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 .
Status of Claims
Claims 1 – 20 are pending and examined herein.
Claims 1 – 20 are rejected under 35 U.S.C. 112(b).
Claims 1 – 20 are rejected under 35 U.S.C. 101.
Claims 1 – 20 are rejected under 35 U.S.C. 103.
Specification
The disclosure is objected to because of the following informalities:
Reference number 110 used to refer both “user” and “server” in [0051].
Reference number 100 used to refer “servers” but [0061] refers to it as device that comprise (displays, feedback device, speaker, etc).
Reference number 204A in [0063] used to refer “ML model training module” seems to be a typo as 204A doesn’t exist in drawings.
Reference number 210A in [0064] used to refer “rApp module”. 210A reference number doesn’t exist in drawing
Reference number 210 used to refer “xApp module” in [0068]. Should be 211 as introduced in Fig. 3
Reference number 210 used to refer “RAN Scenario generator module” in [0071]. Should be 310 as introduced in Fig. 7
Reference number 300 used to refer both “modeling module” and “rApps/xApps module” in [0073]. Reference number 330 should be used for “rApps/xApps module”
Reference number 402 used to refer “UE mobility pattern generator module for offline unit” in [0074]. Ref number 400 should be used here
Reference numbers 208, 216, 218, 316 are never introduced in specification
Appropriate correction is required.
Claim Objection
Claims 6-10, 16-19 objected to because of the following informalities:
Claim 6 recites “the method 1”. It is advised to clearly state “the method of claim 1” as other claims.
Claim 16 recites “the apparatus of claim 11 1”. It is advised to clearly state “the apparatus of claim 11” if referring to apparatus claim.
Claim 20 recites ”replica of a a network”. It is advised to fix the typo.
Claims 7-10, 17-19 are objected to due to their dependence, either directly or indirectly, on their dependence on claims 6, 16
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 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.
Claims 1, 11, 20 recite the limitation “digital replica of a network” and “a digital twin of a network”. Not clear if they are referring to different network and it is uncertain which network is being used in further dependent claims. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “a digital twin of the network” will be used to state they are referring to same network through claims.
Claims 4, 5, 13, 14 recite the limitation “workflow for AI or ML model training” where “a machine learning model” is already introduced in the independent claims 1, 11. It is not clear if these “machine learning model” are newly introduced machine learning model for different training. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “workflow for AI or the ML model training” will be used to state they are referring to same machine learning model through claims.
Claims 6, 16 recite the limitation “a digital twin” which is already introduced in the independent claims 1, 11. It is not clear if they are referring to a different digital twin and it is uncertain which digital twin is used in further dependent claims. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “the digital twin” will be used to state they are referring to same network through claims.
Claims 7, 17 recite the limitation “the received network data”. However, none of the previous claims introduced receiving of network data or received network data. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, “a received network data” will be used to state they are now introducing a received network data from the network.
Claims 2 – 3, 8 – 10, 12, 15, 18 – 19 are dependent on claim rejected above. They do not resolve the issue of indefiniteness and are rejected with the same rationale.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 - 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
MPEP § 2109(III) sets out steps for evaluating whether a claim is drawn to patent-eligible subject matter. The analysis of claims 1-20, in accordance with these steps, follows.
Step 1 Analysis:
Step 1 is to determine whether the claim is directed to a statutory category (process, machine, manufacture, or composition of matter.
Claims 1 – 10 are directed to a method, meaning that it is directed to the statutory category of process. Claims 11 – 19 are directed to an apparatus, which is the statutory category of machine. Claim 20 is directed to a non-transitory computer-readable medium comprising instruction, which can be an article of manufacture.
Step 2A Prong One, Step 2A Prong Two, and Step 2B Analysis:
Step 2A Prong One asks if the claim recites a judicial exception (abstract idea, law of nature, or natural phenomenon). If the claim recites a judicial exception, analysis proceeds to Step 2A Prong Two, which asks if the claim recites additional elements that integrate the abstract idea into a practical application. If the claim does not integrate the judicial exception, analysis proceeds to Step 2B, which asks if the claim amounts to significantly more than the judicial exception. If the claim does not amount to significantly more than the judicial exception, the claim is not eligible subject matter under 35 U.S.C. 101.
