DETAILED ACTION
Notice of Pre-AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Response to Arguments
Applicant's arguments filed 08/26/2025 have been fully considered.
Applicant argues (pp 6-8) that McGee does not teach on grouping, that McGee states where the metrics are being collected from and therefore does not teach on “identifying a peer group classification for the networked environment based at least partly on the metric of interest, wherein the peer group classification comprises a classification of the networked environment among a set of one or more additional networked environments sharing a commonality associated with the metric of interest”.
In response to the arguments, Examiner respectfully disagrees.
McGee teaches on the limitation as recited. McGee teaches specifically on “identifying” a peer group classification of subsystem environments sharing a commonality associated with the metric of interest. Mayerle teaches on networked environments. The limitation is recited broadly. White (as modified by McGee & Mayerle) teaches on the BRI of this limitation. White teaches on most of the limitations of the independent claims.
White teaches on correlation and grouping of metrics (Fig 2A, Col 4 ln 6-12). However, White is silent on identifying a peer group classification for the networked environment based at least partly on the metric of interest, wherein the peer group classification comprises a classification of the networked environment among a set of one or more additional networked environments sharing a commonality associated with the metric of interest; and display, through an electronic interface, data regarding the peer group classification.
McGee teaches identifying a subsystem peer group classification for the subsystem environment based at least partly on the metric of interest, wherein the subsystem peer group classification comprises a classification of the subsystem environment among a set of one or more additional subsystem environments sharing a commonality (ie. closely related metrics) associated with the metric of interest; (See McGee, Col 6 ln 12-16, 43-46, A system and methods for collecting, analyzing and reporting on significant irregularities with respect to a set of system performance metrics. These metrics are collected from the various sub-systems that make up, for example, an e-commerce transaction processing system. Metric collection module 102 includes one or more data adapters 108, installed in the systems to be monitored. Each data adapter 108 collects information relating to the metrics that are being monitored from a particular sub-system. Col 7 ln 7-12, Metric correlation component 116 analyzes pairs of metric values collected from one or more dynamic sampling agent 110. It applies various correlation and regression techniques to determine the statistical relationship between pairs of metrics, and to form groups of closely related metrics. It also tries to determine temporal relationship between metrics)
and display, through an electronic interface, data regarding the subsystem peer group classification. (See McGee, Col 7 ln 33-40, Metric reporting module 106 provides a user, such as a system manager, with detailed reporting on the metrics, alarms, and analysis performed by the system. Reporting module 106 uses a 3D graphical user interface that provides for drill-down exploration of metric data and analysis, permitting, for example, exploration of the activity that caused an abnormal or suspicious condition in the system being monitored.)
It would have been obvious to modify White per McGee as this would allow the modified system to utilize the peer group classification for comparisons of the collected network metrics from other similar peer groups in order to determine accuracy of collected metrics.
White teaches on correlation and grouping of metrics (Fig 2A, Col 4 ln 6-12). McGee teaches on subsystem group classification based on a commonality shared between the groups (Col 7 ln 7-12). However, White (as modified by McGee) is silent on identifying a peer group classification for the networked environment based at least partly on the metric of interest, wherein the peer group classification comprises a classification of the networked environment among a set of one or more additional networked environments sharing a commonality associated with the metric of interest; and display, through an electronic interface, data regarding the peer group classification.
Mayerle teaches identifying a peer group classification for the networked environment based at least partly on the metric of interest, wherein the peer group classification (Fig 1, peer group 128) comprises a classification of the networked environment among a set of one or more additional networked environments (Fig 1, Enterprise 1, Enterprise 2, Enterprise N) sharing a commonality (ie. similar business processes); (See Mayerle, Abstract: A peer group controller configured to receive peer group information, a data collector configured to collect first business data corresponding to the enterprise and second business data corresponding to at least one other enterprise according to the peer group information. [0022] The peer group 128 may include a number of enterprises, e.g., enterprise 1 to enterprise N. Enterprises in the peer group 128 may have one or more similar business processes 132 or be enterprises in the same business area as each other. See Fig 1, System 100 has peer group 128 which is a classification for networked environments Enterprise 1, Enterprise 2, Enterprise N)
and display, through an electronic interface, data regarding the peer group classification. (See Mayerle, Abstract: The communication manager is configured to provide the performance results, over the network, to the benchmarking application for display)
It would have been obvious to modify White (as modified by McGee) by modifying McGee per Mayerle as this would allow the combined system to utilize the peer group classification to provide comparisons of the collected network metrics against other collected business data from the peer classification networked environments.
