associated with one or more monitoring tasks, at least one discrete device 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, wherein generating the at least one discrete device data distribution comprises converting at least one continuous input time series data stream derived from the at least one device into the at least one discrete device data distribution by defining a second set of two or more bins corresponding to respective ones of the first set of two or more bins (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 … top bins”); 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 discrete device data distribution to at least a portion of the at least one discrete reference data distribution (Lambert, Claim 1, “compare data representing … the respective count values … of the second data structure and the saves 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”).
Lambert appears to compare only the raw values of the top bins of the histogram to create their distance measure, and so does not teach the comparing comprising comparing a proportion of values in each of the two or more bins of the at least one discrete device data distribution to a corresponding proportion of values in each of the two or more bins of the at least one discrete reference data distribution. However, Callegari, in the analogous art of anomaly detection using histogram differences, teaches a distance measure between two histograms that acts by comparing a proportion of values in each of the two or more bins of [a first histogram] to a corresponding proportion of values in each of the two or more bins of [a second histogram] (Callegari, pg. 3, 2nd column, 1st paragraph, “we will consider the normalized histograms” where the definition shows a proportion & Eqs. (3-7) each demonstrating comparing a corresponding proportion of values in each of the two or more bins of two histograms). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a distance measure of Callegari, comparing normalized proportional values, in place of the top bin comparison of Lambert. The motivation to do so is that the measures of Callegari are common distance measures which yield competitive results (Callegari, pg. 3, second column, all the distance measures are known and commonly used & pg. 6, Fig. 3, showing the results of using each distance measure).
The limitation thereby retaining information of the at least one continuous input time series data stream and the at least one continuous historical time series data stream while reducing one or more computational requirements of the comparing relative to the at least one continuous input time series data stream to the at least one continuous historical time series data stream is an intended result of the positively recited step of comparing, and thus is given negligible patentable weight. See MPEP 2111.04.
Regarding Claim 2, the Lambert/Callegari combination of Claim 1 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 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 8, the Lambert/Callegari combination of Claim 1 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 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, the Lambert/Callegari combination of Claim 1 teaches the computer-implemented method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Lambert 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, the Lambert/Callegari combination of Claim 1 teaches the computer-implemented method of Claim 1 (and thus the rejection of Claim 1 is incorporated). Lambert 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, 25, 27, and 28 recite a non-transitory computer readable storage medium having stored therein program code to perform precisely the methods of Claims 1, 9, 10, and 10 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, 25, 27, and 28 are rejected for reasons set forth in the rejections of Claims 1, 9, 10, and 10 respectively. Similarly, Claims 16, 20, 22, and 23 recite an apparatus comprising at least one processing device configured to perform precisely the methods of Claims 1, 9, 10, and 10, 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”).
Claims 3, 11, 29, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Lambert, in view of Callegari, and further in view of Gasthaus, US PG Pub 2021/0406671.
Regarding Claim 3, the Lambert/Callegari combination of Claim 1 teaches the computer-implemented method of Claim 1 (and thus the rejection of Claim 1 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 ymin to ymax, 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 11, the Lambert/Callegari combination of Claim 1 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 29 and 24 recite a computer-readable medium and apparatus comprising at least one processing device to perform the method of Claim 3, 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 29 and 24 are rejected for reasons set forth in the rejection of Claim 3.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Lambert, in view of Callegari, and further in view of Kasioumis, US PG Pub 2022/0026228.
Regarding Claim 4, the Lambert/Callegari combination of Claim 1 teaches the computer-implemented method of Claim 1 (and thus the rejection of Claim 1 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 (Kasioumis, [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]).
Claims 7, 26, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Lambert, in view of Callegari, and further in view of Narayanam, US PG Pub 2024/0320538 (with a filing date of 3/20/2023).
Regarding Claim 7, the Lambert/Callegari combination of Claim 1 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]).
Claims 26 and 21 recite a computer-readable medium and apparatus comprising at least one processing device to perform the method of Claim 7, 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 26 and 21 are rejected for reasons set forth in the rejection of Claim 7.
Double Patenting
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 § 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, 8-10; 12, 25, 27, 28; 16, 20, 22, and 23 are rejected on the ground of nonstatutory double patenting as being unpatentable over Claim 1 of U.S. Patent No. 10,866,006, in view of Callegari. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are obvious over Claim 1 of the reference patent, in view of Callegari. See the 35 U.S.C. 103 rejections of the claims for details.
Claims 10, 22, 23, 27, and 28 are rejected on the ground of nonstatutory double patenting as being unpatentable over Claim 3 of U.S. Patent No. 10,866,006, in view of Callegari. Although the claims at issue are not identical, they are not patentably distinct from each other because the instant claims are obvious over Claim 3 of the reference patent, in view of Callegari. See the 35 U.S.C. 103 rejections of the instant claims for details.
Claims 3, 11, 29, and 14 are rejected on the ground of nonstatutory double patenting as being unpatentable over Claim 1 of U.S. Patent No. 10,866,006, in view of Callegari and Gasthaus. The claims are obvious over Claim 1 of the reference patent, as described in the 35 U.S.C. 103 rejections.
Claim 4 is rejected on the ground of nonstatutory double patenting as being unpatentable over Claim 1 of U.S. Patent No. 10,866,006, in view of Callegari and Kasioumis. The claim is obvious over Claim 1 of the reference patent, as described in the 35 U.S.C. 103 rejection.
Claims 7, 26, and 21 are rejected on the ground of nonstatutory double patenting as being unpatentable over Claim 1 of U.S. Patent No. 10,866,006, in view of Callegari and Narayanam, US PG Pub 2024/0320538 (with a filing date of 3/20/2023). The claims are obvious over Claim 1 of the reference patent, as described in the 35 U.S.C. 103 rejections.
Response to Arguments
Applicant’s arguments filed May 25th, 2026 have been fully considered, but are not fully persuasive.
Applicant’s arguments regarding the 35 U.S.C. 101 rejections of the claims have been fully considered, and are persuasive. The rejections have been withdrawn.
Applicant’s arguments regarding the prior art rejections of the independent claims have been fully considered, but are moot because they do not apply to new reference Callegari, to teach the amended limitations regarding comparing a proportion of values in each of the two or more bins.
Applicant’s arguments with respect to the prior art rejections of the dependent claims rely upon features argued with respect to the independent claims, and are thus also unpersuasive.
Applicant’s arguments with respect to the double patenting rejections of the claims also rely upon the non-obviousness of the features argued with respect to the prior art rejection of the independent claims, and are thus also unpersuasive.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Harutyunyan, US PG Pub 2022/0027257, [0196], teaches measuring the distance between histograms of “relative frequencies” of input data, i.e. the proportion of data.
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRIAN M SMITH whose telephone number is (469)295-9104. The examiner can normally be reached Monday - Friday, 8:00am - 4pm Pacific.
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/BRIAN M SMITH/Primary Examiner, Art Unit 2122
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