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 .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on June 2, 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
Applicant’s Preliminary Amendment, filed September 3, 2025, has been fully considered and entered. Accordingly, Claims 1-40 are pending in this application. Claims 1-20 have been cancelled. Claims 21-40 are new claims. Claims 21, 31, and 40 are independent claims.
Claim Objections
Applicant is advised that should Claim 21 be found allowable, Claim 40 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. 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).
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 21-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over Claims 1-20 of the ‘877 Patent (U.S. Patent No. 12,360,877 B1) and further in view of Stocker (PG Pub. No. 2021/0034994 A1).
Regarding Claim 21, the ‘877 Patent discloses a system comprising:
a processor (see ‘877 Patent, Claim 11, at least one processor coupled to the memory); and
a memory storing instructions that, when executed, perform operations comprising (see ‘877 Patent, Claim 11, a memory):
performing, by an alerting component of a first computing device, a procedure for time-series data on a resource of a network (see ‘877 Patent, Claim 11, perform, by an alerting component of the computing device, a … procedure for time-series data occurring on a resource of a network);
identifying a seasonal pattern of the time-series data based on the RBF procedure, wherein the seasonal pattern defines a tendency of the time-series data to exhibit behavior that repeats on the network over a fixed period of time (see ‘877 Patent, Claim 11, identifying a seasonal pattern of the time-series data based on the autoencoding procedure, wherein the seasonal pattern defines a tendency of the time-series data to exhibit behavior that repeats on the network over a fixed period of time and that is characterized by a shape and proportions between values observed in the fixed period of time);
determining a first shape of a first graphical depiction representing the time-series data (see ‘877 Patent, Claim 11, determining a first shape of a first graphical depiction representing the utilization metrics observed on the network in the fixed period at a first time);
determining a second shape of a second graphical depiction representing the time-series data (see ‘877 Patent, Claim 11, determining a second shape of a second graphical depiction representing the utilization metrics observed on the network in the fixed period at a second time);
determining that one or more change points occur in the seasonal pattern based on a difference between the first shape and the second shape (see ‘877 Patent, Claim 11, determine that one or more change points occur in the seasonal pattern based on a difference between the first shape and the second shape); and
transmitting an alert indicating the one or more change points based on determining that the one or more change points occur in the seasonal pattern of the time-series data (see ‘877 Patent, Claim 11, generate, by the alerting component, an alert indicating the one or more change points in the seasonal pattern and transmit the alert to a second device for evaluation).
The ‘877 Patent does not claim a radial basis function (RBF) procedure. Stocker discloses a radial basis function (RBF) procedure (see Stocker, paragraph [0122], where the exemplary inventive computer-based systems of the present disclosure may be configured to utilize one or more exemplary AI/machine learning techniques chosen from, but not limited to … radial basis function network; see also paragraph [0014], where a variable is part of a data asset that may be a time-series data set; change-points, outliers, patterns, and/or anomalies may be identified by a human or one or more detection models).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to substitute the autoencoding procedure in the instant application with the radial basis function procedure in Stocker as it amounts to simple substitution of one prior art element with another to yield predictable results (see MPEP 2143(I)(C)).
Regarding Claim 22, the ‘877 Patent in view of Stocker discloses the system of Claim 21, wherein the time-series data includes at least one of:
processor utilization metrics for the resource (see ‘877 Patent, Claim 11, processor utilization metrics); or
memory utilization metrics for the resource (see ‘877 Patent, Claim 11, memory utilization metrics).
Regarding Claim 23, the ‘877 Patent in view of Stocker discloses the system of Claim 21, wherein the alerting component executes the RBF procedure (see ‘877 Patent, Claim 11, perform, by an alerting component of the computing device, an autoencoding procedure).
The ‘877 Patent does not claim a radial basis function (RBF) procedure. Stocker discloses a radial basis function (RBF) procedure (see Stocker, paragraph [0122], where the exemplary inventive computer-based systems of the present disclosure may be configured to utilize one or more exemplary AI/machine learning techniques chosen from, but not limited to … radial basis function network; see also paragraph [0014], where a variable is part of a data asset that may be a time-series data set; change-points, outliers, patterns, and/or anomalies may be identified by a human or one or more detection models).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to substitute the autoencoding procedure in the instant application with the radial basis function procedure in Stocker as it amounts to simple substitution of one prior art element with another to yield predictable results (see MPEP 2143(I)(C)).
Regarding Claim 24, the ‘877 Patent in view of Stocker discloses the system of Claim 21, wherein the behavior exhibited by the time-series data is further characterized by a shape and proportions between values observed in the fixed period of time (see ‘877 Patent, Claim 11, wherein the seasonal pattern defines a tendency of the time-series data to exhibit behavior that repeats on the network over a fixed period of time and that is characterized by a shape and proportions between values observed in the fixed period of time).
Regarding Claim 25, the ‘877 Patent in view of Stocker discloses the system of Claim 21, wherein the first graphical depiction depicts at least one of first processor or first memory utilization metrics observed on the network in the fixed period at a first time (see ‘877 Patent, Claim 1, determining a first shape of a first graphical depiction representing the at least one of processor or memory utilization metrics observed on the network in the fixed period at a first time).
Regarding Claim 26, the ‘877 Patent in view of Stocker discloses the system of Claim 25, wherein the second graphical depiction depicts at least one of second processor or second memory utilization metrics observed on the network in the fixed period at a second time (see ‘877 Patent, Claim 1, determining a second shape of a second graphical depiction representing the at least one of processor or memory utilization metrics observed on the network in the fixed period at a second time).
Regarding Claim 27, the ‘877 Patent in view of Stocker discloses the system of Claim 11, wherein transmitting the alert comprises transmitting the alert to a second device comprising an analyzing component for evaluating the alert and the one or more change points (see ‘877 Patent, Claim 11, generate, by the alerting component, an alert indicating the one or more change points in the seasonal pattern and transmit the alert to a second device for evaluation).
Regarding Claim 28, the ‘877 Patent in view of Stocker discloses the system of Claim 21, wherein performing the RBF procedure comprises computing a similarity measurement between two points in dimensions of infinite size (see ‘877 Patent, Claim 8, compute a similarity measurement between two points in dimensions of infinite size).
Regarding Claim 29, the ‘877 Patent in view of Stocker discloses the system of Claim 28, wherein performing the RBF procedure further comprises detecting a mean shift value in an infinite-dimensional signal based on the similarity measurement (see ‘877 Patent, Claim 8, detect a mean shift value in the infinite-dimensional signal based on the similarity measurement).
