DETAILED ACTION
Claims 1-18 have been examined and are rejected.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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 1-18 are 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. Claim 1 recite the limitation “various network functions”. The term “various” indicates an indefinite number, and therefore, the term “various” renders the claims indefinite. It is unclear whether the term “various network functions” means an indefinite number or a quantity of network functions or whether it means different types of network functions. Claims 2-18 fail to remedy the deficiency of claim 1 and are rejected.
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, 5-6, 9-12 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Seetharaman et al. (U.S. PGPub 2023/0261932) in view of Kanza et al. (U.S. PGPub 2025/0112841).
Regarding claim 1, Seetharaman teaches A method for modelling, generating intelligence of and optimization of interdependent network entities in a wireless telecom network via a computer utilizing machine learning, the method executing on the computer comprising the steps of: receiving network entity-specific data relating to a network function; (Seetharaman, see figs. 3-5; see paragraph 0058 analyze the fault and/or performance data of constituents (i.e., network functions and connections) in that network segment…; see paragraph 0059 analysis may be required to pin-point exact reason or cause of the fault and/or performance issue, for example, start at the Network Function level, then go to infrastructure level...; see paragraph 0036 reported from the network functions and the AMS...)
associating groups of interdependent network entities to generate aggregated data for joint observation to evaluate an impact each interdependent network entity has on each other; (Seetharaman, see figs. 3-5; see paragraph 0032 perform necessary aggregation...of the fault and/or performance data received from the various network functions at network function level, network function type level, network service level or at any granularity as specified by the E2E-NS-LCM-SS 212....)
predicting Key Performance Indicators (KPIs) of the interdependent network entities based on the aggregated data; (Seetharaman, see figs. 3-5; see paragraph 0029 predict the service level KPIs of the network, network resource utilization, network congestion, user & application-centric network performance…; see paragraph 0026 predict performance of the application based on the fault and/or performance issues predicted for the network...)
generating optimizations to improve the KPIs for the interdependent network entities based on the predicted KPIs; (Seetharaman, see figs. 3-5; see paragraph 0029 performing analytics and/or execution of machine learning algorithms as instructed by the E2E-NS-LCM-SS 212, NET-SA-HM-SS 202, and APP-NET-PERF-CORR-SS 204...examining historical fault and/or performance data and determining fault or performance degradation that may have occurred due to the resolution action, by eliminating non-relevant factors and using machine learning techniques such as reinforcement learning...; see paragraph 0064-0065 The impact on each network service, network slice, other active applications, or user class with a priority higher than the network service, network slice, or other active application (or user class accessing the application) may be listed in an impact list, i.e., IMPACT-LIST...)
testing the optimizations on a model and receiving input from the model on which the optimizations are tested; and (Seetharaman, see figs. 3-5; see paragraph 0065-0066 the impact of each of the resolution actions listed in the RES-ACTION-LIST may be assessed on other network segments, and the IMPACT-LIST may be updated accordingly...each of the resolution actions may be ranked starting with the resolution action with least value of via an equation (model) (2) Rank (input from model)=RES-IMPACT*(1/RES-EFF) (2)...)
implementing control actions to various network functions based on the outcome of the tested optimizations. (Seetharaman, see figs. 3-5; see paragraphs 0066-0067 the resolution action(s) may be implemented...the APP-NET-PERF-CORR-SS 204 may trigger the E2E-NS-LCM-SS 212, or the NET-SA-HM-SS 202 as appropriate to implement first resolution action in the RES-ACTION-LIST corresponding to each entry in the RC-LIST. Based on the impact assessment done in Step 412, the APP-NET-PERF-CORR-SS 204 may initiate actions to address the impacts of implementing the resolution action...)
However, Seetharaman does not explicitly teach imputing missing values in the received network entity-specific data;
Kanza teaches imputing missing values in the received network entity-specific data; (Kanza, see paragraph 0030 collects mobility data from a cellular network in distinct locations across a continent. System 200 collects parameters about the network, like traffic volume, number of served devices, and others, and analyzes the parameters to compute key performance indicators (KPIs)... Missing values might prevent detecting a significant change in the variability of the system. Imputing values before analysis with sliding windows would help reduce the effect of the missing values and enhance detection of a change in variance...)
