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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/11/2025 has been considered by examiner and made of record in the application file.
Response to Arguments
Applicant’s arguments, see pages 1-2, filed 11/20/2025, with respect to the rejection(s) of claim(s) 41-60 under 35 USC § 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Curic (US-20240098568-A1).
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.
Claim(s) 41-44, 46-49, and 51-60 are rejected under 35 U.S.C. 103 as being unpatentable over Tapia (US 20170353991 A1) in view of Arora (US 20240022923 A1) in further view of Curic (US-20240098568-A1).
Regarding Claim 41, Tapia discloses a method comprising:
building (paragraph [0050], training), based on telemetry data from an open radio access network (paragraph [0021], Fig.1:111, "The operation data source 111 may include a data collection that provides performance information…include Radio Access Network (RAN)" (i.e., Tapia discloses collecting data from Radio Access Network. "Open radio access network" will be mapped by a different prior art.)), a machine learning model (paragraph [0049], Fig.2, machine learning models 220A, 220N) that matches performance indicator degradation signatures to respective known configuration solutions that predictably improve corresponding instances of performance degradation within the open radio access network (paragraph [0049], Fig.2, "The model training module 219 may train machine learning models 220A, 220N to analyze the performance data from the data sources 110-114 to determine root causes for the quality of service issues…" and paragraph [0050], "training data input…KPIs, network coverage details…" and paragraph [0058], "The auto fix module 214…can provide a suggestion for resolving each quality of service issue using a machine learning model." (i.e., Tapia discloses training machine learning module to analyze performance data from data sources and one of the data sources is data from Radio Access Network Fig.1:111 and find root cause for quality of service. By finding the root cause, a solution can be matched by the auto fix module 214 that will improve the service issues in the RAN.));
detecting that a specific performance indicator (paragraph [0065], a particular type of service quality issue) of the open radio access network has degraded (paragraph [0064], Fig.4:402, "At block 402, the network fix application analyzes the obtained data in order to detect network service issues.", and paragraph [0065], Fig.5:501, "…At block 501, the issue investigation module 210 conducts issue investigation to identify symptoms regarding service quality in poor performing areas…symptoms that corresponds to a particular type of service quality issue and/or root cause" (i.e., Fig.5 is a detailed step of Fig.4:403. Fig.4 illustrates of monitoring data sources 110-114 and at step 403 detecting a potential data that will cause network degradation.)); and
perform (paragraph [0071], Fig.4:407, implemented), by applying the machine learning model in response to detecting that the specific performance indicator of the open radio access network has degraded (paragraph [0071], Fig.4:406, "…the auto fix module 214 can utilize a machine learning logic to determine a resolution for root causes and improve the quality of service provided to the subscribers."), a corresponding solution (paragraph [0071], network fix) indicated by the machine learning model such that the specific performance indicator is improved (paragraph [0071], Fig.4:407, "…As indicated in block 407, the courses of action or the network fix for each node associated with a problem is implemented in an order of priority based on the network fix prioritization. " (i.e., implementing a solution to a node indicated by the auto fix module that utilize machine learning par.71 Fig.4:406.)).
However, Tapia does not disclose based on telemetry data from an open radio access network.
Arora discloses based on telemetry data from an open radio access network (page 11, Claim 8, “8. The system of claim 1, wherein the network function is enabled using an open radio access network protocol”, and Paragraph [0065], Fig.5, "Generally, operation 503 can comprise identifying impacted databases and interfaces during such identified events, and retrieving metadata form different NFs and performing consistency checks to detect the context of mismatch/misconfiguration. In this way, configuration data of NFs can be proactively retrieved and analyzed, accounting for one or more deficiencies in provisioning of the NFs." (i.e., Examiner first points to Fig.3 of a radio data analysis system 300 that analyze the network function (NF) of Fig.1 which is an open radio access network. Fig.5 is to showing of obtaining data from NF that could be like central unit (CU) or distributed unit (DU).)).