Regarding Claim 30, the ‘877 Patent in view of Stocker discloses the system of Claim 21, wherein the alerting component is further configured to perform an autoencoding procedure to determine whether the one or more change points occur in the seasonal pattern of the time-series data (see ‘877 Patent, Claim 11, perform, by an alerting component of the computing device, an autoencoding procedure for time-series data occurring on a resource of a network, the time-series data).
Regarding Claim 31, the ‘877 Patent discloses a method comprising:
performing, by an alerting component of a first computing device, a procedure for time-series data on a resource of a network (see ‘877 Patent, Claim 11, perform, by an alerting component of the computing device, a … procedure for time-series data occurring on a resource of a network);
identifying a seasonal pattern of the time-series data based on the RBF procedure, wherein the seasonal pattern defines a tendency of the time-series data to exhibit behavior that repeats on the network over a fixed period of time (see ‘877 Patent, Claim 11, identifying a seasonal pattern of the time-series data based on the autoencoding procedure, wherein the seasonal pattern defines a tendency of the time-series data to exhibit behavior that repeats on the network over a fixed period of time and that is characterized by a shape and proportions between values observed in the fixed period of time);
determining a first shape of a first graphical depiction representing the time-series data (see ‘877 Patent, Claim 11, determining a first shape of a first graphical depiction representing the utilization metrics observed on the network in the fixed period at a first time);
determining a second shape of a second graphical depiction representing the time-series data (see ‘877 Patent, Claim 11, determining a second shape of a second graphical depiction representing the utilization metrics observed on the network in the fixed period at a second time);
determining that one or more change points occur in the seasonal pattern based on a difference between the first shape and the second shape (see ‘877 Patent, Claim 11, determine that one or more change points occur in the seasonal pattern based on a difference between the first shape and the second shape); and
transmitting an alert indicating the one or more change points based on determining that the one or more change points occur in the seasonal pattern of the time-series data (see ‘877 Patent, Claim 11, generate, by the alerting component, an alert indicating the one or more change points in the seasonal pattern and transmit the alert to a second device for evaluation).
The ‘877 Patent does not claim a radial basis function (RBF) procedure. Stocker discloses a radial basis function (RBF) procedure (see Stocker, paragraph [0122], where the exemplary inventive computer-based systems of the present disclosure may be configured to utilize one or more exemplary AI/machine learning techniques chosen from, but not limited to … radial basis function network; see also paragraph [0014], where a variable is part of a data asset that may be a time-series data set; change-points, outliers, patterns, and/or anomalies may be identified by a human or one or more detection models).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to substitute the autoencoding procedure in the instant application with the radial basis function procedure in Stocker as it amounts to simple substitution of one prior art element with another to yield predictable results (see MPEP 2143(I)(C)).
Regarding Claim 32, the ‘877 Patent in view of Stocker discloses the method of Claim 31, wherein the resource is a network-specific computing device (see ‘877 Patent, Claim 11, time-series data occurring on a resource of a network).
Regarding Claim 33, the ‘877 Patent in view of Stocker discloses the method of Claim 31, wherein:
The ‘877 Patent does not claim the resource includes a data logger for logging the time-series data. Stocker discloses the resource includes a data logger for logging the time-series data (see Stocker, Fig. 5, time-series-data 59).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the ‘877 Patent with Stocker in order to substitute the autoencoding procedure in the instant application with the radial basis function procedure in Stocker as it amounts to simple substitution of one prior art element with another to yield predictable results (see MPEP 2143(I)(C)).
Regarding Claim 34, the ‘877 Patent in view of Stocker discloses the method of Claim 31, wherein the time-series data includes at least one of processor or memory utilization metrics for the resource (see ‘877 Patent, Claim 11, the time-series data including utilization metrics for the resource, the utilization metrics including at least one of processor utilization metrics or memory utilization metrics).
Regarding Claim 35, the ‘877 Patent in view of Stocker discloses the method of Claim 31, wherein the time-series data includes at least one of:
throughput of traffic on the resource (see ‘877 Patent, Claim 10, throughput of traffic on the resource); or
application-specific events that are definable by application executing on the resource (see ‘877 Patent, Claim 10, application-specific events that are definable by applications execution on the resource).
Regarding Claim 36, the ‘877 Patent in view of Stocker discloses the method of Claim 31, wherein the alerting component executes the RBF procedure (see ‘877 Patent, Claim 11, perform, by an alerting component of the computing device, an autoencoding procedure).
The ‘877 Patent does not claim a radial basis function (RBF) procedure. Stocker discloses a radial basis function (RBF) procedure (see Stocker, paragraph [0122], where the exemplary inventive computer-based systems of the present disclosure may be configured to utilize one or more exemplary AI/machine learning techniques chosen from, but not limited to … radial basis function network; see also paragraph [0014], where a variable is part of a data asset that may be a time-series data set; change-points, outliers, patterns, and/or anomalies may be identified by a human or one or more detection models).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to substitute the autoencoding procedure in the instant application with the radial basis function procedure in Stocker as it amounts to simple substitution of one prior art element with another to yield predictable results (see MPEP 2143(I)(C)).
Regarding Claim 37, the ‘877 Patent in view of Stocker discloses the method of Claim 31, wherein the behavior exhibited by the time-series data is further characterized by a shape and proportions between values observed in the fixed period of time (see ‘877 Patent, Claim 11, wherein the seasonal pattern defines a tendency of the time-series data to exhibit behavior that repeats on the network over a fixed period of time and that is characterized by a shape and proportions between values observed in the fixed period of time)..
Regarding Claim 38, the ‘877 Patent in view of Stocker discloses the method of Claim 31, wherein performing the RBF procedure comprises computing a similarity measurement between two points in dimensions of infinite size (see ‘877 Patent, Claim 8, compute a similarity measurement between two points in dimensions of infinite size).
The ‘877 Patent does not claim a radial basis function (RBF) procedure. Stocker discloses a radial basis function (RBF) procedure (see Stocker, paragraph [0122], where the exemplary inventive computer-based systems of the present disclosure may be configured to utilize one or more exemplary AI/machine learning techniques chosen from, but not limited to … radial basis function network; see also paragraph [0014], where a variable is part of a data asset that may be a time-series data set; change-points, outliers, patterns, and/or anomalies may be identified by a human or one or more detection models).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to substitute the autoencoding procedure in the instant application with the radial basis function procedure in Stocker as it amounts to simple substitution of one prior art element with another to yield predictable results (see MPEP 2143(I)(C)).
