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 .
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 10 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over James (US 2018/0234302 A1) in view of Gupta (US 2016/0088006 A1).
Regarding Claim 1, James discloses the below limitation(s): receiving, from a management service consumer, a first request to create a set of clusters (James [0006] machine learning engine may generate the event monitoring model for a subset of network devices (i.e. a cluster) to be monitored) and to train a machine learning model ([0067] machine learning engine may learn normal behavior of a network using machine learning training sets)
receiving, from the management service consumer, a second request to dynamically monitor the set of clusters ([0007] machine learning engine may run multiple machine learning algorithms sequentially (i.e. at least a second request) to generate the event monitoring model for a group of network devices (i.e. cluster); Fig 5 block 502 receive an event monitoring model and a network query schedule);
creating, based on the first request, the set of clusters, wherein each cluster of the set of clusters is associated with the machine learning model (Fig 5 block 504 update an event monitoring model database with the received event monitoring model);
training, based on the first request, the machine learning model ([0067] machine learning engine may learn normal behavior of a network using machine learning training sets; Fig 5 block 506 update an event monitor with the receive event monitoring model)
performing, based on the second request, a dynamic monitoring procedure to monitor the clusters in the set of clusters (Fig 5 block 512 monitor the observed events based on the event monitoring model);
transmitting cluster reports to the management service consumer based on the creating the set of clusters (Fig 5 block 514 send the raw network traffic feed and network device classifications to the event monitoring service); and
transmitting training reports to the management service consumer based on performing the dynamic monitoring of the set of clusters, and the training of the machine learning model ([0120] feature extractor may extract machine learning feature vectors from the training event and may provide the feature vectors to a machine learning algorithm (i.e. transmitting training reports)).
James does not disclose the below limitation(s): train a machine learning model for one or more clusters in the set of clusters for a plurality of different contexts, wherein each cluster comprises a plurality of network nodes, network functions, and management functions ([0067] teaches that James does not teach training a model for a particular cluster but rather the network overall);
training, based on the first request, the machine learning model for the one or more clusters in the set of clusters for the plurality of different contexts;
In the same field of endeavor of dynamic monitoring of a wireless network using machine learning, Gupta does disclose the below limitation(s):
receiving, from a management service consumer, a first request to create a set of clusters (Gupta Fig 14 block 1402 monitor clusters of nodes) and to train a machine learning model for one or more clusters in the set of clusters for a plurality of different contexts (Fig 14 block 1406 collect data from open time-series database and label as training data and block 1408 build model through machine learning using training data; see also Fig 2 machine-learning model building offline training 206), wherein each cluster comprises a plurality of network nodes, network functions, and management functions (Fig 11 network system 1102 i.e. cluster; [0004] clusters are multiple server machines);
training, based on the first request, the machine learning model for the one or more clusters in the set of clusters for the plurality of different contexts (Fig 14 block 1406 collect data from open time-series database and label as training data and block 1408 build model through machine learning using training data; see also Fig 2 machine-learning model building offline training 206);
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of James to include building a machine learning model for monitoring a subset of network device(s) and/or function(s) as taught by Gupta. The suggestion/motivation to do so would have been to create ML model for a plurality of network configurations such that different subsets of the network may be monitored for a plurality of different criteria in real-time. Therefore, it would have been obvious to combine James and Gupta to obtain the invention, as specified in the instant claim.
Regarding Claim 10, James discloses the below limitation(s): at least one processor; and at least one memory comprising computer program code which, when executed by the at least one processor (James Fig 12 computing device 1290 comprising processor 1203 and memory 1205), cause the apparatus at least to receiving, from a management service consumer, a first request to create a set of clusters ([0006] machine learning engine may generate the event monitoring model for a subset of network devices (i.e. a cluster) to be monitored) and to train a machine learning model ([0067] machine learning engine may learn normal behavior of a network using machine learning training sets)
receiving, from the management service consumer, a second request to dynamically monitor the set of clusters ([0007] machine learning engine may run multiple machine learning algorithms sequentially (i.e. at least a second request) to generate the event monitoring model for a group of network devices (i.e. cluster); Fig 5 block 502 receive an event monitoring model and a network query schedule);
creating, based on the first request, the set of clusters, wherein each cluster of the set of clusters is associated with the machine learning model (Fig 5 block 504 update an event monitoring model database with the received event monitoring model);
training, based on the first request, the machine learning model ([0067] machine learning engine may learn normal behavior of a network using machine learning training sets; Fig 5 block 506 update an event monitor with the receive event monitoring model)
performing, based on the second request, a dynamic monitoring procedure to monitor the clusters in the set of clusters (Fig 5 block 512 monitor the observed events based on the event monitoring model);
transmitting cluster reports to the management service consumer based on the creating the set of clusters (Fig 5 block 514 send the raw network traffic feed and network device classifications to the event monitoring service); and
transmitting training reports to the management service consumer based on performing the dynamic monitoring of the set of clusters, and the training of the machine learning model ([0120] feature extractor may extract machine learning feature vectors from the training event and may provide the feature vectors to a machine learning algorithm (i.e. transmitting training reports)).
