DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/14/2023 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 1-3, and 5-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In reference to claim 1:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“selecting from the third matrix a subset that matches with the second matrix;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could select from a third matrix a subset that matches the second matrix.
“subtracting the selected subset from the second matrix to obtain a fifth matrix;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could subtract the selected subset from the second matrix to obtain a fifth matrix.
“outputting the fifth matrix or information derived from the fifth matrix for the purpose of evaluating performance of the target system to detect problems so that corrective actions can be taken,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could output information based on analysis of the fifth matrix.
“and wherein selecting the subset from the third matrix comprises selecting such rows of the third matrix that are related to at least partially the same properties with rows of the second matrix.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could select rows of the third matrix that relate to at least partially the same properties with rows of the second matrix.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“A computer implemented method for analyzing measurement results of a target system, the method comprising receiving a first matrix comprising first measurement results of the target system;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“training a matrix decomposition model with the first matrix to obtain a third matrix of normal or stable measurement results and a fourth matrix of anomalous or unstable measurement results;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receiving a second matrix comprising second measurement results of the target system, wherein the second measurement results are later measurement results compared to the first measurement results;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“wherein each row of the matrices relates to respective one or more properties,” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“wherein the properties define operating context in which respective measurement result is obtained,” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“A computer implemented method for analyzing measurement results of a target system, the method comprising receiving a first matrix comprising first measurement results of the target system;” (well-understood, routine, conventional MPEP 2106.05(d))
“training a matrix decomposition model with the first matrix to obtain a third matrix of normal or stable measurement results and a fourth matrix of anomalous or unstable measurement results;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receiving a second matrix comprising second measurement results of the target system, wherein the second measurement results are later measurement results compared to the first measurement results;” (well-understood, routine, conventional MPEP 2106.05(d))
“wherein each row of the matrices relates to respective one or more properties,” (well-understood, routine, conventional MPEP 2106.05(d))
“wherein the properties define operating context in which respective measurement result is obtained,” (well-understood, routine, conventional MPEP 2106.05(d))
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 2:
Claim 2 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
In reference to claim 3:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“and wherein the subset that matches the second matrix is selected based on respective combinations of properties.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could select the subset that matches the second matrix based on respective combinations of properties.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“The method claim 1, wherein the first and second matrices are accompanied with a property matrix comprising a combination of properties for each row of the first and second matrices,” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“The method claim 1, wherein the first and second matrices are accompanied with a property matrix comprising a combination of properties for each row of the first and second matrices,” (well-understood, routine, conventional MPEP 2106.05(d))
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 5:
Claim 5 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
In reference to claim 6:
Claim 6 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
In reference to claim 7:
Claim 7 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
In reference to claim 8:
Claim 8 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
In reference to claim 9:
Claim 9 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
In reference to claim 10:
Claim 10 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
In reference to claim 11:
Claim 11 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
In reference to claim 12:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a process
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“The method of claim 1, wherein the fifth matrix identifies anomalies present in the second matrix.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could calculate the fifth matrix to identify anomalies present in the second matrix.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
No
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
No
In reference to claim 13:
Claim 13 is directed to a judicial exception from claim(s) depended on and does not recite additional elements that integrate the judicial exception into a practical application and amount to significantly more than the judicial exception.
In reference to claim 14:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a machine
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“selecting from the third matrix a subset that matches with the second matrix;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could select from a third matrix a subset that matches the second matrix.
“subtracting the selected subset from the second matrix to obtain a fifth matrix;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could subtract the selected subset from the second matrix to obtain a fifth matrix.
“outputting the fifth matrix or information derived from the fifth matrix for the purpose of evaluating performance of the target system to detect problems so that corrective actions can be taken,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could output information based on analysis of the fifth matrix.