Regarding Claim 39, the ‘877 Patent in view of Stocker discloses the method of Claim 38, wherein performing the RBF procedure further comprises detecting a mean shift value in an infinite-dimensional signal based on the similarity measurement (see ‘877 Patent, Claim 8, detect a mean shift value in the infinite-dimensional signal based on the similarity measurement).
The ‘877 Patent does not claim a radial basis function (RBF) procedure. Stocker discloses a radial basis function (RBF) procedure (see Stocker, paragraph [0122], where the exemplary inventive computer-based systems of the present disclosure may be configured to utilize one or more exemplary AI/machine learning techniques chosen from, but not limited to … radial basis function network; see also paragraph [0014], where a variable is part of a data asset that may be a time-series data set; change-points, outliers, patterns, and/or anomalies may be identified by a human or one or more detection models).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to substitute the autoencoding procedure in the instant application with the radial basis function procedure in Stocker as it amounts to simple substitution of one prior art element with another to yield predictable results (see MPEP 2143(I)(C)).
Regarding Claim 40, the ‘877 Patent discloses a computing device comprising:
a processor (see ‘877 Patent, Claim 11, at least one processor coupled to the memory); and
a memory storing instructions that, when executed, perform operations comprising (see ‘877 Patent, Claim 11, a memory):
performing, by an alerting component of a first computing device, a procedure for time-series data on a resource of a network (see ‘877 Patent, Claim 11, perform, by an alerting component of the computing device, a … procedure for time-series data occurring on a resource of a network);
identifying a seasonal pattern of the time-series data based on the RBF procedure, wherein the seasonal pattern defines a tendency of the time-series data to exhibit behavior that repeats on the network over a fixed period of time (see ‘877 Patent, Claim 11, identifying a seasonal pattern of the time-series data based on the autoencoding procedure, wherein the seasonal pattern defines a tendency of the time-series data to exhibit behavior that repeats on the network over a fixed period of time and that is characterized by a shape and proportions between values observed in the fixed period of time);
determining a first shape of a first graphical depiction representing the time-series data (see ‘877 Patent, Claim 11, determining a first shape of a first graphical depiction representing the utilization metrics observed on the network in the fixed period at a first time);
determining a second shape of a second graphical depiction representing the time-series data (see ‘877 Patent, Claim 11, determining a second shape of a second graphical depiction representing the utilization metrics observed on the network in the fixed period at a second time);
determining that one or more change points occur in the seasonal pattern based on a difference between the first shape and the second shape (see ‘877 Patent, Claim 11, determine that one or more change points occur in the seasonal pattern based on a difference between the first shape and the second shape); and
transmitting an alert indicating the one or more change points based on determining that the one or more change points occur in the seasonal pattern of the time-series data (see ‘877 Patent, Claim 11, generate, by the alerting component, an alert indicating the one or more change points in the seasonal pattern and transmit the alert to a second device for evaluation).
The ‘877 Patent does not claim a radial basis function (RBF) procedure. Stocker discloses a radial basis function (RBF) procedure (see Stocker, paragraph [0122], where the exemplary inventive computer-based systems of the present disclosure may be configured to utilize one or more exemplary AI/machine learning techniques chosen from, but not limited to … radial basis function network; see also paragraph [0014], where a variable is part of a data asset that may be a time-series data set; change-points, outliers, patterns, and/or anomalies may be identified by a human or one or more detection models).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to substitute the autoencoding procedure in the instant application with the radial basis function procedure in Stocker as it amounts to simple substitution of one prior art element with another to yield predictable results (see MPEP 2143(I)(C)).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 23 and 36 are rejected under 35 U.S.C. 112(d) as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Regarding Claims 23 and 36, the Claims recite that the RBF function is performed by the alerting component, but this limitation is already in their respective independent Claims. Accordingly, these Claims fail to further limit their respective independent Claims. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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.
Claims 21, 23, 24, 27-31, and 36-40 are rejected under 35 U.S.C. 101 as being directed to an abstract idea without significantly more.
Regarding Independent Claims 21, 31, and 40, the claims recite the following method steps:
performing a radial basis function (RBF) procedure for time-series data on a resource of a network;
identifying a seasonal pattern of the time-series data based on the RBF procedure, wherein the seasonal pattern defines a tendency of the time-series data to exhibit behavior that repeats on the network over a fixed period of time;
determining a first shape of a first graphical depiction representing the time-series data;
determining a second shape of a second graphical depiction representing the time-series data;
determining that one or more change points occur in the seasonal pattern based on a difference between the first shape and the second shape.
It is the position of the Examiner that the method steps set forth above are directed to an abstract mental process, as detecting a change in the seasonal pattern of time series data from a comparison of a shape of a graphical depiction of the time series data constitutes the type of observation, evaluation, judgement, and opinion that can be performed in the human mind or with the aid of pen and paper (see MPEP 2106.04(a)(2)(III)).
The additional method step:
transmitting an alert indicating the one or more change points based on determining that the one or more change points occur in the seasonal pattern of the time-series data.
fails to integrate the abstract mental process into a practical application or provide significantly more because it constitutes insignificant extra-solution activity (see MPEP 2106.05(g)(3), data gathering and outputting).
The additional computer elements, such as:
a processor;
a memory; and
a radial basis function.
Fail to integrate the abstract mental process into a practical application or provide significantly more because they are generic computer elements recited at a high level of generality and thus constitute “apply it” language (see MPEP 2106.05(f)(2), use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more). The claims merely use the radial basis function to perform the abstract idea, they do not improve the radial basis function itself.
Regarding dependent Claim 23 and 36, the claimed alerting component executing the RBF procedure fails to integrate the abstract mental process into a practical application or provide significantly more because they are generic computer elements recited at a high level of generality and thus constitute “apply it” language (see MPEP 2106.05(f)(2)).
Regarding dependent Claims 24 and 37, the claims further disclose how the time-series data is to be evaluated based on shape and proportion, and is thus directed to the same abstract idea set forth above.
Regarding dependent Claim 27, the claimed transmitting of the alert fails to integrate the abstract mental process into a practical application or provide significantly more because it constitutes insignificant extra-solution activity (see MPEP 2106.05(g)(3), data gathering and outputting).
