DETAILED ACTION
This action is responsive to application filed on July 02, 2025.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 4 and 20 are objected to because of the following informalities:
In claim 4 “cause the processor to: causing a change in a configuration”, should read “cause a change in a configuration”; “the automated component” should read “the first automated component”
In claim 20 “cause the processor to identifying” should read “cause the processor to identify”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4, 11 and 18 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 4, 11 and 18 recite the limitation “the performance target”. There is no antecedent basis for “the performance target”. The metes and bounds of the limitation cannot be ascertained, and it is indefinite. For the purpose of examination, “the performance target” is interpreted as a desired level of performance toward which the configuration change is directed.
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 filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual 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/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,387,114 in view of Baumgartner (US Patent Application Publication No. US 20150120901 A1) as to the monitoring limitations, and further in view of Kesin (US Patent Application Publication No. US 20200329064 A1) as to claims 5-7, 12-14 and 19-20.
Instant Application
Patent No. 12,387,114
1. A system comprising: a processor; and a memory device storing programming instructions structured to cause the processor to: monitor performance of a first entity to generate first data, monitor performance of a second entity to generate second data,
determine, based on the first data, a first performance feature value of a first performance feature with respect to the first entity, determine, based on the second data, a second performance feature value of the first performance feature with respect to the second entity,
rank a performance of the first entity and a performance of the second entity based at least on the first performance feature value and the second performance feature value, a rank of the first entity being lower than a rank of the second entity;
identify a performance issue with respect to the first entity based on the rank of the first entity being lower than the rank of the second entity and a comparison of the first performance feature value to the second performance feature value, and
cause an action to be performed with respect a first automated component associated with the first entity to mitigate the performance issue.
1. A system, comprising: a processor; and a memory device that stores program code structured to be executed by the processor, the program code comprising: a ranking component that:
for each automated component within groups of automated components, determines a performance score based on performance feature values of performance features of the automated component and feature importance values of the performance features,
and for each group of automated components, generates a ranking of automated components within the group based on respective performance scores thereof, wherein each group of automated components comprises a respective high ranked automated component and a respective low ranked automated component; and a rank interpreter,
for a first group of the groups: determines a performance target for the respective low ranked automated component based on a comparison of a first performance feature value of a performance feature with respect to the respective high ranked automated component and a second performance feature value of the performance feature with respect to the respective low ranked automated component, and
performs an action based on the determined performance target to improve the performance of the respective low ranked automated component.
8. A computer-implemented method for improving the performance of an entity, the method comprising:
monitoring performance of a first entity to generate first data; monitoring performance of a second entity to generate second data;
determining, based on the first data, a first performance feature value of a first performance feature with respect to the first entity; determining, based on the second data, a second performance feature value of the first performance feature with respect to the second entity;
ranking a performance of the first entity and a performance of the second entity based at least on the first performance feature value and the second performance feature value, a rank of the first entity being lower than a rank of the second entity;
identifying a first performance issue with respect to the first entity based on the rank of the first entity being lower than the rank of the second entity and a comparison of the first performance feature value to the second performance feature value; and
causing an action to be performed with respect the first entity to mitigate the first performance issue.
10. A computer-implemented method for identifying and resolving performance issues of automated components, comprising:
determining a first performance score for a first automated component based on a first performance feature value of a performance feature with respect to the first automated component and a feature importance value of the performance feature; determining a second performance score for a second automated component based on a second performance feature value of the performance feature with respect to the second automated component and the feature importance value;
ranking the first and second automated components based on the first and second performance scores, wherein the first automated component has a higher rank than the second automated component;
determining a performance target for the second automated component based on a comparison of the first performance feature value to the second performance feature value; and
performing an action based on the performance target to improve the performance of the second automated component.
15. A rank interpretation system, comprising: a processor; and a memory device that stores program code structured to cause the processor to:
receive a first rank of a first performance of a first entity and a second rank of a second performance of a second entity, the first rank lower than the second receive a first performance feature value of a first performance feature with respect to the first entity, receive a second performance feature value of the first performance feature with respect to the second entity,
identify a first performance issue with respect to the first entity based on the rank of the first entity being lower than the rank of the second entity and a comparison of the first performance feature value to the second performance feature value, and causes an action to be performed with respect the first entity to mitigate the first performance issue.
