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. Claim Objections Claims 8 and 15 are objected to because of the following informalities: Claim 8 recites “an number of instances” (line 2). For grammatical agreement, this phrase should recite “ [[ an ]] a number of instances . ” Claim 15 recites “an number of instances” in the same manner as claim 8. Appropriate correction is required. 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. Claim s 1- 4, 7, 12, 14-16, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Boukerche, Azzedine, Lining Zheng, and Omar Alfandi (“ Outlier detection: Methods, models, and classification ,” ACM Computing Surveys (CSUR) 53.3 (2020): 1-37 ; hereinafter “Boukerche”). Regarding Claim 1 , Boukerche teaches a method (Title) comprising: receiving, by a computing device, a first instance of data for anomaly detection, wherein the first instance of data includes values from multiple variables (p. 55:11-55:12, section 3—the original data are a first instance of data received for anomaly detection) ; storing, by the computing device, the first instance of data in a queue (section 3—the operations to project the first instance of data into a projection matrix implies that the first instance of data are stored. A queue is a well-known data structure for storing a time series data set. Section 5 describes windowing methods for data streams, which store a window-sized amount of data instances ) ; weighting, by the computing device, instances of data in the queue based on data changing over time (section 5—the Damped window technique assigns a weight to each data point based in its arrival time, with newer data points being assigned higher weights) ; projecting, by the computing device, the instances of the data in the queue into a space, wherein a point in the space represents a correlation of the values for the multiple variables for a respective instance of data (section 3—several projection methods are described, all of which project the instances of data into a space) ; generating, by the computing device, a boundary based on the points in the space (section 1.1—by definition, an outlier {anomaly} is a point that it outside an accepted boundary. Section 3 describes determining outliers/boundaries using projection techniques ) ; and determining, by the computing device, a point in the space that is considered an anomaly based on the boundary ( section 3—outliers are considered anomalies ) . Regarding Claim 2 , Boukerche teaches removing a second instance of data from the queue when storing the first instance of data in the queue (section 5—a sliding window {or the damped window} stores a width w of instances, removing a oldest instance when a new instance is received) . Regarding Claim 3 , Boukerche teaches wherein the second instance of data is an oldest entry in the instances of data in the queue (section 5—a sliding window {or the damped window} stores a width w of instances, removing a oldest instance when a new instance is received) . Regarding Claim 4 , Boukerche teaches wherein weighting the instances of data comprises: weighting the first instance of data higher than a second instance of data in the queue, wherein the second instance of data was stored in the queue before the first instance of data (section 5—the Damped window technique assigns a weight to each data point based in its arrival time, with newer data points being assigned higher weights) . Regarding Claim 7 , Boukerche teaches wherein weighting the instances of data comprises: determining a change point in the instances of data; and weighting instances of data that were stored after the change point higher than instances of data that were stored before the change point in the queue (section 5—the Adaptive window technique determines a change point based on the rate of change from the data within the current window. In response, it contracts the size of the window, effectively weighting the instances that were stored before the change point with a weight of zero by removing them from the window). Regarding Claim 12 , Boukerche teaches wherein projecting the instances of the data comprises: determining a point in the space based on a correlation of values of the variables (section 3—the projection techniques determine a correlation between the variables and the space to map each instance to a point in the space). Regarding Claim 14 , Boukerche teaches wherein each instance of data in the queue is associated with a point in the space (section 3—the projection techniques associate each instance with a point in the space). Regarding Claim 15 , Boukerche teaches wherein generating the boundary comprises: determining the boundary based on a positions of points in the space (section 3—outliers are determined to be outside the boundary by having less similar data points compared to those in the boundary or based on a distance from the boundary). Regarding Claim 16 , Boukerche teaches 1, wherein determining the point in the space that is considered the anomaly based on the boundary comprises: selecting the point based on the point being considered outside of the boundary (section 3). Regarding Claim 18 , Boukerche teaches a non-transitory computer-readable storage medium having stored thereon computer executable instructions (section 5—the application to data streams in computer networks implies a non-transitory computer-readable storage medium having