Please see updated office action below in view of:
Claims 6-8, 12-14, 16-18, 23-24 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over US Patent 7,444,263 White in view of US Patent 6,643,613 McGee further in view of US PGPub 2013/0346161 (Mayerle)
Claims 15, 25-26 are rejected under pre-AIA 35 U.S.C. 103(a) as being unpatentable over US Patent 7,444,263 (White) in view of US Patent 6,643,613 (McGee) further in view of US PGPub 2013/0346161 (Mayerle) more in view of US Patent 7,444,263 (Stone).
Dependent Claims 9-11, 19-21 are allowable over the prior art as no prior art was discovered to teach on these limitations. However, Claims 9-11, 19-21 are rejected under Double Patenting and are dependent on rejected claims.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 6-21, 23-26 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4, 7-13, 17-20 of U.S. Patent No. 11,671,321 in view of US Patent 6,643,613 McGee.
Instant Application
US Patent 11,671,321
6. A computer-implemented method comprising:
under control of a computing system comprising one or more computing devices configured to execute specific instructions, determining a set of one or more network metrics based at least partly on network communications among network resources in a networked environment;
determining that a network metric of the set of one or more network metrics is a non-compliant metric based on the network metric of the set of one or more network metrics falling outside a corresponding compliance range of values;
generating a normalized score for the non-compliant metric based at least partly on a degree to which the non-compliant metric deviates from the corresponding compliance range of values;
identifying the non-compliant metric as a metric of based at least partly on the normalized score for the non-compliant metric;
identifying a peer group classification for the networked environment based at least partly on the metric of interest, wherein the peer group classification comprises a classification of the networked environment among a set of one or more additional networked environments sharing a commonality associated with the metric of interest;
and displaying, through an electronic interface, data regarding the peer group classification.
1. A computer-implemented method comprising:
under control of a computing system comprising one or more computing devices configured to execute specific instructions,
determining a set of one or more network metrics based at least partly on network communications among network resources in a networked environment;
determining that a network metric of the set of one or more network metrics is a non-compliant metric based on the network metric of the set of one or more network metrics falling outside a corresponding compliance range of values;
generating a normalized score for the non-compliant metric based at least partly on a degree to which the non-compliant metric deviates from the corresponding compliance range of values;
identifying the non-compliant metric as a metric of interest based at least partly on the normalized score, wherein the metric of interest is associated with a deficiency;
determining, for each remedial resource of a plurality of remedial resources, a respective relevancy score representing a degree of responsiveness to correct the deficiency;
4. The computer-implemented method of claim 3, further comprising determining a second set of one or more additional networked environments associated with the peer group classification based at least partly on the adjustment to the peer group classification, wherein the second set of one or more additional networked environments is different than the set of one or more additional networked environments.
and displaying, through an electronic interface, data regarding the metric of interest and the plurality of remedial resources.
7. The computer-implemented method of claim 6, further comprising determining a comparison property associated with the metric of interest for the set of one or more additional networked environments.
2. The computer-implemented method of claim 1, further comprising identifying a peer group classification for the networked environment, wherein the peer group classification comprises a classification of the networked environment among a set of one or more additional networked environments.
8. The computer-implemented method of claim 6, further comprising identifying the non-compliant metric as the metric of interest based at least partly on the metric of interest being associated with a deficiency.
Claim 1. … identifying the non-compliant metric as a metric of interest based at least partly on the normalized score, wherein the metric of interest is associated with a deficiency;
9. The computer-implemented method of claim 8, further comprising determining, for each remedial resource of a plurality of remedial resources, a respective relevancy score representing a degree of responsiveness to correct the deficiency.