Regarding claim 1, the following claim elements are abstract ideas:
generating a digital twin of a network, wherein the digital twin is calibrated based on re- ceiving performance metrics data from the network; (Generating a digital twin and calibrating it with data involves mathematical calculations, which is mathematical concept.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
training a machine learning model based on data generated from the digital twin; (This falls under mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
and operating the trained machine learning model within the network. (This falls under mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 2, the rejection of claim 1 is incorporated herein. Further, claim 2 recites the following additional elements:
wherein the network is based on an open radio access network (0- RAN). (This falls under mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 3, the rejection of claim 2 is incorporated herein. Further, claim 3 recites the following additional elements:
operating the digital twin within a non-real-time (Non-RT) radio access network intelli- gent controller (RIC) framework as part of an artificial intelligence (AI) or machine learning (ML) workflow for Al or ML model training and testing. (This falls under mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 4, the rejection of claim 2 is incorporated herein. Further, claim 4 recites the following additional elements:
operating the digital twin within a near-real-time (Near-RT) radio access network intelli- gent controller (RIC) framework as part of an artificial intelligence (AI) or machine learning (ML) workflow for Al or ML model training and testing. (This falls under mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 5, the rejection of claim 2 is incorporated herein. Further, claim 5 recites the following additional elements:
operating the digital twin in parallel with the O-RAN, wherein the digital twin is further operated outside of a non-real-time (Non-RT) radio access network intelligent controller (RIC) framework and a near-real-time (Near-RT) radio access network intelligent controller (RIC) framework. (This falls under mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 6, the rejection of claim 1 is incorporated herein. Further, claim 6 recites the following abstract ideas:
analyzing a performance of the machine learning model; (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
and generating one or more network scenarios via radio access network (RAN) scenario gen- erator based on the performance of the machine learning model. (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 6 does not recite additional elements.
Regarding claim 7, the rejection of claim 6 is incorporated herein. Further, claim 7 recites the following additional elements:
modeling the network based on the received network data from the network and the gen- erated one or more network scenarios from the RAN scenario generator. (This falls under mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 8, the rejection of claim 7 is incorporated herein. Further, claim 8 recites the following abstract idea:
monitoring a performance of the modeled network; (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 8 further recites following additional elements.
providing feedback to the RAN scenario generator based on the monitored performance of the modeled network; (This is mere transmitting data, which is a well-understood, routine conventional activity. It does not integrate the judicial exception into a practical application. See MPEP § 2106.05(d). Therefore, this does not amount to significantly more than the judicial exception.)
and optimizing the modeled network based on the provided feedback to the RAN scenario generator. (This falls under mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 9, the rejection of claim 8 is incorporated herein. Further, claim 9 recites the following additional elements:
wherein the digital twin comprises an offline simulation module and a runtime simulation module. (This falls under mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 10, the rejection of claim 9 is incorporated herein. Further, claim 10 recites the following abstract ideas:
generating user equipment (UE) mobility pattern within the offline simulation module; (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 10 further recites following additional elements
simulating radio frequency (RF) propagation within the offline simulation module, and generating an RF map at least partially representing power and interference at each location within a geographical area; (This falls under mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
and loading the UE mobility pattern and RF map generated to generate training or testing data to an artificial intelligence (Al) or machine learning (ML) model under training or testing in run time. (This is mere data gathering and outputting, an insignificant extra solution activity, which does not integrate the judicial exception into a practical application. The broadest reasonable interpretation of this claim is storing information in memory, which is a well-understood, routine conventional activity. See MPEP § 2106.05(d)(II)(iv). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 11, the following claim elements are additional elements:
An apparatus for creating a lightweight and realistic digital replica of a network for machine learning and training, comprising: a memory storage storing computer-executable instructions; and a processor communicatively coupled to the memory storage, wherein the processor is configured to execute the computer-executable instructions and cause the apparatus to: (This falls under mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
The rest of claim 11 and claims 12 – 19 recite substantially similar subject matter to claim 1 – 9 respectively and are rejected with the same rationale, mutatis mutandis.
Regarding claim 20, the following claim elements are additional elements:
A non-transitory computer-readable medium comprising computer-executable instructions for creating a lightweight and realistic digital replica of a a network for machine learning and training by an apparatus, wherein the computer-executable instructions, when executed by at least one processor of the apparatus, cause the apparatus to: (This falls under mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
The rest of claim 20 recites substantially similar subject matter to claim 1 respectively and is rejected with the same rationale, mutatis mutandis.
