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 .
Specification
The disclosure is objected to because of the following informalities:
In paragraph 0002, the phrase “often fall short, as they are unable to keep up with the real-time demands of the network. They approaches” is unclear in meaning. Presumably, the intended sentence should read “These approaches often fall short, as they are unable to keep up with the real-time demands of the network.” Appropriate correction is required.
In paragraph 0003, the phrase “The manual collection is prone to human error, results in inefficiencies and produces delays in decision-making” is grammatically incorrect, and presumably should read “Manual collection is prone to human error, results in inefficiencies, and produces delays in decision-making”.
In paragraph 0007, the sentence “Specifically the networks are generating terabytes of data daily where it is extremely difficult for humans to characterize in real time” is grammatically incorrect, and presumably should read “Specifically, the networks are generating terabytes of data daily where it is extremely difficult for humans to characterize in real time”.
In paragraph 0007, the sentence “Representing radios with digital twins permits working in an environment, instead of trying them in the physical world, allows for visualizing various radio settings” is unclear in meaning. Appropriate correction is required.
In paragraph 0045, the sentence “Therefore, the cellular network operator may operate
some hardware (such as, RUs and local computing resources on which DUs are executed)
connected with a cloud-computing platform on which other cellular network functions, such as the core and CUs are executed” is grammatically incorrect, and presumably should read “Therefore, the cellular network operator may operate some hardware (such as RUs and local computing resources on which DUs are executed) connected with a cloud-computing platform on which other cellular network functions, such as the core and CUs, are executed”. Appropriate correction is required.
In paragraph 0052, the sentence “That is, chaos test system is not only connected to a DU, but is connected to all the layers and systems above and below a DU, as an example” is grammatically incorrect, and presumably should read “That is, the chaos test system is not only connected to a DU, but is connected to all the layers and systems above and below a DU, as an example”.
In paragraph 0054, the sentence “Chaos testing of these components, as well as other higher layer custom-built components” is unclear in meaning. Appropriate correction is required.
In paragraphs 0079 and 0081, the use of the word “maybe” is unclear in meaning. Presumably, in each instance “maybe” should be replaced with “may be”. Appropriate correction is required.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference characters "406" and "408" have both been used to designate the vector database (see paragraph 0078). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they
do not include the following reference sign(s) mentioned in the description: The Data Network 195 and Access networks 197. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: playback element 420 and Northbound API 504. The playback element is mentioned in paragraph 0077, but not explicitly referenced to as 420. The Northbound API is referenced to as 514 in the description. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application.
Under the “Drawings” subheading of the specification, there is no mention of the attached Figure 6. Furthermore, two figures labelled Figure 5 have been given. The second, comprising elements 500, 502, 504, and 506, is presumably for a separate application and should be removed.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office Action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended”. If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. The replacement sheet(s) should be labeled “Replacement Sheet” in the page header (as per 37 CFR 1.84(c)) so as not to obstruct any portion of the drawing figures. If the changes are not accepted by the Examiner, the Applicant will be notified and informed of any required corrective action in the next Office Action. If a response to the present Office Action fails to include proper drawing corrections, corrected drawings or arguments therefor, the response can be held NON-RESPONSIVE and/or the application could be ABANDONED since the objections/corrections to the drawings are no longer held in abeyance.
Claim Rejections - 35 USC § 112
Claims 3 and 8 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.
With regards to claim 3, it is unclear what is meant by “disseminating the receiving, the computing and the organizing over a plurality of platforms in real-time”, specifically whether the receiving, computing, and organizing are all to be performed by each of the plurality of platforms, or whether these actions are disseminated over the platforms as a group (i.e. platform A performing the receiving, platform B performing the computing, and so on). For examination purposes, the latter will be assumed.
