DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-2, 4-5, 8-9, 11-12, 15-16 and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al., US 20150065121 A1 (hereafter referred to as Gupta) in view of Claridge et al., US 20170127427 A1 (hereafter referred to as Claridge).
Claim 8, Gupta teaches a system comprising:
a traffic orchestrator for a wireless network, the traffic orchestrator comprising:
a set of log receivers comprising a network management log receiver and a call log receiver (p. 25, “the adaptive monitoring manager 132 resides at the source of the CDRs 134 (e.g., the MSC 121 and/or the SGSN 122) and/or the at the source of CDR aggregation (e.g., the server 136).”), wherein the network management log receiver receives network management logs from network nodes of the wireless network (p. 24, from SGSN 122, “A CDR 134 is typically generated by one or more network functions that supervise, monitor, and/or control network access for the device such as the MSC 121 for voice calls and the SGSN 122 for data calls.”), and wherein the call log receiver receives call logs from the network nodes of the wireless network (p. 24, from MSC 121, “A CDR 134 is typically generated by one or more network functions that supervise, monitor, and/or control network access for the device such as the MSC 121 for voice calls and the SGSN 122 for data calls.”);
an orchestration engine operable to receive:
the network management logs and the call logs via the set of log receivers (p. 25, “the adaptive monitoring manager 132 resides at the source of the CDRs 134 (e.g., the MSC 121 and/or the SGSN 122) and/or the at the source of CDR aggregation (e.g., the server 136).”), and traffic patterns of the wireless network (p. 31, “the normal operating characteristic of interest is the normal traffic pattern of voice calls that are observed in a region (e.g., calls placed and received in the area code 914) that has been operated normally. Past network data and/or CDRs collected for that region are analyzed”), network topology of the wireless network (p. 37, “coarse level information can be the originating and terminating base station for a session, whereas finer-level information can be the CDRs per handoff to capture all the base station associations along a user's trajectory.”), and key performance indicators (KPIs) of the wireless network from a database (p. 22, “the network monitor 128 periodically collects network data 120 such as (but not limited to) performance metrics associated with one or more network elements” “The network monitor 128 stores this received data as network data 130.”);
the orchestration engine further operable to:
identify a first network node of the wireless network operating in a degraded manner, wherein identifying the first network node is based upon the network management logs, the call logs, the traffic patterns, the network topology, and the KPIs of the wireless network (p. 28, “Based on these learning operations the adaptive monitoring manager 132 identifies normal operating characteristics/attributes (e.g., behavior) for the network 102 as a whole and/or for one or more of its network elements.”), and
Gupta does not specifically teach generate at least a first traffic advisory for rebalancing traffic in the wireless network to reduce losses to traffic throughput through the first network node; and a traffic advisor operable to distribute at least the first traffic advisory to at least a second network node of the wireless network; in response to receiving the first traffic advisory at least the second network node autonomously updates at least one parameter to reduce traffic throughput through the first network node. However, in the same field of endeavor, Claridge teaches generate at least a first traffic advisory for rebalancing traffic in the wireless network to reduce losses to traffic throughput through the first network node (p. 25, “ … based on the network topology information, the SON system may identify a node that is associated with a potential impairment of network performance … Network Resource 3 being associated with a relatively low throughput as compared to a relatively high throughput of Network Resource 2 …, based on Network Resource 3 being a single point of failure … ”); and a traffic advisor operable to distribute at least the first traffic advisory to at least a second network node of the wireless network (p. 29, “the SON system may recommend adding and/or removing a physical link.” “the SON system may recommend adding the physical link based on Network Resource 1 being associated with a single point of failure for Base Station 2.”);
in response to receiving the first traffic advisory at least the second network node autonomously updates at least one parameter to reduce traffic throughput through the first network node (p. 29, “the SON system improves network performance by reconfiguring a radio access network and a backhaul network of a mobile network, which reduces network downtime, conserves processor resources, conserves storage resources, and conserves bandwidth of base stations and network resources of the mobile network.”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Gupta by incorporating distributing service advisories from Claridge to reduce the negative impact of a prior configuration and thereby improve network performance.
Claim 1 is a method comprising steps similar to the operations of the traffic orchestrator of claim 8 above. Claim 1 is rejected on a similar rationale.
Claim 15 is one or more computer storage devices having computer-executable instructions stored thereon, which, upon execution by a computer (Wang, p. 198-202), cause the computer to perform operations similar to the operations of the traffic orchestrator of claim 8 above. Claim 15 is rejected on a similar rationale.
Claim 9, Gupta-Claridge teaches the system of claim 8, wherein the traffic orchestrator is operable to persist at least the network management logs and the call logs in the database (Gupta, p. 25, “The server 136, in one embodiment, is a datacenter that receives CDRs 134 from a network element such as the MSC 121 and/or the SGSN 122 for billing purposes. ““The server 136, in on embodiment, stores CDRs 134 for a given period of time.”).
Claim 2 is a method comprising steps similar to the operations of the traffic orchestrator
of claim 9 above. Claim 2 is rejected on a similar rationale.
Claim 16 is one or more computer storage devices having computer-executable instructions stored thereon, which, upon execution by a computer to perform operations similar to the operations of claim 9 above. Claim 16 is rejected on the same rationale.
