DETAILED ACTION
This office action is in response to the correspondence filed on 09/06/2024. Claims 1-18 are pending and are examined.
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.
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 09/06/2024 and 05/15/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 13-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the term "computer readable storage medium" is directed to signal per se, thus non-statutory.
Examiner notes that “non-transitory” can be added to the term to make it one of the allowable statutory categories.
Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite generating a detection result by using an anomaly detection model and determining whether the monitored indicator is abnormal based on the detection result.
The limitation of generating a detection result and determining abnormality, 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 “by a processor,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “by a processor” language, “generating” and “determining” in the context of this claim encompasses the user manually and/or mentally analyzing some data in order to determine its abnormality. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using a processor to perform both the generating and determining steps. The processor in both steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of generating a detection result to determine its abnormality) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform both the generating and determining steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 4, 7-8, 10, 13-14, and 16 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Li et al. (US Pub No. 2024/0193068 A1, referred to as Li).
Regarding claims 1, 7, and 13, taking claim 7 as exemplary, Li anticipates,
7. A computing device comprising:
a memory storing executable instructions; and (Li: [0092])
a processor configured to execute the executable instructions in the memory to perform operations of: (Li: [0092])
obtaining time series data of a monitored indicator in a to-be-detected time period; (Li: [0005]; obtaining a plurality of pieces of feature information of time series data. [0027]; it should be noted that the time series data described in this embodiment may be sales record data of a shopping platform, for example, the sales record data of the shopping platform within a period of time (to-be-detected time period) (e.g., one day, one week, or one month), page view record data of the shopping platform, or people traffic record data of a shopping area, which is not limited herein.)
extracting a plurality of features based on a plurality of data slices corresponding to the to-be-detected time period, wherein the plurality of data slices comprise to-be-detected slices, and data of one of the to-be-detected slices comprises the time series data; Li: [0005]; obtaining a plurality of pieces of feature information (features extracted) of time series data. [0027]; it should be noted that the time series data described in this embodiment may be sales record data of a shopping platform, for example, the sales record data (data slices) of the shopping platform within a period of time (to-be-detected time period) (e.g., one day, one week, or one month), page view record data of the shopping platform, or people traffic record data of a shopping area, which is not limited herein.)
generating a detection result by using an anomaly detection model and separately using a plurality of feature combinations as input data of the anomaly detection model; and (Li: [0006-0007]; generating a target feature combination based on the plurality of pieces of feature information; and [0007]; performing anomaly result detection based on the target feature combination. [0054]; at step 201, an abnormal point and a normal point identified by each of a plurality of anomaly detection models are generated by inputting the target feature combination into the plurality of anomaly detection models.)
determining, based on the detection result, whether the monitored indicator is abnormal, wherein the detection result indicates whether the monitored indicator is abnormal, and one of the feature combinations comprises a part or all of the plurality of features. (Li: [0054]; at step 201, an abnormal point and a normal point identified by each of a plurality of anomaly detection models are generated by inputting the target feature combination (a part or all) into the plurality of anomaly detection models.)
Regarding claims 2, 8, and 14, taking claim 8 as exemplary, Li anticipates,
8. The computing device of claim 7, wherein the plurality of features comprises one or more of the following types of features:
a time feature, a difference feature, a sequence feature, or a statistical feature, wherein the time feature identifies a feature value of the monitored indicator in the to-be-detected time period, the difference feature identifies a feature value of a difference between data of the monitored indicator in the to-be-detected time period and data of the monitored indicator in another time period, the sequence feature identifies a feature value of data in one data slice of the monitored indicator, and the statistical feature identifies a distribution feature of the monitored indicator in different data slices. (Li: [0026]; periodic deviation feature (difference feature). trend deviation feature (statistical feature).)
Regarding claims 4, 10, and 16, taking claim 10 as exemplary, Li anticipates,
10. The computing device of claim 7, wherein the plurality of data slices further comprise a historical same-period slice of each of one or more to-be-detected slices, and a time interval between one of the to-be-detected slices and the historical same-period slice of the to-be-detected slice is N days, and N is set to one or more preset positive integers. (Li: [0037]; Xt may be time series data at the current moment (i.e., a time series value, e.g., a sales volume of the shopping platform at the current moment), Xt-1 may be time series data at the previous moment (historical), period may be a periodic deviation interval (time interval) (e.g., a week).)
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.
Claims 3, 9, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Li, in view of Valente et al. (US Pub No. 20230291668 A1, referred to as Valente).
Regarding claims 3, 9, and 15, taking claim 7 as exemplary, Li discloses,
9. The computing device of claim 7,
…preset lengths of time windows corresponding to different to-be-detected slices are different. (Li: [0027]; it should be noted that the time series data described in this embodiment may be sales record data of a shopping platform, for example, the sales record data of the shopping platform within a period of time (e.g., one day, one week, or one month) (different preset lengths can be used for different record data), page view record data of the shopping platform, or people traffic record data of a shopping area, which is not limited herein.)