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.
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, 11 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over Mozo et al. (NPL: “B5GEMINI: AI-Driven Network Digital Twin“) in view of Bonati et al. (NPL: ”Intelligence and Learning in O-RAN for Data-driven NextG Cellular Networks”).
Regarding Claim 1, Mozo teaches
A method of creating a lightweight and realistic digital replica of a network for machine learn- ing and training, the method comprising: generating a digital twin of a network, wherein the digital twin is calibrated based on re- ceiving performance metrics data from the network; (Pg 3. of Mozo states “B5GEMINI goes one step beyond and also focuses on the use of NDT for training and validating AI/ML components in a controlled scenario when potentially harmful situations could occur if trained or tested in a real network. B5GEMINI will provide a virtualized representation of the 5G/6G network meant to analyze, diagnose, emulate, and control the physical network… With the support of AI, complex data sources and telemetry (e.g., IoT sensors, network data) will be fed into sophisticated data interpretation processes to facilitate the replica of 5G/6G network components.” Pg. 7 of Mozo states “From this point of view, using a bidirectional data flow between both worlds, the DT is able to continuously adapt to operational changes based on real-time data and information coming from the physical twin, being able, among other things, to monitor and even predict the future state of the physical twin. In addition, the DT can also be manipulated, and changes made to it can be automatically transferred to the physical twin.“)
training a machine learning model based on data generated from the digital twin; (Pg. 6 of Mozo states “One of the corner stones of B5GEMINI is the Mouseworld Lab [22], a controlled environment set up in the Telefónica I+D premises for running experiments that allow deploying complex network scenarios in a controlled way and generate realistic labeled data sets for training supervised ML components and validate supervised and unsupervised solutions.” Pg. 3 of Mozo states “B5GEMINI will enable an extensive use of advanced AI mechanisms to realize several valuable AI applications, such as (i) the training and testing of ML components to deploy smart applications such as cybersecurity or network management in real-time environments; … (iii) the use of the NDT as a platform to perform distributed training and inference processes for ML and DL models using on-demand GPU virtualization;”)
Mozo does not explicitly teach that and operating the trained machine learning model within the network.
However, Bonati teaches that
and operating the trained machine learning model within the network. (Pg. 5 of Bonati states “We embedded the DRL agents into xApps running in the near real-time RIC (right of Fig. 3), for a total of 12 DRL agents running in parallel and making decisions with a time granularity of 500 ms. Agents connect with the network BSs through the ORAN E2 interface.” Pg. 6 of Bonati states “To train our DRL agents we generated some 7 GB of training data of various performance metrics” Trained DRL agents deployed as xApps in the near RT RIC.)
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to combine the teachings of Mozo and Bonati because both references are directed to data driven control and optimization of cellular networks and address the same type of network systems (5G/O-RAN RANs) and focus on effectively training AI/ML based control algorithm. Bonati teaches an O-RAN architecture where ML based agents are trained using data generated on a large RF emulation and then deployed in the non-RT and near-RT RICs to optimize RAN performance. Mozo teaches using a network digital twin to deploy complex network scenarios in a controlled environment and generate realistic labeled datasets for training and validating ML components, and emphasize two way coupling between the digital twin and the real network. One with ordinary skill in the art would be motivated to incorporate the teachings of Bonati into the teachings of Mozo in order to improve the safety, flexibility, and efficiency of training RIC ML agents, to reuse synthetic and real data across both environments, and to provide manageable environment for testing before deployment. Therefore, it would have been predictable to combine the system of Bonati and Mozo.
Regarding claim 2, the rejection of claim 1 is incorporated herein. Furthermore, the combination of Mozo and Bonati teaches that
wherein the network is based on an open radio access network (O- RAN). (Pg. 1 of Bonati states “This article explores the disaggregated network architecture proposed by the O-RAN Alliance as a key enabler of NextG networks… The second core innovation—which is likely to be even more impactful—is the Radio Access Network (RAN) Intelligent Controller (RIC), a new architectural component that provides a centralized abstraction of the network, allowing operators to implement and deploy custom control plane functions. In both its non and near real-time versions, the RIC facilitates RAN optimization through closed-control loops, i.e., autonomous action and feedback loops between RAN components and their controllers.”)