15. A rank interpretation system, comprising: a processor; and a memory device that stores program code structured to cause the processor to:
receive a first performance feature value of a performance feature with respect to a first automated component of a group of automated components, the first automated component having a first performance score determined based on the first performance feature value and a feature importance value of the performance feature, receive a second performance feature value of the performance feature with respect to a second automated component of the group of automated components, the second automated component having a second performance score determined based on the second performance feature value and the feature importance value, the second performance score lower than the first performance score,
determine a performance target for the second automated component based on a comparison of the first performance feature value to the second performance feature value, and perform an action based on the determined performance target to cause a performance of the second automated component to be improved.
Claims 1, 8 and 15 are a broader version of claims 1, 10 and 15 of US Pat. 12,387,114, respectively; however, this patent also fail to particularly show the limitations not in bold above, as this being the only difference between the claims.
However, Baumgartner teaches “monitor performance of a first entity to generate first data, monitor performance of a second entity to generate second data” (See Baumgartner [0022, 0038, 0044-0045] Disclosing continuously monitoring performance characteristics of computing entities and collecting operational metrics for further analysis.
Therefore, it would have been obvious to a person having ordinary skills in the art to obtain the performance feature values of the patented invention using Baumgartner’s conventional monitoring techniques because the patented ranking process necessarily requires collection of performance information before ranking and comparison can occur.
Claim 2
Claim 1
Claim 3
Claim 1
Claim 4
Claim 2
Claims 5, 12 and 19. determine, based on the first data, a third performance feature value of a second performance feature with respect to the first entity; determine, based on the second data, a fourth performance feature value of the second performance feature with respect to the second entity; and
generate an entity group comprising the first entity and the second entity based on a comparison of the third performance feature value and the fourth performance feature value.
Kesin teaches determining multiple performance metrics for respective computing entities and grouping entities according to comparison of corresponding performance characteristics rather than clustering alone.
Claims 1, 10 and 15. ranking component that: for each automated component within groups of automated components, determines a performance score based on performance feature values of performance features of the automated component and feature importance values of the performance features, and for each group of automated components, generates a ranking of automated components within the group based on respective performance scores thereof,
The patented claims already group automated components before ranking. Kesin [0005-0006, 0066, 0081, 0097-0099] teaches using an additional performance feature comparison to establish the grouping relationship.
Therefore, it would have been obvious to employ Kesin’s known grouping criterion as another predictable grouping mechanism while preserving the patented ranking and performance-target determination.
Claim 6
Claim 1
Claim 7
Claim 3
Claim 9
Claim 10
Claim 10
Claim 10
Claim 11
Claim 11
Claim 13
Claim 10
Claim 14
Claim 12
Claim 16
Claim 15
Claim 17
Claim 15
Claim 18
Claim 16
Claim 20
Claim 15
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,056,620 in view of Baumgartner (US Patent Application Publication No. US 20150120901 A1) as to the monitoring limitations, and further in view of Kesin (US Patent Application Publication No. US 20200329064 A1) as to claims 5-7, 12-14 and 19-20.
Instant Application
Patent No. 12,056,620
1. A system comprising: a processor; and a memory device storing programming instructions structured to cause the processor to: monitor performance of a first entity to generate first data, monitor performance of a second entity to generate second data,
determine, based on the first data, a first performance feature value of a first performance feature with respect to the first entity, determine, based on the second data, a second performance feature value of the first performance feature with respect to the second entity,
rank a performance of the first entity and a performance of the second entity based at least on the first performance feature value and the second performance feature value, a rank of the first entity being lower than a rank of the second entity;
identify a performance issue with respect to the first entity based on the rank of the first entity being lower than the rank of the second entity and a comparison of the first performance feature value to the second performance feature value, and
cause an action to be performed with respect a first automated component associated with the first entity to mitigate the performance issue.