“and wherein selecting the subset from the third matrix comprises selecting such rows of the third matrix that are related to at least partially the same properties with rows of the second matrix.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could select rows of the third matrix that relate to at least partially the same properties with rows of the second matrix.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“An apparatus comprising a processor, and a memory including computer program code; the memory and the computer program code configured to, with the processor, cause the apparatus to perform” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receiving a first matrix comprising first measurement results of the target system;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“training a matrix decomposition model with the first matrix to obtain a third matrix of normal or stable measurement results and a fourth matrix of anomalous or unstable measurement results;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receiving a second matrix comprising second measurement results of the target system, wherein the second measurement results are later measurement results compared to the first measurement results;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“wherein each row of the matrices relates to respective one or more properties,” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“wherein the properties define operating context in which respective measurement result is obtained,” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“An apparatus comprising a processor, and a memory including computer program code; the memory and the computer program code configured to, with the processor, cause the apparatus to perform” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receiving a first matrix comprising first measurement results of the target system;” (well-understood, routine, conventional MPEP 2106.05(d))
“training a matrix decomposition model with the first matrix to obtain a third matrix of normal or stable measurement results and a fourth matrix of anomalous or unstable measurement results;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receiving a second matrix comprising second measurement results of the target system, wherein the second measurement results are later measurement results compared to the first measurement results;” (well-understood, routine, conventional MPEP 2106.05(d))
“wherein each row of the matrices relates to respective one or more properties,” (well-understood, routine, conventional MPEP 2106.05(d))
“wherein the properties define operating context in which respective measurement result is obtained,” (well-understood, routine, conventional MPEP 2106.05(d))
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
In reference to claim 15:
Step 1 - Is the claim to a process, machine, manufacture or composition of matter?
Yes, the claim is directed to a manufacture
Step 2A Prong 1 - Does the claim recite an abstract idea, law of nature, or natural phenomenon?
“selecting from the third matrix a subset that matches with the second matrix;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)(c)). For example, a person could select from a third matrix a subset that matches the second matrix.
“subtracting the selected subset from the second matrix to obtain a fifth matrix;” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could subtract the selected subset from the second matrix to obtain a fifth matrix.
“outputting the fifth matrix or information derived from the fifth matrix for the purpose of evaluating performance of the target system to detect problems so that corrective actions can be taken,” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could output information based on analysis of the fifth matrix.
“and wherein selecting the subset from the third matrix comprises selecting such rows of the third matrix that are related to at least partially the same properties with rows of the second matrix.” which is an abstract idea because it is directed to a mental process, an observation, evaluation, judgement, or opinion. The limitation as drafted, and under a broadest reasonable interpretation, can be performed in the human mind, or by a human using a pen and paper (MPEP 2106.04(a)(2)(Ill)). For example, a person could select rows of the third matrix that relate to at least partially the same properties with rows of the second matrix.
Step 2A Prong 2 - Does the claim recite additional elements that integrate the judicial exception into a practical application?
“A computer program product comprising non-transitory computer executable program code which when executed by a processor causes an apparatus to perform” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receiving a first matrix comprising first measurement results of the target system;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“training a matrix decomposition model with the first matrix to obtain a third matrix of normal or stable measurement results and a fourth matrix of anomalous or unstable measurement results;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receiving a second matrix comprising second measurement results of the target system, wherein the second measurement results are later measurement results compared to the first measurement results;” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“wherein each row of the matrices relates to respective one or more properties,” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
“wherein the properties define operating context in which respective measurement result is obtained,” (insignificant extra-solution activity mere data gathering MPEP 2106.05(g))
The claim does not include additional elements that are integrated into a practical application.
Step 2B - Does the claim recite additional elements that amount to significantly more than the judicial exception?
“A computer program product comprising non-transitory computer executable program code which when executed by a processor causes an apparatus to perform” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receiving a first matrix comprising first measurement results of the target system;” (well-understood, routine, conventional MPEP 2106.05(d))
“training a matrix decomposition model with the first matrix to obtain a third matrix of normal or stable measurement results and a fourth matrix of anomalous or unstable measurement results;” is merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
“receiving a second matrix comprising second measurement results of the target system, wherein the second measurement results are later measurement results compared to the first measurement results;” (well-understood, routine, conventional MPEP 2106.05(d))
“wherein each row of the matrices relates to respective one or more properties,” (well-understood, routine, conventional MPEP 2106.05(d))
“wherein the properties define operating context in which respective measurement result is obtained,” (well-understood, routine, conventional MPEP 2106.05(d))
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
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) 1-3, 5-9, and 11-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bikash Agrawal et al; “Adaptive Anomaly Detection in Cloud using Robust and Scalable Principal Component Analysis” Published 2016 (hereinafter “Agrawal”).
