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 .
This office action is in response to the amendment filed on 02/26/2026. Claims 1, 3-4, 7-15, 17-18 are pending in this application and have been considered below.
Claim Rejections - 35 USC § 101
The rejections to the claims are corrected by the amendment; therefore, the rejections are withdrawn.
Response to Amendment
Applicant's arguments with respect to claims 1, 3-4, 7-15, 17-18 have been considered but are moot in view of the new ground(s) of rejection because of the amendment changes the scope of the invention.
Claim Interpretation
Claims 15, 17-18 recites a method performed by a terminal device, comprising: receiving a signal from a based station followed by “wherein” clauses. The determination of whether each of these clauses is a limitation in a claim depends on the specific facts of the case. See, e.g., Griffin v. Bertina, 285 F.3d 1029, 1034, 62 USPQ2d 1431 (Fed. Cir. 2002) (finding that a "wherein" clause limited a process claim where the clause gave "meaning and purpose to the manipulative steps"). In In re Giannelli, 739 F.3d 1375, 1378, 109 USPQ2d 1333, 1336 (Fed. Cir. 2014), the court found that an "adapted to" clause limited a machine claim where "the written description makes clear that 'adapted to,' as used in the [patent] application, has a narrower meaning, viz., that the claimed machine is designed or constructed to be used as a rowing machine whereby a pulling force is exerted on the handles." In Hoffer v. Microsoft Corp., 405 F.3d 1326, 1329, 74 USPQ2d 1481, 1483 (Fed. Cir. 2005), the court held that when a "‘whereby’ clause states a condition that is material to patentability, it cannot be ignored in order to change the substance of the invention." Id. However, the court noted that a "‘whereby clause in a method claim is not given weight when it simply expresses the intended result of a process step positively recited.’" Id. (quoting Minton v. Nat’l Ass’n of Securities Dealers, Inc., 336 F.3d 1373, 1381, 67 USPQ2d 1614, 1620 (Fed. Cir. 2003)) (MPEP 2111.04). The “wherein” clauses in claims 15, 17-18 do not give “meaning and purposed to the manipulative steps” of “receiving a signal from a base station”. The “wherein” clauses are directed to functions performed at the base station for signal generation and do not gave meaning and purpose to the step of receiving a signal performed by a terminal device. Therefore, the claim scope is not limited by claim language (all the wherein clauses) that suggests or makes optional but does not require steps to be performed.
Claim Rejections - 35 USC § 102
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 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.
Claim(s) 15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kvernvik et al. (WO 2020/173542 A1) (Kvernvik herein after) (IDS).
Re Claim 15, Kvernvik discloses a method performed by a terminal device, comprising:
receiving a signal from a base station (wireless device (WD) refers to a device capable, configured, arranged and/or operable to communicate wirelessly with network nodes and/or other wireless devices. Communicating wirelessly may involve transmitting and/or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and/or other types of signals suitable for conveying information through air. In some embodiments, a WD may be configured to transmit and/or receive information without direct human interaction [0096]),
wherein the signal is transmitted based on the at least one communication parameter, wherein the at least one communication parameter is determined based on filtered measurement data, wherein the filtered measurement data is obtained by using a machine learning algorithm on the measurement data to remove error measurement data, wherein the machine learning algorithm comprises cluster based unsupervised anomaly detection, wherein when the cluster based unsupervised anomaly detection is used to filter the measurement data and when a number of measurement data in a cluster is smaller than a threshold, all measurement data in the cluster is removed from the measurement data, and wherein when the cluster based unsupervised anomaly detection is used to filter the measurement data and when a specific measurement data does not belong to any cluster, the specific measurement data is removed from the measurement data.
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, 7-11, 13-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kvernvik et al. (WO 2020/173542 A1) (Kvernvik herein after) (IDS) in view of Yeddu (US 2021/0064593 A1).
