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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. U.S. Patent No. 12,373,466 in view of Sun (US 20240265273 A1).
Instant Application
U.S. Patent No. 12,373,466
Claim 1:
A computer-implemented method for detecting a seasonality pattern that corresponds to a time-series data set, the computer-implemented method comprising:
for each candidate seasonality pattern of a set of candidate seasonality patterns,
partitioning the time-series data set into a set of subsequences according to a date-time pattern of a selected candidate seasonality pattern,
clustering data points of each of the set of subsequences into two or more clusters, and
determining a silhouette score for the selected candidate seasonality pattern that represents a measure of a clustering quality of the selected candidate seasonality pattern; and
detecting the seasonality pattern from the set of candidate seasonality patterns by selecting the candidate selecting having a highest silhouette score of the candidate seasonality patterns.
Claim 1:
A computer-implemented method, comprising:
obtaining a time-series data set; performing a data regularity check on the time-series data set;
responsive to the data regularity check not satisfying a data regularity threshold, performing a data aggregation process to regularize the time-series data set;
dividing the time-series data set into a set of subsequences based on a first candidate seasonality pattern;
clustering the set of subsequences in accordance with the first candidate seasonality pattern;
determining a silhouette score measuring a quality of the clustering, wherein the silhouette score indicates how well the first candidate seasonality pattern fits the time-series data set;
establishing an anomaly band for the time-series data set based on the first candidate seasonality pattern when the silhouette score satisfies a threshold comparison; and
detecting one or more anomalies within the time-series data set that are outside of the anomaly band.
Claim 1 of U.S. Patent No. 12,373,466 recites obtaining a time-series data set; performing a data regularity check on the time-series data set; responsive to the data regularity check not satisfying a data regularity threshold, performing a data aggregation process to regularize the time-series data set; dividing the time-series data set into a set of subsequences based on a first candidate seasonality pattern; clustering the set of subsequences in accordance with the first candidate seasonality pattern; determining a silhouette score measuring a quality of the clustering, wherein the silhouette score indicates how well the first candidate seasonality pattern fits the time-series data set; establishing an anomaly band for the time-series data set based on the first candidate seasonality pattern when the silhouette score satisfies a threshold comparison; and detecting one or more anomalies within the time-series data set that are outside of the anomaly band.
Claim 1 of U.S. Patent No. 12,373,466 lacks detecting the seasonality pattern from the set of candidate seasonality patterns by selecting the candidate selecting having a highest silhouette score of the candidate seasonality patterns.
Sun teaches detecting the seasonality pattern from the set of candidate seasonality patterns by selecting the candidate selecting having a highest silhouette score of the candidate seasonality patterns (Sun discloses calculating a similarity value (e.g. silhouette score). For example, the processor may calculate how similar characteristics of each set of time series data within a cluster are to each other and how dissimilar those characteristics are to other clusters. Select the cluster results that produced the highest silhouette score. See [0029] and [0047].).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the anomaly detection of claim 1 of U.S. Patent No. 12,373,466 to include the detection of seasonality patterns, as taught by Sun, for the purpose of increasing adaptability and accuracy of predictions (e.g., forecasts) by accounting for time series data attributes (e.g., seasonality, trending), improving alerting capabilities and network planning.
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 1, 8, and 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites the limitation of computing a silhouette score measuring a quality of the clustering, wherein the silhouette score indicates how well the first candidate seasonality pattern fits the time-series data set. The limitation, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “computer-implemented, computing device comprising: a processor; and a non-transitory computer-readable medium having stored thereon instructions that, when executed by the processor, cause the processor to perform operations, a non-transitory computer-readable medium having stored thereon instructions that, when executed by one or more processors, cause the one or more processor to perform operations” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “computer-implemented, computing device, and non-transitory computer-readable medium” language, the claim encompasses a user simply comparing the obtained time-series data set to a predetermined acceptable quality percentage in his/her mind. The mere nominal recitation of a computer does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental process.