It would have been obvious to one of ordinary skill in the art, at the time the invention was filed, to combine Seetharaman and Kanza to provide the technique of imputing missing values in the received network entity-specific data of Kanza in the system of Seetharaman in order to reduce the effect of the missing values and enhance detection of a change in variance (Kanza, see paragraph 0030).
Regarding claim 5, Seetharaman-Kanza teaches wherein the interdependent network entities comprise individual mobile network cells. (Seetharaman, see fig. 1; see paragraph 0074 employ communication protocols/methods such as...cellular (for example, code-division multiple access (CDMA)...global system for mobile communications (GSM), long-term evolution (LTE); see paragraph 0076 also)
Regarding claim 6, Seetharaman-Kanza teaches further comprising the steps of: validating the entity-specific data; and generating an alert when the data exceeds a threshold value or if the data has any sanitation issues. (Seetharaman, see figs. 3-5; see abstract one or more alerts related to fault and/or performance issues associated with the application…; see paragraph 0036 the one or more alerts related to the fault and/or performance issues may be proactively or reactively generated...; see also paragraph 0041)
Regarding claim 9, Seetharaman-Kanza teaches wherein the optimizations relate to adjustments to configuration parameters or control decision variables. (Seetharaman, see figs. 3-5; see paragraphs 0066-0067 the resolution action(s) may be implemented...the APP-NET-PERF-CORR-SS 204 may trigger the E2E-NS-LCM-SS 212, or the NET-SA-HM-SS 202 as appropriate to implement first resolution action in the RES-ACTION-LIST corresponding to each entry in the RC-LIST. Based on the impact assessment done in Step 412, the APP-NET-PERF-CORR-SS 204 may initiate actions to address the impacts of implementing the resolution action...; see paragraph 0038 at least one of rules, parameters, and thresholds may be adapted, based on an effectiveness factor determined for the at least one appropriate resolution action implemented...; see paragraph 0061 each entry in the RC-LIST, the possible resolution actions may be based on historical data and adapted rules (i.e., the provisioned rules which have subsequently been adopted as described in Step 416)...)
Regarding claim 10, Seetharaman-Kanza teaches further comprising the steps of: exercising closed-loop control action to modify the configuration parameters and/or the control decision variables on the network function; or modifying configurable hyper-parameters of the machine learning models; or triggering machine learning life cycle management operations via a graphic user interface. (Seetharaman, see figs. 4-5; see paragraph 0049 the NET-SEG-PERF-CONTRIB-APPL-PERF may also be adjusted based on an adjustment factor, i.e., ADJ-FACTOR. The ADJ-FACTOR may be determined during self-learning process...the NET-SEG-PERF-CONTRIB-APPL-PERF may also be adjusted based on an adjustment factor, i.e., ADJ-FACTOR. The ADJ-FACTOR may be determined during self-learning process...)
Regarding claim 11, Seetharaman-Kanza teaches further comprising the step of determining a causal factor underlying each network entity-specific data. (Seetharaman, see figs. 3-5; see paragraph 0029 performing analytics and/or execution of machine learning algorithms as instructed by the E2E-NS-LCM-SS 212, NET-SA-HM-SS 202, and APP-NET-PERF-CORR-SS 204...examining historical fault and/or performance data and determining fault or performance degradation that may have occurred due to the resolution action, by eliminating non-relevant factors and using machine learning techniques such as reinforcement learning...; see paragraph 0064-0065 The impact on each network service, network slice, other active applications, or user class with a priority higher than the network service, network slice, or other active application (or user class accessing the application) may be listed in an impact list, i.e., IMPACT-LIST...)