Claim 1. … determining, for each remedial resource of a plurality of remedial resources, a respective relevancy score representing a degree of responsiveness to correct the deficiency;
10. The computer-implemented method of claim 9, further comprising displaying, through the electronic interface, data regarding the metric of interest and the plurality of remedial resources.
Claim 1. …and displaying, through an electronic interface, data regarding the metric of interest and the plurality of remedial resources.
11. The computer-implemented method of claim 9, further comprising ranking the plurality of remedial resources according to one of a priority or a preference.
7. The computer-implemented method of claim 1, further comprising ranking the plurality of remedial resources according to a priority.
8. The computer-implemented method of claim 1, further comprising ranking the plurality of remedial resources according to a preference.
12. The computer-implemented method of claim 6, further comprising receiving, via the electronic interface, user input regarding an adjustment to the peer group classification.
3. The computer-implemented method of claim 2, further comprising receiving, via the electronic interface, user input regarding an adjustment to the peer group classification.
13. The computer-implemented method of claim 12, further comprising determining a second set of one or more additional networked environments associated with the peer group classification based at least partly on the adjustment to the peer group classification.
4. The computer-implemented method of claim 3, further comprising determining a second set of one or more additional networked environments associated with the peer group classification based at least partly on the adjustment to the peer group classification, wherein the second set of one or more additional networked environments is different than the set of one or more additional networked environments.
14. The computer-implemented method of claim 6, further comprising generating a roster of metrics of interest, wherein the roster of metrics of interest comprises a subset of the set of one or more network metrics, wherein individual metrics of interest of the roster of metrics of interest satisfy a selection criterion, and wherein individual network metrics of the set of one or more network metrics not included in the roster of metrics of interest do not satisfy the selection criterion.
9. The computer-implemented method of claim 1, further comprising generating a roster of metrics of interest, wherein the roster of metrics of interest comprises a subset of the set of one or more network metrics, wherein individual metrics of interest of the roster of metrics of interest satisfy a selection criterion, and wherein individual network metrics of the set of one or more network metrics not included in the roster of metrics of interest do not satisfy the selection criterion.
15. The computer-implemented method of claim 14, further comprising generating a hierarchical representation of the roster of metrics of interest and corresponding remedial resources.
10. The computer-implemented method of claim 9, further comprising generating a hierarchical representation of the roster of metrics of interest and corresponding remedial resources.
16. A system comprising: computer-readable memory storing executable instructions;
and one or more processors in communication with the computer-readable memory and configured by the executable instructions to at least:
determine a set of one or more network metrics based at least partly on network communications among network resources in a networked environment;
determine that a network metric of the set of one or more network metrics is a non-compliant metric based on the network metric of the set of one or more network metrics falling outside a corresponding compliance range of values;
generate a normalized score for the non-compliant metric based at least partly on a degree to which the non-compliant metric deviates from the corresponding compliance range of values;
identify the non-compliant metric as a metric of based at least partly on the normalized score for the non-compliant metric;
identify a peer group classification for the networked environment based at least partly on the metric of interest, wherein the peer group classification comprises a classification of the networked environment among a set of one or more additional networked environments sharing a commonality associated with the metric of interest;
and display, through an electronic interface, data regarding the peer group classification.
11. A system comprising:
computer-readable memory storing executable instructions;
and one or more processors in communication with the computer-readable memory and configured by the executable instructions to at least:
determine a set of one or more network metrics based at least partly on network communications among network resources in a networked environment;
determine that a network metric of the set of one or more network metrics is a non-compliant metric based on the network metric of the set of one or more network metrics falling outside a corresponding compliance range of values;
generate a normalized score for the non-compliant metric based at least partly on a degree to which the non-compliant metric deviates from the corresponding compliance range of values;
identify the non-compliant metric as a metric of interest based at least partly on the normalized score, wherein the metric of interest is associated with a deficiency;
determine, for each remedial resource of a plurality of remedial resources, a respective relevancy score representing a degree of responsiveness to correct the deficiency;
12. The system of claim 11, wherein the one or more processors are configured by further executable instructions to identify a peer group classification for the networked environment, wherein the peer group classification comprises a classification of the networked environment among a set of one or more additional networked environments.
and display, through an electronic interface, data regarding the metric of interest and the plurality of remedial resources.