Claim 11, Gupta-Claridge teaches the system of claim 8, wherein the traffic orchestrator is further operable to:
associate call log data from different call logs by subscriber (Gupta, p. 24, “A CDR 134 is a formatted measure of a UE's service usage information (placing a phone call, accessing the Internet, etc.). For example, a CDR 134 includes information related to a telephone voice or data call such as … call termination and error codes; and other details of the call.”);
identify an anomaly in the call log data for a particular subscriber (Gupta, p. 44, “If the result of this determination is negative, the adaptive monitor 132, at step 316, determines if any anomalous behavior has been detected based on a comparison of the information within the received set of CDRs and/or network data and the historical set of CDRs 134 and/or historical set of network data 130.”); and
based on at least identifying the anomaly, generate at least a second traffic advisory to initiate an automatic corrective action for the identified anomaly (Gupta, p. 44, “If the result of this determination is positive, the adaptive monitor 132, at step 318, increases the rate and type of metrics logged by the NMS 126.”) or generate an alert.
Claim 4 is a method comprising steps similar to the operations of the traffic orchestrator of claim 11 above. Claim 4 is rejected on a similar rationale.
Claim 18 is one or more computer storage devices having computer-executable instructions stored thereon, which, upon execution by a computer to perform operations similar to the operations of claim 11 above. Claim 18 is rejected on the same rationale.
Claim 12, Gupta-Claridge teaches the system of claim 8, wherein the traffic orchestrator is further operable to:
monitor conditions of the wireless network subsequent to distributing the traffic advisories (Claridge, p. 86, The previous traffic advisory has been applied. “Now assume that SON system 210 receives additional enhanced network topology information indicating that the particular cell is still associated with a failure to satisfy the service level agreement of subscribers in the particular cell. In that case, SON system 210 may determine that the first modification is ineffective…”); and
wherein the orchestration engine is further operable to either: perform self-learning to improve determination of when and/or where to distribute a traffic advisory; or perform self-learning to improve traffic advisory content (Claridge, p. 87, “In that case, SON system 210 may determine that the first modification is ineffective, and may train the predictive model to determine a second modification to a network parameter.”).
Claim 5 is a method comprising steps similar to the operations of the traffic orchestrator
of claim 12 above. Claim 5 is rejected on a similar rationale.
Claim 19 is one or more computer storage devices having computer-executable
instructions stored thereon, which, upon execution by a computer to perform operations similar to the operations of claim 12 above. Claim 19 is rejected on the same rationale.
Claim(s) 3 and 10 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gupta and Claridge as applied to claims above, and further in view of Johri et al., US 2016011505 A1 (hereafter referred to Johri).
Claim 10, Gupta-Claridge teaches the system of claim 9, as recited above. Gupta-Claridge does not specifically teach an extract, transform, load (ETL) process disposed between the set of log receivers and the database, operable to transform information received from the set of log receivers to a form used for storage in the database. However, in the same field of endeavor, Johri teaches an extract, transform, load (ETL) process disposed between the set of log receivers and the database, operable to transform information received from the set of log receivers to a form used for storage in the database (p. 1, “In computing, extract, transform, and load (ETL) refers to a process in database usage and especially in data warehousing that extracts data from outside sources, transforms the data to fit operational needs, which can include quality levels, loads the data into an end target database.” And p. 28, “In some implementations ETL server 25 retrieves … call log files from a customer service database. The ETL server 25 may then automatically store … and call log files into a server database.”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Gupta-Claridge by incorporating ETL from Johri to ensure that the data is formatted for operational needs and can thereby be accessed effectively.
Claim 3 is a method comprising steps similar to the operations of the traffic orchestrator
of claim 10 above. Claim 3 is rejected on a similar rationale.
Claim 17 is one or more computer storage devices having computer-executable
instructions stored thereon, which, upon execution by a computer performing operations similar to the operations of claim 10 above. Claim 17 is rejected on the same rationale.
Allowable Subject Matter
Claims 6-7, 13-14, 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Parker, US 2016001391 A1, teaches key performance indicators are analyzed before and after the broadcasting of a policy change and the effect thereon may be measured. Policy changes that produce no, little, or negative changes in the key performance indicators may be reversed or otherwise rolled back; while other effective policy changes may be thereafter enacted.
Tapia et al., US 20170078171 A1 , teaches a communication analytics engine 124 may execute on the data collection platform 118 to analyze the call data 116 and/or the network performance data 122 and provide call analysis reports and alerts. The communication analytics engine 124 may generate key performance indicators from the call data based on settings.
Kurt et al., US 20230047057 A1, teaches (i) parameter recommendation, in which a parameter configuration is recommended to optimize the network performance (i.e., improve one or more target KQIs or KPIs) based on existing cell-level parameter diversity, (ii) root cause analysis to determine if any configuration change has been the cause of a network anomaly, and (iii) parameter sensitivity analysis, in which the impact of individual parameters on the network performance is determined.
Khalid, US 20240236709 A1, teaches new and/or improved methods and apparatus for determining and/or using accurate Key Performance Indicator information for monitoring and optimizing the performance of wireless systems which offload and/or migrate communications sessions from a first wireless network to a second wireless network.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PATRICE L WINDER whose telephone number is (571)272-3935. The examiner can normally be reached M-F 10am-6pm.
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/Patrice L Winder/Primary Examiner, Art Unit 2453