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 .
Continued Examination
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on Novermber 13, 2025 has been entered.
This Office Action is in response to amendment filed on October 15, 2025 and wherein Claims 1, 10, 15, 18, 20 and 21 are amended.
In virtue of this communication, claims 1, 3-21 are currently pending in this Office Action.
The Office appreciates the explanation of the amendment and analyses of the prior arts, and however, although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993) and MPEP 2145.
Response to Arguments
Applicant's arguments filed on 10/15/2025 (Remarks, pages 13-15) with respect to the prior art rejection of claim 1 has been fully considered and it is persuasive, but it is necessitated in view of the new ground(s) of rejection by the applicant amendment. The Office has thoroughly reviewed Applicants' arguments but firmly believes that the cited references to reasonably and properly meet the claimed limitations.
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.
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.
Claims 1, 3-21 are rejected under 35 U.S.C. 103 as being unpatentable over Chou et al. (US 20210258866 A1, hereinafter Chou) in view of Akhtar et al. (US 20220295309 A1, hereinafter Akhtar), and in view of Niknam et al. (Intelligent O-RAN for beyond 5G and 6G Wireless Networks, provided in 12/12/2022 IDS), and further in view of O-RAN-Grp2 (O-RAN Working group 2 AI/ML workflow description and requirement, provide in 12/12/2022 IDS)
Claim 1: Chou teaches a method performed by a network function (Fig. 5, 6, Abstract, [0121], [0122]), the method comprising:
receiving an initial inter-cell interference management policy corresponding to a device from a radio access network (RAN) intelligent controller (RIC) (Fig, 5, Fig. 6, [0124], “ the non-RT RIC 612 provides a query-able catalog for an ML designer/developer to publish/install trained ML models … the non-RT RIC 612 may provide a discovery mechanism if a particular ML model can be executed in a target ML inference host (MF), and what number and type of ML models can be executed in the MF … The non-RT RIC 612 may also implement policies to switch and activate ML model instances under different operating conditions”, [0123], wherein the ML training host and/or ML model host/actor can be part of the non-RT RIC 612 and/or the near-RT RIC 614 for supervised learning, [0230], “the near-RT RIC may enforce the policies received over the A1 interface using the E2 interface and other network components … the non-RT RIC provides updated policies over the A1 interface to near-RT RIC which results in deletion of previously sent policies once the system has achieved the desired level of balance and control”. Combining [0123-0124] and [230], wherein near-RT RIC receiving management policy from non-RT RIC using A1 interface, and near-RT RIC perform the policies enforcement with collecting real time data via E2 interface);
receiving modeling information from the RIC, wherein the modeling information corresponds to the device, the modeling information comprises one or more of UE traffic parameters, UE radio parameters, or UE mobility parameters (Fig. 5, Fig. 6, [0230], “an O-RAN system of any disclosed examples is associated with the non-RT RIC providing enhanced policies over the A1 interface taking into account various UE related factors such as spectrum utilization by different UEs based on their traffic patterns, speeds of different UEs, mobility patterns”), and the modeling information further comprises at least one trained machine learning model (Fig. 6, [0121], wherein non-RT RIC 612 perform insertion of AI/ML models to near-RT RIC and other RAN functions, [0124], “the non-RT RIC 612 may provide a discovery mechanism if a particular ML model can be executed in a target ML inference host (MF), what number and type of ML models can be executed in the MF”, [0104], “an ML model may be any object or data structure created after an ML algorithm is trained with one or more training datasets. After training, an ML model may be used to make predictions on new datasets”, [0124], “the non-RT RIC 612 provides a query-able catalog for an ML designer/developer to publish/install trained ML models”);
determining a management policy for the device based on the modeling information and the initial inter-cell interference management policy (Fig. 6, [0230], “an O-RAN system of any disclosed examples is associated with the non-RT RIC providing enhanced policies over the A1 interface taking into account various UE related factors such as spectrum utilization by different UEs based on their traffic patterns, speeds of different UEs, mobility patterns”, [0143], “A1 interface is between Non-RT-RIC and the Near-RT RIC functions; A1 is associated with policy guidance for control-plane and user-plane functions; Impacted O-RAN elements associated with A1 include O-RAN nodes.