Regarding claim 1, Agrawal anticipates A computer implemented method for analyzing measurement results of a target system, the method comprising receiving a first matrix comprising first measurement results of the target system; (Agrawal Page 102 Paragraph 3; “We collect a uniform set of metrics from the nodes and concatenate them into one matrix, X. In this paper, we collect several metrics from the data center, inducing CPU, memory, disk IO , and page cache.” Examiner notes that a first matrix (matrix X) is received/collected where first matrix comprises first measurement results (several metrics) of the target system (data center))
training a matrix decomposition model with the first matrix to obtain a third matrix of normal or stable measurement results and a fourth matrix of anomalous or unstable measurement results; (Agrawal Page 102 Paragraph 5; “A matrix decomposition algorithm decomposes the input matrix X into the sum of three parts X = L + S + E using Robust Principal Component Pursuit [20]. Where, L is a low-rank representation matrix illustrating a smooth X, S is a sparse matrix containing corrupted data, and E is noise.” Examiner notes that matrix decomposition model (matrix decomposition algorithm) is trained by Robust Principal Component Pursuit with the first matrix (input matrix X) to obtain a third matrix of normal or stable measurement results (matrix L) and a fourth matrix of anomalous or unstable measurement results (matrix S))
receiving a second matrix comprising second measurement results of the target system, wherein the second measurement results are later measurement results compared to the first measurement results; (Agrawal Page 102 Paragraph 5; “Let Xt be the vector that represents the measurement of CPU usage at time t.” Examiner notes that a second matrix is received comprising a second measurement results (CPU usage) of the target system (data center), wherein the second measurement results (CPU usage at time t) are later measurement results compared to the first measurement results (CPU usage at time t-1); this second measurement is concatenated into second matrix)
selecting from the third matrix a subset that matches with the second matrix; (Agrawal Page 102 Paragraph 4; “Metric Selection: There are different metrics present in a large system as mentioned in Table 1, it is necessary to select an optimal subset of metrics. Metric selection is also known as dimensional reduction.” Examiner notes that a subset (optimal subset of metrics) is selected from the third matrix (matrix of a large system with many metrics) that matches with the second matrix (matrix of a small system with less metrics); a subset of a third matrix can be the entire matrix that matches the second matrix)
subtracting the selected subset from the second matrix to obtain a fifth
matrix; (Agrawal Page 103 Algorithm 1 Line 7 shows subtracting the selected subset (L) from the second matrix (X) to obtain a fifth matrix (E))
outputting the fifth matrix or information derived from the fifth matrix for the purpose of evaluating performance of the target system to detect problems so that corrective actions can be taken, (Agrawal Page 100 Paragraph 1; “Early detection of these anomalous time series is critical for taking preemptive action to protect users and provide a better user-experience.” Agrawal Page 100 Paragraph 3; “Our method analyzes the log files and predicts resource usage to create labeled anomalies.” Agrawal Page 103 Algorithm 1 and Paragraph 5; “The outlier presented in the sparse matrix S contains large variance that is calculated in algorithm 1.” Examiner notes that information (outlier) derived from the fifth matrix (matrix E) is outputted/presented for the purpose of evaluating performance of the target system to detect problems (predict resource usage to create labeled anomalies) so that corrective actions can be taken (taking preemptive action))
wherein the target system is a communications network, or an industrial process, (Agrawal Page 102 Paragraph 3; “we collect several metrics from the data center” Examiner notes that target system is a communications network (data center that facilitates communications network))
wherein each row of the matrices relates to respective one or more properties,
wherein the properties define operating context in which respective measurement result is obtained, (Agrawal Page 102 Paragraph 3; “We collect a uniform set of metrics from the nodes and concatenate them into one matrix, X. In this paper, we collect several metrics from the data center, including CPU, memory, disk I/O and page cache. For example, there might be a memory leakage that may affect the CPU utilization rate and other resources in the system.” Examiner notes that each row of the matrices relates to respective one or more properties (matrix X is formed of several metrics), wherein the properties define operating context in which respective measurement result is obtained (CPU, memory, disk I/O and page cache))
and wherein selecting the subset from the third matrix comprises selecting such rows of the third matrix that are related to at least partially the same properties with rows of the second matrix. (Agrawal Page 102 Paragraph 3; “We collect a uniform set of metrics from the nodes and concatenate them into one matrix, X.” Agrawal Page 102 Paragraph 4; “Metric Selection: There are different metrics present in a large system as mentioned in Table 1, it is necessary to select an optimal subset of metrics. Metric selection is also known as dimensional reduction.” Examiner notes that a subset (optimal subset of metrics) is selected from the third matrix (matrix of a large system with many metrics) that matches with the second matrix (matrix of a small system with less metrics); properties (metrics) are selected from rows that they reside in from the concatenation)