With regards to claim 8, it is unclear what is meant by “wherein the similarity search query is performed per a selected network site model”. The intended meaning of “per” in relating the similarity search query to the selected network site model is unclear. Furthermore, selection of a network site model is unsubstantiated in the specification. For purposes of prior art examination, it will be assumed that “per” is equivalent to “with respect to”, such that the claim is interpreted as “wherein the similarity search query is performed with respect to a selected network site model”.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-9 and 13-15 are rejected under 35 U.S.C. 103 as being unpatentable over Kwan (US 20160381580 A1) in view of Ramsey et al. (US 11226992 B1).
Consider claim 1, Kwan discloses a computer-implemented method for characterizing a Radio Access Network (RAN) comprising network elements, the method comprising:
receiving streams of performance indicators from network elements (see Figure 1, the performance counters 22(1)-22(x) are “measurable metrics of mobile network 20 that can be logged and maintained by (or reported to) network management system 40 or stored in some other storage element or device, such as network monitoring database 30” (see paragraph 0033));
computing Key Performance Indicators (KPIs) and associated correlations based on the performance indicators (“The process can include selecting a set of desired performance counters, converting the set of counters into a set of Key Performance Indicators (KPIs), quantizing each KPI value into one of a set of possible values, and creating a new item set where each item within the set corresponds to a particular KPI associated with each of the possible quantized values” (see paragraph 0037));
organizing the KPIs and the associated correlations into a vector database (“An iterative process may be used to train the SOM (Self Organizing Map) and generate a plurality of neurons (or nodes) that corresponds to a vector of feature weights (feature weight vector), where each feature corresponds to a key performance indicator (KPI) of the wireless network” (see paragraph 0098), said SOM can reasonably be equated to a vector database); and
wherein the performance indicators comprise counters, events and alarms (“Examples of performance counters can include, but are not limited to, packet switching (PS) dropped calls, circuit switching (CS) dropped calls, PS setup failure, CS setup failure, setup failure for specific reasons (e.g., radio link problems, etc.), lu release requests, etc.” (see paragraph 0035)).
However, Kwan fails to disclose wherein the method comprises depicting, in real-time, a network behavior of the RAN with a similarity search query of the vector database.
In the same field of endeavor, Ramsey et al. disclose wherein the method comprises depicting, in real-time, a network behavior of the RAN with a similarity search query of the vector database (refer to Figure 2A, incoming data is added to the data set and added to a visual cluster diagram in real time. As part of this method, the “process queries a centroid similarity search index with the input vector to find clusters closest to the input vector” (see Col. 6, lines 49-52). Ramsey et al. disclose a method for clustering data in general, but this can be easily applied to the network behavior data disclosed by Kwan.
Therefore, it would have been obvious to a person having ordinary skill in the art to modify the method disclosed by Kwan to depict real-time data with a similarity search query of the vector database as disclosed by Ramsey et al. in order to allow the system to actively adapt to new data.
Consider claim 2, and as applied to claim 1 above, Kwan, as modified by Ramsey et al., further discloses wherein the organizing comprises indexing the KPIs and the associated correlations and the computing is performed prior to the organizing (see Figure 3, step 304 comprises the computation of KPIs. Steps 306 and 308 comprise indexing and organization of KPIs (see paragraphs 0058-0060)).
Consider claim 3, and as applied to claim 1 above, Kwan, as modified by Ramsey et al., further discloses a method further comprising disseminating the receiving, the computing and the organizing over a plurality of platforms in real-time (see Figure 1, the receiving is performed by network monitoring database 30 (see paragraph 0036), whereas the computing and organizing are performed by network management system 40. System 40 can also include an “administration module 42, a Self-Organizing Map (SOM) data analysis module 46, and an association rule mining module 44. Appropriate hardware, software, and firmware may also be provided including at least one memory element 47 and at least one processor 49” (see paragraph 0033)).
Consider claim 4, and as applied to claim 1 above, Kwan, as modified by Ramsey et al., further discloses wherein the KPIs are structured KPIs or matrices (see Figures 8A and 8B depicting the hierarchical KPI structure 800).
Consider claim 5, and as applied to claim 1 above, Kwan fails to disclose a method
wherein the associated correlations comprise a running average.