Tapia and Arora are considered to be analogous to the claimed invention because they are in the same Supervisory, monitoring or testing arrangements. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified Tapia to implemented open radio access network (open ran) because carriers are moving towards open ran in order have interoperability and the open ran does not require to rely on a single inventor for hardware and software. Thus, there is a need to collect telemetry data from open ran in order to find root cause and provide a solution because mixing and matching hardware can create new issues and Arora provides a way to collect data from components of open ran like central unit, distribution unit, and radio unit (Arora, paragraph [0028], “To account for one or more of these deficiencies, one or more systems, methods and/or non-transitory computer readable mediums are defined herein that can provide in-situ, dynamic and/or realtime diagnostics of NF configurations and specifications, such as by analyzing systems aspects for suspected and/or probable KPI degradations.”).
However, Tapia in view of Arora do not disclose wherein: a radio access network intelligent controller performs, by applying the machine learning model in response to detecting that the specific performance indicator of the open radio access network has degraded, the corresponding solution indicated by the machine learning model such that the specific performance indicator is improved.
Curic discloses wherein: a radio access network intelligent controller (par.92, near-RT RICs 114) performs, by applying the machine learning model (par.92, non-RT RIC 112) in response to detecting that the specific performance indicator of the open radio access network has degraded (paragraph [0068], Fig.1, "FIG. 10 depicts an example impact matrix 1000 that establishes an awareness in the non-RT RIC 112 about the xApp activities in all of the associated near-RT RICs 114. Correlation of the global xApp activity log 800 and performance and fault data matrix 310 facilitates obtaining the information about the network response that followed each action performed by the xApps 132. Therefore, non-RT RIC 112 has holistic overview of the xApp activities and their impact on the KPIs for both intra- and inter-domain case. This information is leveraged to create patterns that assist in conflict detection." Paragraph [0056], “conflicts can cause network instability or performance degradation” (i.e., non-RT RIC utilize machine learning guides the Near-RT RIC and to assist in conflict.)),
the corresponding solution indicated by the machine learning model such that the specific performance indicator is improved (paragraph [0045], Fig.1, "The non-RT RIC 112 is a logical function within the SMO framework 102 that enables non-real-time (>1 second operation times) control and optimization of RAN elements and resources; AI/machine learning (ML) workflow(s) including model training, inferences, and updates; and policy-based guidance of applications/features in the near-RT RIC 114." and paragraph [0093], Fig.3, "In general, the xApp actions at any time must be such that they maintain the satisfactory KPIs or improve the degraded KPIs, while keeping intent into consideration. The analytics module 308 is used for this purpose. The analytics module 308 is aware of the optimization goals of each xApp 132 and parameters that each xApp 132 can affect. Only then the analytics module 308 can tune the xApp activity according to the intent requirements." (i.e., The analytic module of non-RT RIC optimizes the xAPP actions in order to improve the degraded KPIs.)).
Tapia in view of Arora and Curic are considered to be analogous to the claimed invention because they are in the same Supervisory, monitoring or testing arrangements. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified Tapia to implemented the method of Curic because Curic discloses an improvement to O-RAN, such as facilitating automated conflict detection (Curic, paragraph [0031], “One or more embodiments of the present invention facilitate improvements to radio access networks (RANs), particularly open-RAN (O-RAN) networks. Embodiments of the present invention provide technical solutions that facilitate automated conflict detection. One or more embodiments of the present invention detect indirect and implicit conflicts among operations of a near-RT RICs, regardless of whether the conflicts occur on the same or different (neighboring) Near-RT RICs. Further, embodiments of the present invention detect and mitigate any type of conflict that occur inter- or intra-domain (as defined herein).”).
Regarding Claim 42, Tapia in view of Arora in further view of Curic discloses the limitations of claim 41.