Regarding claim 2, Agrawal anticipates The method of claim 1, wherein the information derived from the fifth matrix comprises an aggregated score for each row of the fifth matrix. (Agrawal Page 103 Algorithm 1 line 7 and Paragraph 5; “where || ||∗ and ||L||1 are the nuclear norm and l1 norm, respectively” Examiner notes that the information derived from the fifth matrix (matrix E) comprises an aggregated score for each of the fifth matrix (nuclear norm is the sum of singular values of a matrix; each value of each row is summed))
Regarding claim 3, Agrawal anticipates The method claim 1, wherein the first and second matrices are accompanied with a property matrix comprising a combination of properties for each row of the first and second matrices, and wherein the subset that matches the second matrix is selected based on respective combinations of properties. (Agrawal Page 102 Table 1 Metrics list and Paragraph 4; “Metric selection is also known as dimensional reduction. The data presented in a low dimensional subspace is easier to separate into different classes.” Examiner notes that the first and second matrices are accompanied with a property matrix (metrics list) comprising a combination of properties for each row of the first and second matrices (CPU rate, Memory usage, etc) and wherein subset that matches the second matrix is selected based on respective combinations of properties (optimal subset of metrics))
Regarding claim 5, Agrawal anticipates The method of claim 1, wherein the properties comprise one or more of the following: time, location, device type, device identifier, logical element, event type, product type, production phase, production equipment, management system. (Agrawal Page 102 Table 1 shows time as disk IO time)
Regarding claim 6, Agrawal anticipates The method of claim 1, wherein the target system is a communications network and the properties comprise one or more of the following: time, location, subscriber type, subscription type, network technology, cell type, cell identifier, device type, device identifier, logical element, event type, antenna type, roaming network, management system. (Agrawal Page 102 Paragraph 3; “we collect several metrics from the data center” Agrawal Page 102 Paragraph 5; “If a matrix X consists of trends, we represent the trend in each column. For example, weekly seasonality would be where each row is a day of a week, and one column is one full week” Examiner notes that target system is a communications network (data center that facilitates communications network); properties comprise time/trend)
Regarding claim 7, Agrawal anticipates The method of claim 1, wherein the first measurement results comprise measurement results for a 24 hour time period or multiple thereof. (Agrawal Page 102 Paragraph 5; “If a matrix X consists of trends, we represent the trend in each column. For example, weekly seasonality would be where each row is a day of a week, and one column is one full week” Examiner notes that first measurements results comprise measurement results (trends) for a 24 hour time period (each row is a day of a week))
Regarding claim 8, Agrawal anticipates The method of claim 1, wherein each row of the first and second matrices comprise measurement results aggregated over a 5-30 minute time period. (Agrawal Page 104 Fig 2 and Page 102 Paragraph 4; “To collect different features we aggregate the metrics in per second intervals for given time frames.” Examiner notes that each row of the first and second matrices comprise measurements results (CPU utilization) aggregated over a 5-30 minute time period (Fig 2 shows measurement taken for 1500 seconds = 25 minutes))
Regarding claim 9, Agrawal anticipates The method of claim 1, wherein the second measurement results comprise measurement results for a 5-30 minute time period or multiple thereof. (Examiner references to previous mapping to show that the second measurement results comprise measurement results (CPU utilization) for a 5-30 minute time period or multiple thereof (Fig 2 shows measurement taken for 1500 seconds = 25 minutes))
Regarding claim 11, Agrawal anticipates The method of claim 1, wherein first measurement results and the second measurement results relate to measurement of the same phenomena over different time periods. (Agrawal Page 102 Paragraph 5; “Let Xt be the vector that represents the measurement of CPU usage at time t.” Examiner notes that wherein first measurements results and the second measurement results relate to measurement of the same phenomena (CPU usage) over different time periods (time t))
Regarding claim 12, Agrawal anticipates The method of claim 1, wherein the fifth matrix identifies anomalies present in the second matrix. (Agrawal page 103 Algorithm 1 Line 8 shows identifying/calculating anomalies using fifth matrix (matrix E) present in the second matrix (matrix X))
Regarding claim 13, Agrawal anticipates The method of claim 12, further comprising providing the identified anomalies for the purpose of performing corrective actions in the target system. (Agrawal Page 100 Paragraph 1; “Early detection of these anomalous time series is critical for taking preemptive action to protect users and provide a better user-experience.” Examiner notes that providing/detecting the identified anomalies is used for performing corrective actions (preemptive actions) in the target system)
Regarding claim 14, Agrawal anticipates An apparatus comprising a processor, and a memory including computer program code; the memory and the computer program code configured to, with the processor, cause the apparatus to perform (Agrawal Page 103 Paragraph 7; “Setup: Our cluster is comprised of 11 nodes with Cen tOS Linux distro, one node each for Namenode, Secondary Namenode, Job Tracker, and Zookeeper. The remaining 6 nodes act as Data Nodes, and Task Trackers. All nodes have an AMD Opteron(TM) 4180 six-core 2.6GHz processor, 16 GB of ECC DDR-2 RAM, 3x3 TeraBytes secondary storage and HP ProCurve 2650 switch. Experiments were conducted using Apache Spark, Hadoop-0.20 releases. Our default HDFS configuration had a block size of 64 MB and the replication factor of 3.”)