UE groups, and UEs”, [0230], “the near-RT RIC may enforce the policies received over the A1 interface using the E2 interface and other network components”).
providing the management policy to the device (Fig. 6, [0143], “A1 is associated with policy guidance for control-plane and user-plane functions; Impacted O-RAN elements associated with A1 include O-RAN nodes. UE groups, and UEs”, wherein any one of O-RAN nodes, UE groups and UEs is reading as device. [0146], “E2 is associated with control-plane and user-plane control functions: Impacted O-RAN elements associated with E2 include mostly individual UEs”, [0230], “the near-RT RIC may enforce the policies received over the A1 interface using the E2 interface and other network components”, thus, near-RT RIC enforcing management policy using E2 interface, which associated with O-RAN elements (e.g., individual UEs.).
However, Chou does not explicitly indicate a management policy is a predictive inter-cell interference management policy.
modeling information comprising one or more of a first expectation of RAN resource conditions for a predefined period of time, a second expectation of wireless backhaul resource conditions for the predefined period of time, a third expectation of UE mobility parameters, or an expectation of UE positioning information, or both for UEs in a geographic area, or a fourth expectation of performance metrics for the UEs in the geographic area.
Akhtar, from the same or similar field of endeavor, teaches a management policy
is a predictive inter-cell interference management policy ([0013-0014], wherein an xAPP of the NearRT-RIC reads the interference measurements and uses this to predict future interference levels and make handover decision to mitigate future interference congestion).
Chou and Akhtar are both considered to be analogous to the claimed invention because they are in the same field of wireless communication. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the management policy of Chou with supporting an inter-cell interference management policy as taught by Akhtar, for the benefit of allowing network to predict future interference levels and make handover decision to mitigate future interference congestion ([0014]).
Niknam and O-RAN-Grp2 teaches modeling information comprising one or more of a first expectation of RAN resource conditions for a predefined period of time (Niknam, page 2, third paragraph of column 1, “The LSTM model is trained at non-real-time radio intelligence controller (non-RT RIC) in the O-RAN architecture, using long term data gathered from RAN. The trained model is then sent to near-real-time radio intelligence controller (near-RT RIC) of the O-RAN for inference …Traffic prediction and the corresponding congestion treatments are continuously applied until the target KPI values are met”. page 3, fourth paragraph of column 2 , “identify the congestion event and the corresponding thresholds are defined based on the operator service level agreement(SLA) and can be re-configured by the operator based on their hardware or software requirements … Using RNN, the temporal pattern of the mentioned parameters are learned through the current values to predict future values and the potential congested cells.”. page 4, second paragraph of column, “The parameters of an RNN model that include 2 layers of 12 LSTM units, are learned to predict the future traffic for the next hour. This can be configured by operators as per the available data and its periodicity”), a second expectation of wireless backhaul resource conditions for the predefined period of time (alternative), a third expectation of UE mobility parameters (O-RAN-Grp2, Page 36, section 7.1, disclose predict KQI/QoE output based on mobility related metric. Page 21, lines 1-7 disclose RF signal strength, KPI and QoE are predicted within x+Δ) , or an expectation of UE positioning information (alternative), or both for UEs in a geographic area (alternative), or a fourth expectation of performance metrics for the UEs in the geographic area (alternative).
Chou and NikNam are both considered to be analogous to the claimed invention because they are in the same field of wireless communication. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the modeling information of Chou with expectation of RAN resource conditions as taught by NikNam, for the benefit of allowing network to handle traffic congestion and demonstrate its efficacy on a real-world dataset obtained from a large operator (abstract).
Chou and O-RAN-Grp2 are both considered to be analogous to the claimed invention because they are in the same field of wireless communication. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify the modeling information of Chou with expectation of UE mobility parameters as taught by O-RAN-Grp2, for the benefit of improving RAN or network performance by using AI/ML based model (page 9, first paragraph).
Claim 21 is analyzed and rejected according to claim 1 and Chou further teaches at least one memory (Fig. 17, elements 1704, 1706, 1707); and at least one processor (Fig. 17, elements 1702) coupled with the at least one memory ([0225], “one or any combination of the hardware processor 1702, the main memory 1704, the static memory 1706, or the mass storage 1716 may constitute the device-readable medium 1722”).