Regarding claim 12, Seetharaman-Kanza teaches further comprising the step of generating an alert notification based on a predicted KPI data point. (Seetharaman, see figs. 3-5; see abstract one or more alerts related to fault and/or performance issues associated with the application…; see paragraph 0036 the one or more alerts related to the fault and/or performance issues may be proactively (predicted) or reactively generated...; see also paragraph 0041)
Regarding claim 16, Seetharaman-Kanza teaches wherein the network entity-specific data is generated by mobile telecom systems having different types of network data originating from different network functions with different network function types. (Seetharaman, see figs. 3-5; see paragraph 0058 analyze the fault and/or performance data of constituents (i.e., network functions and connections) in that network segment…; see paragraph 0059 analysis may be required to pin-point exact reason or cause of the fault and/or performance issue, for example, start at the Network Function level, then go to infrastructure level...; see paragraph 0036 reported from the network functions and the AMS...)
Regarding claim 17, Seetharaman-Kanza teaches wherein the network entity-specific data is generated by mobile telecom systems having different types of data from the management functions of the network functions at different levels of granularity. (Seetharaman, see figs. 3-5; see paragraph 0058 analyze the fault and/or performance data of constituents (i.e., network functions and connections) in that network segment…; see paragraph 0059 analysis may be required to pin-point exact reason or cause of the fault and/or performance issue, for example, start at the Network Function level, then go to infrastructure level...; see paragraph 0036 reported from the network functions and the AMS...)
Regarding claim 18, Seetharaman-Kanza teaches wherein the network entity-specific data is generated by mobile telecom systems at different instances of time, during a reporting window. (Seetharaman, see fig. 4; see paragraph 0026 monitoring and collection of fault and/or performance data may include, for example, granularity and periodicity of data collection, what data to collect…; see paragraph 0039 application health and performance data may be obtained from the AMS (periodically) and analyzed...; see paragraph 0036 a time window associated with the application, and location information associated with application client...)
Claims 2-4 are rejected under 35 U.S.C. 103 as being unpatentable over Seetharaman-Kanza in view of Molleti et al. (U.S. PGPub 2024/0314025).
Regarding claim 2, Seetharaman-Kanza teaches all of the features of claim 1. However, Seetharaman-Kanza does not explicitly teach wherein the step of generating aggregated data further comprises: performing a time-correlated aggregation of Operations, Administration, and Maintenance of Fault, Configuration, Accounting, Performance, and Security (OAM FCAPS) data of different types from across different network functions and/or different interdependent network entities and at different levels.
Molleti teaches wherein the step of generating aggregated data further comprises: performing a time-correlated aggregation of Operations, Administration, and Maintenance of Fault, Configuration, Accounting, Performance, and Security (OAM FCAPS) data of different types from across different network functions and/or different interdependent network entities and at different levels. (Molleti, see figs. 3A and 4A-4C; see paragraph 0054 OPPRM 104 isolates, parses, and streams data from NFs, such as NFs 110A, 110B, 110C through 110N, to OAM 130 and eSON 128 modules…; see paragraphs 0059-0060 E2 service model is used for three applications Key Performance Indicators (KPI),...E2EO 106 includes eSON 128, OAM 130, and FCAPS 132...; see paragraph 0028 OAM or operations, administration, and maintenance (OA&M or OAM) are the processes, activities, tools, and standards involved with operating, administering, managing, and maintaining systems....; see paragraph 0005 a fault, configuration, accounting, performance, and security (FCAPS) application; see paragraph 0075 provides log collection, centralized log aggregation)
It would have been obvious to one of ordinary skill in the art, at the time the invention was filed, to combine Seetharaman-Kanza and Molleti to provide the technique of performing a time-correlated aggregation of Operations, Administration, and Maintenance of Fault, Configuration, Accounting, Performance, and Security (OAM FCAPS) data of different types from across different network functions and/or different interdependent network entities and at different levels of Molleti in the system of Seetharaman-Kanza in order to enhance proactive measures that help significantly improve network stability (Molleti, see paragraph 0064).