1. A system, comprising: one or more processors; and one or more memory devices that store program code to be executed by the one or more processors, the program code comprising: a clustering component configured to:
for each of a plurality of automated components, receive a respective set of segmentation feature values corresponding to a set of segmentation features;
and segment the plurality of automated components into groups by applying a K-means clustering algorithm to the plurality of automated components based on the segmentation feature values associated therewith, wherein applying the K- means clustering algorithm comprises initializing a set of cluster centroids used in applying the K-means clustering algorithm by applying a set of context rules to the plurality of automated components;
a ranking component configured to rank a performance of the automated components within each of the groups by:… for each group of automated components, generating a ranking of the automated components within the group based on the respective performance scores thereof, wherein each group of automated components comprises a respective high ranked automated component and a respective low ranked automated component;
a rank interpreter configured to, for a group of the groups: determine a performance target for the respective low ranked automated component based on a comparison of respective performance feature values of a performance feature of the set of performance features of the respective high ranked automated component and
the respective low ranked automated component, and perform an action based on the determined performance target to improve the performance of the respective low ranked automated component.
8. A computer-implemented method for improving the performance of an entity, the method comprising:
monitoring performance of a first entity to generate first data; monitoring performance of a second entity to generate second data;
determining, based on the first data, a first performance feature value of a first performance feature with respect to the first entity; determining, based on the second data, a second performance feature value of the first performance feature with respect to the second entity;
ranking a performance of the first entity and a performance of the second entity based at least on the first performance feature value and the second performance feature value, a rank of the first entity being lower than a rank of the second entity;
identifying a first performance issue with respect to the first entity based on the rank of the first entity being lower than the rank of the second entity and a comparison of the first performance feature value to the second performance feature value; and
causing an action to be performed with respect the first entity to mitigate the first performance issue.
14. A computer-implemented method for identifying and resolving performance issues of automated components, comprising:
for each of a plurality of automated components, receiving a respective set of segmentation feature values corresponding to a set of segmentation features and a respective set of performance feature values corresponding to a set of performance features;
segmenting the plurality of automated components into groups by applying a clustering algorithm to the plurality of automated components based on the segmentation feature values respectively associated therewith; and
ranking a performance of the automated components within each of the groups by:… for each group of automated components, generating a ranking of the automated components within the group based on the respective performance scores thereof, wherein each group of automated components comprises a respective high ranked automated component and a respective low ranked automated component; and
for a group of the groups: determining a performance target for the respective low ranked automated component based on a comparison of respective performance feature values of a performance feature of the set of performance features of the respective high ranked automated component and the respective low ranked automated component, and
performing an action based on the determined performance target to improve the performance of the respective low ranked automated component.
15. A rank interpretation system, comprising: a processor; and a memory device that stores program code structured to cause the processor to:
receive a first rank of a first performance of a first entity and a second rank of a second performance of a second entity, the first rank lower than the second receive a first performance feature value of a first performance feature with respect to the first entity, receive a second performance feature value of the first performance feature with respect to the second entity,
identify a first performance issue with respect to the first entity based on the rank of the first entity being lower than the rank of the second entity and a comparison of the first performance feature value to the second performance feature value, and
causes an action to be performed with respect the first entity to mitigate the first performance issue.
1. A system, comprising: one or more processors; and one or more memory devices that store program code to be executed by the one or more processors, the program code comprising: a clustering component configured to:
for each of a plurality of automated components, receive a respective set of segmentation feature values corresponding to a set of segmentation features;
and segment the plurality of automated components into groups by applying a K-means clustering algorithm to the plurality of automated components based on the segmentation feature values associated therewith, wherein applying the K- means clustering algorithm comprises initializing a set of cluster centroids used in applying the K-means clustering algorithm by applying a set of context rules to the plurality of automated components;
a ranking component configured to rank a performance of the automated components within each of the groups by:… for each group of automated components, generating a ranking of the automated components within the group based on the respective performance scores thereof, wherein each group of automated components comprises a respective high ranked automated component and a respective low ranked automated component;
a rank interpreter configured to, for a group of the groups: determine a performance target for the respective low ranked automated component based on a comparison of respective performance feature values of a performance feature of the set of performance features of the respective high ranked automated component and
the respective low ranked automated component, and perform an action based on the determined performance target to improve the performance of the respective low ranked automated component.
Claims 1, 8 and 15 are a broader version of claims 1, 10 and 15 of US Pat. 12,056,620; however, this patent also fail to particularly show the limitations not in bold above, as this being the only difference between the claims.
However, Baumgartner teaches “monitor performance of a first entity to generate first data, monitor performance of a second entity to generate second data” (See Baumgartner [0022, 0038, 0044-0045] Disclosing continuously monitoring performance characteristics of computing entities and collecting operational metrics for further analysis.