This judicial exception is not integrated into a practical application because the claim recites three additional elements: obtaining a time-series data set, dividing the time-series data set into a set of subsequences based on a first candidate seasonality pattern, clustering the set of subsequences resulting in a set of clustered subsequences and that a generic “computer” performs the obtaining, dividing, clustering and comparing steps. The partitioning, clustering, determining, and detecting steps are recited at a high level of generality (i.e., as a general means of gathering and organizing time-series data for use in the comparison step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The computer that performs the partitioning, clustering, determining, and detecting steps is also recited at a high level of generality, and merely automates the partitioning, clustering, determining, and detecting steps. Each of the recitations of the computer-implemented are no more than mere instructions to apply the exception using a generic computer component.
The combination of these additional elements is no more than insignificant extra solution activity (collecting time-series data) that provides data for the exception, with mere instructions to apply the exception using a generic computer component (the computer). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as explained below, the recitation of the computer amounts to nothing more than applying the exception with a generic, off-the-shelf-computer component and the recitation of collecting traffic data was determined to be insignificant extra solution activity that is well understood, routine, conventional. As discussed above, the computer amounts to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application nor can it provide an inventive concept. The obtaining step was considered to be insignificant extra-solution activity (mere data gathering). The background does not provide any indication that the computer was anything other than a generic, off-the-shelf computer component. Additionally, the Symantec, TLI, and OIP Techs court decisions cited in MPEP 2106.05(d)(II) indicate that mere collection or receipt of data over a network is a well- understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the partitioning, clustering, determining, and detecting steps are a well-understood, routine, and conventional activity is supported.
Even when considered in combination, these additional elements represent mere instructions to apply an exception with well understood, routine, and conventional insignificant extra-solution activity, which does not provide significantly more to the abstract idea. The claim is not patent eligible.
Claims 2, 9, and 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “generating a graphical user interface (GUI) configured to receive a search query that includes (i) a search or query command, and (ii) a data source from which to retrieve the time- series data set; and obtaining the time-series data set as a result of execution of the search query” . This limitation allow for enhanced comparison of time-series data using a set of parameters, which is a method of comparing data, that falls within the “mental process” grouping of abstract ideas. The mere nominal recitation of a generic computer and computing device does not take the claim out of the “mental process” grouping. Thus, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application because the claims as a whole merely describes how to generally “apply” the concept of obtaining and comparing time-series data in a computer environment. The claimed “non-transitory storage devices” and “computing device” are each recited at a high level of generality and are merely invoked as tools to perform an existing data gathering and analysis process. Even considered in combination, simply implementing the abstract idea on a generic computer with storage devices recited at a high level of generality is not a practical application of the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because as noted previously, the content server and the storage devices individually and in combination merely describe how to generally “apply” the concept of data gathering and analysis in a computer environment. The same applies here. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. The claim is ineligible.
Claims 3, 10, and 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “selected candidate seasonality pattern corresponds to a particular date-time pattern for partitioning the time-series data set into the set of subsequences”. This limitation allow for enhanced grouping of time-series data using a patterns of similarity, which is a method of gathering and comparing data, that falls within the “mental process” grouping of abstract ideas. The mere nominal recitation of a generic computer and computing device does not take the claim out of the “mental process” grouping. Thus, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application because the claims as a whole merely describes how to generally “apply” the concept of obtaining and comparing time-series data in a computer environment. The claimed “non-transitory storage devices” and “computing device” are each recited at a high level of generality and are merely invoked as tools to perform an existing data gathering and analysis process. Even considered in combination, simply implementing the abstract idea on a generic computer with storage devices recited at a high level of generality is not a practical application of the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because as noted previously, the content server and the storage devices individually and in combination merely describe how to generally “apply” the concept of data gathering and analysis in a computer environment. The same applies here. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. The claim is ineligible.
Claims 4, 11, and 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “performing anomaly detection operations on the time-series data set including generating an anomaly band based on the selected seasonality pattern and detecting data points outside of the anomaly band”. As is evident from the background, the claimed computation is a mathematical calculation of the value of the extent of anomalies within data sets, using mathematical formulas including thresholds. Thus, the limitation recites a concept that falls into the “mathematical concepts” group of abstract ideas. This limitation also falls into the “mental process” group of abstract ideas, because the recited mathematical calculation can be practically performed in the human mind, e.g., scientists and engineers have been using thresholds to make data groupings in their minds since it was first proposed.