In the same field of endeavor, Ramsey et al. disclose wherein the associated correlations comprise a running average (“Implementations of a dynamic data clustering system can make use of one or more of the following three components…A centroid update mechanism such as a weighted average or exponential moving average of the item representation” (see Col.6, lines 18-33)).
Therefore, it would have been obvious to a person having ordinary skill in the art to
modify the method disclosed by Kwan such that the associated correlations comprise a running average as disclosed by Ramsey et al. in order to provide up-to-date information to the operator.
Consider claim 6, and as applied to claim 1 above, Kwan, as modified by Ramsey et al., further discloses wherein the associated correlations are selected by a Subject Matter Expert (SME) (see Figure 1, “Administration module 42 may be provided in network management system 40 to allow network operators or other authorized users to provide input to configure the systems for analyzing mobile network performance data. For example, network operators may select desired performance counters to be used in analyzing mobile network performance using association rule mining techniques or SOM data analysis” (see paragraph 0039)).
Consider claim 7, and as applied to claim 1 above, Kwan, as modified by Ramsey et al., further discloses wherein the computing further comprises computing a feature dimension for the KPIs (see Figures 8A and 8B, “group 2 KPIs such as call drop rate for voice and video can be separately computed” (see paragraph 0075)).
Consider claim 8, and as applied to claim 1 above, Kwan further discloses wherein the similarity search query is performed per a selected network site model (the described method and SOM can be applied to model one or several network sites (“Therefore, it should also be appreciated that the system of FIG. 1 (and its teachings) is readily scalable. The system can accommodate a large number of components, as well as more complicated or sophisticated arrangements and configurations”, see paragraph 0140). “Access points may be selected for association rule mining based on certain prioritizations, which may be configurable by a network operator (or other user)” (see paragraph 0088)).
However, Kwan fails to disclose a similarity search query of a vector database.
In the same field of endeavor, Ramsey et al. disclose a similarity search query of a vector database (the “process queries a centroid similarity search index with the input vector to find clusters closest to the input vector” (see Col. 6, lines 49-52)).
Therefore, it would have been obvious to a person having ordinary skill in the art to modify the method disclosed by Kwan to incorporate a similarity search query of a vector database as disclosed by Ramsey et al. in order to efficiently extract associations from the network site model.
Consider claim 9, and as applied to claim 1 above, Kwan, as modified by Ramsey et al., further discloses wherein the depicting comprises a workflow automation to monitor performance of the RAN (the method of analyzing network performance “can include pre-processing performance data, using SOM to organize data on the basis of similarity, applying clustering techniques to group the similar patterns of data, and to automate the interpretation of the clusters by classifying and labeling the clusters in a meaningful way” (see paragraph 0038)).
Consider claim 13, and as applied to claim 1 above, Kwan further discloses a method comprising performing cause analysis of a RAN cell by querying the vector database (“Association rules among KPIs can be systematically created. Based on these rules, the hidden relationships among KPIs can be revealed. Subsequently, the causes of specific problems can be discovered in an automated way” (see paragraph 0053)).
However, Kwan fails to disclose wherein the data is compared via querying a vector database.
In the same field of endeavor, Ramsey et al. disclose wherein data is retrieved via querying a vector database (refer to Figure 2A, incoming data is added to the data set and added to a visual cluster diagram in real time. As part of this method, the “process queries a centroid similarity search index with the input vector to find clusters closest to the input vector” (see Col. 6, lines 49-52)).
Therefore, it would have been obvious to a person having ordinary skill in the art to modify the method disclosed by Kwan such that data is compared via querying a vector database as taught by Ramsey et al. in order to efficiently compare new information against existing data.
Consider claim 14, and as applied to claim 1 above, Kwan, as modified by Ramsey et al., further discloses a method comprising associating a decoration comprising an operating environment description with some of the KPIs (see Figure 8A, the KPI groups 1, 2, and 3 are grouped by operating environment of the contained KPIs (see paragraph 0075)).