Therefore, it would have been obvious to a person having ordinary skills in the art to obtain the performance feature values of the patented invention using Baumgartner’s conventional monitoring techniques because the patented ranking process necessarily requires collection of performance information before ranking and comparison can occur.
Claim 2
Claim 6
Claim 3
Claim 1
Claim 4
Claim 7
Claims 5, 12 and 19. determine, based on the first data, a third performance feature value of a second performance feature with respect to the first entity; determine, based on the second data, a fourth performance feature value of the second performance feature with respect to the second entity; and
generate an entity group comprising the first entity and the second entity based on a comparison of the third performance feature value and the fourth performance feature value.
Kesin teaches determining multiple performance metrics for respective computing entities and grouping entities according to comparison of corresponding performance characteristics rather than clustering alone.
Claims 1 and 14. segment the plurality of automated components into groups by applying a K-means clustering algorithm to the plurality of automated components based on the segmentation feature values associated therewith… a ranking component configured to rank a performance of the automated components within each of the groups by: for each of the plurality of automated components, receive a respective set of performance feature values corresponding to a set of performance features;
The patented claims already group automated components before ranking. Kesin [0005-0006, 0066, 0081, 0097-0099] teaches using an additional performance feature comparison to establish the grouping relationship.
Therefore, it would have been obvious to employ Kesin’s known grouping criterion as another predictable grouping mechanism while preserving the patented ranking and performance-target determination.
Claim 6
Claim 1
Claim 7
Claim 1
Claim 9
Claim 17
Claim 10
Claim 14
Claim 11
Claim 18
Claim 13
Claim 14
Claim 14
Claim 14
Claim 16
Claim 6
Claim 17
Claim 1
Claim 18
Claim 7
Claim 20
Claim 1
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.
Claims 1-3, 8-10 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Baumgartner (US Patent Application Publication No. US 20150120901 A1), in view of Minh (US Patent Application Publication No. US 20170316005 A1), in view of Zeng (US Patent Application Publication No. US 20220240123 A1).
Regarding claim 1, Baumgartner teaches a system comprising: a processor; and a memory device storing programming instructions structured to cause the processor to: monitor performance of a first entity to generate first data, (See Baumgartner [0022, 0038, 0044-0045] “ Performance device 270 may include a device capable of monitoring the performance of one or more devices associated with network 220… performance device 270 may determine the performance information [e.g. data] associated with base station 230 and user devices 210 [e.g. first entity]… performance device 270 may parse the connection records to determine values for each performance indicator… for one or more user devices 210 associated with base station 230… performance device 270 may determine a performance record. [Thus, monitor performance of a first entity to generate first data]”)
monitor performance of a second entity to generate second data, (See Baumgartner [0045] “The performance record [e.g. first, second data]may include performance information associated with… user devices 210 [e.g. second entity].” See also Baumgartner [0067, 0078-0079] “FIG. 5 is a diagram of an example data structure that stores performance information [e.g. first, second data]… The second row in data structure 500 of FIG. 5 may correspond to a first user device 210-1[e.g. second entity]… [which] may have experienced a total quantity of calls (e.g., “1”), a quantity of attach failures (e.g., “1”)… The third row in data structure 500 of FIG. 5 may correspond to a second user device 210-2 [e.g. first entity] … [which] may have experienced a total quantity of calls (e.g., “1”), a quantity of attach failures (e.g., “0”) [Thus, the first and second data are the performance information/performance records that performance device 270 generates for each device 210-2 and `210-1 respectively]”)
determine, based on the first data, a first performance feature value of a first performance feature with respect to the first entity, (See Baumgartner [0086] “performance device 270 may determine a performance problem based on the performance information [e.g. based on the first data]… 12 [e.g. first value] dropped calls [e.g. first performance feature] were associated with second user device 210-2 [Thus, with respect to the first entity] (e.g., second user device 210-2 was associated with 100% of the dropped calls)”)
determine, based on the second data, a second performance feature value of the first performance feature with respect to the second entity, (See Baumgartner [0083] “As shown by reference number 630, the first connection record [e.g. based on the second data] may indicate that… first user device 210-1 [Thus, with respect to the second entity] was associated with 0 handovers, 0 dropped handovers, 1 call, and 0 [e.g. second value] dropped calls [e.g. first performance feature]”)
rank a performance of the first entity and a performance of the second entity based at least on the first performance feature value and the second performance feature value, a rank of the first entity being lower than a rank of the second entity;
Baumgartner [0083, 0086] compares the two entities’ values of the feature to order them by performance where for example device 210-2 is 100% of the dropped calls and device 210-1 is 0%.