Arora further discloses further comprising adjusting a configuration parameter (paragraph [0066], a default configuration or a configuration) to an updated value (paragraph [0066], a configuration can be retrieved) as part of a specific solution from the respective known configuration solutions (paragraph [0066], Fig.5:504, "in the case of an inconsistency (e.g., mismatch and/or misconfiguration) among the NFs (e.g., between NFs and/or between an NF and a default configuration or a configuration), a configuration can be retrieved from the SMO 360 and transmitted to the affected NF, such as by the controller component 312" (i.e., Fig.5 uses a machine learning or Agent to monitor network function (NFs) of Open RAN and when the parameter is detected as mismatch or misconfiguration the radio analysis system 300 retrieves known parameters that can update the parameter of NF as "a specific solution".)).
The proposed combination as well as the motivations for combining the references presented in the rejection of the parent claim apply to this claim and are incorporated herein by reference.
Regarding Claim 43, Tapia in view of Arora in further view of Curic discloses the limitations of claim 42.
Tapia further discloses monitoring the specific performance indicator prior to adjusting the configuration parameter (paragraph [0064], Fig.4:402, "…The network fix application may analyze trends and conduct clustered analysis and/or individual analysis. For example, the network fix application may cluster data sources for different regions or analyze each issue individually…" (i.e., Tapia discloses analyzes RAN data and other types of data prior to adjusting in order to detect possible issues.)); and
monitoring the specific performance indicator after adjusting the configuration parameter (paragraph [0073], Fig.4:408, "As indicated in block 408, the verification module 217 of the action tracker 121 tracks or reviews performances of each change made on nodes." (i.e., Tapia discloses monitoring each change made on nodes.)).
The proposed combination as well as the motivations for combining the references presented in the rejection of the parent claim apply to this claim and are incorporated herein by reference.
Regarding Claim 44, Tapia in view of Arora in further view of Curic discloses the limitations of claim 43.
Tapia further discloses further comprising maintaining the configuration parameter at the updated value for a predetermined amount of time during which the specific performance indicator can be monitored (paragraph [0032], "…a network fix prioritization is optimal if the order in which network fixes are made according to the network fix prioritization can increase the network performance level within a predetermined period of time." and paragraph [0076], Fig.4:409, "If the expected performance improvement is not achieved or the network performance level is not above the predetermined threshold, then the auto fix module 214 of the recommendation module 120 implements a different network fix associated with the quality of service issue." (i.e., par.76 discloses when the performance is not above a threshold then another fix is implemented, and there must be a predetermined time period before implementing another fix associated with the service issue because measuring performance takes time if there will be improvement or not.)).
The proposed combination as well as the motivations for combining the references presented in the rejection of the parent claim apply to this claim and are incorporated herein by reference.
Regarding Claim 46, Tapia in view of Arora in further view of Curic discloses the limitations of claim 41.
Arora further discloses wherein the corresponding solution indicated by the machine learning model is performed as part of a closed-loop radio access network optimization loop in which the corresponding solution is autonomously applied by the open radio access network in repeated iterations (paragraph [0066], Fig.5, "At operation 504, the process flow 500 can comprise enforcing a configuration that can account for and/or resolve a mismatch, misconfiguration and/or the like…executing the configuration enforcement…in the case of an inconsistency (e.g., mismatch and/or misconfiguration) among the NFs…a configuration can be retrieved from the SMO 360 and transmitted to the affected NF, such as by the controller component 312." and paragraph [0067], “At operation 505, the process flow 500 can comprise analysis of the knowledge base 328 and/or analytical model 330 for updates and/or unknown or unspecified context. Likewise, operation 505 can comprise updating of the knowledge base 328 and/or training of the analytical model 330 where a new context is determined.” (i.e., Tapia Fig.4 discloses suggesting a solution and implemented a solution that is done by a person. Arora Fig.5 discloses monitoring and configuring network functions (NFs) of open ran. In the instance the only issues detected by Tapia system is NFs of open ran, Tapia can use Fig.5 to configure the affected NF without the user as this would provide faster implementation of the solution. Thus, performing the solution in "a closed-loop radio access network optimization loop". Examiner further points Tapia Fig.4:407 to 409 and 409 to 401 is a closed loop to collect data and keep implementing solutions until the improvement is achieved.)).