receiving a first matrix comprising first measurement results of the target system; (Agrawal Page 102 Paragraph 3; “We collect a uniform set of metrics from the nodes and concatenate them into one matrix, X. In this paper, we collect several metrics from the data center, inducing CPU, memory, disk IO , and page cache.” Examiner notes that a first matrix (matrix X) is received/collected where first matrix comprises first measurement results (several metrics) of the target system (data center))
training a matrix decomposition model with the first matrix to obtain a third matrix of normal or stable measurement results and a fourth matrix of anomalous or unstable measurement results; (Agrawal Page 102 Paragraph 5; “A matrix decomposition algorithm decomposes the input matrix X into the sum of three parts X = L + S + E using Robust Principal Component Pursuit [20]. Where, L is a low-rank representation matrix illustrating a smooth X, S is a sparse matrix containing corrupted data, and E is noise.” Examiner notes that matrix decomposition model (matrix decomposition algorithm) is trained by Robust Principal Component Pursuit with the first matrix (input matrix X) to obtain a third matrix of normal or stable measurement results (matrix L) and a fourth matrix of anomalous or unstable measurement results (matrix S))
receiving a second matrix comprising second measurement results of the target system, wherein the second measurement results are later measurement results compared to the first measurement results; (Agrawal Page 102 Paragraph 5; “Let Xt be the vector that represents the measurement of CPU usage at time t.” Examiner notes that a second matrix is received comprising a second measurement results (CPU usage) of the target system (data center), wherein the second measurement results (CPU usage at time t) are later measurement results compared to the first measurement results (CPU usage at time t-1); this second measurement is concatenated into second matrix)
selecting from the third matrix a subset that matches with the second matrix; (Agrawal Page 102 Paragraph 4; “Metric Selection: There are different metrics present in a large system as mentioned in Table 1, it is necessary to select an optimal subset of metrics. Metric selection is also known as dimensional reduction.” Examiner notes that a subset (optimal subset of metrics) is selected from the third matrix (matrix of a large system with many metrics) that matches with the second matrix (matrix of a small system with less metrics); a subset of a third matrix can be the entire matrix that matches the second matrix)
subtracting the selected subset from the second matrix to obtain a fifth
matrix; (Agrawal Page 103 Algorithm 1 Line 7 shows subtracting the selected subset (L) from the second matrix (X) to obtain a fifth matrix (E))
outputting the fifth matrix or information derived from the fifth matrix for the purpose of evaluating performance of the target system to detect problems so that corrective actions can be taken, (Agrawal Page 100 Paragraph 1; “Early detection of these anomalous time series is critical for taking preemptive action to protect users and provide a better user-experience.” Agrawal Page 100 Paragraph 3; “Our method analyzes the log files and predicts resource usage to create labeled anomalies.” Agrawal Page 103 Algorithm 1 and Paragraph 5; “The outlier presented in the sparse matrix S contains large variance that is calculated in algorithm 1.” Examiner notes that information (outlier) derived from the fifth matrix (matrix E) is outputted/presented for the purpose of evaluating performance of the target system to detect problems (predict resource usage to create labeled anomalies) so that corrective actions can be taken (taking preemptive action))
wherein the target system is a communications network, or an industrial process, (Agrawal Page 102 Paragraph 3; “we collect several metrics from the data center” Examiner notes that target system is a communications network (data center that facilitates communications network))
wherein each row of the matrices relates to respective one or more properties,
wherein the properties define operating context in which respective measurement result is obtained, (Agrawal Page 102 Paragraph 3; “We collect a uniform set of metrics from the nodes and concatenate them into one matrix, X. In this paper, we collect several metrics from the data center, including CPU, memory, disk I/O and page cache. For example, there might be a memory leakage that may affect the CPU utilization rate and other resources in the system.” Examiner notes that each row of the matrices relates to respective one or more properties (matrix X is formed of several metrics), wherein the properties define operating context in which respective measurement result is obtained (CPU, memory, disk I/O and page cache))