Li does not explicitly disclose, however Valente teaches,
…wherein the plurality of data slices comprises a plurality of to-be-detected slices, one of the to-be-detected slices is obtained by sliding a time window with one preset length on a time series data column of the monitored indicator, and (Valente: [0045]; a plurality of time series samples relating to respective time windows and having a predefined window size and a predefined stride, by sliding the time windows to overlap the time series data. In the preferred embodiment, the extracting defines a plurality of time series samples from the time series data retrieved in a predetermined actual retrieving time window or for a predetermined retrieving amount of data, but different kind of extracting can be used.)
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to implement the teachings of Valente into the teachings of Li with a motivation to continuously detect anomalies by devices in a network by using sliding time windows to retrieve time series data (Valente abstract and [0044]).
Claims 5-6, 11-12, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Li in view of He et al. (US Pub No. 20240062120 A1, referred to as He).
Regarding claims 5, 11, and 17, taking claim 11 as exemplary, Li discloses,
11. The computing device of claim 7, wherein one of the plurality of feature combinations is input data of one or more anomaly detection models, and different feature combinations are input data of different anomaly detection models, and (Li: [0054]; an abnormal point and a normal point identified by each of a plurality of anomaly detection models are generated by inputting the target feature combination into the plurality of anomaly detection models. There may be multiple abnormal points and multiple normal points, and the multiple anomaly detection models can include: a KNN (k-NearestNeighbor) model, a one-class SVM (One-Class Support Vector Machine) model, and an Isolation Forest model. [0057]; after obtaining the target feature combination, the electronic device may input the target feature combination into the plurality of anomaly detection models separately, so that the target feature combination is processed separately by the plurality of anomaly detection models, to cause each anomaly detection model to output the abnormal point and the normal point, i.e., the abnormal point and the normal point identified by each anomaly detection model.)
…anomaly detection models corresponding to the plurality of feature combinations (Li: [0054])
Li does not explicitly disclose, however He teaches,
wherein the operation of determining whether the monitored indicator is abnormal comprises: (He: [0033])
when a quantity of detection results indicating that the monitored indicator is abnormal in detection results output by the plurality of anomaly detection models… reaches a preset threshold, determining that the monitored indicator is abnormal; or (He: [0033]; inputting, with at least one processor, at least a portion of the multivariate sequence data into each respective anomaly detection model of a plurality of anomaly detection models to generate a plurality of scores comprising a respective score for each respective anomaly detection model; combining, with at least one processor, the multivariate sequence data with the plurality of scores to generate combined intermediate data; inputting, with at least one processor, the combined intermediate data into a combined ensemble model to generate an output score, the combined ensemble model based on a model-domain ensemble model, a time-domain ensemble model, and a feature-domain ensemble model; determining, with at least one processor, that the output score satisfies a threshold (output reaches a threshold); and in response to determining that the output score satisfies the threshold, at least one of: communicating, with at least one processor, an alert to a user device (determining abnormality).)
when a quantity of detection results indicating that the monitored indicator is abnormal in detection results output by the plurality of anomaly detection… does not reach a preset threshold, determining that the monitored indicator is not abnormal. (He: [0033]; inputting, with at least one processor, at least a portion of the multivariate sequence data into each respective anomaly detection model of a plurality of anomaly detection models to generate a plurality of scores comprising a respective score for each respective anomaly detection model; combining, with at least one processor, the multivariate sequence data with the plurality of scores to generate combined intermediate data; inputting, with at least one processor, the combined intermediate data into a combined ensemble model to generate an output score, the combined ensemble model based on a model-domain ensemble model, a time-domain ensemble model, and a feature-domain ensemble model; determining, with at least one processor, that the output score satisfies a threshold (output reaches a threshold); and in response to determining that the output score satisfies the threshold, at least one of: communicating, with at least one processor, an alert to a user device (no alert is sent if threshold is not reached because it is determined to be not abnormal).)
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to implement the teachings of He into the teachings of Li with a motivation to improve the way of determining anomaly detection of different anomaly detection models and avoid false positives by using a multi-domain ensemble learning (He abstract and [0004]).
Regarding claims 6, 12, and 18, taking claim 12 as exemplary, the combination of Li and He discloses,
12. The computing device of claim 11,
Li further discloses,
wherein algorithms of a part or all of the plurality of anomaly detection models are different, or algorithms of a part or all of the plurality of anomaly detection models are the same but values of at least one parameter comprised in the algorithms are different, and algorithms of the plurality of anomaly detection models comprise an unsupervised algorithm. (Li: [0054]; an abnormal point and a normal point identified by each of a plurality of anomaly detection models are generated by inputting the target feature combination into the plurality of anomaly detection models. There may be multiple abnormal points and multiple normal points, and the multiple anomaly detection models can include: a KNN (k-NearestNeighbor) model, a one-class SVM (One-Class Support Vector Machine) model, and an Isolation Forest model (different algorithms).)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Karis; Steven et al. US-PGPUB US 20250165376 A1 Generating span related metric data streams by an analytic engine
Patel; Parth Arvindbhai et al. US-PGPUB US 20240163185 A1 Efficient detection and prediction of data pattern changes in a cloud-based application acceleration as a service environment
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/KA SHAN CHOY/Primary Examiner, Art Unit 2435