Curic further discloses wherein the corresponding solution indicated by the machine learning model is performed as part of a closed-loop radio access network optimization loop in which the corresponding solution is autonomously applied by the open radio access network in repeated iterations (paragraph [0092], Fig.10, “FIG. 10 depicts an example impact matrix 1000 that establishes an awareness in the non-RT RIC 112 about the xApp activities in all of the associated near-RT RICs 114. Correlation of the global xApp activity log 800 and performance and fault data matrix 310 facilitates obtaining the information about the network response that followed each action performed by the xApps 132…” and paragraph [0094], “the analytics module 308 uses reinforcement learning to determine the policies (224).” and paragraph [101], “the near-RT RIC 114 creates snapshots of the KPIs of an xApp 132 before and after that xApp 132 has made certain updates. Thus, the delta of the impact can be analyzed and assessed if it is meeting the operation targets.” and paragraph [0102], “Analytics Module 308 can also revert the policy if it identifies that it did not result with the desired network behavior. The policy revert activity is also logged for future reference.” (i.e., Fig.10 and par.92-103 discloses continuously monitor xApp activity and using machine learning that is indicated by the analytics module to improve the degraded KPIs, and examiner reading as a closed-loop since par.92-103 is implying its continuously updating the parameter of xApp behavior.)).
The proposed combination as well as the motivations for combining the references presented in the rejection of the parent claim apply to this claim and are incorporated herein by reference.
Regarding Claim 47, Tapia in view of Arora in further view of Curic discloses the limitations of claim 41.
Tapia discloses wherein the machine learning model comprises a library of classifiers that classify telemetry data as matching one or more of the performance indicator degradation signatures to predict the respective known configuration solutions (paragraph [0049], Fig.2:220A,220N, "The model training module 219 may train machine learning models 220A, 220N to analyze the performance data from the data sources 110-114 to determine root causes for the quality of service issues for subscribers and to prioritize network fix for each problem related to the root causes." and paragraph [0050], "machine learning training module 219 may receive a training corpus comprised of one or more input datasets from the data adaptor platform 116." and paragraph [0051], " To generate training models, the training module 119 is configured to select an initial type of machine learning algorithm to training a machine learning model using the training corpus… the different types of machine learning algorithms may include a Bayesian algorithm, a decision tree algorithm, an SVM algorithm, an ensemble of trees algorithm…" (i.e., Tapia discloses multiple models 220A, 220N therefor "a library" and "classifiers" is the different types of algorithms that each machine learning model is using to determine the root cause in order to obtain a solution.)).
The proposed combination as well as the motivations for combining the references presented in the rejection of the parent claim apply to this claim and are incorporated herein by reference.
Regarding Claim 48, Tapia in view of Arora in further view of Curic discloses the limitations of claim 41.
Arora further discloses wherein the telemetry data comprises at least two of performance management data (paragraph [0052], Fig.3:328, "such knowledge base 328 can comprise a set of events that have correlation or causality with configuration mismatch… additional context can comprise user entity (UE) events, key logs, metadata, telemetry, traces" (i.e., Par.50-53 radio data analysis system 300 has a knowledge base 328 and that stores data of O-RAN.)),
fault management data (paragraph [0054], Fig.3:328, "Generally, the AAs 314 can report to the controller component 312 upon detection of a context relating to possible KPI degradation. Such context can be any specified event, message and/or the like of a knowledge base 328." (i.e., Par.53 also talks about updated the knowledge base when there is a degradation of KPI.)).
The proposed combination as well as the motivations for combining the references presented in the rejection of the parent claim apply to this claim and are incorporated herein by reference.
Regarding Claim 49, Tapia in view of Arora in further view of Curic discloses the limitations of claim 41.
Arora further discloses wherein the telemetry data is continuously streamed from the open radio access network to a centralized data platform (paragraph [0013], " The analytical model 330 can be employed by the AAs 314 and/or receive information from the AAs regarding current conditions, configurations, KPIs and/or the like of the NFs and/or other aspects of the radio system 100." (i.e., Examiner pointes to Fig.3 of NF being CU, DU, or RU and data is collected to a radio analysis system 300.)).