Claim 15 is analyzed and rejected according to claim 1 and the combination of Chou and Akhtar further teaches
transmitting at least one monitoring report corresponding to a device to an interference management device (Chou, Fig, 8, element 810, Fig. 9, element 901, [0186], “receiving data related to performance measurements of NSSI. The data may be received from network functions over an O1 interface”, [0230], “These include measurement reports with RSRP/RSRQ/CQI information for serving and neighboring cells and intra-RAT and inter-RAT measurement reports, cell quality thresholds”, [0122], “The O-RAN near-RT RIC 614 is a logical function that enables near-real-time control and optimization of RAN elements and resources via fine-grained data collection and actions over the E2 interface”, [0143], “A1 interface is between Non-RT-RIC and the Near-RT RIC functions; A1 is associated with policy guidance for control-plane and user-plane functions; Impacted O-RAN elements associated with A1 include O-RAN nodes. UE groups, and UEs”);
wherein the predictive interference management policy is determined based on modeling information (Akhtar ,[0013-0014], wherein an xAPP of the NearRT-RIC reads the interference measurements and uses this to predict future interference levels and make handover decision to mitigate future interference congestion), the at least one monitoring report (Chou, Fig. 6, [0230], “the near-RT RIC may enforce the policies received over the A1 interface using the E2 interface and other network components”, wherein NON-RT RIC initialized NEAR-RT policy via A1 interface, [0122], “The O-RAN near-RT RIC 614 is a logical function that enables near-real-time control and optimization of RAN elements and resources via fine-grained data collection and actions over the E2 interface”) and an initial inter-cell interference management policy (Chou, [0143], “A1 interface is between Non-RT-RIC and the Near-RT RIC functions; A1 is associated with policy guidance for control-plane and user-plane functions; Impacted O-RAN elements associated with A1 include O-RAN nodes. UE groups, and UEs”).
Claim 18 is analyzed and rejected according to claim 1 and Chou further teaches
a network function, the method (Fig. 5, 6, Abstract, [0121], [0122]) comprising:
RIC from a service entity (Fig. 6, element 602,[0121], “the non-RT RIC 612 is a function that sits within the SMO platform (or SMO framework) 602 in the O-RAN architecture”, [0141-0148], wherein Near-RT RIC receive measurement data via E2 path, and E2 connects to O-e/gNB, O-DU, O-CU-CP, O-CU-UP, etc), or a management entity (alternative), or both to an application (alternative);
receiving a request for modeling information in response to transmitting the initial inter-cell interference management policy and transmitting the modeling information in response to receiving the request (Chou, [0180], Table 1, disclose Non-RT RIC determines the action based on model inference to update the NSSI resource, executes the action at the time determined by the model inference, after executing action, starting Post Near-RT RIC to continue monitoring the NSSI resource usages. [0186], “receiving data related to performance measurements of NSSI … The data may be received periodically or upon request”. [0230], “the non-RT RIC provides enhanced policies over A1 interface to near-RT RIC which results in policy-based cell selection … the near-RT RIC may enforce the policies received over the A1 interface using the E2 interface and other network components”. Akhtar , Fig. 13, [0180], “ client application 532 may receive request data from host application 512 and provide user data in response to the request data” ),
Claim 3: Chou teaches the method of claim 1, wherein the initial inter-cell interference management policy is transmitted from a service entity, or a management entity, or both (Fig. 5, Fig. 6, [0121], “the non-RT RIC 612 is a function that sits within the SMO platform (or SMO framework) 602 in the O-RAN architecture”, [0141-0148], wherein Near-RT RIC receive measurement data via E2 path, and E2 connects to O-e/gNB, O-DU, O-CU-CP, O-CU-UP, etc.).
Claim 4: Chou teach wherein the initial inter-cell interference management policy comprises a cell identifier, a network slice identifier, a service type, an application type, a profile, a policy identifier list, per policy metrics, per policy thresholds, an interference management preference, an enforcement flag, a middleware flag, a middleware identifier, a time validity indicator, a geographic area, vertical specific parameters, or cross-vertical parameters ([0180], TABLE 1, “Non-RT RIC determines the action based on model inference to update the NSSI resources that may include the following information: a) the time/date, b) locations (e.g. gNB ID), c) NSSI ID, d) slice subnet attributes [z], e) VNF resources update”).
Claim 19 is analyzed and rejected according to claim 18 and claim 4.