This judicial exception is not integrated into a practical application because the claims as a whole merely describes how to generally “apply” the concept of obtaining and comparing time-series data in a computer environment. The claimed “non-transitory storage devices” and “computing device” are each recited at a high level of generality and are merely invoked as tools to perform an existing data gathering and analysis process. Even considered in combination, simply implementing the abstract idea on a generic computer with storage devices recited at a high level of generality is not a practical application of the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because as noted previously, the content server and the storage devices individually and in combination merely describe how to generally “apply” the concept of data gathering and analysis in a computer environment. The same applies here. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. The claim is ineligible.
Claims 5, 12, and 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “performing anomaly detection operations on the time-series data set including applying a set of heuristics to the time-series data set”. As is evident from the background, the claimed computation is a mathematical calculation of the value of the extent of anomalies within data sets, using mathematical formulas including thresholds. Thus, the limitation recites a concept that falls into the “mathematical concepts” group of abstract ideas. This limitation also falls into the “mental process” group of abstract ideas, because the recited mathematical calculation can be practically performed in the human mind, e.g., scientists and engineers have been using thresholds to make data groupings in their minds since it was first proposed.
This judicial exception is not integrated into a practical application because the claims as a whole merely describes how to generally “apply” the concept of obtaining and comparing time-series data in a computer environment. The claimed “non-transitory storage devices” and “computing device” are each recited at a high level of generality and are merely invoked as tools to perform an existing data gathering and analysis process. Even considered in combination, simply implementing the abstract idea on a generic computer with storage devices recited at a high level of generality is not a practical application of the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because as noted previously, the content server and the storage devices individually and in combination merely describe how to generally “apply” the concept of data gathering and analysis in a computer environment. The same applies here. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. The claim is ineligible.
Claims 6, 13, and 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “determining the silhouette score for the selected candidate seasonality pattern includes determining a statistical measure of the silhouette scores of the subsequences forming the selected candidate seasonality pattern, wherein determining a silhouette score of each of the set of subsequences includes: (i) determining silhouette scores of data points comprising each cluster, (ii) determining silhouette scores of the clusters, which is a statistical measure of silhouette scores of data points comprising each cluster, and (iii) determining the silhouette score of each of the set of subsequences, which is a statistical measure of the silhouette scores of the clusters of each of the set of subsequences”. As is evident from the background, the claimed computation is a mathematical calculation of the value of the extent of similarity, using mathematical formulas including min and max between data points. Thus, the limitation recites a concept that falls into the “mathematical concepts” group of abstract ideas. This limitation also falls into the “mental process” group of abstract ideas, because the recited mathematical calculation can be practically performed in the human mind, e.g., scientists and engineers have been using calculating similarity scores in their minds since it was first proposed.
This judicial exception is not integrated into a practical application because the claims as a whole merely describes how to generally “apply” the concept of obtaining and comparing time-series data in a computer environment. The claimed “non-transitory storage devices” and “computing device” are each recited at a high level of generality and are merely invoked as tools to perform an existing data gathering and analysis process. Even considered in combination, simply implementing the abstract idea on a generic computer with storage devices recited at a high level of generality is not a practical application of the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because as noted previously, the content server and the storage devices individually and in combination merely describe how to generally “apply” the concept of data gathering and analysis in a computer environment. The same applies here. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. The claim is ineligible.
Claims 7, 14, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “the statistical measure for determining the silhouette scores of clusters of each of the set of subsequences is a mean, wherein the statistical measure for determining the silhouette scores of the data points comprising each cluster is a mean, and wherein the statistical measure for determining the silhouette score for the selected candidate seasonality pattern is a mean”. As is evident from the background, the claimed computation is a mathematical calculation of the value of the extent of similarity, using mathematical formulas including min and max between data points. Thus, the limitation recites a concept that falls into the “mathematical concepts” group of abstract ideas. This limitation also falls into the “mental process” group of abstract ideas, because the recited mathematical calculation can be practically performed in the human mind, e.g., scientists and engineers have been using calculating mean in their minds since it was first proposed.
This judicial exception is not integrated into a practical application because the claims as a whole merely describes how to generally “apply” the concept of obtaining and comparing time-series data in a computer environment. The claimed “non-transitory storage devices” and “computing device” are each recited at a high level of generality and are merely invoked as tools to perform an existing data gathering and analysis process. Even considered in combination, simply implementing the abstract idea on a generic computer with storage devices recited at a high level of generality is not a practical application of the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because as noted previously, the content server and the storage devices individually and in combination merely describe how to generally “apply” the concept of data gathering and analysis in a computer environment. The same applies here. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea. The claim is ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 3-8, 10-15, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sun (US 20240265273 A1) in view of Kawabata et al (US 20130179381 A1).