17. The system of claim 16, wherein the one or more processors are configured by further executable instructions to determine a comparison property associated with the metric of interest for the set of one or more additional networked environments.
12. The system of claim 11, wherein the one or more processors are configured by further executable instructions to identify a peer group classification for the networked environment, wherein the peer group classification comprises a classification of the networked environment among a set of one or more additional networked environments.
18. The system of claim 16, wherein the one or more processors are configured by further executable instructions to identify the non-compliant metric as a metric of interest based at least partly on a deficiency.
Claim 11. … identify the non-compliant metric as a metric of interest based at least partly on the normalized score, wherein the metric of interest is associated with a deficiency;
19. The system of claim 18, wherein the one or more processors are configured by further executable instructions to determine, for each remedial resource of a plurality of remedial resources, a respective relevancy score representing a degree of responsiveness to correct the deficiency.
Claim 11. … determine, for each remedial resource of a plurality of remedial resources, a respective relevancy score representing a degree of responsiveness to correct the deficiency;
20. The system of claim 19, wherein the one or more processors are configured by further executable instructions to display, through the electronic interface, data regarding the metric of interest and the plurality of remedial resources.
Claim 11. … display, through an electronic interface, data regarding the metric of interest and the plurality of remedial resources.
21. The system of claim 19, wherein the one or more processors are configured by further executable instructions to rank the plurality of remedial resources according to one of a priority or a preference.
17. The system of claim 11, wherein the one or more processors are configured by further executable instructions to rank the plurality of remedial resources according to a priority.
18. The system of claim 11, wherein the one or more processors are configured by further executable instructions to rank the plurality of remedial resources according to a preference.
23. The system of claim 22, wherein the one or more processors are configured by further executable instructions to determine a second set of one or more additional networked environments associated with the peer group classification based at least partly on user input regarding an adjustment to the peer group classification
12. The system of claim 11, wherein the one or more processors are configured by further executable instructions to identify a peer group classification for the networked environment, wherein the peer group classification comprises a classification of the networked environment among a set of one or more additional networked environments.
13. The system of claim 12, wherein the one or more processors are configured by further executable instructions to receive, via the electronic interface, user input regarding an adjustment to the peer group classification.
24. The system of claim 16, wherein the one or more processors are configured by further executable instructions to generate a roster of metrics of interest, wherein the roster of metrics of interest comprises a subset of the set of one or more network metrics, wherein individual metrics of interest of the roster of metrics of interest satisfy a selection criterion, and wherein individual network metrics of the set of one or more network metrics not included in the roster of metrics of interest do not satisfy the selection criterion.
19. The system of claim 11, wherein the one or more processors are configured by further executable instructions to generate a roster of metrics of interest, wherein the roster of metrics of interest comprises a subset of the set of one or more network metrics, wherein individual metrics of interest of the roster of metrics of interest satisfy a selection criterion, and wherein individual network metrics of the set of one or more network metrics not included in the roster of metrics of interest do not satisfy the selection criterion.
25. The system of claim 24, wherein the one or more processors are configured by further executable instructions to generate a hierarchical representation of the roster of metrics of interest and corresponding remedial resources.
20. The system of claim 19, wherein the one or more processors are configured by further executable instructions to generate a hierarchical representation of the roster of metrics of interest and corresponding remedial resources.
26. The system of claim 16, wherein the one or more processors are configured by further executable instructions to generate a hierarchical representation of metrics of interest paired to potential optimizers (remedial resources) based on criticality rankings of the respective metrics of interest.
17. The system of claim 11, wherein the one or more processors are configured by further executable instructions to rank the plurality of remedial resources according to a priority.
18. The system of claim 11, wherein the one or more processors are configured by further executable instructions to rank the plurality of remedial resources according to a preference.
20. The system of claim 19, wherein the one or more processors are configured by further executable instructions to generate a hierarchical representation of the roster of metrics of interest and corresponding remedial resources.
US Patent 11,671,321 does not teach on “wherein the peer group classification comprises a classification of the networked environment among a set of one or more additional networked environments sharing a commonality associated with the metric of interest” of the instant application.