Baumgartner compares the two values but does not explicitly disclose assigning a rank to each entity.
However, Minh teaches ranking monitored entities by their performance attribute values. (See Minh [0003, 0026] “sample values of attributes [e.g. a first performance feature value and a second performance feature value] for entities running on a monitored environment… sort the entities [e.g. first, second entities] by the combined value based on the sort order to assign an entity rank for each entity… The sort order can indicate whether smaller values should be ranked lower or higher.” See also Minh [0023, 0048] “the machines in a node can be ranked to determine which machine is using the most (or least) amount of resources…” Thus, assigns each entity a numbered rank from its attribute values [e.g. based at least on a first performance feature value and a second performance feature value]. Thus, a rank of the first entity being lower than a rank of the second entity [Thus, a first and a second rank].)
Both Baumgartner and Minh monitor pluralities of like entities and quantify each entity’s performance from monitored feature values; applying Minh ranking to Baumgartner’s already-computer per-device values is the use of a known technique (assigning ranks by sorting metric values) to improve a similar known system (per-device performance comparison), yielding predictable results of an ordered list of devices.
One of ordinary skills in the art would have been motivated to do so because Minh teaches that ranking “can improve troubleshooting by allowing the user to focus on the top offenders” [Minh 0016] and prioritize which underperforming device to address with a reasonably expectation of success since both operate on the same kind of per-entity metric values.
Baumgartner further in view of Minh, [hereinafter Baumgartner-Minh] additionally disclose identify a performance issue with respect to the first entity based on the rank of the first entity being lower than the rank of the second entity and a comparison of the first performance feature value to the second performance feature value, and (Baumgartner teaches identifying the performance issue with respect to the first entity based on the comparison of the entities’ values of the performance feature. See Baumgartner [0053, 0062-0063] “performance device 270 may determine the performance problem based on performance information associated with base station 230… performance device 270 may determine the extent to which user device 210 is associated with the performance problem… performance device 270 may determine that a particular user device 210 is associated with the problem (e.g., that 91% of the dropped calls are associated with the particular user device 210)” See also Baumgartner Fig. 6B
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Thus, in the example of Fig. 6B, the issue is identified with respect to device 210-2 [e.g. first entity] because its 12 dropped calls (100%) compared to device 210-1’s 0%.
Baumgartner does not explicitly teach that the identification is also based on the first entity’s rank being lower than the second entity’s rank.
However, Zeng teaches identify a performance issue with respect to the first entity based on the rank of the first entity being lower than the rank of the second entity in more details. (See Zeng [0026, 0038] teaches that performance of serving cells of the network [e.g. entities] are measured by the KPI samples of the user sessions it serves. See also Zeng [0008, 0038-0041] “The network analysis platform can then rank the cells [e.g. first, second entity] and nominate a certain number of worst-ranked cells [e.g. first entity] within the whole grid for remediation… The nominated serving cells can then be targeted for remedial actions. Adjusting these cells can potentially fix the problem(s) identified in stage 120 for the entire grid. [Thus, the first entity reads on the “worst-ranked” nominated cell and the second entity on a better-ranked cell that is not nominated, thus the first entity’s rank is inferior to its peer’s.]” See also Zeng [0007] teaches that the problem entity’s rank is the “lower” one “the weights can instead be lowest in another example that distinguishes poor performance with lower weights.”)
Baumgartner and Zeng both identify poorly performing entities from monitored performance values. Applying Zeng’s worst-ranked identification to Baumgartner’s per-device values is the predictable use of a known technique, and one of ordinary skills would have been motivated to do so because Zeng teaches it and replaces “very manual” problem identification [Zeng 0003] and enables automatic remediation of the entities so identified [Zeng 0044].
