DETAILED ACTION
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 .
Response to Amendment
This office action is in response to amendment/reconsideration filed on 12/19/2025, the amendment/reconsideration has been considered. Claims 2, 7, 12, 17, 18 and 20 have been amended. Claims 2-21 are pending for examination as cited below.
Response to Arguments
Applicant's arguments filed on 12/19/2025 have been fully considered but they are not persuasive. In remarks application argues that:
(a) Applicant argues that cited references “Jain and Jagota” does not disclose the amended limitations e.g. “receiving streams of data points, wherein individual ones of the streams have different data resolutions, wherein individual ones of the different
data resolutions correspond to an inter-arrival time between data points in a respective stream.”
Examiner respectfully disagree because, Jain discloses the concept of time-series captured at time interval e.g.; [0001], Time series data is a sequence of data points indexed in time order. Typically, the data points of time series data are captured at equally-spaced time intervals. This establishes that a time-series data points are separated by inter-arrival times e.g. sampling interval. Jain further discloses in [0036], describes a multi-dimensional time series as “several two-dimensional time-series, where each time series represents the same metric over interval of time” and refers to “discrete points in time.” Jain further discloses configurable binning / sample resolution fig.13, readconfig “bin size”: “0010:00” and “TimecolumnName”: “time”. The BinsSize parameter explicitly ties a processing resolution to a time interval (here, ten minutes), i.e., a resolution that corresponds to a time interval between data points or aggregation buckets. Also see fig.11 and 12 example plots labeled with time points t1, t2, t3….t5 and different sets of dimensions (Fig.12 adds tenant ID), which demonstrates handling of multiple streams / series and visualization at particular time points/granularities.
(b) Applicant further argues that cited reference does not teach, “generating second output data points based upon the data points in individual ones of the streams at each of the second input resolutions;”
Examiner respectfully disagree because Jain in light of fig.13 clearly discloses, different BinSize values produce different output point sets derived for the input stream. Additionally, Jain literally provides a mechanism to specify a time-based processing resolution (“BinSize”) and shows processing / plotting at discrete time points which suggests, generating output points at a specified time resolution derived for input data.
(c ) Applicant yet again argues that cited references do not teach, “determining second input resolution values based on second data resolutions for individual ones of the streams”.
Examiner respectfully disagree because Jain discloses time-indexed samples / sampling semantics in [0001], Jain also discloses, multi-dimensional time series and discrete points in [0036], Jain further discloses, processing pipeline and time-series filter, see fig.4, [0037]-[0044], a component that filters /aggregates time-series data prior to anomaly detection i.e. a place where input resolution values would be determined/applied and also see fig.13, “BinSize”.
Claim Rejections - 35 USC § 103
3. 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 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.
4. 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 non-obviousness.
5. Claims 2-10, 12-20 are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al. (US 2019/0236177 hereinafter referred to as Jain) in view of Jagota (US 2022/0121983 hereinafter referred to as Jagota).
Regarding claim 2,
Jain teaches:
“A computer-implemented method, comprising:
receiving streams of data points, wherein individual ones of the streams have different data resolutions, (Jain [0048] [0026], Anomaly detector providing a notification to a user when the anomaly is detected in a time-series data. The time-series data is sequences of data points. Paragraphs [0054]- [0057] teaches various predetermined thresholds (series of trigger parameters) to detect the anomaly in the time-series data), wherein individual ones of the streams have different data resolutions, wherein individual ones of the different data resolutions correspond to an inter-arrival time between data points in a respective stream (Jain, [0001], Time series data is a sequence of data points indexed in time order. Typically, the data points of time series data are captured at equally-spaced time intervals. This establishes that a time-series data points are separated by inter-arrival times e.g. sampling interval. Jain further discloses in [0036], describes a multi-dimensional time series as “several two-dimensional time-series, where each time series represents the same metric over interval of time” and refers to “discrete points in time.”);
“determining first input resolution values based on data resolutions for individual ones of the streams” (Jain [0042] [0026] [0032], receiving a time-series data (stream of data points) representing operational or performance metrics of a service. The time-series data represent a sequence of data points (first plurality of data points) captured at equally-spaced time intervals and indexed in time order (time stamp). The received data points comprising values (first data resolution) representing the performance metric of the service/system).
“setting an output resolution to a first value based, at least in part, on the first input resolution values;” (Jain [0032] [0055] [0056], teaches acceptable standard, normal or expected behavior values to detect the anomaly in the time-series data when the data points value deviate from the acceptable values. For example, an acceptable average percentage value (first value) if average percentage techniques is selected for anomaly detection. Acceptable standard deviation value (first value) if average percentage techniques is selected for anomaly detection).