McGee teaches wherein the subsystem peer group classification comprises a classification of the subsystem environment among a set of one or more additional subsystem environments sharing a commonality (ie. closely related metrics) associated with the metric of interest; (Metric correlation component 116 analyzes pairs of metric values collected from one or more dynamic sampling agent 110. It applies various correlation and regression techniques to determine the statistical relationship between pairs of metrics, and to form groups of closely related metrics. It also tries to determine temporal relationship between metrics, Col 7 ln 7-12.)
It would have been obvious for a person having ordinary skill in the art at the time the claimed invention was made, to modify US Patent 11,671,321 per McGee as this would allow the combined system to utilize the group classification to provide comparisons of the collected network metrics against other collected metrics from the other/additional subsystem environments.
However, US Patent 11,671,321 (as modified by McGee) is silent on wherein the peer group classification comprises a classification of the networked environment among a set of one or more additional networked environments sharing a commonality associated with the metric of interest;
Mayerle teaches wherein the peer group classification (Fig 1, peer group 128) comprises a classification of the networked environment among a set of one or more additional networked environments (Fig 1, Enterprise 1, Enterprise 2, Enterprise N) sharing a commonality (ie. similar business processes); (A peer group controller configured to receive peer group information, a data collector configured to collect first business data corresponding to the enterprise and second business data corresponding to at least one other enterprise according to the peer group information, Abstract. [0022] The peer group 128 may include a number of enterprises, e.g., enterprise 1 to enterprise N. Enterprises in the peer group 128 may have one or more similar business processes 132 or be enterprises in the same business area as each other. See Fig 1, System 100 has peer group 128 which is a classification for networked environments Enterprise 1, Enterprise 2, Enterprise N)
It would have been obvious for a person having ordinary skill in the art at the time the claimed invention was made, to modify US Patent 11,671,321 (as modified by McGee) by modifying McGee per Mayerle as this would allow the combined system to utilize the peer group classification to provide comparisons of the collected network metrics against other collected business data from the peer classification networked environments.
Claims 6-11, 13-21, 23-26 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4, 6-9, 12, 14-16 of U.S. Patent No. 10,958,520 in view of US Patent 6,643,613 McGee.
Instant Application
US Patent 10,958,520
6. A computer-implemented method comprising:
under control of a computing system comprising one or more computing devices configured to execute specific instructions,
determining a set of one or more network metrics based at least partly on network communications among network resources in a networked environment;
determining that a network metric of the set of one or more network metrics is a non-compliant metric based on the network metric of the set of one or more network metrics falling outside a corresponding compliance range of values;
generating a normalized score for the non-compliant metric based at least partly on a degree to which the non-compliant metric deviates from the corresponding compliance range of values;
identifying the non-compliant metric as a metric of based at least partly on the normalized score for the non-compliant metric;
identifying a peer group classification for the networked environment based at least partly on the metric of interest, wherein the peer group classification comprises a classification of the networked environment among a set of one or more additional networked environments sharing a commonality associated with the metric of interest;
and displaying, through an electronic interface, data regarding the peer group classification.
1. A method comprising:
under control of a computing system comprising one or more computing devices configured to execute specific instructions,
identifying network resources in a networked environment, wherein the network resources comprise a plurality of network devices and a plurality of applications; monitoring network communications among the network resources, the monitoring the network communications including collecting network metrics;
evaluating each network metric of the network metrics against a corresponding compliance range of values, wherein a compliant metric falls within the corresponding compliance range of values, wherein a non-compliant metric falls outside the corresponding compliance range of values, and wherein non-compliant metrics indicate corresponding deficiencies in the networked environment;
generating a normalized score for individual non-compliant metrics of the non- compliant metrics based at least partly on a degree to which the individual non-compliant metrics deviate from their corresponding compliance range of values;
identifying a metric of interest from the non-compliant metrics based at least partly on the normalized scores for the individual non-compliant metrics;
6. The method of claim 1, further comprising
identifying a peer group classification for the networked environment, wherein the peer group classification comprises a classification of the networked environment among the one or more additional networked environments.
associating a deficiency, corresponding to the metric of interest, with a plurality of remedial resources for correcting the deficiency;
determining, for each remedial resource of the plurality of remedial resources, a respective relevancy score representing a degree of responsiveness to correct the deficiency;
determining a comparison property associated with the metric of interest for one or more additional networked environments separate from the networked environment;
and displaying, through an electronic interface, data regarding the metric of interest, the plurality of remedial resources, and the comparison property.