and wherein selecting the subset from the third matrix comprises selecting such rows of the third matrix that are related to at least partially the same properties with rows of the second matrix. (Agrawal Page 102 Paragraph 3; “We collect a uniform set of metrics from the nodes and concatenate them into one matrix, X.” Agrawal Page 102 Paragraph 4; “Metric Selection: There are different metrics present in a large system as mentioned in Table 1, it is necessary to select an optimal subset of metrics. Metric selection is also known as dimensional reduction.” Examiner notes that a subset (optimal subset of metrics) is selected from the third matrix (matrix of a large system with many metrics) that matches with the second matrix (matrix of a small system with less metrics); properties (metrics) are selected from rows that they reside in from the concatenation)
Regarding claim 15, Agrawal anticipates A computer program product comprising non-transitory computer executable program code which when executed by a processor causes an apparatus to perform (Agrawal Page 103 Paragraph 7; “Setup: Our cluster is comprised of 11 nodes with Cen tOS Linux distro, one node each for Namenode, Secondary Namenode, Job Tracker, and Zookeeper. The remaining 6 nodes act as Data Nodes, and Task Trackers. All nodes have an AMD Opteron(TM) 4180 six-core 2.6GHz processor, 16 GB of ECC DDR-2 RAM, 3x3 TeraBytes secondary storage and HP ProCurve 2650 switch. Experiments were conducted using Apache Spark, Hadoop-0.20 releases. Our default HDFS configuration had a block size of 64 MB and the replication factor of 3.”)
receiving a first matrix comprising first measurement results of the target system; (Agrawal Page 102 Paragraph 3; “We collect a uniform set of metrics from the nodes and concatenate them into one matrix, X. In this paper, we collect several metrics from the data center, inducing CPU, memory, disk IO , and page cache.” Examiner notes that a first matrix (matrix X) is received/collected where first matrix comprises first measurement results (several metrics) of the target system (data center))
training a matrix decomposition model with the first matrix to obtain a third matrix of normal or stable measurement results and a fourth matrix of anomalous or unstable measurement results; (Agrawal Page 102 Paragraph 5; “A matrix decomposition algorithm decomposes the input matrix X into the sum of three parts X = L + S + E using Robust Principal Component Pursuit [20]. Where, L is a low-rank representation matrix illustrating a smooth X, S is a sparse matrix containing corrupted data, and E is noise.” Examiner notes that matrix decomposition model (matrix decomposition algorithm) is trained by Robust Principal Component Pursuit with the first matrix (input matrix X) to obtain a third matrix of normal or stable measurement results (matrix L) and a fourth matrix of anomalous or unstable measurement results (matrix S))
receiving a second matrix comprising second measurement results of the target system, wherein the second measurement results are later measurement results compared to the first measurement results; (Agrawal Page 102 Paragraph 5; “Let Xt be the vector that represents the measurement of CPU usage at time t.” Examiner notes that a second matrix is received comprising a second measurement results (CPU usage) of the target system (data center), wherein the second measurement results (CPU usage at time t) are later measurement results compared to the first measurement results (CPU usage at time t-1); this second measurement is concatenated into second matrix)
selecting from the third matrix a subset that matches with the second matrix; (Agrawal Page 102 Paragraph 4; “Metric Selection: There are different metrics present in a large system as mentioned in Table 1, it is necessary to select an optimal subset of metrics. Metric selection is also known as dimensional reduction.” Examiner notes that a subset (optimal subset of metrics) is selected from the third matrix (matrix of a large system with many metrics) that matches with the second matrix (matrix of a small system with less metrics); a subset of a third matrix can be the entire matrix that matches the second matrix)
subtracting the selected subset from the second matrix to obtain a fifth
matrix; (Agrawal Page 103 Algorithm 1 Line 7 shows subtracting the selected subset (L) from the second matrix (X) to obtain a fifth matrix (E))