“generating first output data points based upon the data points in individual ones of the streams at each of the first input resolutions;” (Jain [0053] [0026], applying selected anomaly detection technique to first set of dimensions (first set of output data points) of the time-series data. Dimensions are various attributes’ values represented by the data points in the time-series data).
“outputting the first output data points according to the first value;” (Jain [0053] [0056] [0048], applying selected anomaly detection technique to the first set of dimension of the time-series data to detect anomaly. If standard deviation is applied, detecting anomaly in the time-series data if the standard deviation difference between the historical portion and current potion (interval) of the data points is equal to or greater than predetermined threshold values (series of trigger parameters) for anomaly detection. Displaying anomaly notification on user interface when anomaly is detected in time-series data. In addition to standard deviation, an average percentage and zero-threshold anomaly detection techniques also detected anomaly based on predetermined threshold values (see paragraphs [0074] [0073])).
“determining second input resolution values based on second data resolutions for individual ones of the streams;” (Jain [0032] [0055] [0056], teaches anomaly detection method based on acceptable standard, normal or expected behavior values deviation. Detecting the acceptable average percentage values (first value) is changed (second value) in the time series data points if average percentage techniques is selected. Detecting standard deviation values (first value) is changed (second value) if the standard deviation is selected. Thus, the values in the data points changed (change in data resolution)).
“generating second output data points based upon the data points in individual ones of the streams at each of the second input resolutions;” (Jain [0059], generating second set of dimension (second set of output data points) reflecting additional data points (second plurality of data points) of a service based on the received time-series data points and (Jain [0060] [0053] [0056] [0048] [0067], performing additional anomaly redetecting procedure based on previously detected anomaly by repeating one of anomaly detection techniques to the second set of dimensions. Thus, the changed values from the first anomaly detection ion is incorporated into the redetecting. Applying selected anomaly detection technique to detect anomaly. If standard deviation is applied, detecting anomaly in the time-series data if the standard deviation difference between the historical portion and current potion (interval) of the data points is equal to or greater than predetermined threshold values (series of trigger parameters) for anomaly detection. Displaying anomaly notification on user interface when anomaly is detected in time-series data. The anomaly detection steps repeatedly performed if needed).
Jain does not explicitly teach:
“outputting the second output data points according to the second value”
Jagota teaches:
“outputting the second output data points according to the second value” (Jagota [0023], teaches continuously training anomaly machine learning system with a new value that indicates anomalous).
Both Jain and Jagota teaches anomaly detection in time series data. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention to modify Jain to adapt new anomalous indicating values as disclosed by Jagota, such inclusion allows to implement an artificial intelligence system that has the ability to automatically learning and improve from experience without being explicitly programmed (Jagota [0023]).
Regarding claim 12, is rejected for same rationale as applied to claim 2 above.
Regarding claim 18, is rejected for same rationale as applied to claim 2 above.
Regarding claims 3,13 and 19, the combination of Jain and Jagota teaches all the limitations of claims 2, and 12.
Jain teaches:
“wherein setting the output value to the second value is responsive to detecting the second input resolution values.” (Jain [0048] [0026], the anomaly detector configured to detect anomalies in the time-series data. The time series data represent various services performance values).
Regarding claim 16, wherein the first ingestion system specifies a first input resolution, and the second ingestion system specifies one or more second input resolutions (Jain, [0063]).
Regarding claim 4 , the combination of Jain and Jagota teaches all the limitations of claims 2.
Jain teaches:
“wherein the streams of data points are received from a plurality of emitters, wherein individual ones of the emitters are associated with one or more of a computing instance, or a port.” (Jain [0056], A standard deviation is calculated for a historical portion of the time series data, and for each data point of the current portion. The historical standard deviation is compared to the standard deviation calculated for each data point.).
Regarding claims 5 and 15, the combination of Jain and Jagota teaches all the limitations of claims 2 and 12.
Jagota teaches:
“further comprising ingestion systems, wherein a first stream of data points are received by a first ingestion system and a second stream of data points is received by a second ingestion system.” (Jagota, fig.2, [0059]).
Regarding Claim 6, is rejected for same rationale as applied to claim 5 and it is further noted the limitation as recited is an intended use.
Regarding claim 7, the combination of Jain and Jagota teaches all the limitations of claim 1.
Jain teaches:
“wherein the first input resolution is based, at least in part on, on one or more of an average data resolution, or a highest data resolution of one or more streams received by the first ingestion system.” (Jain [0063], Subsequently, the number of dimensions may be increased while eliminating some of the dimension values of the prior dimensions, to focus more closely on the source of the anomalies, which may more easily be determined based on the reduced dimension.).