Regarding dependent Claim 28 and 38, the RBF procedure fails to integrate the abstract mental process into a practical application or provide significantly more because they are generic computer elements recited at a high level of generality and thus constitute “apply it” language (see MPEP 2106.05(f)(2)).
Regarding dependent Claim 29 and 39, the RBF procedure fails to integrate the abstract mental process into a practical application or provide significantly more because they are generic computer elements recited at a high level of generality and thus constitute “apply it” language (see MPEP 2106.05(f)(2)).
Regarding dependent Claim 30, the autoencoding procedure fails to integrate the abstract mental process into a practical application or provide significantly more because they are generic computer elements recited at a high level of generality and thus constitute “apply it” language (see MPEP 2106.05(f)(2)).
Regarding dependent Claim 33, the data logger fails to integrate the abstract mental process into a practical application or provide significantly more because it constitutes insignificant extra-solution activity (see MPEP 2106.05(g)(3), data gathering and outputting).
Regarding dependent Claims 22, 25, 26, and 32-35, the claims involve receiving network, processor, and memory utilization metrics, which cannot be practically performed within the human mind. Accordingly, Claims 22, 25, 26, and 32-35 are not directed to an abstract idea.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 21-27, 31-37, and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Stocker (PG Pub. No. 2021/0034994 A1), and further in view of Garvey (PG Pub. No. 2017/0249564 A1).
Regarding Claim 21, Stocker discloses a system, comprising:
a processor (see Stocker, paragraph [0028], where ECS 17 may include e.g., one or more processing devices, such as a processor or multiple processors along with memory and storage devices); and
a memory storing instructions (see Stocker, paragraph [0028], where ECS 17 may include e.g., one or more processing devices, such as a processor or multiple processors along with memory and storage devices) that, when executed, perform operations comprising:
performing a radial basis function (RBF) procedure for time-series data on a resource of a network (see Stocker, paragraph [0122], where the exemplary inventive computer-based systems of the present disclosure may be configured to utilize one or more exemplary AI/machine learning techniques chosen from, but not limited to … radial basis function network; see also paragraph [0014], where a variable is part of a data asset that may be a time-series data set; change-points, outliers, patterns, and/or anomalies may be identified by a human or one or more detection models); and
identifying a seasonal pattern of the time-series data based on the RBF procedure (see Stocker, paragraph [0011], where a detection model at a centralized platform can communicate with data stores, including any data storage solution (e.g., databases) storing multidimensional and multivariate data to detect anomalies, change-points, patterns, and/or outliers).
Stocker does not disclose:
an alerting component of a first computing device;
wherein the seasonal pattern defines a tendency of the time-series data to exhibit behavior that repeats on the network over a fixed period of time;
determining a first shape of a first graphical depiction representing the time-series data;
determining a second shape of a second graphical depiction representing the time-series data;
determining that one or more change points occur in the seasonal pattern based on a difference between the first shape and the second shape; and
transmitting an alert indicating the one or more change points based on determining that the one or more change points occur in the seasonal pattern of the time-series data.
Garvey discloses:
an alerting component of a first computing device (see Garvey, Claim 6, where the method further comprises displaying, for each respective state change of the plurality of state changes, an indication of a change time of the respective state change).
wherein the seasonal pattern defines a tendency of the time-series data to exhibit behavior that repeats on the network over a fixed period of time (see Garvey, [0044], where seasonality analytic 132 includes logic for detecting and classifying seasonal behaviors within an input time-series signal);
determining a first shape of a first graphical depiction representing the time-series data (see Garvey, paragraph [0034], where systems and methods are described through which state changes within a time-series signal may be automatically detected and accommodated in a forecast or other analytical model; a ‘state change’ in this context refers to a change in the ‘normal’ characteristics of the signal; for instance, time-series data before a particular point in time (a ‘change time’) may exhibit a first pattern or set of one or more characteristics that are determined to be normal; after the change time, the time-series data may exhibit a second pattern or second set of characteristics that were not normal before the change time; in other words, a state change results in a ‘new normal’ for the time-series signal; see also paragraph [0051], where the seasonal behavior/pattern may shift such that the new seasonal behavior of the signal differs from the seasonal behavior that were detected in the first sub-period … a patch or other update of a target resource may cause a recurring shape, amplitude, phase, and/or other characteristic of a time-series signal to change);
determining a second shape of a second graphical depiction representing the time-series data (see Garvey, paragraph [0034], where systems and methods are described through which state changes within a time-series signal may be automatically detected and accommodated in a forecast or other analytical model; a ‘state change’ in this context refers to a change in the ‘normal’ characteristics of the signal; for instance, time-series data before a particular point in time (a ‘change time’) may exhibit a first pattern or set of one or more characteristics that are determined to be normal; after the change time, the time-series data may exhibit a second pattern or second set of characteristics that were not normal before the change time; in other words, a state change results in a ‘new normal’ for the time-series signal; see also paragraph [0051], where the seasonal behavior/pattern may shift such that the new seasonal behavior of the signal differs from the seasonal behavior that were detected in the first sub-period … a patch or other update of a target resource may cause a recurring shape, amplitude, phase, and/or other characteristic of a time-series signal to change);
determining that one or more change points occur in the seasonal pattern based on a difference between the first shape and the second shape (see Garvey, paragraph [0034], where systems and methods are described through which state changes within a time-series signal may be automatically detected and accommodated in a forecast or other analytical model; a ‘state change’ in this context refers to a change in the ‘normal’ characteristics of the signal; for instance, time-series data before a particular point in time (a ‘change time’) may exhibit a first pattern or set of one or more characteristics that are determined to be normal; after the change time, the time-series data may exhibit a second pattern or second set of characteristics that were not normal before the change time; in other words, a state change results in a ‘new normal’ for the time-series signal; see also paragraph [0051], where the seasonal behavior/pattern may shift such that the new seasonal behavior of the signal differs from the seasonal behavior that were detected in the first sub-period … a patch or other update of a target resource may cause a recurring shape, amplitude, phase, and/or other characteristic of a time-series signal to change); and
transmitting an alert indicating the one or more change points based on determining that the one or more change points occur in the seasonal pattern of the time-series data (see Garvey, Claim 6, where the method further comprises displaying, for each respective state change of the plurality of state changes, an indication of a change time of the respective state change).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Stocker with Garvey for the benefit of improved accurate forecasting of time-series data (see Garvey, paragraph [0004]).