outputting the fifth matrix or information derived from the fifth matrix for the purpose of evaluating performance of the target system to detect problems so that corrective actions can be taken, (Agrawal Page 100 Paragraph 1; “Early detection of these anomalous time series is critical for taking preemptive action to protect users and provide a better user-experience.” Agrawal Page 100 Paragraph 3; “Our method analyzes the log files and predicts resource usage to create labeled anomalies.” Agrawal Page 103 Algorithm 1 and Paragraph 5; “The outlier presented in the sparse matrix S contains large variance that is calculated in algorithm 1.” Examiner notes that information (outlier) derived from the fifth matrix (matrix E) is outputted/presented for the purpose of evaluating performance of the target system to detect problems (predict resource usage to create labeled anomalies) so that corrective actions can be taken (taking preemptive action))
wherein the target system is a communications network, or an industrial process, (Agrawal Page 102 Paragraph 3; “we collect several metrics from the data center” Examiner notes that target system is a communications network (data center that facilitates communications network))
wherein each row of the matrices relates to respective one or more properties,
wherein the properties define operating context in which respective measurement result is obtained, (Agrawal Page 102 Paragraph 3; “We collect a uniform set of metrics from the nodes and concatenate them into one matrix, X. In this paper, we collect several metrics from the data center, including CPU, memory, disk I/O and page cache. For example, there might be a memory leakage that may affect the CPU utilization rate and other resources in the system.” Examiner notes that each row of the matrices relates to respective one or more properties (matrix X is formed of several metrics), wherein the properties define operating context in which respective measurement result is obtained (CPU, memory, disk I/O and page cache))
and wherein selecting the subset from the third matrix comprises selecting such rows of the third matrix that are related to at least partially the same properties with rows of the second matrix. (Agrawal Page 102 Paragraph 3; “We collect a uniform set of metrics from the nodes and concatenate them into one matrix, X.” Agrawal Page 102 Paragraph 4; “Metric Selection: There are different metrics present in a large system as mentioned in Table 1, it is necessary to select an optimal subset of metrics. Metric selection is also known as dimensional reduction.” Examiner notes that a subset (optimal subset of metrics) is selected from the third matrix (matrix of a large system with many metrics) that matches with the second matrix (matrix of a small system with less metrics); properties (metrics) are selected from rows that they reside in from the concatenation)
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.
Claim(s) 10 is rejected under 35 U.S.C. 103 as being unpatentable over Bikash Agrawal et al; “Adaptive Anomaly Detection in Cloud using Robust and Scalable Principal Component Analysis” Published 2016 (hereinafter “Agrawal”) in view of Brian Xu et al ; US 11288111 B2 filed on Jan 23, 2020 (hereinafter “Xu”).
Regarding claim 10, Agrawal does not teach The method of claim 1, wherein the first measurement results of the first matrix comprise measurement results of a previous day and the second measurement results of the second matrix comprise at least part of measurement results of a current day.
However, Xu does teach The method of claim 1, wherein the first measurement results of the first matrix comprise measurement results of a previous day and the second measurement results of the second matrix comprise at least part of measurement results of a current day. (Xu Fig 3A and Column 10 Line 23; “Some embodiments may use an analysis window 306 that selects a number of previous days' histograms for comparison to the histogram for a current day 308.” Examiner notes that first measurement results of the first matrix comprise measurement results (action counts) of a previous day (Day 1 –
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Day 91) and the second measurement results of the second matrix comprise at least part of measurement results of a current day (Day 92))
It would have obvious to one of ordinary skill in the art before the effective filing date of the present application to combine Agrawal and Xu. Agrawal teaches a self-adaptive based anomaly detection method to detect abnormal behaviors. Xu teaches a method for distinguishing between human and computer actions in a cloud environment. One of ordinary skill would have motivation to combine Agrawal and Xu to implement aspects of Xu in order to distinguish between human and bot users to better identify potential malicious attacks “It may be of particular interest to distinguish between human users and bots in the cloud environment because most malicious attacks on a cloud environment may involve bots.” (Xu Column 16 Line 39).
Conclusion
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/D.D.T./Examiner, Art Unit 2147
/VIKER A LAMARDO/Supervisory Patent Examiner, Art Unit 2147