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 .
Claims 1-20 are pending.
Claim Objections
Claim 2 is objected to because of the following informalities: Claim 2 recites “the composite vector being begin representative of” in line 3 of the claim which appears to have mistakenly added the word ‘begin’. For the purposes of the examination, “being begin” will simply be treated as “being”. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 2 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2 recites the limitation "the at least one or more processors" in line 1 of the claim. There is insufficient antecedent basis for this limitation in the claim.
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 1-18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3-11, 13-20 of U.S. Patent No. 12,386,717 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because limitations of the claims of the instant application are anticipated by the claims of the patent. See the comparison table below for some of the claims from both the patent and the instant application.
Instant Application
Pat. 12,386,717
1. An anomaly detection system comprising: one or more processors; and a memory storing instructions that, when executed by at least one processor in the one or more processors, cause the at least one processor to perform at least the following operations: encode one or more configuration settings for a campaign in a composite vector, the composite vector being representative of at least a portion of the configuration settings;
1. An anomaly detection system comprising: one or more processors; and a memory storing instructions that, when executed by at least one processor in the one or more processors, cause the at least one processor to perform at least the following operations: encode one or more configuration settings for a campaign in a composite vector using an encoder, the composite vector being representative of at least a portion of the configuration settings;
generate a composite vector for multiple campaigns to create a training sample including a set of composite vectors;
generate a composite vector for multiple campaigns to create a set of composite vectors;
train multiple anomaly detection machine learning models using the training sample; and
aggregate the set of composite vectors to generate an output of the encoder, the output including a training sample for modeling; transmit the output of the encoder to a training model for training multiple anomaly detection machine learning models;
determine, using an ensemble model, an anomaly occurrence probability for one or more configuration settings of a target campaign, the ensemble model including two or more of the trained anomaly detection machine learning models.
select two or more of the trained anomaly detection machine learning models to include in an ensemble model; and use the ensemble model to determine an anomaly occurrence probability for one or more configuration settings of a target campaign.
2. The system of claim 1, wherein the at least one or more processors are further configured to determine a composite vector for the target campaign, the composite vector being representative of at least a portion of the configuration settings of the target campaign.
3. The system of claim 1, wherein the one or more processors are further configured to determine a composite vector for the target campaign, the composite vector being representative of at least a portion of the configuration settings of the target campaign.
Claims 19-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 11, 18-20 of U.S. Patent No. 12,386,717 B2 in view of Eberhardt, III et al. (PG Pub. 2013/0,198,119 A1) [hereafter Eberhardt].
As per claim 19, most of the limitations of the claim are anticipated by the claims 11, 18-20 of the patent 12,386,717. The claims of the patent fail to anticipate determining, using the trained anomaly detection machine learning model, a joint probability distribution for multiple sets of at least two configuration settings of a target campaign; and determining a conditional probability for each set of at least two configuration settings based on the joint probability distributions.
However, Eberhardt in an analogous art teaches anomaly detection machine-learning algorithm that establishes conditional probabilities and joint probability distribution (Eberhardt, ¶ [0022], ¶ [0096]). It would have been obvious to a person of ordinary skill of the art before the effective filing date of the invention to incorporate teachings of Eberhardt into the claims of the patent to provide a method of determining, using the trained anomaly detection machine learning model, a joint probability distribution for multiple sets of at least two configuration settings of a target campaign; and determining a conditional probability for each set of at least two configuration settings based on the joint probability distributions. The modification would be obvious because machine-learned Bayesian Belief Networks (BBNs) are a computationally compact and efficient way to represent highly complex rule sets (Eberhardt, ¶ [0081]).
As per claim 20, while the claims of the patent does not specifically anticipate identifying a set of at least two or more configuration settings that contain an anomaly based on the conditional probabilities. Eberhardt in an analogous art teaches calculating anomaly score from conditional probabilities between individual event attributes (Eberhardt, ¶ [0022], ¶ [0096]). It would have been obvious to a person of ordinary skill of the art before the effective filing date of the invention to incorporate teachings of Eberhardt into the claims of the patent to provide a method of identifying a set of at least two or more configuration settings that contain an anomaly based on the conditional probabilities. The modification would be obvious because machine-learned Bayesian Belief Networks (BBNs) are a computationally compact and efficient way to represent highly complex rule sets (Eberhardt, ¶ [0081]).
Allowable Subject Matter
Claims 1-20 are allowable if terminally disclaimed to overcome the double patenting rejection set forth in this OFFICE ACTION.
The following is a statement of reasons for the indication of allowable subject matter:
As per claim 1, the examiner found no prior arts that teach or fairly suggest, either alone or in combination, each and every limitations of the claim when the claim is taken into the consideration as a whole. The closest prior arts cited by the examiner are Retinraj et al. (PG Pub. 2023/0,410,143 A1) [hereafter Retinraj] and Ardel et al. (PG Pub. 2023/0,325,292 A1) [hereafter Ardel]. Retinraj discloses detecting anomalies/outliers in marketing campaign data/metrics using machine learning algorithm and training the machine learning model on an ongoing basis to update the machine learning model to improve the detection of the anomalies/outliers. Retinraj also discusses using at least one or more machine-learning-derived baseline performance metrics to update set of one or more anomaly detection rules on an ongoing basis. Ardel discloses detecting anomalous behavior of monitored processes using ensemble model in which a model can be used in combination with one or more other models. However, neither Retinraj nor Ardel disclose "generate a composite vector for multiple campaigns to create a training sample including a set of composite vectors;" and "determine, using an ensemble model, an anomaly occurrence probability for one or more configuration settings of a target campaign".
Claim 10 is a method claim corresponding to the system claim 1 and is allowable for the same reasons.
Claims 2-9, 11-18 depend either directly or indirectly on claim 1 or 10 and are allowable as a result.
As per claim 19, the examiner found no prior arts that teach or fairly suggest, either alone or in combination, each and every limitations of the claim when the claim is taken into the consideration as a whole. In particular, no prior arts teach “generating a composite vector for multiple campaigns to create a training sample including a set of composite vectors;" and "determining, using the trained anomaly detection machine learning model, a joint probability distribution for multiple sets of at least two configuration settings of a target campaign;".
Claim 20 depends on claim 19 and is allowable as a result.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Pat. 11,575,697 B2 discloses techniques for automated anomaly detection including a technique comprising training an ensemble of deep learning models using clustered time series training data from numerous components in an Information Technology (IT) infrastructure.
US Pat. 12,197,418 B1 discloses techniques for detecting regressions with respect to the accuracy of an anomaly detection compute service in detecting anomalies in users' time series data.
PG Pub. 2022/0,253,699 A1 discloses a method for training a machine learning model for detecting anomalous patterns in general data, which may be structured (e.g., as graphs, spatially, or temporally) or unstructured. General data may be, e.g., numerical, any univariate and/or multivariate time-series data, attribute-based data, vectors (structured or unstructured), graphs, image data, video data, tabular data, and/or a combination of any thereof.
PG Pub. 2020/0,311,603 A1 discloses training one or more machine learning models, with the historical data, to generate trained machine learning models that generate outputs, and trains a model with the outputs to generate a trained model.
PG Pub. 2024/0,046,314 discloses method for predicting click through rate (CTR) in an ad campaign by extracting a hidden vector and appending to the settings and performance features of an ad campaign node.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHAE M KO whose telephone number is (571)270-3886. The examiner can normally be reached M-F 9 am - 5 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ashish Thomas can be reached at 571-272-0631. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHAE M KO/Primary Examiner, Art Unit 2114
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