Regarding claim 3, the rejection of claim 2 is incorporated herein. Furthermore, the combination of Mozo and Bonati teaches that
operating the digital twin within a non-real-time (Non-RT) radio access network intelli- gent controller (RIC) framework as part of an artificial intelligence (AI) or machine learning (ML) workflow for Al or ML model training and testing. (Pg. 3 of Bonati states “Indeed, since AI/ML techniques usually rely upon a randomized initialization, O-RAN requires all ML models to be trained and validated offline before their deployment [10] ... Online AI/ML techniques could still be used in O-RAN compliant architectures by allowing models to be trained with offline data in the non real-time RIC, and then perform online learning in the near real-time RIC… The O-RAN Alliance defines non real-time any control loop that operates on a timescale of at least one second. As shown in Fig. 2, this involves the coordination between the non real-time and near real-time RIC through the A1 interface. This control loop manages the orchestration of resources at the infrastructure level, making decisions and applying policies that impact thousands of devices. These actions can be performed using data-driven optimization algorithms processing data from multiple sources, and inference models deployed on the non real-time RIC itself.” Pg. 3 of Mozo states “It is worth noting that current NDT proposals tend to apply AI to add intelligence in different ways to network orchestration processes. B5GEMINI goes one step beyond and also focuses on the use of NDT for training and validating AI/ML components in a controlled scenario when potentially harmful situations could occur if trained or tested in a real network.”)
Regarding claim 4, the rejection of claim 2 is incorporated herein. Furthermore, the combination of Mozo and Bonati teaches that
operating the digital twin within a near-real-time (Near-RT) radio access network intelli- gent controller (RIC) framework as part of an artificial intelligence (AI) or machine learning (ML) workflow for Al or ML model training and testing. (Pg. 4 of Bonati states “Near real-time control loops operate on a timescale between 10 ms and 1 s… Because one near real-time RIC is associated to multiple gNBs, these control loops can make decisions affecting up to thousands of UEs, using user-session aggregated data and Medium Access Control (MAC)/PHY layer KPIs. ML-based algorithms are implemented as external applications, i.e., xApps, and are deployed on the near real-time RIC to deliver specific services such as inference, classification, and prediction pipelines to optimize the per-user quality of experience, controlling load balancing and handover processes, or the scheduling and beamforming design.” Pg. 5 of Bonati states “We embedded the DRL agents into xApps running in the near real-time RIC (right of Fig. 3), for a total of 12 DRL agents running in parallel and making decisions with a time granularity of 500 ms. Agents connect with the network BSs through the ORAN E2 interface.” Pg. 3 of Mozo states “It is worth noting that current NDT proposals tend to apply AI to add intelligence in different ways to network orchestration processes. B5GEMINI goes one step beyond and also focuses on the use of NDT for training and validating AI/ML components in a controlled scenario when potentially harmful situations could occur if trained or tested in a real network.” Combining Mozo and Bonati, we get a DT whose data and behavior feed into the near RT RIC ML workflow.)