Consider claim 15, and as applied to claim 1 above, Kwan, as modified by Ramsey et al., further discloses a method comprising post-processing the vector database to make an informed decision as to a RAN state relative to a RAN operator policy (”Using SOM, an operator can group a number of KPIs (or, equally, performance counters), and analyze them simultaneously, which may allow some patterns to emerge. Post-processing may be done in order to further cluster these patterns and each cluster can correspond to a non-overlapping set of neurons” (see paragraph 0101)).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Kwan (US 20160381580 A1) in view of Ramsey et al. (US 11226992 B1), and further in view of Tabak et al. (US 20190102440 A1).
Consider claim 10, and as applied to claim 9 above, Kwan, as modified by Ramsey et
al., fails to disclose a method wherein the workflow automation performs a performance analysis through an Application Programming Interface (API).
In the same field of endeavor, Tabak et al. disclose wherein the workflow automation performs a performance analysis through an Application Programming Interface (API) (“using REST APIs to visualize key performance indicators (KPIs) and breakdowns associated with both the collected external data and internal PA (Performance Analysis) data on a single dashboard” (see paragraph 0023)).
Therefore, it would have been obvious to a person having ordinary skill in the art to modify the method disclosed by Kwan and modified by Ramsey et al. such that the workflow automation performs a performance analysis through an Application Programming Interface (API) as taught by Tabak et al. in order to use an existing infrastructure for performance analysis.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Kwan (US 20160381580 A1) in view of Ramsey et al. (US 11226992 B1), and further in view of Burgarella et al. (WO 2023047170 A1) and Nam et al. (US 20230362681 A1).
Consider claim 11, and as applied to claim 1 above, Kwan, as modified by Ramsey et al., fails to disclose a method comprising conducting a comparison check of an operational requirement against a running average associated with one or more of the KPIs of a RAN cell, wherein the RAN cell is represented with a digital replica in the vector database.
In the same field of endeavor, Burgarella et al. disclose a method comprising conducting a comparison check of an operational requirement against a running average associated with one or more of the KPIs of a RAN cell (see Figure 3, “In some embodiments, the KPI classifier 305 checks how well the KPIs can be predicted (i.e., determines or rates predictability)…KPI classifier 305 can be implemented as a vector autoregressive (VAR)/ seasonal autoregressive integrated moving average (SARIMA) process” (see paragraph 0048). The “divergency detector 309 is a software function that checks if the predicted values of the predictable KPIs cross KPI limits or warning/error thresholds specific to the type of the KPI” (see paragraph 0051)).
Therefore, it would have been obvious to a person having ordinary skill in the art to modify the method disclosed by Kwan and modified by Ramsey et al. to include conducting a comparison check of an operational requirement against a running average associated with one or more of the KPIs of a RAN cell as taught by Burgarella et al. in order to monitor relevant KPIs with respect to the operational parameters of the network.
However, Kwan as modified by Ramsey et al. and Burgarella et al. fails to disclose wherein the RAN cell is represented with a digital replica.
In the same field of endeavor, Nam et al. disclose wherein the RAN cell is represented with a digital replica (“an electronic device for building a digital twin with respect to a network base station includes a memory configured to store one or more instructions and a simulator of the digital twin; a transceiver; and at least one processor configured to execute the one or more instructions to: obtain at least one simulated key performance indicator (KPI)” (see paragraph 0005)).
Therefore, it would have been obvious to a person having ordinary skill in the art to modify the method disclosed by Kwan and modified by Ramsey et al. and Burgarella et al. such that the RAN cell is represented with a digital replica as taught by Nam et al. in order to create a secondary virtual test environment without affecting network services.
However, Kwan, Burgarella et al., and Nam et al. fail to disclose wherein data is compared via querying a vector database.
In the same field of endeavor, Ramsey et al. disclose wherein data is compared via querying a vector database (refer to Figure 2A, incoming data is added to the data set and added to a visual cluster diagram in real time. As part of this method, the “process queries a centroid similarity search index with the input vector to find clusters closest to the input vector” (see Col. 6, lines 49-52)).