Regarding claim 3, Seetharaman-Kanza-Molleti teaches wherein the OAM FCAPS comprises Fault Management (FM), Configuration Management (CM), Performance Measurement (PM), and Trace Reporting (TR) based on network events. (Molleti, see figs. 3A and 4A-4C; see paragraph 0054 OPPRM 104 isolates, parses, and streams data from NFs, such as NFs 110A, 110B, 110C through 110N, to OAM 130 and eSON 128 modules…; see paragraphs 0059-0060 E2 service model is used for three applications Key Performance Indicators (KPI),...E2EO 106 includes eSON 128, OAM 130, and FCAPS 132...; see paragraph 0028 OAM or operations, administration, and maintenance (OA&M or OAM) are the processes, activities, tools, and standards involved with operating, administering, managing, and maintaining systems....; see paragraph 0005 a fault, configuration, accounting, performance, and security (FCAPS) application) The motivation regarding to the obviousness to claim 2 is also applied to claim 3.
Regarding claim 4, Seetharaman-Kanza-Molleti teaches wherein the different levels are selected from the group consisting of: User Equipment (UE) level, Network Function (NF) level, UE group level, bearer level, slice level, slice subnet level, Quality of Service (QoS) level, Protocol Data Unit (PDU) session level and combinations thereof. (Seetharaman, see figs. 3-5; see paragraph 0058 analyze the fault and/or performance data of constituents (i.e., network functions and connections) in that network segment…; see paragraph 0059 analysis may be required to pin-point exact reason or cause of the fault and/or performance issue, for example, start at the Network Function level, then go to infrastructure level...; see paragraph 0036 reported from the network functions and the AMS...)
Claims 7 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Seetharaman-Kanza in view of Trivedi et al. (U.S. PGPub 2023/0246901).
Regarding claim 7, Seetharaman-Kanza teaches all of the features of claim 1. However, Seetharaman-Kanza does not explicitly teach wherein the step of generating optimizations further comprises: analyzing data distribution and statistics relating to the entity-specific data; and
generating the predicted KPI's based in part on the analysis of data distribution and statistics.
Trivedi teaches wherein the step of generating optimizations further comprises: analyzing data distribution and statistics relating to the entity-specific data; and (Trivedi, see figs. 11-12; see paragraph 0134 KPI prediction and anomaly prediction with confidence bands formed by an upper threshold and a lower threshold. In some embodiments, the confidence bands are dynamic values that vary over time, providing upper and lower threshold values. In this example, the confidence band is shown in user interface 1100 as a shaded portion demonstrating the upper and lower threshold values within which the KPI being viewed should be if the KPI is considered to be normal...)
generating the predicted KPI's based in part on the analysis of data distribution and statistics. (Trivedi, see figs. 11-12; see paragraph 0134 KPI prediction and anomaly prediction with confidence bands formed by an upper threshold and a lower threshold. In some embodiments, the confidence bands are dynamic values that vary over time, providing upper and lower threshold values. In this example, the confidence band is shown in user interface 1100 as a shaded portion demonstrating the upper and lower threshold values within which the KPI being viewed should be if the KPI is considered to be normal...)
It would have been obvious to one of ordinary skill in the art, at the time the invention was filed, to combine Seetharaman-Kanza and Trivedi to provide the technique of analyzing data distribution and statistics relating to the entity-specific data and generating the predicted KPI's based in part on the analysis of data distribution and statistics of Trivedi in the system of Seetharaman-Kanza in order to assist a network operator with scheduling maintenance and avoid an interruption of expected performance of the communication network (Trivedi, see paragraph 0028).
Regarding claim 13, Seetharaman-Kanza teaches all of the features of claim 1. However, Seetharaman-Kanza does not explicitly teach wherein the network entity-specific data relating to a network function is received in real time and the optimizations to improve the KPIs are based on the real time data feeds.
Trivedi teaches wherein the network entity-specific data relating to a network function is received in real time and the optimizations to improve the KPIs are based on the real time data feeds. (Trivedi, see fig. 11; see paragraphs 0122-0124 monitors the target KPI(s) defined in the profiles, and causes an action (e.g., sending an alert to a user, perform a network function, etc.) ...view KPIs in a real-time process...User interface 1100 is a graphical representation of KPI prediction and anomaly detection in real-time…)
It would have been obvious to one of ordinary skill in the art, at the time the invention was filed, to combine Seetharaman-Kanza and Trivedi to provide the technique of the network entity-specific data relating to a network function is received in real time and the optimizations to improve the KPIs are based on the real time data feeds of Trivedi in the system of Seetharaman-Kanza in order to assist a network operator with scheduling maintenance and avoid an interruption of expected performance of the communication network (Trivedi, see paragraph 0028).