Regarding claim 5, the rejection of claim 2 is incorporated herein. Furthermore, the combination of Mozo and Bonati teaches that
operating the digital twin in parallel with the O-RAN, wherein the digital twin is further operated outside of a non-real-time (Non-RT) radio access network intelligent controller (RIC) framework and a near-real-time (Near-RT) radio access network intelligent controller (RIC) framework. (Pg. 8 of Mozo states “One of the crucial elements that differentiates B5GEMINI from Mouseworld is the two‐way communication capability with the real network, which allows real‐time synchronization between the real and virtual networks… That is, the configuration of the real network can be replicated in the virtual twin in real-time and the optimizations applied to the virtual twin can be seamlessly deployed in the real network” Pg. 3 of Bonati states “This control loop manages the orchestration of resources at the infrastructure level, making decisions and applying policies that impact thousands of devices. These actions can be performed using data-driven optimization algorithms processing data from multiple sources, and inference models deployed on the non real-time RIC itself.“ Pg. 4 of Bonati states “Moreover, since the non real-time RIC is endowed with service management and orchestration capabilities, this control loop can also handle the association between the near real-time RIC and the DUs/CUs. This is particularly useful in virtualized systems where DUs and CUs are dynamically instantiated on-demand to match the requests and load of the RAN.” B5GEMINI describes a virtual twin running alongside the real network, synchronized. Bonati’s Non-RT RIC is the ML framework with the DT/NDT as component. )
Regarding claim 6, the rejection of claim 1 is incorporated herein. Furthermore, the combination of Mozo and Bonati teaches that
analyzing a performance of the machine learning model; (Pg. 3 of Mozo states “It is worth noting that current NDT proposals tend to apply AI to add intelligence in different ways to network orchestration processes. B5GEMINI goes one step beyond and also focuses on the use of NDT for training and validating AI/ML components in a controlled scenario when potentially harmful situations could occur if trained or tested in a real network.” Pg. 25 of Mozo states “To demonstrate the advantages provided by the architecture proposed in Section 4.3, we present a use case for energy optimization that builds on the DMap concept to optimize the energy consumption of an NDT system by managing its resources.” B5GEMINI’s training, validation, and energy optimization use cases require monitoring ML performance in the NDT.)
and generating one or more network scenarios via radio access network (RAN) scenario gen- erator based on the performance of the machine learning model (Pg. 6 of Mozo states “One of the corner stones of B5GEMINI is the Mouseworld Lab [22], a controlled environment set up in the Telefónica I+D premises for running experiments that allow deploying complex network scenarios in a controlled way and generate realistic labeled data sets for training supervised ML components and validate supervised and unsupervised solutions.” Pg. 7 of Mozo states “herefore, the Machine Learning Orchestration is part of the Digital Twin Entity Management and is composed of a Topology Generator and an Experiment Launcher… The Experiment Launcher makes day-2 configurations and triggers the emulations functions for dataset generation, and it is described with OSM’s ProxyCharms or with dedicated scripts... The current version uses a configuration file to define the statistical distribution of the traffic, the number of intervals in which the experiment is divided and the duration, and the type of service that it is emulating. Currently, there are defined three scenarios: traffic classification, crypto mining malware detection, and DoH attacks.” The topology generator and experiment launcher are like a scenario generator. They define scenarios then trigger emulation runs that feed the ML training pipeline.)
Regarding claim 7, the rejection of claim 6 is incorporated herein. Furthermore, the combination of Mozo and Bonati teaches that
modeling the network based on the received network data from the network and the gen- erated one or more network scenarios from the RAN scenario generator. (Pg. 8 of Mozo states “the development of smart agents that can be deployed in the target network, which are in charge of collecting all the information necessary for the DT generation (topological information, hardware and software information, states, etc.). This information is introduced to the deployment module in the JSON or XML format” Pg. 3 of Mozo states “With the support of AI, complex data sources and telemetry (e.g., IoT sensors, network data) will be fed into sophisticated data interpretation processes to facilitate the replica of 5G/6G network components.”Pg. 7 of Mozo states “herefore, the Machine Learning Orchestration is part of the Digital Twin Entity Management and is composed of a Topology Generator and an Experiment Launcher… The Experiment Launcher makes day-2 configurations and triggers the emulations functions for dataset generation, and it is described with OSM’s ProxyCharms or with dedicated scripts... The current version uses a configuration file to define the statistical distribution of the traffic, the number of intervals in which the experiment is divided and the duration, and the type of service that it is emulating. Currently, there are defined three scenarios: traffic classification, crypto mining malware detection, and DoH attacks.”)