As to claims 1, 8, and 15, Sun teaches A computer-implemented method for detecting a seasonality pattern that corresponds to a time-series data set, the computer-implemented method comprising:
for each candidate seasonality pattern of a set of candidate seasonality patterns (Sun discloses obtain (e.g., receive, collect) sets of time series data in [0027].);
clustering data points of each of the set of subsequences into two or more clusters (Sun discloses clustering time series data based on similarity characteristics in [0029].); and
determining a silhouette score for the selected candidate seasonality pattern that represents a measure of a clustering quality of the selected candidate seasonality pattern (Sun discloses calculating a similarity value (e.g. silhouette score). For example, the processor may calculate how similar characteristics of each set of time series data within a cluster are to each other and how dissimilar those characteristics are to other clusters. See [0029] and [0047].); and
detecting the seasonality pattern from the set of candidate seasonality patterns by selecting the candidate selecting having a highest silhouette score of the candidate seasonality patterns (Sun discloses calculating a similarity value (e.g. silhouette score). For example, the processor may calculate how similar characteristics of each set of time series data within a cluster are to each other and how dissimilar those characteristics are to other clusters. Select the cluster results that produced the highest silhouette score. See [0029] and [0047].).
Sun fails to teach partitioning the time-series data set into a set of subsequences according to a date-time pattern of a selected candidate seasonality pattern.
However, Kawabata teaches partitioning the time-series data set into a set of subsequences according to a date-time pattern of a selected candidate seasonality pattern (Kawabata discloses dividing the time series data for the data operation into a plurality of data operation sets based on similarity among data sets in [0016].).
Before the effective filing date, it would have been obvious to one of ordinary skill in the art, to modify the teachings of Sun to incorporate the pattern extraction system of Kawabata for the purpose of extracting a pattern of sequences that frequently appear among sequences.
As to claims 3, 10, and 17, Sun discloses the selected candidate seasonality pattern corresponds to a particular date-time pattern for (Sun discloses calculating accumulative variance scores for each time series prediction (e.g. expected pattern) using the selected machine learning model and calculate an average variance score across time series data associated with a metric (e.g. Parameter). The machine learning model selection may adapt with data pattern changes in the time series data over time. See [0057] and [0060]-[0068].).
Sun fails to teach partitioning the time-series data set into a set of subsequences according to a date-time pattern of a selected candidate seasonality pattern.
However, Kawabata teaches partitioning the time-series data set into a set of subsequences according to a date-time pattern of a selected candidate seasonality pattern (Kawabata discloses dividing the time series data for the data operation into a plurality of data operation sets based on similarity among data sets in [0016].).
Before the effective filing date, it would have been obvious to one of ordinary skill in the art, to modify the teachings of Sun to incorporate the pattern extraction system of Kawabata for the purpose of extracting a pattern of sequences that frequently appear among sequences.
As to claims 4, 11, and 18, Sun teaches performing anomaly detection operations on the time-series data set including generating an anomaly band based on the selected seasonality pattern and detecting data points outside of the anomaly band (Sun discloses generate thresholds (e.g., an upper bound and a lower bound) for determining anomalies in time series data. See [0014], [0026], [0038], [0040], [0054], and [0056].).
As to claims 5, 12, and 19, Sun teaches performing anomaly detection operations on the time-series data set including applying a set of heuristics to the time-series data set (Sun discloses generate thresholds (e.g., an upper bound and a lower bound) for determining anomalies in time series data and provide predictions (i.e. heuristics) for the next set of time-series data. See [0012]-[0014], [0026], [0038]-[0040], [0054], and [0056].).