Therefore, it would have been obvious to a person having ordinary skill in the art to modify the method disclosed by Kwan such that data is compared via querying a vector database as taught by Ramsey et al. in order to efficiently enter and compare new information against existing data.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Kwan (US 20160381580 A1) in view of Ramsey et al. (US 11226992 B1), and further in view of Burgarella et al. (WO 2023047170 A1).
Consider claim 12, and as applied to claim 1 above, Kwan fails to disclose a method wherein the depicting illustrates a running average and a similarity check for an operational requirement.
In the same field of endeavor, Ramsey et al. disclose wherein the depicting illustrates a running average (refer to Figure 2A, incoming data is added to the data set and added to a visual cluster diagram in real time. “Implementations of a dynamic data clustering system can make use of one or more of the following three components…A centroid update mechanism such as a weighted average or exponential moving average of the item representation” (see Col.6, lines 30-33)).
Therefore, it would have been obvious to a person having ordinary skill in the art to modify the method disclosed by Kwan such that wherein the depicting illustrates a running average as disclosed by Ramsey et al. in order to provide up-to-date information to the operator.
However, Kwan as modified by Ramsey et al. fail to disclose a method comprising a similarity check for an operational requirement.
In the same field of endeavor, Burgarella et al. disclose a method comprising a similarity check for an operational requirement (see Figure 3, “In some embodiments, the KPI classifier 305 checks how well the KPIs can be predicted (i.e., determines or rates predictability)…KPI classifier 305 can be implemented as a vector autoregressive (VAR)/ seasonal autoregressive integrated moving average (SARIMA) process” (see paragraph 0048). The “divergency detector 309 is a software function that checks if the predicted values of the predictable KPIs cross KPI limits or warning/error thresholds specific to the type of the KPI” (see paragraph 0051)).
Therefore, it would have been obvious to a person having ordinary skill in the art to modify the method disclosed by Kwan and modified by Ramsey et al. to include conducting a similarity check for an operational requirement as taught by Burgarella et al. in order to monitor relevant KPIs with respect to the operational parameters of the network.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Kwan (US 20160381580 A1) in view of Ramsey et al. (US 11226992 B1), and further in view of Nam et al. (US 20230362681 A1).
Consider claim 16, and as applied to claim 1 above, Kwan as modified by Ramsey et al. further discloses a method comprising a vector database (“An iterative process may be used to train the SOM (Self Organizing Map) and generate a plurality of neurons (or nodes) that corresponds to a vector of feature weights (feature weight vector), where each feature corresponds to a key performance indicator (KPI) of the wireless network” (see paragraph 0098), said SOM can reasonably be equated to a vector database).
However, Kwan as modified by Ramsey et al. fails to disclose a representation of a network digital twin of a RAN cell.
In the same field of endeavor, Nam et al. disclose a representation of a network digital twin of a RAN cell (“an electronic device for building a digital twin with respect to a network base station includes a memory configured to store one or more instructions and a simulator of the digital twin; a transceiver; and at least one processor configured to execute the one or more instructions to: obtain at least one simulated key performance indicator (KPI)” (see paragraph 0005)).
Therefore, it would have been obvious to a person having ordinary skill in the art to modify the method disclosed by Kwan and modified by Ramsey et al. to incorporate a representation of a network digital twin of a RAN cell as taught by Nam et al. in order to create a secondary virtual test environment without affecting network services.
Claims 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Kwan (US 20160381580 A1) in view of Digikar et al. (US 20230370347 A1) and further in view of Nam et al. (US 20230362681 A1) and Vittal et al. (US 20200366756 A1).