Claim 5: Chou teaches the method of claim 1, further comprising obtaining a monitoring event report related to the device (Fig. 8, element 804, Fig.6, [0141-0148], wherein Near-RT RIC receive measurement data via E2 path, and E2 connects to O-e/gNB, O-DU, O-CU-CP, O-CU-UP, etc., [0230], “an O-RAN system of any disclosed examples is associated with the non-RT RIC providing enhanced policies over the A1 interface taking into account various UE related factors such as spectrum utilization by different UEs based on their traffic patterns, speeds of different UEs, mobility patterns, … UE subscription, type of service requested by UEs … and measurement gaps on per-UE or per-frequency”).
Claim 6: The combination of Chou and Akhtar teaches the method of claim 5, wherein the predictive inter-cell interference management policy (Akhtar, Fig. 8, element 816, Fig. 9, element 912, [0151], “the NonRT-RIC network node transmits the A1 interface message to a NearRT-RIC. In some embodiments, the NearRT-RIC may comprise one or more xAPPs for consuming the enrichment information EI elements in the A1 message”, [0013-0014], wherein an xAPP of the NearRT-RIC reads the interference measurements and uses this to predict future interference levels and make handover decision to mitigate future interference congestion) is determined in response to obtaining the monitoring event report (Chou, Fig. 9, element 902, [0187], “training an AI/ML model using the data. Once trained, the AI/ML model may generate a number of inferences about the network operation at various times/locations”).
The motivation for combining Chou and Akhtar regarding to the claim 1 is also applied to claim 6.
Claim 7: The combination of Chou and Akhar teaches the method of claim 5, wherein the monitoring event report comprises one or more of a cell identifier ID, a UE ID, a network slice ID, a resource ID, a resource pool ID, a UE quality of experience downgrade indication, a UE quality of service downgrade indication, a high resource load indication, a high radio access network delay indication, a low backhaul resource availability indication, a quality of service fluctuation indication, a radio link failure indication, a bandwidth adaptation requirement, a radio resource adaptation requirement, or a traffic steering requirement(Chou, [0230], “an O-RAN system of any disclosed examples is associated with the non-RT RIC providing enhanced policies over the A1 interface taking into account various UE related factors such as spectrum utilization by different UEs based on their traffic patterns, speeds of different UEs, mobility patterns, QoS/QCI characteristics, load balancing information, UE subscription, type of service requested by UEs or traffic class (tile time versus non-real-time, data versus voice, etc”, [0180], TABLE 1, “Non-RT RIC determines the action based on model inference to update the NSSI resources that may include the following information: a) the time/date, b) locations (e.g. gNB ID), c) NSSI ID, d) slice subnet attributes [z], e) VNF resources update”. Akhtar, Fig. 3, wherein enrichment information includes “Policy Type”, “UE Identity” and “Encirclement Data”).
Claim 16 is analyzed and rejected according to claim 15 and claim 7.
Claim 8: Chou teaches the method of claim 5, further comprising subscribing to a RAN node, or a subscription function, or both for receiving the monitoring event report (Fig. 8, element 804, Fig.6, [0141-0148], wherein Near-RT RIC receive measurement data via E2 path, and E2 connects to O-e/gNB, O-DU, O-CU-CP, O-CU-UP, etc. ).
Claim 9: Chou teaches the method of claim 1, wherein the device comprises at least one network unit, or at least one UE, or both (Fig. 6, elements 610, 601).
Claim 10: The combination of Chou and O-RAN-Grp2 teaches the method of claim 1, wherein the modeling information comprises one or more of an expected distribution (Chou, [0180], TABLE 1, “Non-RT RIC determines the action based on model inference to update the NSSI resources that may include the following information: a) the time/date, b) locations (e.g. gNB ID), c) NSSI ID, d) slice subnet attributes [z], e) VNF resources update”, [0230], “an O-RAN system of any disclosed examples is associated with the non-RT RIC providing enhanced policies over the A1 interface taking into account various UE related factors such as spectrum utilization by different UEs based on their traffic patterns, speeds of different UEs, mobility patterns, QoS/QCI characteristics, load balancing information, UE subscription, type of service requested by UEs or traffic class (tile time versus non-real-time, data versus voice, etc”) of one or more of the first expectation, the second expectation, the third expectation, or the fourth expectation over the predefined period of time; a confidence level metric for one or more of the first expectation (O-RAN-Grp2, Page 36, section 7.1, disclose predict KQI/QoE output based on mobility input data. Page 21, lines 1-7 disclose RF sigbak strength, KPI and QoE are predicted within x+Δ), the second expectation, the third expectation, or the fourth expectation over the predefined period of time; an expected probability density function over RAN resources (chou, [0230], “an O-RAN system of any disclosed examples is associated with the non-RT RIC providing enhanced policies over the A1 interface taking into account various UE related factors such as spectrum utilization by different UEs based on their traffic patterns, speeds of different UEs, mobility patterns, QoS/QCI characteristics, load balancing information, UE subscription, type of service requested by UEs or traffic class (tile time versus non-real-time, data versus voice, etc”), or backhaul resources (alternative), or both; or an expectation of a sequence of inter-cell handovers for the UEs in the geographic area (alternative).