James does not disclose the below limitation(s): train a machine learning model for one or more clusters in the set of clusters for a plurality of different contexts, wherein each cluster comprises a plurality of network nodes, network functions, and management functions ([0067] teaches that James does not teach training a model for a particular cluster but rather the network overall);
training, based on the first request, the machine learning model for the one or more clusters in the set of clusters for the plurality of different contexts;
In the same field of endeavor of dynamic monitoring of a wireless network using machine learning, Gupta does disclose the below limitation(s):
receiving, from a management service consumer, a first request to create a set of clusters (Gupta Fig 14 block 1402 monitor clusters of nodes) and to train a machine learning model for one or more clusters in the set of clusters for a plurality of different contexts (Fig 14 block 1406 collect data from open time-series database and label as training data and block 1408 build model through machine learning using training data; see also Fig 2 machine-learning model building offline training 206), wherein each cluster comprises a plurality of network nodes, network functions, and management functions (Fig 11 network system 1102 i.e. cluster; [0004] clusters are multiple server machines);
training, based on the first request, the machine learning model for the one or more clusters in the set of clusters for the plurality of different contexts (Fig 14 block 1406 collect data from open time-series database and label as training data and block 1408 build model through machine learning using training data; see also Fig 2 machine-learning model building offline training 206);
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of James to include building a machine learning model for monitoring a subset of network device(s) and/or function(s) as taught by Gupta. The suggestion/motivation to do so would have been to create ML model for a plurality of network configurations such that different subsets of the network may be monitored for a plurality of different criteria in real-time. Therefore, it would have been obvious to combine James and Gupta to obtain the invention, as specified in the instant claim.
Regarding Claim 19, James discloses the below limitation(s):A non-transitory computer readable medium comprising program instructions which, when executed by an apparatus (James Fig 12 computing device 1290 comprising processor 1203 and memory 1205), cause the apparatus at least to:
receiving, from a management service consumer, a first request to create a set of clusters ([0006] machine learning engine may generate the event monitoring model for a subset of network devices (i.e. a cluster) to be monitored) and to train a machine learning model ([0067] machine learning engine may learn normal behavior of a network using machine learning training sets)
receiving, from the management service consumer, a second request to dynamically monitor the set of clusters ([0007] machine learning engine may run multiple machine learning algorithms sequentially (i.e. at least a second request) to generate the event monitoring model for a group of network devices (i.e. cluster); Fig 5 block 502 receive an event monitoring model and a network query schedule);
creating, based on the first request, the set of clusters, wherein each cluster of the set of clusters is associated with the machine learning model (Fig 5 block 504 update an event monitoring model database with the received event monitoring model);
training, based on the first request, the machine learning model ([0067] machine learning engine may learn normal behavior of a network using machine learning training sets; Fig 5 block 506 update an event monitor with the receive event monitoring model)
performing, based on the second request, a dynamic monitoring procedure to monitor the clusters in the set of clusters (Fig 5 block 512 monitor the observed events based on the event monitoring model);
transmitting cluster reports to the management service consumer based on the creating the set of clusters (Fig 5 block 514 send the raw network traffic feed and network device classifications to the event monitoring service); and
transmitting training reports to the management service consumer based on performing the dynamic monitoring of the set of clusters, and the training of the machine learning model ([0120] feature extractor may extract machine learning feature vectors from the training event and may provide the feature vectors to a machine learning algorithm (i.e. transmitting training reports)).
James does not disclose the below limitation(s): train a machine learning model for one or more clusters in the set of clusters for a plurality of different contexts, wherein each cluster comprises a plurality of network nodes, network functions, and management functions ([0067] teaches that James does not teach training a model for a particular cluster but rather the network overall);
training, based on the first request, the machine learning model for the one or more clusters in the set of clusters for the plurality of different contexts;
In the same field of endeavor of dynamic monitoring of a wireless network using machine learning, Gupta does disclose the below limitation(s):
receiving, from a management service consumer, a first request to create a set of clusters (Gupta Fig 14 block 1402 monitor clusters of nodes) and to train a machine learning model for one or more clusters in the set of clusters for a plurality of different contexts (Fig 14 block 1406 collect data from open time-series database and label as training data and block 1408 build model through machine learning using training data; see also Fig 2 machine-learning model building offline training 206), wherein each cluster comprises a plurality of network nodes, network functions, and management functions (Fig 11 network system 1102 i.e. cluster; [0004] clusters are multiple server machines);
training, based on the first request, the machine learning model for the one or more clusters in the set of clusters for the plurality of different contexts (Fig 14 block 1406 collect data from open time-series database and label as training data and block 1408 build model through machine learning using training data; see also Fig 2 machine-learning model building offline training 206);
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of James to include building a machine learning model for monitoring a subset of network device(s) and/or function(s) as taught by Gupta. The suggestion/motivation to do so would have been to create ML model for a plurality of network configurations such that different subsets of the network may be monitored for a plurality of different criteria in real-time. Therefore, it would have been obvious to combine James and Gupta to obtain the invention, as specified in the instant claim.
Allowable Subject Matter
Claims 2-9, 11-18 and 20 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.
The following is a statement of reasons for the indication of allowable subject matter: a thorough and complete search has been conducted and no prior art has been found that solely, or in any reasonable combination, reads on each element of the indicated claim(s). In particular, the dependent claims provide additional detail not present in the prior art. For example, the details of the triggering mechanism (e.g. Claim 2) or attributes used for the creation of clusters (e.g. Claim 7) overcome the prior art of record which is directed generally to monitoring of network devices using an ML model but lacks specificity contained in the instant dependent claim(s).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAWN D MILLER whose telephone number is (571)272-8599. The examiner can normally be reached M-TR 8-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles C Jiang can be reached at (571) 270-7191. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SHAWN D MILLER/Primary Examiner, Art Unit 2412
Prosecution Insights