Re Claim 1, Kvernvik discloses a method performed by a network node, comprising:
obtaining measurement data for at least one terminal device (network node 120A is operative to receive first values of a first network parameter, where the first values are not trusted by the network node 120A and receive second values of a second network parameter that are trusted [0034]);
filtering the measurement data to remove error measurement data by a machine learning algorithm (network node 120A is then operative to determine whether the first values are anomalies and based on this determination, the network node 120 is operative to transmit or not the first values to the network prediction model 130 to be used during a training phase of the prediction mode [0035]);
determining at least one communication parameter based on the filtered measurement data (prediction model 130 can be used to determine a Block Error Rate (BLER) based on one or more of the following network parameters: CQI that is obtained from the communication device 140A, Timing Advance determined in the network, path loss indicating the quality of the channel between the communication device 140A and the ND 120A, and neighbor cell activity. The BLER allows the network to transmit data with different transport block size and is used to optimize the channel quality between the network node 120A and the communication device 140A [0045]); and
causing transmission of a signal to at least one terminal device based on the at least one communication parameter (optimization of traffic in the communication [0006]).
Kvernvik discloses the claimed invention except wherein the machine learning algorithm comprises cluster based unsupervised anomaly detection, wherein when the cluster based unsupervised anomaly detection is used to filter the measurement data and when a number of measurement data in a cluster is smaller than a threshold, all measurement data in the cluster is removed from the measurement data, and wherein when the cluster based unsupervised anomaly detection is used to filter the measurement data and when a specific measurement data does not belong to any cluster, the specific measurement data is removed from the measurement data.
However, Yeddu discloses unsupervised anomaly detection method and system wherein anomaly detection system 102 can compare the new data 138 to adjusted clustered data set A 132 and anomaly thresholds 136. If the new data 138 is a value that falls within any cluster of adjusted clustered data set A 132, then the new data 138 is classified as non-anomalous. If the new data 138 is a value that falls outside of any cluster of adjusted clustered data set A 132, but the value of the new data 138 is within an anomaly threshold 136 of a minimum or maximum value of at least one cluster of adjusted clustered data set A 132, then the new data 138 is classified as non-anomalous. If the new data 138 is outside of each cluster in adjusted clustered data set A 132 and further outside of any extended range provided by each cluster in adjusted clustered data set A 132 using anomaly threshold 136, then the new data 138 is classified as anomalous ([0041]).
Therefore, it would have been obvious at the time the invention was made to one of ordinary skill in the art to modify method and system of Kvernvik, by making use of the technique taught by Yeddu, in order to improve the data processing efficiency.
Both references are within the same field of telecommunication, and in particular of xxxx, the modification does not change a fundamental operating principle of Kvernvik, nor does Kvernvik teach away from the modification (Kvernvik merely discloses a preferred embodiment). The combination has a reasonable expectation of success in that the modifications can be made using conventional and well known engineering and/or programming techniques, the anomaly detection system taught by Yeddu is not altered and continues to perform the same function as separately, and the resultant combination produces the highly predictable result of wherein the machine learning algorithm comprises cluster based unsupervised anomaly detection, wherein when the cluster based unsupervised anomaly detection is used to filter the measurement data and when a number of measurement data in a cluster is smaller than a threshold, all measurement data in the cluster is removed from the measurement data, and wherein when the cluster based unsupervised anomaly detection is used to filter the measurement data and when a specific measurement data does not belong to any cluster, the specific measurement data is removed from the measurement data.
Re Claim 7, Kvernvik discloses the method according to claim 1, wherein the measurement data comprises at least one of: reference signal received power (RSRP), time-of-arrival (TOA), time difference of arrival (TDOA), path loss, power headroom, interference measurement, or downlink channel state information report (first network parameter can be an indicator of quality of a radio channel between the communication device and the radio access network node such as e.g., the Channel Quality Indicator (CQI) [0039]).
Re Claim 8, Kvernvik discloses the method according to claim 7, wherein a downlink channel state information report comprises at least one of: precoding matrix indicator (PMI), channel quality indicator (CQI), or timestamp (first network parameter can be an indicator of quality of a radio channel between the communication device and the radio access network node such as e.g., the Channel Quality Indicator (CQI) [0039]).