Consider claim 17, Kwan discloses a system to characterize a Radio Access Network (RAN) comprising network elements, the system comprising: a vector database to organize the KPIs and the associated correlations (“An iterative process may be used to train the SOM (Self Organizing Map) and generate a plurality of neurons (or nodes) that corresponds to a vector of feature weights (feature weight vector), where each feature corresponds to a key performance indicator (KPI) of the wireless network” (see paragraph 0098), said SOM can reasonably be equated to a vector database); and
a similarity search query of the vector database (the “SOM organizes data on the basis of similarity by placing similar neurons (also referred to herein as ‘nodes’) geometrically close to each other…new input is then assimilated within the node to which it is mapped” (see paragraph 0096)).
wherein the performance indicators comprise counters, events and alarms(“Examples of performance counters can include, but are not limited to, packet switching (PS) dropped calls, circuit switching (CS) dropped calls, PS setup failure, CS setup failure, setup failure for specific reasons (e.g., radio link problems, etc.), lu release requests, etc.” (see paragraph 0035)).
However, Kwan fails to disclose a Southbound Application Processing Interface (API) to receive streams of performance indicators from network elements.
In the same field of endeavor, Digikar et al. disclose a Southbound Application Processing Interface (API) to receive streams of performance indicators from network elements (“an illustrative method herein may comprise: sending, from a server instrumentation agent configured on a transaction server, instrumented server performance data regarding the transaction server and an associated correlation token to an application programming
interface (API) monitoring agent” (see paragraph 0013)).
Therefore, it would have been obvious to a person having ordinary skill in the art to modify the method disclosed by Kwan to incorporate a Southbound Application Processing Interface (API) to receive streams of performance indicators from network elements as taught by Digikar et al. in order to use existing infrastructure to obtain performance information on the network.
However, Kwan as modified by Digikar et al. fails to disclose a Radio Digital Twin (RDT) Observability Platform to compute Key Performance Indicators (KPIs) and associated correlations based on the performance indicators.
In the same field of endeavor, Nam et al. disclose a Radio Digital Twin (RDT) Observability Platform to compute Key Performance Indicators (KPIs) and associated correlations based on the performance indicators (“an electronic device for building a digital twin with respect to a network base station includes a memory configured to store one or more instructions and a simulator of the digital twin; a transceiver; and at least one processor configured to execute the one or more instructions to: obtain at least one simulated key performance indicator (KPI)… [and] calculate a degree of similarity between the at least one simulated KPI and at least one network KPI received through the transceiver; and update the at least one input parameter, based on the calculated degree of similarity.” (see paragraph 0005)).
Therefore, it would have been obvious to a person having ordinary skill in the art to modify the method disclosed by Kwan and modified by Digikar et al. to incorporate a Radio Digital Twin (RDT) Observability Platform to compute Key Performance Indicators (KPIs) and associated correlations based on the performance indicators as taught by Nam et al. in order to
create a secondary virtual test environment without affecting network services.
However, Kwan as modified by Digikar et al. and Nam et al. fail to disclose a Northbound API to depict, in real-time, a network behavior of the RAN with a similarity search query of the vector database.
In the same field of endeavor, Vittal discloses a Northbound API to depict, in real-time, a network behavior of the RAN (refer to Figures 5A-5C, “a device 200 may comprise a service graph generator that generates and stores a service graph 505 based on a topology 510. A service graph monitor 516 may monitor the network elements of the topology and service, such as via API calls, for metrics 518 to update dynamically or in real-time the elements and metrics 518” (see paragraph 0102)).
Therefore, it would have been obvious to a person having ordinary skill in the art to modify the method disclosed by Kwan and modified by Digikar et al. and Nam et al. to incorporate a Northbound API to depict, in real-time, a network behavior of the RAN as taught by Vittal in order to use existing infrastructure for the purpose of data visualization.
Consider claim 18, and as applied to claim 17 above, Kwan, as modified by Digikar et al., Nam et al., and Vittal et al., further discloses wherein the KPIs are structured KPIs or matrices (see Figures 8A and 8B depicting the hierarchical KPI structure 800).
Consider claim 19, and as applied to claim 17 above, Kwan, as modified by Digikar et al. and Nam et al., further discloses wherein the method comprises a workflow automation to monitor performance of the RAN (the method of analyzing network performance “can include pre-processing performance data, using SOM to organize data on the basis of similarity, applying clustering techniques to group the similar patterns of data, and to automate the interpretation of the clusters by classifying and labeling the clusters in a
meaningful way” (see paragraph 0038)).