Baumgartner-Minh further in view of Zeng, [hereinafter Baumgartner-Minh-Zeng] additionally disclose cause an action to be performed with respect a first automated component associated with the first entity to mitigate the performance issue. (See Baumgartner [0065] “performance device 270 may provide a notification to user device 210 (e.g., the particular user device 210 associated with the performance problem)… performance device 270 may prevent user device 210 from accessing network 220 (e.g., may drop user device 210, may block user device 210, etc.). [Thus, cause an action to be performed with respect a first automated component associated with the first entity to mitigate the performance issue]”
Thus, the user device 210-2 is an automated component (the specification paragraph [0024] identifies “a telecommunication device in a telecommunications network” as an automated component), and is associated with the first entity, the claim’s “associated with” encompassing the component being the monitored entity itself, consistent with the specification’s [0006, 0024-0025] embodiments in which the monitored and ranked entities are the automated components. Alternatively, the entity is the endpoint identified by the device identifier of field 510 ([0069, 0078-0079]) and the associated automated component is the physical device hardware acted upon ([0016, 0065]).
Zeng further teaches the action performed on a component associated with the identified entity. See Zeng [0009, 0044-0045] “the network analysis platform can automate the fixes by sending commands to the nominated cells… the network analysis platform can automatically attempt to apply the remedial action, such as by making application programming interface (“API”) calls to interfaces for the nominated serving cells… The remedial action can include changing one or more of antenna tilt, transmission power, and load balancing parameters”)
Regarding claim 2, Baumgartner-Minh-Zeng teaches all limitations and motivations of claim 1, wherein to identify the performance issue with respect to the first entity, the programming instructions are further structured to cause the processor to: identify the performance issue with respect to the first automated component. (See Baumgartner [0063-0064] “Based on the performance information associated with user devices 210… may determine that a particular user device 210 is associated with the problem (e.g., that 91% of the dropped calls are associated with the particular user device 210)… process 400 may include providing information that identifies the user device [e.g. identify the performance issue with respect to the first automated component]”)
Regarding claim 3, Baumgartner-Minh-Zeng teaches all limitations and motivations of claim 2, wherein to identify the performance issue with respect to the first automated component, the programming instructions are further structured to cause the processor to: determine a difference in a performance of the first automated component and a performance of a second automated component associated with the second entity. (See Baumgartner [0086, 0083] determines the per-device performance difference: 210-2 with “12 dropped calls… 100% of the dropped calls” versus 210-1 with “0 dropped calls” See also Baumgartner Fig. 5, [0078-0079]
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Greyscale
Thus, determine a difference in a performance of the first automated component [e.g. user device 210-2] and a performance of a second automated component [e.g. user device 210-1] associated with the second entity.
Regarding claim 8, Baumgartner-Minh-Zeng teaches all of the elements of claim 1 in system form rather than method form. Therefore, the supporting rationale of the rejection to claim 1 applies equally as well to those elements of claim 8.
Regarding claim 9, Baumgartner-Minh-Zeng teaches all of the elements of claim 2 in system form rather than method form. Therefore, the supporting rationale of the rejection to claim 2 applies equally as well to those elements of claim 9.
Regarding claim 10, Baumgartner-Minh-Zeng teaches all of the elements of claim 3 in system form rather than method form. Therefore, the supporting rationale of the rejection to claim 3 applies equally as well to those elements of claim 10.
Regarding claim 15, Baumgartner-Minh-Zeng teaches all of the elements of claim 1 in system form. Therefore, the supporting rationale of the rejection to claim 1 applies equally as well to those elements of claim 15.
Regarding claim 16, Baumgartner-Minh-Zeng teaches all of the elements of claim 2 in system form. Therefore, the supporting rationale of the rejection to claim 2 applies equally as well to those elements of claim 16.
Regarding claim 17, Baumgartner-Minh-Zeng teaches all of the elements of claim 3 in system form. Therefore, the supporting rationale of the rejection to claim 3 applies equally as well to those elements of claim 17.
Claims 4, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Baumgartner-Minh-Zeng in view of Hussein (US Patent Application Publication No. US 20120087269 A1).
Regarding claim 4, Baumgartner-Minh-Zeng teaches all limitations and motivations of claim 1, wherein to cause the action to be performed with respect to the automated component associated with the first entity, the programming instructions are further structured to cause the processor to: causing a change in a configuration of the automated component based on the performance target. (Zeng [0045] teaches “The remedial action can include changing one or more of antenna tilt, transmission power, and load balancing parameters”)
Baumgartner-Minh-Zeng does not explicitly disclose change the component’s configuration toward a performance target.