Re Claim 9, Kvernvik discloses the method according to claim 1, wherein filtering the measurement data to remove error measurement data by the machine learning algorithm comprises: for a terminal device, filtering measurement data related to the terminal device to remove error measurement data by the machine learning algorithm (training data determiner 122 also includes a data filter 126 that is operative to receive one or more anomaly indication for a first value and determine whether to transmit or not the first value to the prediction model [0041]).
Re Claim 10, Kvernvik discloses the method according to claim 1, further comprising: removing time information from the filtered measurement data (second network parameters include one or more of a timing advance parameter that represents a length of time a signal takes to reach the radio access network node from the communication device through a radio channel between the communication device and the radio access network node, an estimation of path loss for the radio channel, and measures of activity of neighbor cells. In some embodiments, the first values and/or the second values can be time series [0034]).
Re Claim 11, Kvernvik discloses the method according to claim 1, further comprising: removing repetitive measurement data from the filtered measurement data (a determination of whether the first values of the first network parameters satisfy a set of one or more filtering rules is performed. The filtering rules identify anomaly categories and are based on analysis of previous values of the first network parameters obtained through one or more network devices in the communication network [0078]).
Re Claim 13, Kvernvik discloses the method according to claim 1, wherein the network node is a server (server [0098]), and wherein causing transmission of the signal comprise sending the at least one communication parameter to a base station for transmission of the signal (base station, [0033]-[0034]).
Re Claim 14, Kvernvik discloses the method according to claim 1, wherein the network node is a base station, and wherein causing transmission of the signal comprises transmitting the signal to at least one terminal device based on the at least one communication parameter (wireless device [0095]-[0100]).
Re Claim 15, Kvernvik discloses a method performed by a terminal device, comprising:
receiving a signal from a base station (wireless device (WD) refers to a device capable, configured, arranged and/or operable to communicate wirelessly with network nodes and/or other wireless devices. Communicating wirelessly may involve transmitting and/or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and/or other types of signals suitable for conveying information through air. In some embodiments, a WD may be configured to transmit and/or receive information without direct human interaction [0096]),
wherein the signal is transmitted based on the at least one communication parameter (network node 120A is operative to receive first values of a first network parameter, where the first values are not trusted by the network node 120A and receive second values of a second network parameter that are trusted [0034]), wherein the at least one communication parameter is determined based on filtered measurement data (network node 120A is then operative to determine whether the first values are anomalies and based on this determination, the network node 120 is operative to transmit or not the first values to the network prediction model 130 to be used during a training phase of the prediction mode [0035]), wherein the filtered measurement data is obtained by using a machine learning algorithm on the measurement data to remove error measurement data (prediction model 130 can be used to determine a Block Error Rate (BLER) based on one or more of the following network parameters: CQI that is obtained from the communication device 140A, Timing Advance determined in the network, path loss indicating the quality of the channel between the communication device 140A and the ND 120A, and neighbour cell activity. The BLER allows the network to transmit data with different transport block size and is used to optimize the channel quality between the network node 120A and the communication device 140A [0045]).
Kvernvik discloses the claimed invention except wherein the machine learning algorithm comprises cluster based unsupervised anomaly detection, wherein when the cluster based unsupervised anomaly detection is used to filter the measurement data and when a number of measurement data in a cluster is smaller than a threshold, all measurement data in the cluster is removed from the measurement data, and wherein when the cluster based unsupervised anomaly detection is used to filter the measurement data and when a specific measurement data does not belong to any cluster, the specific measurement data is removed from the measurement data.
However, Yeddu discloses unsupervised anomaly detection method and system wherein anomaly detection system 102 can compare the new data 138 to adjusted clustered data set A 132 and anomaly thresholds 136. If the new data 138 is a value that falls within any cluster of adjusted clustered data set A 132, then the new data 138 is classified as non-anomalous. If the new data 138 is a value that falls outside of any cluster of adjusted clustered data set A 132, but the value of the new data 138 is within an anomaly threshold 136 of a minimum or maximum value of at least one cluster of adjusted clustered data set A 132, then the new data 138 is classified as non-anomalous. If the new data 138 is outside of each cluster in adjusted clustered data set A 132 and further outside of any extended range provided by each cluster in adjusted clustered data set A 132 using anomaly threshold 136, then the new data 138 is classified as anomalous ([0041]).