Regarding Claim 22, Stocker in view of Garvey discloses the system of Claim 21, wherein the time-series data includes at least one of:
Stocker does not disclose processor utilization metrics for the resource or memory utilization metrics for the resource. Garvey discloses processor utilization metrics (see Garvey, paragraph [0004], where an inaccurate forecast may result in poor capacity planning decisions, leading to an inefficient allocation of resources; for instance, a forecast that underestimates future demand may lead to insufficient hardware and/or software resources being deployed to handle incoming requests; as a result, deployed resources may be over-utilized, increasing the time spent on processing each request) for the resource or memory utilization metrics for the resource.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Stocker with Garvey for the benefit of improved accurate forecasting of time-series data (see Garvey, paragraph [0004]).
Regarding Claim 23, Stocker in view of Garvey discloses the system of Claim 21, wherein:
Stocker does not disclose the alerting component executes the RBF procedure. Stocker in view of Garvey discloses the alerting component (see Garvey, Claim 6, where the method further comprises displaying, for each respective state change of the plurality of state changes, an indication of a change time of the respective state change) executes the RBF procedure (see Stocker, paragraph [0122], where the exemplary inventive computer-based systems of the present disclosure may be configured to utilize one or more exemplary AI/machine learning techniques chosen from, but not limited to … radial basis function network; see also paragraph [0014], where a variable is part of a data asset that may be a time-series data set; change-points, outliers, patterns, and/or anomalies may be identified by a human or one or more detection models).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Stocker with Garvey for the benefit of improved accurate forecasting of time-series data (see Garvey, paragraph [0004]).
Regarding Claim 24, Stocker in view of Garvey discloses the system of Claim 21, wherein:
Stocker does not disclose the behavior exhibited by the time-series data is further characterized by a shape and proportions between values observed in the fixed period of time. Garvey discloses the behavior exhibited by the time-series data is further characterized by a shape and proportions between values observed in the fixed period of time (see Garvey, paragraph [0034], where systems and methods are described through which state changes within a time-series signal may be automatically detected and accommodated in a forecast or other analytical model; a ‘state change’ in this context refers to a change in the ‘normal’ characteristics of the signal; for instance, time-series data before a particular point in time (a ‘change time’) may exhibit a first pattern or set of one or more characteristics that are determined to be normal; after the change time, the time-series data may exhibit a second pattern or second set of characteristics that were not normal before the change time; in other words, a state change results in a ‘new normal’ for the time-series signal; see also paragraph [0051], where the seasonal behavior/pattern may shift such that the new seasonal behavior of the signal differs from the seasonal behavior that were detected in the first sub-period … a patch or other update of a target resource may cause a recurring shape, amplitude, phase, and/or other characteristic of a time-series signal to change).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Stocker with Garvey for the benefit of improved accurate forecasting of time-series data (see Garvey, paragraph [0004]).
Regarding Claim 25, Stocker in view of Garvey discloses the system of Claim 21, wherein:
Stocker does not disclose the first graphical depiction depicts at least one of first processor or first memory utilization metrics observed on the network in the fixed period at a first time. Garvey discloses the first graphical depiction depicts at least one of first processor or first memory utilization metrics observed on the network in the fixed period at a first time (see Garvey, paragraph [0004], where an inaccurate forecast may result in poor capacity planning decisions, leading to an inefficient allocation of resources; for instance, a forecast that underestimates future demand may lead to insufficient hardware and/or software resources being deployed to handle incoming requests; as a result, deployed resources may be over-utilized, increasing the time spent on processing each request; see also paragraph [0093], where Fig. 8A illustrates an example forecast visualization that is generated in a manner that accounts for detected state changes; see also Fig. 8A where both actual time series data and forecast are shown, along with change point in time 812).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Stocker with Garvey for the benefit of improved accurate forecasting of time-series data (see Garvey, paragraph [0004]).
Regarding Claim 26, Stocker in view of Garvey discloses the system of Claim 25, wherein:
Stocker does not disclose the second graphical depiction depicts at least one of second processor or second memory utilization metrics observed on the network in the fixed period at a second time. Garvey discloses the second graphical depiction depicts at least one of second processor or second memory utilization metrics observed on the network in the fixed period at a second time (see Garvey, paragraph [0004], where an inaccurate forecast may result in poor capacity planning decisions, leading to an inefficient allocation of resources; for instance, a forecast that underestimates future demand may lead to insufficient hardware and/or software resources being deployed to handle incoming requests; as a result, deployed resources may be over-utilized, increasing the time spent on processing each request; see also paragraph [0093], where Fig. 8A illustrates an example forecast visualization that is generated in a manner that accounts for detected state changes; see also Fig. 8A where both actual time series data and forecast are shown, along with change point in time 812).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Stocker with Garvey for the benefit of improved accurate forecasting of time-series data (see Garvey, paragraph [0004]).
Regarding Claim 27, Stocker in view of Garvey discloses the system of Claim 25, wherein transmitting the alert comprises:
Stocker does not disclose transmitting the alert to a second device comprising an analyzing component for evaluating the alert and the one or more change points. Garvey discloses transmitting the alert to a second device comprising an analyzing component for evaluating the alert and the one or more change points (see Garvey, Claim 7, wherein the analytical output comprises a representation of a forecast, the method further comprising receiving a selection of a graphical object that represents a particular state change in the plurality of state changes and in response to receiving the selection of the graphical object that represents the particular state change: updating the set of values used to train the model and generating … an updated forecast using the retrained model [it is the position of the Examiner that the user selecting a graphical object representing a state change in the time series data is not patentably distinguishable from an analyzing component for evaluating the alert and the one or more change points).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Stocker with Garvey for the benefit of improved accurate forecasting of time-series data (see Garvey, paragraph [0004]).
Regarding Claim 31, Stocker discloses a method, comprising:
performing a radial basis function (RBF) procedure for time-series data on a resource of a network (see Stocker, paragraph [0122], where the exemplary inventive computer-based systems of the present disclosure may be configured to utilize one or more exemplary AI/machine learning techniques chosen from, but not limited to … radial basis function network; see also paragraph [0014], where a variable is part of a data asset that may be a time-series data set; change-points, outliers, patterns, and/or anomalies may be identified by a human or one or more detection models); and
identifying a seasonal pattern of the time-series data based on the RBF procedure (see Stocker, paragraph [0011], where a detection model at a centralized platform can communicate with data stores, including any data storage solution (e.g., databases) storing multidimensional and multivariate data to detect anomalies, change-points, patterns, and/or outliers).