Regarding claim 17, is rejected for same rationale as applied to claim 7 above.
Regarding claim 8, the combination of Jain and Jagota teaches all the limitations of claim 1.
Jain teaches:
“further comprising inspecting timestamps associated with the data points to determine an interval, and based, at least in part on, the interval updating an input data resolution associated with the data points.:” (Jain [0032] [0055] [0056], teaches anomaly detection method based on acceptable standard, normal or expected behavior values deviation. Detecting the acceptable average percentage values (first value) is changed (second value) in the time series data points if average percentage techniques is selected. Detecting standard deviation values (first value) is changed (second value) if the standard deviation is selected. Thus, the values in the data points are changed (change in data resolution) and [0079] [0080], teaches seasonally collected time-series data at regular interval such as on a daily, weekly, monthly or yearly (series of resolution interval) basis also see [0066]).
Regarding claim 9, the combination of Jain and Jagota teaches all the limitations of claim 2.
Jain teaches:
“further comprising monitoring the streams of data points, and wherein determining the second input resolution values is based on the monitoring.” (Jain [0062], the time series-series data comprising dimension values (set input resolution) representing different metrics. Also see [0026]).
Regarding claim 10, the combination of Jain and Jagota teaches all the limitations of claim 2.
Jain teaches:
“further comprising transmitting an alert message in response to a change in the first value to the second value.” (Jagota [0015], the first set of dimension include different dimension values).
Regarding claim 14, is rejected for same rationale as applied to claim 4 above.
Regarding claim 20, is rejected for same rationale as applied to claim 7 above.
Claims 11 and 21 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
The non-statutory 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 non-statutory 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 non-statutory 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 non-statutory 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.
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Claims 2, 12 and 18 rejected on the ground of non-statutory double patenting as being unpatentable over claims1, 11 and 15 of U.S. Patent No. 12013880 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because see the table below:
Instant application: 18/666,603 U.S. Patent No.: 12013880 B2
2. (New) A computer-implemented method, comprising: receiving streams of data points, wherein individual ones of the streams have different data resolutions, wherein individual ones of the streams have different data resolutions, wherein individual ones of the different data resolutions correspond to an inter-arrival time between data points in a respective stream; determining first input resolution values based on data resolutions for individual ones of the streams; setting an output resolution to a first value based, at least in part, on the first input resolution values; generating first output data points based upon the data points in individual ones of the streams at each of the first input resolutions; outputting the first output data points according to the first value; determining second input resolution values based on second data resolutions for individual ones of the streams; setting the output resolution to a second value based, at least in part, on the second input resolution values; generating second output data points based upon the data points in individual ones of the streams at each of the second input resolutions; and outputting the second output data points according to the second value.
1. A computer-implemented method, comprising: implementing a detector for a client, the detector configured to process a stream of data points and provide alert messages responsive to determining that the stream of data points correspond with a series of trigger parameters; receiving, via a stream of data points, a first plurality of data points at a first data resolution, each data point in the stream of data points associated with a time stamp, wherein the first data resolution corresponds to an inter-arrival time between the data points in the first plurality of data points; setting an output resolution to a first value; generating a first set of output data points based upon the data points in the first plurality of data points received via the stream of data points; based on determining, at an interval corresponding with the first value, the first set of output data points correspond with any of the series of trigger parameters, generating a first alert message; based upon monitoring of the stream of data points, detecting a change in data resolution of the data points received via the stream of data points from the first value to a second value that is different from the first value, wherein the first value corresponds to a first frequency at which the first plurality of data points is received, wherein the second value corresponds to a second frequency at which the data points received via the stream of data points are received, wherein the first frequency and the second frequency are different such that the change in data resolution corresponds to a change in a frequency at which the data points are received; responsive to detecting the change in data resolution for the stream of data points from the first value to the second value, setting the output resolution to the second value; generating a second set of output data points based upon the data points in a second plurality of data points received via the stream of data points; and based on determining, at an interval corresponding with the second value, the second set of output data points correspond with any of the series of trigger parameters, generating a second alert message.
12. (New) A data resolution monitoring system comprising:
a processor; and
a computer-readable medium including instructions thereon that, when executed by the processor, cause the processor to: receiving streams of data points, wherein individual ones of the streams have different data resolutions resolutions, wherein individual ones of the different data resolutions correspond to an inter-arrival time between data points in a respective stream;
determining first input resolution values based on data resolutions for individual ones of the streams;
setting an output resolution to a first value based, at least in part, on the first input resolution values;
generating first output data points based upon the data points in individual ones of the streams at each of the first input resolutions;
outputting the first output data points according to the first value;
determining second input resolution values based on second data resolutions for individual ones of the streams;
setting the output resolution to a second value based, at least in part, on the second input resolution values; generating second output data points based upon the data points in individual ones of the streams at each of the second input resolutions; and
outputting the second output data points according to the second value.