7. The computer-implemented method of claim 6, further comprising determining a comparison property associated with the metric of interest for the set of one or more additional networked environments.
6. The method of claim 1, further comprising
identifying a peer group classification for the networked environment, wherein the peer group classification comprises a classification of the networked environment among the one or more additional networked environments.
8. The computer-implemented method of claim 6, further comprising identifying the non-compliant metric as the metric of interest based at least partly on the metric of interest being associated with a deficiency.
Claim 1. … identifying a metric of interest from the non-compliant metrics based at least partly on the normalized scores for the individual non-compliant metrics; associating a deficiency, corresponding to the metric of interest, with a plurality of remedial resources for correcting the deficiency;
9. The computer-implemented method of claim 8, further comprising determining, for each remedial resource of a plurality of remedial resources, a respective relevancy score representing a degree of responsiveness to correct the deficiency.
Claim 1. … determining, for each remedial resource of the plurality of remedial resources, a respective relevancy score representing a degree of responsiveness to correct the deficiency;
10. The computer-implemented method of claim 9, further comprising displaying, through the electronic interface, data regarding the metric of interest and the plurality of remedial resources.
Claim 1 … displaying, through an electronic interface, data regarding the metric of interest, the plurality of remedial resources, and the comparison property.
11. The computer-implemented method of claim 9, further comprising ranking the plurality of remedial resources according to one of a priority or a preference.
4. The method of claim 1, further comprising ranking the plurality of remedial resources according to a priority.
13. The computer-implemented method of claim 12, further comprising determining a second set of one or more additional networked environments associated with the peer group classification based at least partly on the adjustment to the peer group classification.
6. The method of claim 1, further comprising identifying a peer group classification for the networked environment, wherein the peer group classification comprises a classification of the networked environment among the one or more additional networked environments.
14. The computer-implemented method of claim 6, further comprising generating a roster of metrics of interest, wherein the roster of metrics of interest comprises a subset of the set of one or more network metrics, wherein individual metrics of interest of the roster of metrics of interest satisfy a selection criterion, and wherein individual network metrics of the set of one or more network metrics not included in the roster of metrics of interest do not satisfy the selection criterion.
7. The method of claim 1, further comprising
generating a roster of metrics of interest, wherein the roster of metrics of interest comprises a subset of the network metrics,
wherein individual metrics of interest of the roster of metrics of interest satisfy a selection criterion, and wherein individual network metrics of the network metrics not included in the roster of metrics of interest do not satisfy the selection criterion.
15. The computer-implemented method of claim 14, further comprising generating a hierarchical representation of the roster of metrics of interest and corresponding remedial resources.
8. The method of claim 7, further comprising generating a hierarchical representation of the roster of metrics of interest and corresponding remedial resources.
16. A system comprising: computer-readable memory storing executable instructions; and one or more processors in communication with the computer-readable memory and configured by the executable instructions to at least:
determine a set of one or more network metrics based at least partly on network communications among network resources in a networked environment;
determine that a network metric of the set of one or more network metrics is a non-compliant metric based on the network metric of the set of one or more network metrics falling outside a corresponding compliance range of values;
generate a normalized score for the non-compliant metric based at least partly on a degree to which the non-compliant metric deviates from the corresponding compliance range of values;
identify the non-compliant metric as a metric of based at least partly on the normalized score for the non-compliant metric;
identify a peer group classification for the networked environment based at least partly on the metric of interest, wherein the peer group classification comprises a classification of the networked environment among a set of one or more additional networked environments sharing a commonality associated with the metric of interest;
and display, through an electronic interface, data regarding the peer group classification.