Regarding claim 14, Seetharaman-Kanza-Trivedi teaches wherein the step of testing the optimizations on a model and receiving input from the model comprises (Seetharaman, see figs. 3-5; see paragraph 0065-0066 the impact of each of the resolution actions listed in the RES-ACTION-LIST may be assessed on other network segments, and the IMPACT-LIST may be updated accordingly...each of the resolution actions may be ranked starting with the resolution action with least value of via an equation (model) (2) Rank (input from model)=RES-IMPACT*(1/RES-EFF) (2)...)
continuous performance monitoring based on the real time data received. (Trivedi, see fig. 11; see paragraphs 0122-0124 monitors the target KPI(s) defined in the profiles, and causes an action (e.g., sending an alert to a user, perform a network function, etc.) ...view KPIs in a real-time process...User interface 1100 is a graphical representation of KPI prediction and anomaly detection in real-time…; see paragraph 0045 continuously evaluates the KPI(s) included in the evaluation profile in real-time, and performs an action (e.g., sending alert to the network operator/network service provider, scheduling maintenance, etc.) based on the evaluation) The motivation regarding to the obviousness to claim 13 is also applied to claim 14.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Seetharaman-Kanza in view of Chase et al. (U.S. PGPub 2023/0105039).
Regarding claim 8, Seetharaman-Kanza teaches all of the features of claim 1. However, Seetharaman-Kanza does not explicitly teach further comprising the step of scaling and normalizing the entity-specific data.
Chase teaches further comprising the step of scaling and normalizing the entity-specific data. (Chase, see paragraph 0013 where the data about the organization may be obtained from multiple sources, and each source may not organize and scale its data in the same way...organization data is collected and normalized, so that similar data is organized and scaled in an identical manner, no matter which organization’s data is being analyzed...)
It would have been obvious to one of ordinary skill in the art, at the time the invention was filed, to combine Seetharaman-Kanza and Chase to provide the technique of scaling and normalizing the entity-specific data of Chase in the system of Seetharaman-Kanza in order to make the data more meaningful for analysis purposes (Chase, see paragraph 0014).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Seetharaman-Kanza in view of Monteiro (U.S. PGPub 2024/0243973).
Regarding claim 15, Seetharaman-Kanza teaches all of the features of claim 1. However, Seetharaman-Kanza does not explicitly teach wherein the model comprises a digital twin of the underlying network from which the network entity-specific data is received.
Monteiro teaches wherein the model comprises a digital twin of the underlying network from which the network entity-specific data is received. (Monteiro, see fig. 1; see paragraph 0027 run at an “edge” computing site, enabling an edge solution team to test features close to “real” customer environments (e.g., the industrial sites 107)...allowing end-users to build a solution and test all features at an organization's premises... mirrored within the solution from a safe, controlled environment... test new configurations...)
It would have been obvious to one of ordinary skill in the art, at the time the invention was filed, to combine Seetharaman-Kanza and Monteiro to provide the technique of a digital twin of the underlying network from which the network entity-specific data is received of Monteiro in the system of Seetharaman-Kanza in order to perform tests from a safe, controlled environment (Monteiro, see paragraph 0027).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. This includes:
U.S. Patent No. 9900790, which describes a system and method of predicting cellular network performance based on observed performance indicator data from cells in the cellular network;
U.S. PGPub 2022/0210682, which describes a system and method for artificial intelligence (AI) driven voice over long-term evolution (VoLTE) analytics; and
U.S. PGPub 2020/0313985, which describes methods and systems for effective data collection, aggregation, and analysis in a distributed heterogeneous communication network for effective fault detection or performance anomaly detection.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MENG VANG whose telephone number is (571)270-7023. The examiner can normally be reached M-F 8AM-2PM, 3PM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, NICHOLAS TAYLOR can be reached at (571) 272-3889. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MENG VANG/Primary Examiner, Art Unit 2443
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