However, Hussein teaches changing the component’s configuration toward a performance target. (See Hussein [0048-0049] “configuration modifications are calculated and added to the modification queue in step 212 for application to the wireless network… the critical cell site/sector antenna down tilt will be increased and/or the critical cell site/sector transmitted power will be decreased [e.g. the change in configuration]” See also Hussein abstract, [0012] “altering wireless network parameters of the critical cell sites or sectors, or the best neighbor cells sites or sectors for achieving the desired improvement in communications [e.g. performance target]… determining if the desired improvement in communications has been achieved by altering the wireless network parameters.”)
Baumgartner-Minh-Zeng already identifies the underperforming component; Hussein teaches making the remedial action an automatic, target-verified reconfiguration because manual optimization consumes a high amount of human resources [Hussen 0009], and automated alteration proceeds continuously until the desired improvement is achieved [Hussein 0013], having a reasonable expectation of success given the shared KPI domain which includes KPIs dropped calls rate [Hussein 0029].
Regarding claim 11, Baumgartner-Minh-Zeng in view of Hussein teaches all of the elements of claim 4 in system form rather than method form. Therefore, the supporting rationale of the rejection to claim 4 applies equally as well to those elements of claim 11.
Regarding claim 18, Baumgartner-Minh-Zeng in view of Hussein teaches all of the elements of claim 4 in system form. Therefore, the supporting rationale of the rejection to claim 4 applies equally as well to those elements of claim 18.
Claims 5-7, 12-14 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Baumgartner-Minh-Zeng in view of Kesin (US Patent Application Publication No. US 20200329064 A1).
Regarding claim 5, Baumgartner-Minh-Zeng teaches all limitations and motivations of claim 1, wherein the programming instructions are further structured to cause the processor to: determine, based on the first data, a third performance feature value of a second performance feature with respect to the first entity; (See Baumgartner [0079] device 210-2’s row of data structure 500 includes “a quantity of PDN connection failures (e.g., ‘1’) [e.g. the third performance feature value]… 100% of the PDN connection failures [e.g. of a second performance feature]”, determined from 210-2’s records [0044].)
determine, based on the second data, a fourth performance feature value of the second performance feature with respect to the second entity; and (See Baumgartner [0078] device 210-1’s row of data structure 500 includes “a quantity of attach failures (e.g., ‘1’)” and, for the common second feature, its PDN-failure value (e.g., ‘1’) [e.g. the fourth performance feature value], determined from 210-1’s records [0044].)
generate an entity group comprising the first entity and the second entity based on a comparison of the third performance feature value and the fourth performance feature value.
Baumgartner Fig. 5 teaches a data structure grouping each metric for each entity.
Minh teaches tiers group nodes for treating metrics as a unit. (See Minh [0073] “identical nodes can be grouped into a single tier (such as a cluster of redundant servers). In some implementations, any set of nodes, identical or not, can be grouped [e.g. generate an entity group] for the purpose of treating certain performance metrics as a unit into a single tier.”
Baumgartner-Minh-Zeng does not explicitly generate the group based on a comparison of the two entities’ feature values.
However, Kesin teaches generate the group based on a comparison of the two entities’ feature values, including its own third/fourth values. (See Kesin [0007, 0099] “calculate a plurality of similarity scores… comprising a first similarity score between a first user [e.g. the first entity]… and a second user [e.g. the second entity]… assign, based at least in part on the first similarity score [e.g. the comparison of the third and fourth values], the first user and the second user to a first cohort [e.g. the entity group comprising the first entity and the second entity]… Users 103 a, 103 b, and 103 e are grouped into a first cohort 501.” See also Kesin [0005-0006, 0066, 0097] “sort the plurality of users into a plurality of cohorts based at least in part on the similarity scores… calculating the similarity scores can include a determination of at least one of a cosine similarity score and a Jaccard similarity score… performing an inverse user frequency transform… the users can be divided into cohorts based, at least in part, on the similarity score.. the similarity score can be calculated based on the amount of data transferred to access resource [Thus, the similarity score is a comparison of the two entities’ logged feature values (e.g. comparison of the third performance feature value and the fourth performance feature value)]” See also Kesin [0081] “Applied to the logged user activity shown in table 400 without scale factors, user 103 a and user 103 b both accessed resources 401 and 402 [e.g. the third and fourth performance feature values], so the size of the intersection of resources accessed is 2. The union of resources accessed by user 103 a and user 103 b includes resources 401, 402, and 404, so the size of the union of resources accessed is 3. The similarity score for users 103 a and 103 b would be calculated as ⅔ [Thus, the comparison of the third value to the fourth value].”)