9. A system comprising: computer-readable memory storing executable instructions; and one or more processors in communication with the computer-readable memory and configured by the executable instructions to at least:
identify network resources in a networked environment, wherein the network resources comprise a plurality of network devices and a plurality of applications;
determine network metrics based on monitoring network communications among the network resources;
evaluate each network metric of the network metrics against a corresponding compliance range of values, wherein a compliant metric falls within the corresponding compliance range of values, wherein a non-compliant metric falls outside the corresponding compliance range of values, and wherein non-compliant metrics indicate corresponding deficiencies in the networked environment;
generate a normalized score for individual non-compliant metrics based at least partly on a degree to which the individual non-compliant metrics deviate from their corresponding compliance range of values;
identify a metric of interest from the non-compliant metrics based at least partly on the normalized scores for the individual non-compliant metrics;
14. The system of claim 9, wherein the one or more processors are further configured by the executable instructions to identify a peer group classification for the networked environment, wherein the peer group classification comprises a classification of the networked environment among the one or more additional networked environments.
associate a deficiency, corresponding to the metric of interest, with a plurality of remedial resources for correcting the deficiency;
determine, for each remedial resource of the plurality of remedial resources, a respective relevancy score representing a degree of responsiveness to correct the deficiency;
determine a comparison property associated with the metric of interest for one or more additional networked environments separate from the networked environment;
and display, through an electronic interface, data regarding the metric of interest, the plurality of remedial resources, and the comparison property.
17. The system of claim 16, wherein the one or more processors are configured by further executable instructions to determine a comparison property associated with the metric of interest for the set of one or more additional networked environments.
14. The system of claim 9, wherein the one or more processors are further configured by the executable instructions to identify a peer group classification for the networked environment, wherein the peer group classification comprises a classification of the networked environment among the one or more additional networked environments.
18. The system of claim 16, wherein the one or more processors are configured by further executable instructions to identify the non-compliant metric as a metric of interest based at least partly on a deficiency.
Claim 9. … identify a metric of interest from the non-compliant metrics based at least partly on the normalized scores for the individual non-compliant metrics; associate a deficiency, corresponding to the metric of interest, with a plurality of remedial resources for correcting the deficiency;
19. The system of claim 18, wherein the one or more processors are configured by further executable instructions to determine, for each remedial resource of a plurality of remedial resources, a respective relevancy score representing a degree of responsiveness to correct the deficiency.
Claim 9. … determine, for each remedial resource of the plurality of remedial resources, a respective relevancy score representing a degree of responsiveness to correct the deficiency;
20. The system of claim 19, wherein the one or more processors are configured by further executable instructions to display, through the electronic interface, data regarding the metric of interest and the plurality of remedial resources.
Claim 9…. display, through an electronic interface, data regarding the metric of interest, the plurality of remedial resources, and the comparison property.
21. The system of claim 19, wherein the one or more processors are configured by further executable instructions to rank the plurality of remedial resources according to one of a priority or a preference.
12. The system of claim 9, wherein the one or more processors are further configured by the executable instructions to rank the plurality of remedial resources according to a priority.
23. The system of claim 22, wherein the one or more processors are configured by further executable instructions to determine a second set of one or more additional networked environments associated with the peer group classification based at least partly on user input regarding an adjustment to the peer group classification
14. The system of claim 9, wherein the one or more processors are further configured by the executable instructions to identify a peer group classification for the networked environment, wherein the peer group classification comprises a classification of the networked environment among the one or more additional networked environments.
24. The system of claim 16, wherein the one or more processors are configured by further executable instructions to generate a roster of metrics of interest, wherein the roster of metrics of interest comprises a subset of the set of one or more network metrics, wherein individual metrics of interest of the roster of metrics of interest satisfy a selection criterion, and wherein individual network metrics of the set of one or more network metrics not included in the roster of metrics of interest do not satisfy the selection criterion.
15. The system of claim 9, wherein the one or more processors are further configured by the executable instructions to generate a roster of metrics of interest,
wherein the roster of metrics of interest comprises a subset of the network metrics,
wherein individual metrics of interest of the roster of metrics of interest satisfy a selection criterion, and wherein individual network metrics of the network metrics not included in the roster of metrics of interest do not satisfy the selection criterion.
25. The system of claim 24, wherein the one or more processors are configured by further executable instructions to generate a hierarchical representation of the roster of metrics of interest and corresponding remedial resources.