Stocker does not disclose:
an alerting component of a first computing device;
wherein the seasonal pattern defines a tendency of the time-series data to exhibit behavior that repeats on the network over a fixed period of time;
determining a first shape of a first graphical depiction representing the time-series data;
determining a second shape of a second graphical depiction representing the time-series data;
determining that one or more change points occur in the seasonal pattern based on a difference between the first shape and the second shape; and
transmitting an alert indicating the one or more change points based on determining that the one or more change points occur in the seasonal pattern of the time-series data.
Garvey discloses:
an alerting component of a first computing device (see Garvey, Claim 6, where the method further comprises displaying, for each respective state change of the plurality of state changes, an indication of a change time of the respective state change).
wherein the seasonal pattern defines a tendency of the time-series data to exhibit behavior that repeats on the network over a fixed period of time (see Garvey, [0044], where seasonality analytic 132 includes logic for detecting and classifying seasonal behaviors within an input time-series signal);
determining a first shape of a first graphical depiction representing the time-series data (see Garvey, paragraph [0034], where systems and methods are described through which state changes within a time-series signal may be automatically detected and accommodated in a forecast or other analytical model; a ‘state change’ in this context refers to a change in the ‘normal’ characteristics of the signal; for instance, time-series data before a particular point in time (a ‘change time’) may exhibit a first pattern or set of one or more characteristics that are determined to be normal; after the change time, the time-series data may exhibit a second pattern or second set of characteristics that were not normal before the change time; in other words, a state change results in a ‘new normal’ for the time-series signal; see also paragraph [0051], where the seasonal behavior/pattern may shift such that the new seasonal behavior of the signal differs from the seasonal behavior that were detected in the first sub-period … a patch or other update of a target resource may cause a recurring shape, amplitude, phase, and/or other characteristic of a time-series signal to change);
determining a second shape of a second graphical depiction representing the time-series data (see Garvey, paragraph [0034], where systems and methods are described through which state changes within a time-series signal may be automatically detected and accommodated in a forecast or other analytical model; a ‘state change’ in this context refers to a change in the ‘normal’ characteristics of the signal; for instance, time-series data before a particular point in time (a ‘change time’) may exhibit a first pattern or set of one or more characteristics that are determined to be normal; after the change time, the time-series data may exhibit a second pattern or second set of characteristics that were not normal before the change time; in other words, a state change results in a ‘new normal’ for the time-series signal; see also paragraph [0051], where the seasonal behavior/pattern may shift such that the new seasonal behavior of the signal differs from the seasonal behavior that were detected in the first sub-period … a patch or other update of a target resource may cause a recurring shape, amplitude, phase, and/or other characteristic of a time-series signal to change);
determining that one or more change points occur in the seasonal pattern based on a difference between the first shape and the second shape (see Garvey, paragraph [0034], where systems and methods are described through which state changes within a time-series signal may be automatically detected and accommodated in a forecast or other analytical model; a ‘state change’ in this context refers to a change in the ‘normal’ characteristics of the signal; for instance, time-series data before a particular point in time (a ‘change time’) may exhibit a first pattern or set of one or more characteristics that are determined to be normal; after the change time, the time-series data may exhibit a second pattern or second set of characteristics that were not normal before the change time; in other words, a state change results in a ‘new normal’ for the time-series signal; see also paragraph [0051], where the seasonal behavior/pattern may shift such that the new seasonal behavior of the signal differs from the seasonal behavior that were detected in the first sub-period … a patch or other update of a target resource may cause a recurring shape, amplitude, phase, and/or other characteristic of a time-series signal to change); and
transmitting an alert indicating the one or more change points based on determining that the one or more change points occur in the seasonal pattern of the time-series data (see Garvey, Claim 6, where the method further comprises displaying, for each respective state change of the plurality of state changes, an indication of a change time of the respective state change).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Stocker with Garvey for the benefit of improved accurate forecasting of time-series data (see Garvey, paragraph [0004]).
Regarding Claim 32, Stocker in view of Garvey discloses the method of Claim 31, wherein:
Stocker does not disclose the resource is a network-specific computing device. Garvey discloses the resource is a network-specific computing device (see Garvey, paragraph [0004], where an inaccurate forecast may result in poor capacity planning decisions, leading to an inefficient allocation of resources; for instance, a forecast that underestimates future demand may lead to insufficient hardware and/or software resources being deployed to handle incoming requests; as a result, deployed resources may be over-utilized, increasing the time spent on processing each request) for the resource or memory utilization metrics for the resource.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Stocker with Garvey for the benefit of improved accurate forecasting of time-series data (see Garvey, paragraph [0004]).
Regarding Claim 33, Stocker in view of Garvey discloses the method of Claim 31, wherein the resource includes a data logger for logging the time-series data. Garvey discloses the resource includes a data logger for logging the time-series data (see Stocker, Fig. 5, time-series-data 59).
Regarding Claim 34, Stocker in view of Garvey discloses the method of Claim 31, wherein the time-series data includes at least one of:
Stocker does not disclose processor utilization metrics for the resource or memory utilization metrics for the resource. Garvey discloses processor utilization metrics (see Garvey, paragraph [0004], where an inaccurate forecast may result in poor capacity planning decisions, leading to an inefficient allocation of resources; for instance, a forecast that underestimates future demand may lead to insufficient hardware and/or software resources being deployed to handle incoming requests; as a result, deployed resources may be over-utilized, increasing the time spent on processing each request) for the resource or memory utilization metrics for the resource.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Stocker with Garvey for the benefit of improved accurate forecasting of time-series data (see Garvey, paragraph [0004]).
Regarding Claim 35, Stocker in view of Garvey discloses the method of Claim 31, wherein the time-series data includes at least one of:
Stocker does not disclose throughput of traffic on the resource or application-specific events that are definable by application executing on the resource. Garvey discloses throughput of traffic on the resource (see Garvey, paragraph [0029], where in the event that online traffic is greatly reduced in the late evening hours, the linear regression model may underestimate future peak values or overestimate future trough values, both of which lead to a wasteful use of computational resources (including computer hardware, software, storage, and processor resources, and any services or other resources built on top of those resources) or application-specific events that are definable by application executing on the resource.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Stocker with Garvey for the benefit of improved accurate forecasting of time-series data (see Garvey, paragraph [0004]).