Regarding claim 8, the rejection of claim 7 is incorporated herein. Furthermore, the combination of Mozo and Bonati teaches that
monitoring a performance of the modeled network; providing feedback to the RAN scenario generator based on the monitored performance of the modeled network; and optimizing the modeled network based on the provided feedback to the RAN scenario generator. (Pg. 7 of Mozo states “From this point of view, using a bidirectional data flow between both worlds, the DT is able to continuously adapt to operational changes based on real-time data and information coming from the physical twin, being able, among other things, to monitor and even predict the future state of the physical twin.” Pg. 4 of Mozo states “Thanks to this two-way communication, NDT is expected to enable a new wave of AI-powered applications that can more accurately predict outcomes and make better decisions to support network operators in the deployment and operation procedures involved in network management.” Pg. 3 of Bonati states “Each closed-control loop optimizes RAN parameters and operations by running at different timescales, with different number of UEs, and using different sources for the input data. The ORAN Alliance is also looking into how to standardize the data-driven workflows for these control loops.” Pg. 6 of Bonati states “Specifically, agents process the performance metrics received by the BS they are controlling—which possibly expresses the performance of several UEs—through an encoder. This allows them to cast the dimensionality of the input data to a fixed size and to process it regardless of the number of active UEs of the slice. As a consequence, the DRL agents do not need to be aware of the number of UEs and BSs in the network, which makes our approach general and scalable. Through the RIC Indication messages sent via the O-RAN E2 interface (Fig. 3), the agent is fed realtime performance measurements of the slice it controls… The agent uses a fully connected neural network with 5 layers and 30 neurons each to determine the best scheduling policy for the corresponding slice.” B5GEMINI in Mozo has experiment launcher and configurable scenarios. Bonati’s closed loops use performance metrics to update control policies. A POSITA would feed those performance metrics to scenario generator to refine scenarios.)
Regarding claim 9, the rejection of claim 8 is incorporated herein. Furthermore, the combination of Mozo and Bonati teaches that
wherein the digital twin comprises an offline simulation module and a runtime simulation module. (Pg. 3 of Mozo states “It is worth noting that current NDT proposals tend to apply AI to add intelligence in different ways to network orchestration processes. B5GEMINI goes one step beyond and also focuses on the use of NDT for training and validating AI/ML components in a controlled scenario when potentially harmful situations could occur if trained or tested in a real network… (iii) the use of the NDT as a platform to perform distributed training and inference processes for ML and DL models using on-demand GPU virtualization;” Pg. 8 of Mozo states “One of the crucial elements that differentiates B5GEMINI from Mouseworld is the two-way communication capability with the real network, which allows real-time synchronization between the real and virtual networks. That is, the configuration of the real network can be replicated in the virtual twin in real-time and the optimizations applied to the virtual twin can be seamlessly deployed in the real network.” Pg. 3 of Bonati states “Second, data-driven solutions must be trained and validated offline to avoid causing inefficiencies—or even outages—to the RAN. Indeed, since AI/ML techniques usually rely upon a randomized initialization, O-RAN requires all ML models to be trained and validated offline before their deployment [10]… Online AI/ML techniques could still be used in O-RAN compliant architectures by allowing models to be trained with offline data in the non real-time RIC, and then perform online learning in the near real-time RIC..” The NDT/.DT environment used for training and testing ML components, and O-RAN’s requirement that models be trained or validated offline maps to offline simulation module. The part of the twin that is synchronized with live data and participates in real or near real time control loops from Bonati maps to runtime simulation module.)
Claims 11 – 20 recite substantially similar subject matter as claims 1 – 9 and 1 (for claim 20) respectively, and are rejected with the same rationale, mutatis mutandis.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Mozo et al. (NPL: “B5GEMINI: AI-Driven Network Digital Twin“) in view of Bonati et al. (NPL: ”Intelligence and Learning in O-RAN
for Data-driven NextG Cellular Networks”), further in view of Yilmaz et al. (NPL: “Radio Environment Map as Enabler for Practical Cognitive Radio Networks”).
Regarding claim 10, the rejection of claim 9 is incorporated herein. Furthermore, the combination of Mozo and Bonati teaches that
generating user equipment (UE) mobility pattern within the offline simulation module; (Pg. 5 of Bonati states “We have emulated a 5G network with 4 BSs and 40 UEs (Fig. 3, left) in the dense urban scenario of Rome, Italy.” Pg. 6 of Bonati states “Agents have been trained under network configurations obtained by varying the distance between BSs and UEs and the mobility of the UEs.” Pg. 7 of Mozo states “The Experiment Launcher makes day-2 configurations and triggers the emulations functions for dataset generation, and it is described with OSM’s ProxyCharms or with dedicated scripts. The current version uses a configuration file to define the statistical distribution of the traffic, the number of intervals in which the experiment is divided and the duration, and the type of service that it is emulating. Currently, there are defined three scenarios: traffic classification, crypto mining malware detection, and DoH attacks.” Bonati teaches generating UE trajectories in an emulator by varying distances and mobility of the UEs. Mozo’s experiment launcher shows such patterns are part of a configurable offline simulation module for emulation.)