16. The system of claim 15, wherein the one or more processors are further configured by the executable instructions to generate a hierarchical representation of the roster of metrics of interest and corresponding remedial resources.
26. The system of claim 16, wherein the one or more processors are configured by further executable instructions to generate a hierarchical representation of metrics of interest paired to potential optimizers (remedial resources) based on criticality rankings of the respective metrics of interest.
12. The system of claim 9, wherein the one or more processors are further configured by the executable instructions to rank the plurality of remedial resources according to a priority.
16. The system of claim 15, wherein the one or more processors are further configured by the executable instructions to generate a hierarchical representation of the roster of metrics of interest and corresponding remedial resources.
US Patent 10,958,520 does not teach on “wherein the peer group classification comprises a classification of the networked environment among a set of one or more additional networked environments sharing a commonality associated with the metric of interest” of the instant application.
McGee teaches wherein the subsystem peer group classification comprises a classification of the subsystem environment among a set of one or more additional subsystem environments sharing a commonality (ie. closely related metrics) associated with the metric of interest; (Metric correlation component 116 analyzes pairs of metric values collected from one or more dynamic sampling agent 110. It applies various correlation and regression techniques to determine the statistical relationship between pairs of metrics, and to form groups of closely related metrics. It also tries to determine temporal relationship between metrics, Col 7 ln 7-12.)
It would have been obvious for a person having ordinary skill in the art at the time the claimed invention was made, to modify US Patent 10,958,520 per McGee as this would allow the combined system to utilize the group classification to provide comparisons of the collected network metrics against other collected metrics from the other/additional subsystem environments.
However, US Patent 10,958,520 (as modified by McGee) is silent on wherein the peer group classification comprises a classification of the networked environment among a set of one or more additional networked environments sharing a commonality associated with the metric of interest;
Mayerle teaches wherein the peer group classification (Fig 1, peer group 128) comprises a classification of the networked environment among a set of one or more additional networked environments (Fig 1, Enterprise 1, Enterprise 2, Enterprise N) sharing a commonality (ie. similar business processes); (A peer group controller configured to receive peer group information, a data collector configured to collect first business data corresponding to the enterprise and second business data corresponding to at least one other enterprise according to the peer group information, Abstract. [0022] The peer group 128 may include a number of enterprises, e.g., enterprise 1 to enterprise N. Enterprises in the peer group 128 may have one or more similar business processes 132 or be enterprises in the same business area as each other. See Fig 1, System 100 has peer group 128 which is a classification for networked environments Enterprise 1, Enterprise 2, Enterprise N)
It would have been obvious for a person having ordinary skill in the art at the time the claimed invention was made, to modify US Patent 10,958,520 (as modified by McGee) by modifying McGee per Mayerle as this would allow the combined system to utilize the peer group classification to provide comparisons of the collected network metrics against other collected business data from the peer classification networked environments.
Claim 12 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of US Patent 10,958,520 in view of US Patent 6,643,613 McGee further in view of US PGPub 2013/0346161 (Mayerle).
OBVIOUSNESS
Cited Prior Art
12. The computer-implemented method of claim 6, further comprising receiving, via the electronic interface, user input regarding an adjustment to the peer group classification.
US PGPub 2013/0346161 (Mayerle) [0022] The peer group 128 may include a number of enterprises, e.g., enterprise 1 to enterprise N. Enterprises in the peer group 128 may have one or more similar business processes 132 or be enterprises in the same business area as each other. As further explained below, the peer group 128 may be selectable or adjustable by the user of the cloud benchmarking service 104 by industry, Standard Industrial Classification (SIC) code, region, country, size, and number/type of enterprises
US Patent 10,958,520 (as modified by McGee) does not teach on Claim 12 of the instant application.
Mayerle teaches on these claims (see table above).
It would have been obvious for a person having ordinary skill in the art at the time the claimed invention was made, to modify US Patent 10,958,520 (as modified by McGee) by modifying US Patent 10,958,520 per Mayerle as this would allow the combined system to utilize the peer group classification to provide comparisons of the collected network metrics against other collected business data that share metric commonalities.
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 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.
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