Therefore, it would have been obvious at the time the invention was made to one of ordinary skill in the art to modify method and system of Kvernvik, by making use of the technique taught by Yeddu, in order to improve the data processing efficiency.
Both references are within the same field of telecommunication, and in particular of xxxx, the modification does not change a fundamental operating principle of Kvernvik, nor does Kvernvik teach away from the modification (Kvernvik merely discloses a preferred embodiment). The combination has a reasonable expectation of success in that the modifications can be made using conventional and well known engineering and/or programming techniques, the anomaly detection system taught by Yeddu is not altered and continues to perform the same function as separately, and the resultant combination produces the highly predictable result of wherein the machine learning algorithm comprises cluster based unsupervised anomaly detection, wherein when the cluster based unsupervised anomaly detection is used to filter the measurement data and when a number of measurement data in a cluster is smaller than a threshold, all measurement data in the cluster is removed from the measurement data, and wherein when the cluster based unsupervised anomaly detection is used to filter the measurement data and when a specific measurement data does not belong to any cluster, the specific measurement data is removed from the measurement data.
Claim(s) 3-4, 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kvernvik et al. (WO 2020/173542 A1) (Kvernvik herein after) (IDS) and Yeddu (US 2021/0064593 A1), further in view of Triplet et al (US 2020/0082013 A1) (Triplet herein after).
Re Claims 3 and 17, the combined teachings disclose the method according to claim 1 and the method according to claim 15, except explicitly teaches wherein the unsupervised machine learning algorithm comprises at least one of: distance based unsupervised anomaly detection, density based unsupervised anomaly detection, or tree based unsupervised anomaly detection.
However, Triplet discloses a system and method provide a feature-selection engine that enables automatic determination of most relevant input PM data, prior to feeding a data-driven software application. The systems and methods are scalable and enable hierarchical feature selection and density-based unsupervised pattern detection in multivariate time-series, without prior knowledge about the data, making the approach suitable in an IoT or multi-vendor context, for a networking application ([0041]).
Therefore, it would have been obvious at the time the invention was made to one of ordinary skill in the art to modify method and system of the combined teachings, by making use of the technique taught by Triplet, in order to improve the large volume data analysis.
Both references are within the same field of telecommunication, and in particular of wireless networking, the modification does not change a fundamental operating principle of the combined teachings, nor does the combined teachings teach away from the modification (the combined teachings merely discloses a preferred embodiment). The combination has a reasonable expectation of success in that the modifications can be made using conventional and well known engineering and/or programming techniques, the detection taught by Triplet is not altered and continues to perform the same function as separately, and the resultant combination produces the highly predictable result of wherein the unsupervised machine learning algorithm comprises at least one of: distance based unsupervised anomaly detection, density based unsupervised anomaly detection, or tree based unsupervised anomaly detection.
Re Claims 4 and 18, the combined teachings disclose the method according to claim 1 and the method according to claim 15, except wherein the cluster based unsupervised anomaly detection comprises at least one of: density-based spatial clustering of applications with noise (DBSCAN), shared nearest neighbor (SNN), clustering, K-Means clustering, self-organizing map (SOM), clustering, cluster-based local outlier factor (CBLOF), or local density cluster-based outlier factor (LDCOF).
Triplet discloses wherein the cluster based unsupervised anomaly detection comprises at least one of: density-based spatial clustering of applications with noise (DBSCAN), shared nearest neighbor (SNN), clustering, K-Means clustering, self-organizing map (SOM), clustering, cluster-based local outlier factor (CBLOF), or local density cluster-based outlier factor (LDCOF) (eliminate user input and automatically compute the optimal threshold t using an unsupervised density-based clustering algorithm such as DBSCAN [0112]).
Therefore, it would have been obvious at the time the invention was made to one of ordinary skill in the art to modify method and system of the combined teachings, by making use of the technique taught by Triplet, in order to improve the large volume data analysis.