Claim 20 is analyzed and rejected according to claim 18 and claim 10.
Claim 11: The combination of Chou and Akhtar teaches the method of claim 1, wherein the predictive inter-cell interference management policy comprises one or more of a cell identifier (ID), an application ID, a group of UE IDs, a network slice ID, a central unit CU ID, a distributed unit DU ID, a current policy ID, a new policy ID, a current traffic steering policy ID, a new traffic steering policy ID, a confidence level parameter, an enforcement flag, a time validity ID, an area ID, an overload indication, a high interference indication, a relative narrowband transmit power, almost blank subframe pattern information, a coordinated multipoint coordination area, a coordinated multipoint scheme, or a resource restriction (Chou, Fig. 6, [0180], TABLE 1, “Non-RT RIC determines the action based on model inference to update the NSSI resources that may include the following information: a) the time/date, b) locations (e.g. gNB ID), c) NSSI ID, d) slice subnet attributes [z], e) VNF resources update”. Akhtar, Fig. 3, wherein enrichment information includes “Policy Type”, “UE Identity” and “Encirclement Data”).
Claim 17 is analyzed and rejected according to claim 15 and claim 11.
Claim 12: The combination of Chou and Akhtar teaches the method of claim 1, Akhtar additionally teaches further comprising requesting a validation of the predictive inter-cell interference management policy from a conflict mitigation function and receiving a validation response from the conflict mitigation function ([0013-0014], wherein an xAPP of the NearRT-RIC reads the interference measurements and uses this to predict future interference levels, and publishes the predicted interference levels as refined data, consume the predicted interference levels (refined data) and based on this xAPP “b” makes handover decisions to mitigate future interference congestion).
Chou and Akhtar are both considered to be analogous to the claimed invention because they are in the same field of wireless communication. Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the system of Chou and the features of validation of the predictive inter-cell interference management policy as taught by Akhtar, for the benefit for using user friendly interface to make handover decisions to mitigate future interference congestion (see paragraph [0014]).
Claim 13: The combination of Chou and Akhtar teaches the method of claim 1, wherein communications are transmitted and received using an open application program interface (API) (Chou, Fig. 6, [0124], “The non-RT RIC 612 may also include and/or operate one or more ML engines, which are packaged software executable libraries that provide methods , routines, data types, etc., used to run ML model ”. Akhtar, Fig.2, [0012], “the NearRT-RIC is further divided in a framework part and a multitude of xAPPs. The framework part is responsible for the terminating the interfaces E2 and A1. The NearRT-RIC further exposes an API (e.g., R1 interface) towards the xAPPs”).
Claim 14: The combination of Chou and Akhtar teaches the method of claim 1, Akhtar additionally teaches wherein the predictive inter-cell interference management policy is provided to the device via an application exposure function ([0013], “The xAPPs realize the NearRT-RIC RRM by consuming the RAN data and use the E2 interface, via the NearRT-RIC framework, to control handovers and radio bearers”, [0014], “The xAPP “a” then publishes the predicted interference levels as refined data. An xAPP “b” is designed to consume the predicted interference levels (refined data) and based on this xAPP “b” makes handover decisions to mitigate future interference congestion”).
The motivation for combining Chou and Akhtar regarding to the claim 12 is also applied to claim 14.
Conclusion
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/Y.Z./Examiner, Art Unit 2472
/NICHOLAS A JENSEN/Supervisory Patent Examiner, Art Unit 2472