Regarding Claim 36, Stocker in view of Garvey discloses the method of Claim 21, wherein:
Stocker does not disclose the alerting component causes executes the RBF procedure on the computing device. Stocker in view of Garvey discloses the alerting component (see Garvey, Claim 6, where the method further comprises displaying, for each respective state change of the plurality of state changes, an indication of a change time of the respective state change) causes executes the RBF procedure on the computing device (see Stocker, paragraph [0122], where the exemplary inventive computer-based systems of the present disclosure may be configured to utilize one or more exemplary AI/machine learning techniques chosen from, but not limited to … radial basis function network; see also paragraph [0014], where a variable is part of a data asset that may be a time-series data set; change-points, outliers, patterns, and/or anomalies may be identified by a human or one or more detection models).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Stocker with Garvey for the benefit of improved accurate forecasting of time-series data (see Garvey, paragraph [0004]).
Regarding Claim 37, Stocker in view of Garvey discloses the method of Claim 21, wherein:
Stocker does not disclose the behavior exhibited by the time-series data is further characterized by a shape and proportions between values observed in the fixed period of time. Garvey discloses the behavior exhibited by the time-series data is further characterized by a shape and proportions between values observed in the fixed period of time (see Garvey, paragraph [0034], where systems and methods are described through which state changes within a time-series signal may be automatically detected and accommodated in a forecast or other analytical model; a ‘state change’ in this context refers to a change in the ‘normal’ characteristics of the signal; for instance, time-series data before a particular point in time (a ‘change time’) may exhibit a first pattern or set of one or more characteristics that are determined to be normal; after the change time, the time-series data may exhibit a second pattern or second set of characteristics that were not normal before the change time; in other words, a state change results in a ‘new normal’ for the time-series signal; see also paragraph [0051], where the seasonal behavior/pattern may shift such that the new seasonal behavior of the signal differs from the seasonal behavior that were detected in the first sub-period … a patch or other update of a target resource may cause a recurring shape, amplitude, phase, and/or other characteristic of a time-series signal to change).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Stocker with Garvey for the benefit of improved accurate forecasting of time-series data (see Garvey, paragraph [0004]).
Regarding Claim 40, Stocker discloses a computing device comprising:
a processor (see Stocker, paragraph [0028], where ECS 17 may include e.g., one or more processing devices, such as a processor or multiple processors along with memory and storage devices); and
a memory storing instructions (see Stocker, paragraph [0028], where ECS 17 may include e.g., one or more processing devices, such as a processor or multiple processors along with memory and storage devices) that, when executed, perform operations comprising:
performing a radial basis function (RBF) procedure for time-series data on a resource of a network (see Stocker, paragraph [0122], where the exemplary inventive computer-based systems of the present disclosure may be configured to utilize one or more exemplary AI/machine learning techniques chosen from, but not limited to … radial basis function network; see also paragraph [0014], where a variable is part of a data asset that may be a time-series data set; change-points, outliers, patterns, and/or anomalies may be identified by a human or one or more detection models); and
identifying a seasonal pattern of the time-series data based on the RBF procedure (see Stocker, paragraph [0011], where a detection model at a centralized platform can communicate with data stores, including any data storage solution (e.g., databases) storing multidimensional and multivariate data to detect anomalies, change-points, patterns, and/or outliers).
Stocker does not disclose:
wherein the seasonal pattern defines a tendency of the time-series data to exhibit behavior that repeats on the network over a fixed period of time;
determining a first shape of a first graphical depiction representing the time-series data;
determining a second shape of a second graphical depiction representing the time-series data;
determining that one or more change points occur in the seasonal pattern based on a difference between the first shape and the second shape; and
transmitting an alert indicating the one or more change points based on determining that the one or more change points occur in the seasonal pattern of the time-series data.
Garvey discloses:
wherein the seasonal pattern defines a tendency of the time-series data to exhibit behavior that repeats on the network over a fixed period of time (see Garvey, [0044], where seasonality analytic 132 includes logic for detecting and classifying seasonal behaviors within an input time-series signal);
determining a first shape of a first graphical depiction representing the time-series data (see Garvey, paragraph [0034], where systems and methods are described through which state changes within a time-series signal may be automatically detected and accommodated in a forecast or other analytical model; a ‘state change’ in this context refers to a change in the ‘normal’ characteristics of the signal; for instance, time-series data before a particular point in time (a ‘change time’) may exhibit a first pattern or set of one or more characteristics that are determined to be normal; after the change time, the time-series data may exhibit a second pattern or second set of characteristics that were not normal before the change time; in other words, a state change results in a ‘new normal’ for the time-series signal; see also paragraph [0051], where the seasonal behavior/pattern may shift such that the new seasonal behavior of the signal differs from the seasonal behavior that were detected in the first sub-period … a patch or other update of a target resource may cause a recurring shape, amplitude, phase, and/or other characteristic of a time-series signal to change);
determining a second shape of a second graphical depiction representing the time-series data (see Garvey, paragraph [0034], where systems and methods are described through which state changes within a time-series signal may be automatically detected and accommodated in a forecast or other analytical model; a ‘state change’ in this context refers to a change in the ‘normal’ characteristics of the signal; for instance, time-series data before a particular point in time (a ‘change time’) may exhibit a first pattern or set of one or more characteristics that are determined to be normal; after the change time, the time-series data may exhibit a second pattern or second set of characteristics that were not normal before the change time; in other words, a state change results in a ‘new normal’ for the time-series signal; see also paragraph [0051], where the seasonal behavior/pattern may shift such that the new seasonal behavior of the signal differs from the seasonal behavior that were detected in the first sub-period … a patch or other update of a target resource may cause a recurring shape, amplitude, phase, and/or other characteristic of a time-series signal to change);
determining that one or more change points occur in the seasonal pattern based on a difference between the first shape and the second shape (see Garvey, paragraph [0034], where systems and methods are described through which state changes within a time-series signal may be automatically detected and accommodated in a forecast or other analytical model; a ‘state change’ in this context refers to a change in the ‘normal’ characteristics of the signal; for instance, time-series data before a particular point in time (a ‘change time’) may exhibit a first pattern or set of one or more characteristics that are determined to be normal; after the change time, the time-series data may exhibit a second pattern or second set of characteristics that were not normal before the change time; in other words, a state change results in a ‘new normal’ for the time-series signal; see also paragraph [0051], where the seasonal behavior/pattern may shift such that the new seasonal behavior of the signal differs from the seasonal behavior that were detected in the first sub-period … a patch or other update of a target resource may cause a recurring shape, amplitude, phase, and/or other characteristic of a time-series signal to change); and
transmitting an alert indicating the one or more change points based on determining that the one or more change points occur in the seasonal pattern of the time-series data (see Garvey, Claim 6, where the method further comprises displaying, for each respective state change of the plurality of state changes, an indication of a change time of the respective state change).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Stocker with Garvey for the benefit of improved accurate forecasting of time-series data (see Garvey, paragraph [0004]).