Kesin teaches that cohorts of compatible peers (“a group of network users who perform similar activities… more similar when compared to activities performed by members of a different group [Kesin 0034]“ enable the system to “better determine what is normal… to more accurately detect anomalous activity” with fewer false positives [Kesin 0040].
One of ordinary skills would have applied Kesin’s comparison-based cohort generation to Baumgartner’s monitored devices so that the ranking and issue identification occur within a group of comparable peers; improving accuracy and reducing false positives, with a reasonable expectation of success, as both operate on logged per-entity feature values.
Regarding claim 6, Baumgartner-Minh-Zeng further in view of Kesin, [hereinafter Baumgartner-Minh-Zeng-Kesin] teaches all limitations and motivations of claim 5, wherein to identify the performance issue, the programming instructions are further structured to cause the processor to: identify the performance issue subsequent to generating the entity group. (See Kesin [0108-0110] “At block 701, users can be sorted into a cohort [Thus, generating the entity group]… At block 705, new activity by a cohort member is detected. At block 707, the new activity can be compared to the set of user activity. At block 709, it can be determined if the new activity is within the set of user activity… If the new activity is within the set of user activity, then the new activity is normal. At block 711, new user activity can continue to be monitored and logged. If, on the other hand, the new activity is not within the set of user activity, then the new activity is anomalous. At block 713, a warning can be generated to warn of the anomalous activity [Thus, identify the performance issue subsequent to generating the entity group].”)
Regarding claim 7, Baumgartner-Minh-Zeng-Kesin teaches all limitations and motivations of claim 6, wherein the first entity is the top performing entity of the entity group. (Baumgartner [0078-0079] teaches the first entity being the top performing entity of the group of the second feature within the group of devices, where the first user device 210-1 [e.g. second entity] may have experienced 100% of attached failures [e.g. worst value of the second feature], and the second user device 210-2 [e.g. first entity] experienced 0 [e.g. best value of the second feature] of attached failures [Thus, the top performing entity of the entity group].
See Minh [0023, 0037] teaches that “the machines in a node can be ranked to determine which machine is using the most (or least [e.g. the top performer entity]) amount of resources… ranking technique can be used to identify the lowest resource consumers… identifying the lowest resource consuming entities is to highlight lightly-loaded machines [e.g. the top performer entities of the group]”
See also Kesin generated group (claim 5) contains devices 210-2 and 210-1, where device 210-2 [e.g. first entity] is the group’s top performing entity in the second feature.
Regarding claim 12, Baumgartner-Minh-Zeng in view of Kesin teaches all of the elements of claim 5 in system form rather than method form. Therefore, the supporting rationale of the rejection to claim 5 applies equally as well to those elements of claim 12.
Regarding claim 13, Baumgartner-Minh-Zeng in view of Kesin teaches all of the elements of claim 6 in system form rather than method form. Therefore, the supporting rationale of the rejection to claim 6 applies equally as well to those elements of claim 13.
Regarding claim 14, Baumgartner-Minh-Zeng in view of Kesin teaches all of the elements of claim 7 in system form rather than method form. Therefore, the supporting rationale of the rejection to claim 7 applies equally as well to those elements of claim 14.
Regarding claim 19, Baumgartner-Minh-Zeng in view of Kesin teaches all of the elements of claim 5 in system form. Therefore, the supporting rationale of the rejection to claim 5 applies equally as well to those elements of claim 19.
Regarding claim 20, Baumgartner-Minh-Zeng in view of Kesin teaches all of the elements of claim 6 in system form. Therefore, the supporting rationale of the rejection to claim 6 applies equally as well to those elements of claim 20.
Conclusion
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/OSCAR WEHOVZ/Examiner, Art Unit 2161
/APU M MOFIZ/Supervisory Patent Examiner, Art Unit 2161
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