Both references are within the same field of telecommunication, and in particular of wireless networking, the modification does not change a fundamental operating principle of the combined teachings, nor does the combined teachings teach away from the modification (the combined teachings merely discloses a preferred embodiment). The combination has a reasonable expectation of success in that the modifications can be made using conventional and well known engineering and/or programming techniques, the detection taught by Triplet is not altered and continues to perform the same function as separately, and the resultant combination produces the highly predictable result of wherein the cluster based unsupervised anomaly detection comprises at least one of: density-based spatial clustering of applications with noise (DBSCAN), shared nearest neighbor (SNN), clustering, K-Means clustering, self-organizing map (SOM), clustering, cluster-based local outlier factor (CBLOF), or local density cluster-based outlier factor (LDCOF)
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kvernvik et al. (WO 2020/173542 A1) (Kvernvik herein after) (IDS) and Yeddu (US 2021/0064593 A1), further in view of Ge et al (US 2022/0416866 A1) (Ge herein after).
Re Claim 12, the combined teachings disclose the method according to claim 1, except wherein the at least one communication parameter comprises common beamforming weight, determining at least one communication parameter based on the filtered measurement data comprises: extracting channel information of a terminal device from the filtered measurement data; selecting channel information of the terminal device with a channel quality smaller than a threshold; building a spatial channel matrix of the terminal device based on the selected channel information of the terminal device; building a summed spatial channel matrix based on the spatial channel matrix of at least one terminal device; and using singular value decomposition, SVD, on the summed spatial channel matrix to calculate the common beamforming weight.
However, Ge discloses a MIMO communication method and system wherein the at least one communication parameter comprises common beamforming weight (precoding matrix may be obtained by performing singular value decomposition (SVD) on the channel matrix or a covariance matrix of the channel matrix, or may be obtained by performing eigenvalue decomposition (EVD) on the covariance matrix of the channel matrix [0148]), determining at least one communication parameter comprises: extracting channel information of a terminal device (network device needs to perform modulation and coding and signal precoding based on CSI fed back by the terminal device [0145]); selecting channel information of the terminal device with a channel quality smaller than a threshold (network device may process a to-be-sent signal by using a precoding matrix that matches a channel resource, so that the precoded to-be-sent signal is adapted to a channel, and received signal quality (for example, a signal-to-interference-plus-noise ratio (SINR)) of a receiving device is improved [0146]); building a spatial channel matrix of the terminal device based on the selected channel information of the terminal device (matrix constructed by a spatial-domain vector and a frequency-domain vector may be referred to as, for example, a spatial-frequency component matrix [0172]); building a summed spatial channel matrix based on the spatial channel matrix of at least one terminal device (weighted sum of the one or more spatial-frequency component matrices may be used to construct a spatial-frequency precoding matrix corresponding to a spatial layer. In other words, the spatial-frequency precoding matrix may be approximately a weighted sum of spatial-frequency component matrices constructed by the selected one or more spatial-domain vectors and one or more frequency-domain vectors [0172]); and using singular value decomposition, SVD, on the summed spatial channel matrix to calculate the common beamforming weight (precoding matrix may be determined by performing SVD on the channel matrix or a covariance matrix of the channel matrix. In the SVD process, different spatial layers may be distinguished based on magnitude of eigenvalues [0186]).
Therefore, it would have been obvious at the time the invention was made to one of ordinary skill in the art to modify method and system of the combined teachings, by making use of the technique taught by Ge, in order to improve the beamforming signal quality.
Both references are within the same field of telecommunication, and in particular of MIMO system, the modification does not change a fundamental operating principle of the combined teachings, nor does the combined teachings teach away from the modification (the combined teachings merely discloses a preferred embodiment). The combination has a reasonable expectation of success in that the modifications can be made using conventional and well known engineering and/or programming techniques, the beamforming technique taught by Ge is not altered and continues to perform the same function as separately, and the resultant combination produces the highly predictable result of wherein the at least one communication parameter comprises common beamforming weight, determining at least one communication parameter based on the filtered measurement data comprises: extracting channel information of a terminal device from the filtered measurement data; selecting channel information of the terminal device with a channel quality smaller than a threshold; building a spatial channel matrix of the terminal device based on the selected channel information of the terminal device; building a summed spatial channel matrix based on the spatial channel matrix of at least one terminal device; and using singular value decomposition, SVD, on the summed spatial channel matrix to calculate the common beamforming weight.