Claims 28, 29, 38 and 39 are rejected under 35 U.S.C. 103 as being unpatentable over Stocker and Garvey as applied to Claims 21-27, 31-37, and 40 above, and further in view of Miguelanez (PG Pub. No. 2010/0088054 A1).
Regarding Claim 28, Stocker in view of Garvey discloses the system of Claim 21, wherein performing the RBF procedure comprises:
Stocker does not disclose computing a similarity measurement between two points in dimensions of infinite size. Miguelanez discloses computing a similarity measurement between two points in dimensions of infinite size (see Miguelanez, paragraph [0084], where the difference between the raw data point Rn and the preceding smoothed data point Sn-1 exceeds the threshold T1, it is assumed that the exceeded threshold corresponds to a significant departure from the smoothed data and indicates a shift in the data; see also paragraph [0205], where classifiers in the two stages 3210, 3212 may comprise any suitable systems for analyzing data, such as intelligent systems, for example, neural networks, particle swarm optimization (PSO) systems, generic algorithm (GA) systems, radial basis function (RBF) neural networks).
Both Stocker and Miguelanez are directed to radial basis functions. Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Stocker with Miguelanez as it amounts to combining prior art elements according to known techniques to yield predictable results (see MPEP 2143(I)(A)).
Regarding Claim 29, Stocker in view of Garvey and Miguelanez discloses the system of Claim 28, wherein performing the RBF procedure further comprises:
Stocker does not disclose detecting a mean shift value in an infinite-dimensional signal based on the similarity measurement. Miguelanez discloses detecting a mean shift value in an infinite-dimensional signal based on the similarity measurement (see Miguelanez, paragraph [0084], where the difference between the raw data point Rn and the preceding smoothed data point Sn-1 exceeds the threshold T1, it is assumed that the exceeded threshold corresponds to a significant departure from the smoothed data and indicates a shift in the data; see also paragraph [0205], where classifiers in the two stages 3210, 3212 may comprise any suitable systems for analyzing data, such as intelligent systems, for example, neural networks, particle swarm optimization (PSO) systems, generic algorithm (GA) systems, radial basis function (RBF) neural networks).
Both Stocker and Miguelanez are directed to radial basis functions. Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Stocker with Miguelanez as it amounts to combining prior art elements according to known techniques to yield predictable results (see MPEP 2143(I)(A)).
Regarding Claim 38, Stocker in view of Garvey discloses the method of Claim 31, wherein performing the RBF procedure comprises:
Stocker does not disclose computing a similarity measurement between two points in dimensions of infinite size. Miguelanez discloses computing a similarity measurement between two points in dimensions of infinite size (see Miguelanez, paragraph [0084], where the difference between the raw data point Rn and the preceding smoothed data point Sn-1 exceeds the threshold T1, it is assumed that the exceeded threshold corresponds to a significant departure from the smoothed data and indicates a shift in the data; see also paragraph [0205], where classifiers in the two stages 3210, 3212 may comprise any suitable systems for analyzing data, such as intelligent systems, for example, neural networks, particle swarm optimization (PSO) systems, generic algorithm (GA) systems, radial basis function (RBF) neural networks).
Both Stocker and Miguelanez are directed to radial basis functions. Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Stocker with Miguelanez as it amounts to combining prior art elements according to known techniques to yield predictable results (see MPEP 2143(I)(A)).
Regarding Claim 39, Stocker in view of Garvey and Miguelanez discloses the method of Claim 38, wherein performing the RBF procedure further comprises:
Stocker does not disclose detecting a mean shift value in an infinite-dimensional signal based on the similarity measurement. Miguelanez discloses detecting a mean shift value in an infinite-dimensional signal based on the similarity measurement (see Miguelanez, paragraph [0084], where the difference between the raw data point Rn and the preceding smoothed data point Sn-1 exceeds the threshold T1, it is assumed that the exceeded threshold corresponds to a significant departure from the smoothed data and indicates a shift in the data; see also paragraph [0205], where classifiers in the two stages 3210, 3212 may comprise any suitable systems for analyzing data, such as intelligent systems, for example, neural networks, particle swarm optimization (PSO) systems, generic algorithm (GA) systems, radial basis function (RBF) neural networks).
Both Stocker and Miguelanez are directed to radial basis functions. Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine Stocker with Miguelanez as it amounts to combining prior art elements according to known techniques to yield predictable results (see MPEP 2143(I)(A)).
Claim 30 is rejected under 35 U.S.C. 103 as being unpatentable over Stocker and Garvey as applied to Claims 21-27, 31-37, and 40 above, and further in view of Ben Simhon (PG Pub. No. 2016/0210556 A1).
Regarding Claim 30, Stocker in view of Garvey discloses the method of Claim 21, wherein:
Stocker does not disclose the alerting component is further configured to perform an autoencoding procedure to determine whether the one or more change points occur in the seasonal pattern of the time-series data. Ben Simhon discloses the alerting component is further configured to perform an autoencoding procedure to determine whether the one or more change points occur in the seasonal pattern of the time-series data (see Ben Simhon, paragraph [0017], where the system includes a stacked autoencoder module configured to (i) train a stacked autoencoder using each of the time series portions of each of the plurality of metrics).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to substitute the radial basis function in Stocker with the autoencoding procedure in Ben Simhon as it amounts to simple substitution of one prior art element with another to yield predictable results (see MPEP 2143(I)(C)).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FARHAD AGHARAHIMI whose telephone number is (571)272-9864. The examiner can normally be reached M-F 9am - 5pm ET.
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, Apu Mofiz can be reached at 571-272-4080. 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.
/FARHAD AGHARAHIMI/Examiner, Art Unit 2161
/APU M MOFIZ/Supervisory Patent Examiner, Art Unit 2161
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