11. A data resolution monitoring system comprising:
a processor; and
a computer-readable medium including instructions thereon that, when executed by the processor, cause the processor to: implement a detector for a client, the detector configured to process a stream of data points and provide alert messages responsive to determining that the stream of data points correspond with a series of trigger parameters; receive, via a stream of data points, a first plurality of data points at a first data resolution, each data point in the stream of data points associated with a time stamp, wherein the first data resolution corresponds to an inter-arrival time between the data points in the first plurality of data points; generate a first set of output data points based upon the data points in the first plurality of data points received via the stream of data points; generate a first alert message responsive to determining, at an interval corresponding with an output resolution set at a first value, the first set of output data points correspond with any of the series of trigger parameters; detect a change in data resolution of the data points received via the stream from the first data resolution to a second data resolution that is different from the first data resolution, wherein the first data resolution corresponds to a first frequency at which the first plurality of data points is received, wherein the second data resolution corresponds to a second frequency at which the data points received via the stream of data points are received, wherein the first frequency and the second frequency are different such that the change in data resolution corresponds to a change in a frequency at which the data points are received; responsive to detecting the change in data resolution for the stream of data points from the first data resolution to the second data resolution, set the output resolution to a second value different from the first value; generate a second set of output data points based upon the data points in a second plurality of data points received via the stream of data points; and based on determining, at an interval corresponding with the second value, the second set of output data points correspond with any of the series of trigger parameters, generate a second alert message.
18. (New) A non-transitory computer-readable medium including stored thereon a sequence of instructions which, when executed by a processor causes the processor to execute a process comprising:
receiving streams of data points, wherein individual ones of the streams have different data resolutions, wherein individual ones of the different data resolutions correspond to an inter-arrival time between data points in a respective stream;
determining first input resolution values based on data resolutions for individual ones of the streams;
setting an output resolution to a first value based, at least in part, on the first input resolution values;
generating first output data points based upon the data points in individual ones of the streams at each of the first input resolutions;
outputting the first output data points according to the first value;
determining second input resolution values based on second data resolutions for individual ones of the streams;
setting the output resolution to a second value based, at least in part, on the second input resolution values; generating second output data points based upon the data points in individual ones of the streams at each of the second input resolutions; and
outputting the second output data points according to the second value.
15. A non-transitory computer-readable medium including stored thereon a sequence of instructions which, when executed by a processor causes the processor to execute a process comprising: implementing a detector for a client, the detector configured to process multiple streams of data points and provide alert messages responsive to determining that any of the multiple streams of data points correspond with a series of trigger parameters; receiving the multiple streams of data points, each of the multiple streams of data points comprising a data resolution, wherein each data resolution corresponds to an inter-arrival time between data points in the each of the multiple streams of data points; deriving a first set of input resolution values based on data resolutions for each of multiple portions of the multiple streams of data points, where each of the multiple portions correspond to data blocks; setting an output resolution to a first value based on the first set of input resolution values; generating a first set of output data points based upon the data points in each of the multiple streams of data points at each of the first set of input resolution values; based on determining, at an interval corresponding with the first value, the first set of output data points correspond with any of the series of trigger parameters, generating a first alert message; based upon monitoring the multiple streams of data points, detecting a second set of input resolution values that are different than the first set of input resolution values, wherein the first set of input resolution values corresponds to a first set of frequencies at which the data points in the each of the multiple streams of data points are received, wherein the second set of input resolution values corresponds to a second set of frequencies at which the data points in the each of the multiple streams of data points are later received, and wherein at least one frequency in the second set of frequencies is different from the first set of frequencies; responsive to detecting the second set of input resolution values, setting the output resolution to a second value different from the first value; generating a second set of output data points based upon the data points in a second plurality of data points received via the stream of data points; and based on determining, at an interval corresponding with the second value, the second set of output data points correspond with any of the series of trigger parameters, generating a second alert message.
The instant claims merely broaden the scope of the conflicting claims. It is well settled that broadening the scope of claims would have been obvious to one of ordinary skill in the art in view of the narrower issued claims. In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982) and In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993).
The dependent claims 3-11, 13-17 and 19-21 carry the deficiencies from the base claims.
Conclusion
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure because the references teach time-series data analysis.
Chen et al. (US 2015/0134586)
Mills (US 2013/0262035)
9. 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.
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/Tauqir Hussain/Primary Examiner, Art Unit 2446