Tapia discloses a centralized data platform (paragraph [0013], "a network fix application can continuously monitor performance data related to…network components in a wireless carrier network…" and paragraph [0016], Fig.1 "Further, in a networked deployment, new computing nodes 126 may be added on the fly without affecting the operational integrity of the data adaptor platform 116," (i.e., Examiner pointes to Fig.1 where the telemetry data of radio access network such as Fig.1:111 is streamed to the Fig.1:126 and all the operation of providing a solution happens at the computing nodes using Fig.4.)).
The proposed combination as well as the motivations for combining the references presented in the rejection of the parent claim apply to this claim and are incorporated herein by reference.
Regarding Claim 51, which is similar in scope to claim 41, thus rejected under the same rationale.
Examiner notes Tapia discloses a physical computing processor; and a non-transitory computer-readable medium (paragraph [0035], Fig.2, “The computing nodes 126 may include a communication interface 201, one or more processors 202, and memory 204.”).
Regarding Claim 52, which is similar in scope to claim 42, thus rejected under the same rationale.
Regarding Claim 53, which is similar in scope to claim 43, thus rejected under the same rationale.
Regarding Claim 54, which is similar in scope to claim 44, thus rejected under the same rationale.
Regarding Claim 55, which is similar in scope to claim 45, thus rejected under the same rationale.
Regarding Claim 56, which is similar in scope to claim 46, thus rejected under the same rationale.
Regarding Claim 57, which is similar in scope to claim 47, thus rejected under the same rationale.
Regarding Claim 58, which is similar in scope to claim 48, thus rejected under the same rationale.
Regarding Claim 59, which is similar in scope to claim 49, thus rejected under the same rationale.
Regarding Claim 60, which is similar in scope to claim 41, thus rejected under the same rationale.
Examiner notes Tapia discloses a non-transitory computer-readable medium (paragraph [0036], Fig.2:204, “The memory 204 may be implemented using computer-readable media, such as computer storage media… any other non-transmission medium that can be used to store information for access by a computing device.”).
Claim(s) 45 is rejected under 35 U.S.C. 103 as being unpatentable over Tapia (US 20170353991 A1) in view of Arora (US 20240022923 A1) in further view of in view of Curic (US-20240098568-A1) MEDITHE (US-20230091638-A1).
Regarding Claim 45, Tapia in view of Arora in further view of Curic discloses the limitations of claim 41.
Tapia further discloses wherein: the machine learning model is built in response to detecting that the specific performance indicator of the open radio access network has degraded at (paragraph [0041], “The network fix application 118 may process real time or non-real time data from various geographic locations, in which data from multiple data sources may be aggregated, converged, or otherwise consolidated.” and paragraph [0049], “The artificial intelligence module 122 comprises at least one machine learning training module for issue prioritization and predicting root cause 219, at least one machine learning logic 221, and one or more machine learning trained models 220A, 220N. The model training module 219 may train machine learning models 220A, 220N to analyze the performance data from the data sources 110-114 to determine root causes for the quality of service issues for subscribers and to prioritize network fix for each problem related to the root causes.” and paragraph [0050], "In the initial training data input phase of the machine learning training pipeline, it is contemplated that the machine learning training module 219 may receive a training corpus comprised of one or more input datasets from the data adaptor platform 116. The training corpus may include training data that emulates data collected from the multiple data sources 110-114 and optionally a set of desired outputs for the training data. For example, the data that is received during the training data input phase may include Operating Support System (OSS) counters, KPIs, network coverage details…" (i.e., the machine learning is built on data from adaptor platform 116 wherein includes RAN data OSS counters as disclosed in par.21, and is built in order to detect KPIs issues or degradation.));
detecting that the specific performance indicator of the open radio access network has degraded is subsequently performed also at a second and distinct site (paragraph [0065], Fig.5:503, " At block 501, the issue investigation module 210 conducts issue investigation to identify symptoms regarding service quality in poor performing areas…At block 502, the issue investigation module 210 correlates one or more complex symptoms to a service quality problem by matching the symptoms with previously stored performance pattern or symptoms that corresponds to a particular type of service quality issue and/or root cause. Accordingly, at block 503, the issue investigation module 210 identifies specific long-term and/or short-term problems in poor performing areas. At block 504, the root cause analysis module predicts a root cause for the identified wireless carrier network issues based on a set of live input data using the machine learning model." (i.e., Examiner reading "previously stored performance pattern" as the performance pattern on the first site and "poor performing areas" as the second and distinct site. Because there is a plurality of “poor performing areas” that is reading as distinct areas but with similar or matching symptoms e.g., the specific performance indicator.)).