simulating radio frequency (RF) propagation within the offline simulation module, (Pg. 4 of Bonati states “Colosseum includes 128 compute nodes, called Standard Radio Nodes (SRNs), equipped with USRP X310 SDRs that can be used to run generic protocol stacks. These are connected in a mesh topology through 128 additional USRPs X310 of the Massive Channel Emulator (MCHEM) for emulating realistic RF scenarios. The wireless channel between each pair of devices is modeled through complex-valued finite impulse response filter taps. In this way, scenarios are able to capture effects such as path loss, multi-path and fading as if the SDRs were operating in a real RF environment.”)
and loading the UE mobility pattern… to generate training or testing data to an artificial intelligence (Al) or machine learning (ML) model under training or testing in run time. (Pg. 5 of Bonati states “We have emulated a 5G network with 4 BSs and 40 UEs (Fig. 3, left) in the dense urban scenario of Rome, Italy.” Pg. 6 of Bonati states “Agents have been trained under network configurations obtained by varying the distance between BSs and UEs and the mobility of the UEs.” Pg. 6 of Mozo states “One of the corner stones of B5GEMINI is the Mouseworld Lab [22], a controlled environment set up in the Telefónica I+D premises for running experiments that allow deploying complex network scenarios in a controlled way and generate realistic labeled data sets for training supervised ML components and validate supervised and unsupervised solutions.” Pg. 7 of Mozo states “The Experiment Launcher makes day-2 configurations and triggers the emulations functions for dataset generation, and it is described with OSM’s ProxyCharms or with dedicated scripts. The current version uses a configuration file to define the statistical distribution of the traffic, the number of intervals in which the experiment is divided and the duration, and the type of service that it is emulating. Currently, there are defined three scenarios: traffic classification, crypto mining malware detection, and DoH attacks.”)
However, the combination of Mozo and Bonati does not explicitly teach that
generating an RF map at least partially representing power and interference at each location within a geographical area;
and loading the RF map generated
Yilmaz teaches that
generating an RF map at least partially representing power and interference at each location within a geographical area; (Pg. 162 of Yilmaz states “In that regard, Radio Environment Map (REM) is a promising tool that provides a practical means for the realization of cognitive radio networks (CRNs). It constructs a comprehensive map of the CRN by utilizing multi-domain information from geolocation databases, characteristics of spectrum use, geographical terrain models, propagation environment, and regulations.” Pg. 163 of Yilmaz states “Essential functionality of a REM is the construction of dynamic interference map for each frequency at each location of interest.”)
and loading the RF map generated (Pg. 162 of Yilmaz states “In that regard, Radio Environment Map (REM) is a promising tool that provides a practical means for the realization of cognitive radio networks (CRNs). It constructs a comprehensive map of the CRN by utilizing multi-domain information from geolocation databases, characteristics of spectrum use, geographical terrain models, propagation environment, and regulations.” Pg. 163 of Yilmaz states “Essential functionality of a REM is the construction of dynamic interference map for each frequency at each location of interest.”)
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to combine the teachings of Yilmaz into the combination of Mozo and Bonati. Bonati teaches an O-RAN architecture where ML based agents are trained using data generated on a large RF emulation and then deployed in the non-RT and near-RT RICs to optimize RAN performance. Mozo teaches using a network digital twin to deploy complex network scenarios in a controlled environment and generate realistic labeled datasets for training and validating ML components, and emphasize two way coupling between the digital twin and the real network. Yilmaz teaches the well known concept of a Radio Environment Map, which constructs a map of the radio network to construct a dynamic interference map for each frequency at each location of interest. One with ordinary skill in the art would be motivated to incorporate the teachings of Yilmaz into the teachings of Mozo and Bonati in order to capture the simulated RF conditions in a precise, reusable form indexed by geolocation to manage RF environments across scenarios, and provide location aware features to the ML training pipeline. It would have been predictable to combine to improve the RF propagation already simulated in Bonati’s emulator with RF map representation of Yilmaz.
Conclusion
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/BYUNGKWON HAN/ Examiner, Art Unit 2121
/Li B. Zhen/ Supervisory Patent Examiner, Art Unit 2121