Claim(s) 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kvernvik et al. (WO 2020/173542 A1) (Kvernvik herein after) (IDS), in view of Triplet et al (US 2020/0082013 A1) (Triplet herein after).
Re Claim 17, Kvernvik discloses the method according to claim 15, except explicitly teaches wherein the unsupervised machine learning algorithm comprises at least one of: distance based unsupervised anomaly detection, density based unsupervised anomaly detection, or tree based unsupervised anomaly detection.
However, Triplet discloses a system and method provide a feature-selection engine that enables automatic determination of most relevant input PM data, prior to feeding a data-driven software application. The systems and methods are scalable and enable hierarchical feature selection and density-based unsupervised pattern detection in multivariate time-series, without prior knowledge about the data, making the approach suitable in an IoT or multi-vendor context, for a networking application ([0041]).
Therefore, it would have been obvious at the time the invention was made to one of ordinary skill in the art to modify method and system of Kvernvik, by making use of the technique taught by Triplet, in order to improve the large volume data analysis.
Both references are within the same field of telecommunication, and in particular of wireless networking, the modification does not change a fundamental operating principle of Kvernvik, nor does Kvernvik teaches away from the modification (Kvernvik merely discloses a preferred embodiment). The combination has a reasonable expectation of success in that the modifications can be made using conventional and well known engineering and/or programming techniques, the detection taught by Triplet is not altered and continues to perform the same function as separately, and the resultant combination produces the highly predictable result of wherein the unsupervised machine learning algorithm comprises at least one of: distance based unsupervised anomaly detection, density based unsupervised anomaly detection, or tree based unsupervised anomaly detection.
Re Claim 18, Kvernvik discloses the method according to claim 15, except wherein the cluster based unsupervised anomaly detection comprises at least one of: density-based spatial clustering of applications with noise (DBSCAN), shared nearest neighbor (SNN), clustering, K-Means clustering, self-organizing map (SOM), clustering, cluster-based local outlier factor (CBLOF), or local density cluster-based outlier factor (LDCOF).
Triplet discloses wherein the cluster based unsupervised anomaly detection comprises at least one of: density-based spatial clustering of applications with noise (DBSCAN), shared nearest neighbor (SNN), clustering, K-Means clustering, self-organizing map (SOM), clustering, cluster-based local outlier factor (CBLOF), or local density cluster-based outlier factor (LDCOF) (eliminate user input and automatically compute the optimal threshold t using an unsupervised density-based clustering algorithm such as DBSCAN [0112]).
Therefore, it would have been obvious at the time the invention was made to one of ordinary skill in the art to modify method and system of Kvernvik, by making use of the technique taught by Triplet, in order to improve the large volume data analysis.
Both references are within the same field of telecommunication, and in particular of wireless networking, the modification does not change a fundamental operating principle of Kvernvik, nor does Kvernvik teach away from the modification (Kvernvik merely discloses a preferred embodiment). The combination has a reasonable expectation of success in that the modifications can be made using conventional and well known engineering and/or programming techniques, the detection taught by Triplet is not altered and continues to perform the same function as separately, and the resultant combination produces the highly predictable result of wherein the cluster based unsupervised anomaly detection comprises at least one of: density-based spatial clustering of applications with noise (DBSCAN), shared nearest neighbor (SNN), clustering, K-Means clustering, self-organizing map (SOM), clustering, cluster-based local outlier factor (CBLOF), or local density cluster-based outlier factor (LDCOF)
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KENNETH T LAM whose telephone number is (571)270-1862. The examiner can normally be reached M-F 8:30-5:00 PM.
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/KENNETH T LAM/Primary Examiner, Art Unit 2631