Curic further discloses and the radio access network intelligent controller performs, by applying the machine learning model in response to detecting that the specific performance indicator of the open radio access network has degraded at the second and distinct site, the corresponding solution indicated by the machine learning model such that the specific performance indicator is improved (paragraph [0045], Fig.1, "The non-RT RIC 112 is a logical function within the SMO framework 102 that enables non-real-time (>1 second operation times) control and optimization of RAN elements and resources; AI/machine learning (ML) workflow(s) including model training, inferences, and updates; and policy-based guidance of applications/features in the near-RT RIC 114." and paragraph [0093], "In general, the xApp actions at any time must be such that they maintain the satisfactory KPIs or improve the degraded KPIs, while keeping intent into consideration. The analytics module 308 is used for this purpose. The analytics module 308 is aware of the optimization goals of each xApp 132 and parameters that each xApp 132 can affect. Only then the analytics module 308 can tune the xApp activity according to the intent requirements." (i.e., Similar to Claim 41, the near-RT RIC performs the changes of xApp as indicated by the non-RT RIC.)).
However, Tapia in view of Arora in further view of Curic do not explicitly disclose a first site.
MEDITHE discloses First site (par.24, Fig.1, cell) (paragraph [0024], "At operation 205, data of a plurality of features of a plurality of cells are collected over a period of time…the collected data correspond to the training data 142 described with respect to FIG. 1." and paragraph [0025], "Examples of cell features for which data are collected include key performance indicators (KPIs)." and paragraph [0035], "At operation 235, one or more trained machine learning models obtained by one or more machine learning techniques performed at operation 225 are saved…the one or more trained machine learning models are provided to a cell anomaly detecting device for use in cell anomaly detection, as described with respect to FIG. 1 where one or more machine learning models 144 are provided to the cell anomaly detecting device 150." (i.e., Fig.1 shows plurality of cells and the data such as KPI are collected to train machine learning to detect anomalies or degradation.)).
Tapia in view of Arora in further view of Curic and MEDITHE are considered to be analogous to the claimed invention because they are in the same field wireless communication. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have modified Tapia to implement the method of MEDITH of collecting data from a first cite as MEDITH enables to collect data from a plurality of cells by clustering and reducing the amount of processing and time required for cell anomaly detection (MEDITH, paragraph [0026], “when a group of two or more KPIs are related to each other most of the times, there is a high probability that if one KPI in the group shows that the cell is anomalous, the other KPI(s) in the group will also show that the cell is anomalous. In such situations, it is sufficient to consider one KPI in the group and ignore the other KPI(s) for cell anomaly detection, thereby reducing the amount of processing and time required for cell anomaly detection… As a result, the number of KPIs and the corresponding of KPI data to be considered for cell anomaly detection are significantly reduced, in one or more embodiments.”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Erkin S. Abdullaev whose telephone number is (571)272-4135. The examiner can normally be reached Monday - Friday - 8:00 am - 5:00 pm.
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ERKIN S. ABDULLAEV
Examiner
Art Unit 2648
/ERKIN ABDULLAEV/Examiner, Art Unit 2648
/WESLEY L KIM/Supervisory Patent Examiner, Art Unit 2648
Prosecution Insights