However, Kwan fails to disclose a Northbound API.
In the same field of endeavor, Vittal discloses a Northbound API (refer to Figures 5A-5C, “a device 200 may comprise a service graph generator that generates and stores a service graph 505 based on a topology 510. A service graph monitor 516 may monitor the network elements of the topology and service, such as via API calls, for metrics 518 to update dynamically or in real-time the elements and metrics 518” (see paragraph 0102)).
Therefore, it would have been obvious to a person having ordinary skill in the art to modify the method disclosed by Kwan to incorporate a Northbound API as taught by Vittal in order to use existing infrastructure to automate performance monitoring.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Kwan (US 20160381580 A1) in view of Digikar et al. (US 20230370347 A1) and further in view of Nam et al. (US 20230362681 A1) , Vittal et al. (US 20200366756 A1), and Burgarella et al. (WO 2023047170 A1).
Consider claim 20, and as applied to claim 17 above, Kwan, as modified by Digikar et al., further discloses a vector database (“An iterative process may be used to train the SOM (Self Organizing Map) and generate a plurality of neurons (or nodes) that corresponds to a vector of feature weights (feature weight vector), where each feature corresponds to a key performance indicator (KPI) of the wireless network” (see paragraph 0098), said SOM can reasonably be equated to a vector database).
However, Kwan, as modified by Digikar, fails to disclose the Northbound API.
In the same field of endeavor, Vittal discloses a Northbound API (refer to Figures 5A-5C, “a device 200 may comprise a service graph generator that generates and stores a service graph 505 based on a topology 510. A service graph monitor 516 may monitor the network elements of the topology and service, such as via API calls, for metrics 518 to update dynamically or in real-time the elements and metrics 518” (see paragraph 0102)).
Therefore, it would have been obvious to a person having ordinary skill in the art to modify the method disclosed by Kwan to incorporate a Northbound API as taught by Vittal in order to use existing infrastructure to automate performance monitoring.
However, Kwan as modified by Vittal fails to disclose a comparison check of an operational requirement against a running average associated with one or more of the KPIs of a RAN cell.
In the same field of endeavor, Burgarella et al. disclose a method comprising conducting a comparison check of an operational requirement against a running average associated with one or more of the KPIs of a RAN cell (see Figure 3, “In some embodiments, the KPI classifier 305 checks how well the KPIs can be predicted (i.e., determines or rates predictability)…KPI classifier 305 can be implemented as a vector autoregressive (VAR)/ seasonal autoregressive integrated moving average (SARIMA) process” (see paragraph 0048). The “divergency detector 309 is a software function that checks if the predicted values of the predictable KPIs cross KPI limits or warning/error thresholds specific to the type of the KPI” (see paragraph 0051)).
Therefore, it would have been obvious to a person having ordinary skill in the art to modify the method disclosed by Kwan and modified by Vittal et al. to include conducting a comparison check of an operational requirement against a running average associated with one or more of the KPIs of a RAN cell as taught by Burgarella et al. in order to monitor relevant KPIs with respect to the operational parameters of the network.
However, Kwan as modified by Vittal and Burgarella et al. fails to disclose wherein the RAN cell is represented with a digital replica.
In the same field of endeavor, Nam et al. disclose wherein the RAN cell is represented with a digital replica (“an electronic device for building a digital twin with respect to a network base station includes a memory configured to store one or more instructions and a simulator of the digital twin; a transceiver; and at least one processor configured to execute the one or more instructions to: obtain at least one simulated key performance indicator (KPI)” (see paragraph 0005)).
Therefore, it would have been obvious to a person having ordinary skill in the art to modify the method disclosed by Kwan and modified by Vittal and Burgarella et al. such that the RAN cell is represented with a digital replica as taught by Nam et al. in order to create a secondary virtual test environment without affecting network services.
Conclusion
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/ALEXANDER WU/Examiner, Art Unit 2642
/Rafael Pérez-Gutiérrez/Supervisory Patent Examiner, Art Unit 2642
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