As to claim 6, 13, and 19, Sun teaches determining the silhouette score for the selected candidate seasonality pattern includes determining a statistical measure of the silhouette scores of the subsequences forming the selected candidate seasonality pattern, wherein determining a silhouette score of each of the set of subsequences includes: (i) determining silhouette scores of data points comprising each cluster, (ii) determining silhouette scores of the clusters, which is a statistical measure of silhouette scores of data points comprising each cluster, and (iii) determining the silhouette score of each of the set of subsequences, which is a statistical measure of the silhouette scores of the clusters of each of the set of subsequences (Sun discloses calculating a similarity value (e.g. silhouette score). For example, the processor may calculate how similar characteristics of each set of time series data within a cluster are to each other and how dissimilar those characteristics are to other clusters. See [0029] and [0047].)(Sun discloses utilizing a silhouette coefficient equation (e.g., including mean intra-cluster distance and mean nearest-cluster distance) and determining a center (e.g., calculate an arithmetic mean, calculate which set of time series data is closest to each other set within a cluster) of time series data for each cluster in [0029].).
As to claim 7, 14, and 20, Sun teaches the statistical measure for determining the silhouette scores of clusters of each of the set of subsequences is a mean, wherein the statistical measure for determining the silhouette scores of the data points comprising each cluster is a mean, and wherein the statistical measure for determining the silhouette score for the selected candidate seasonality pattern is a mean (Sun discloses compute a silhouette score that is based on a distance between each time series within a cluster and each time series of another cluster using an automatic clustering algorithm (e.g., K-Means, GMM) in [0029] and [0047].).
Claim(s) 2, 9, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sun (US 20240265273 A1) in view of Kawabata et al (US 20130179381 A1), and further in view of PANG (US 11762853 B2).
As to claims 2, 9, and 16, Sun and Kawabata fail to teach generating a graphical user interface (GUI) configured to receive a search query that includes (i) a search or query command, and (ii) a data source from which to retrieve the time- series data set; and obtaining the time-series data set as a result of execution of the search query.
However, PANG teaches generating a graphical user interface (GUI) configured to receive a search query that includes (i) a search or query command, and (ii) a data source from which to retrieve the time- series data set; and obtaining the time-series data set as a result of execution of the search query (PANG column 4, line 41 through column 5, line 14 discloses querying a time-series data points from a source database.).
Before the effective filing date, it would have been obvious to one of ordinary skill in the art, to modify the teachings of Sun and Kawabata to incorporate the Querying A Variably Partitioned Time Series Database of PANG for the purpose of decreasing query response time by providing adaptation of time series database schema for time series data stored in a time series database.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Xu et al (US 20230368069 A1) - Technologies for monitoring input feature health for machine learning models and detecting anomalies are described. Embodiments include receiving, by a trained machine learning model and from an online system, a set of historical values and a current feature value of a time series feature, the time series feature represents one or more measured values of the online system. Embodiments include predicting an expected feature value and an expected range of values using the set of historical values. Embodiments include receiving the expected feature value and the expected range of values. Embodiments include receiving the current feature value. Embodiments include determining that an anomaly condition is present based on the current feature value, the expected feature value, and the expected range of values. Embodiments include generating an alert for the anomaly condition that includes the severity metric and the duration metric.
JIANG et al (US 20220218261 A1) - A method for processing time series data comprises: dividing time series data into a plurality of data fragments according to an objective function, the plurality of data fragments having a greatest similarity; in response to determining that at least one data fragment does not satisfy an iteration termination condition, performing following iteration operations on the at least one data fragment; and constructing a time series base pattern library by using a plurality of time series base patterns. The iteration operations includes: using the at least one data fragment as at least one update time series fragment; dividing each update time series fragment into a plurality of update data fragments; using each update data fragment that does not satisfy the iteration termination condition as a new update time series fragment; and using each update data fragment that satisfies the iteration termination condition as a time series base pattern.
HAN et al (US 20160219067 A1) - Disclosed is a method of detecting anomalies suspected of an attack based on time series statistics according to the present invention. The method of detecting anomalies suspected of an attack according to the present invention includes the steps of: collecting log data and traffic data in real-time and extracting at least one piece of preset traffic feature information from the collected log data and traffic data; and training through a time series analysis-based normal traffic training model using the extracted traffic feature information, and detecting abnormal network traffic according to a result of the training.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JARED M BIBBEE whose telephone number is (571)270-1054. The examiner can normally be reached Monday-Thursday 8AM-6PM.
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/JARED M BIBBEE/ Primary Examiner, Art Unit 2161
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