stored thereon computer executable instructions), which when executed by a computing device, cause the computing device to be operable for: receiving a first instance of data for anomaly detection, wherein the first instance of data includes values from multiple variables (p. 55:11-55:12, section 3—the original data are a first instance of data received for anomaly detection); storing the first instance of data in a queue; weighting instances of data in the queue based on data changing over time (section 3—the operations to project the first instance of data into a projection matrix implies that the first instance of data are stored. A queue is a well-known data structure for storing a time series data set. Section 5 describes windowing methods for data streams, which store a window-sized amount of data instances); projecting the instances of the data in the queue into a space, wherein a point in the space represents a correlation of the values for the multiple variables for a respective instance of data (section 3—several projection methods are described, all of which project the instances of data into a space); generating a boundary based on the points in the space (section 1.1—by definition, an outlier {anomaly} is a point that it outside an accepted boundary. Section 3 describes determining outliers/boundaries using projection techniques); and determining a point in the space that is considered an anomaly based on the boundary (section 3—outliers are considered anomalies). Regarding Claim 20 , Boukerche teaches an apparatus comprising: one or more computer processors; and a computer-readable storage medium comprising instructions for controlling the one or more computer processors (section 5—the application to data streams in computer networks implies an apparatus comprising: one or more computer processors; and a computer-readable storage medium comprising instructions for controlling the one or more computer processors) to be operable for: receiving a first instance of data for anomaly detection, wherein the first instance of data includes values from multiple variables (p. 55:11-55:12, section 3—the original data are a first instance of data received for anomaly detection); storing the first instance of data in a queue (section 3—the operations to project the first instance of data into a projection matrix implies that the first instance of data are stored. A queue is a well-known data structure for storing a time series data set. Section 5 describes windowing methods for data streams, which store a window-sized amount of data instances); weighting instances of data in the queue based on data changing over time (section 5—the Damped window technique assigns a weight to each data point based in its arrival time, with newer data points being assigned higher weights); projecting the instances of the data in the queue into a space, wherein a point in the space represents a correlation of the values for the multiple variables for a respective instance of data (section 3—several projection methods are described, all of which project the instances of data into a space); generating a boundary based on the points in the space (section 1.1—by definition, an outlier {anomaly} is a point that it outside an accepted boundary. Section 3 describes determining outliers/boundaries using projection techniques); and determining a point in the space that is considered an anomaly based on the boundary (section 3—outliers are considered anomalies). Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Boukerche, as applied to claim 7 , above, in view of Apostol, Elena-Simona, et al. (“ Change point enhanced anomaly detection for IoT time series data ,” Water 13.12 (2021): 1633 ; hereinafter “Apostol”). Regarding Claim 8 , Boukerche does not specifically teach wherein detecting the change point comprises: analyzing different windows of an number of instances of data in the queue; and determining the change point when differences between windows meet a threshold. However, Apostol teaches detecting a change point comprises: analyzing different windows of an number of instances of data in a queue; and determining the change point when differences between windows meet a threshold (section 3.3.1—a window-based segmentation algorithm computes discrepancies for two windows and determines a change point when the discrepancies exceed a threshold) . All of the claimed elements were known in Boukerche and Apostol and could have been combined by known methods with no change in their respective functions. It therefore would have been obvious to a person of ordinary skill in the art at the time of filing of the applicant’s invention to combine the window-based segmentation of Apostol with the change point detection of Boukerche to yield the predictable result of wherein detecting the change point comprises: analyzing different windows of an number of instances of data in the queue; and determining the change point when differences between windows meet a threshold. One would be motivated to make this combination for the purpose of minimizing a false positive rate in anomaly detection by detecting sudden change points in normal behavior (Apostol, Abstract). Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Boukerche, as applied to claim 1, above, in view of Fainberg et al. (U.S. 2021/0099473, hereinafter “Fainberg”). Regarding Claim 9 , Boukerche does not specifically teach wherein weighting instances of data comprises: w eighting a first variable in the instance of data with a first weight; and w eighting a second variable in the instance of data with a second weight. However, Fainberg teaches wherein weighting instances of data comprises: weighting a first variable in the instance of data with a first weight; and weighting a second variable in the instance of data with a second weight (¶ [0027] – [0029]—different properties {i.e. variables} of an instance are weighted differently, with properties that are more important or sensitive being assigned a higher weight). All of the claimed elements were known in Boukerche and Fainberg and could have been combined by known methods with no change in their respective functions. It therefore would have been obvious to a person of ordinary skill in the art at the time of filing of the applicant’s invention to combine the variable weighting of Fainberg with the weighting and instances of data of Boukerche to yield the predictable result of wherein weighting instances of data comprises: weighting a first variable in the instance of data with a first weight; and weighting a second variable in the instance of data with a second weight. One would be motivated to make this combination for the purpose of securing a network by preventing an attack on the network (Fainberg, ¶ [0002]). Allowable Subject Matter Claims 5-6, 10-11, 13, 17, and 19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. None of the prior art of record teaches “ The method of claim 1, wherein weighting the instances of data comprises: weighting instances of data in the queue based on an amount of traffic load being handled by a delivery entity ” as recited by claim 5. Fainberg monitors network traffic and uses the traffic and weights assigned to properties to detect anomalies, but does not weight instances based on an amount of traffic load being handled by a delivery entity . None of the prior art of record teaches “ wherein the projecting of the instances of data is dynamically trained to detect the anomaly when data changes over time based on a changing of the weights assigned to instances of data in the queue ” as recited by claims 6 and 19. In Boukerche, the projecting is not based on the assigned weights, and no other prior art of record teaches the details of the projecting recited by the present claim. None of the prior art of record teaches “th e method of claim 9, wherein: the first variable is weighted higher than the second variable when the first variable includes data that is changing more than the second variable ” as recited by claim 10. Fainberg teaches weighting the first and second variables differently as described for claim 9, but does not assign those weights based on one variable including data that is changing more than another variable. None of the prior art of record teaches “ The method of claim 10, wherein: the first variable is considered more important than the second variable based on the data that is changing more ” as recited by claim 11. Although Fainberg teaches assigning a higher weight to a variable that is considered more important, it does not do so based on the data that is changing more. Claim 11 also contains allowable subject matter by virtue of its dependence on claim 10. None of the prior art of record teaches “ The method of claim 12, wherein the space comprises a higher dimensional space than a number of the variables ” as recited by claim 13. In Boukerche, the space is described as having a lower dimension than the data instances of the data set, and the purpose of projecting the instances onto the space is to reduce the dimensionality. So, it would not make sense to use a space that comprises a higher dimensional space than a number of the variables with the systems and methods of Boukerche. None of the prior art of record teaches “ The method of claim 1, wherein further comprising: transforming the point to be a value on the boundary; and outputting the value for the point as the value in which the point will not be an anomaly ” as recited by claim 17. Boukerche does not perform such a transformation, an none of the prior art of record remedies the deficiency of Boukerche. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. This art includes: Huang et al. (U.S. 2018/0027004) teaches anomaly detection in a computer network that maps data from a time window into hyperplanes to determine outliers Johnston et al. (U.S. Patent 11,658,994) teaches anomaly detection in network transactions, weighting more recent transactions higher than older ones Shumpert (U.S. 2016/0342903) teaches anomaly detection for streaming data that weights newer instances more for clustering and training Putina, Andrian, and Dario Rossi (“ Online anomaly detection leveraging stream-based clustering and real-time telemetry ,” IEEE Transactions on Network and Service Management 18.1 (2020): 839-854 ) teaches online anomaly detection using clustering that assigns higher weights to newer data instances in a damped decay window Fukuda, Kensuke (“ On the use of weighted syslog time series for anomaly detection ,” 12th IFIP/IEEE International Symposium on Integrated Network Management (IM 2011) and Workshops. IEEE, 2011 ) teaches anomaly detection that assigns weights by message type and message frequency, and performs change point detection. 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