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 .
Response to Arguments
Applicant’s arguments with respect to pending claim have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 54-73 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 pre-AIA the applicant regards as the invention.
Regarding claims 54 and 65, the limitation “remain useful” in claims 54 and 65 are relative terms which render the claim indefinite. The term “remain useful” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
Claims 55-64 and 66-73 are rejected as being dependent of rejected claims.
Claim Rejections – 35 USC§ 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 54, 64 and 65 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al(US 2023/0112127 A1) in view of Pillalamarri et al(US 2018/0067779 A1).
Regarding claims 54 and 65, Lee ‘127 teaches, an edge compute node ([0030]- [0037] and Figs. 1-6, radio access network intelligent controller (RIC) 101), comprising:
memory circuitry configured to store instructions for operating an application (app) manager and a set of edge apps ([0030]- [0037], [0052] and Figs. 1-6, the RIC 101 comprising storage device 130, processor 120 and xApp Manager 401 and xApps 211, 221 and 231); and
processor circuitry connected to the memory circuitry, wherein the processor circuitry is configured to operate the app manager to([0030]-[0037], [0052] and Figs. 1-6, the RIC 101’s xAPP manager 401):
receive, via network interface circuitry (NIC), measurement data from a set of network access nodes (NANs) connected to the edge compute node( [0035], [0046], [0070] and Figs. 1A, 4, 6, radio access network intelligent controller (RIC) 101 receiving key performance indicator (KPI) messages from E2 nodes(e.g. DU, CU-CP, CU-UP) ,[0035], RIC 101 receiving message size processed per unit time from the E2 Nodes[0070] via its network interface (the O-RAN E2 interface));
receive, via the NIC, telemetry data from one or more telemetry agents implemented by the edge compute node( [0063], [0069], [0070], table 4 and Figs. 4, 6, using its network interface (the O-RAN E2 interface), the RIC 101’s xApp manager 401/601 receiving information about the available and used resources via the K8s node manager),
determine a resource allocation for a corresponding edge app of the set of edge apps based on the measurement data and the telemetry data([0052], [0063], 0070] and Figs. 4-6, the RIC’s xAPP manager selecting a deployment node with enough available resources, using available resource information (telemetry) and message size processed per unit time (measurement data from the E2 node)); and
configure at least one NAN of the set of NANs or the edge compute node according to the determined resource allocation such that resources indicated by the resource allocation are allocated to the corresponding edge app([0043], [0035], [0070] and Figs. 4-6, the RIC after selecting the target node with enough available resources, deploys the xAPP to the selected node in order to allocate the available resources to the xAPP).
Lee ‘127 does not explicitly teach, the measurement data and/or the telemetry data are to be classified into data classifications based upon how long the measurement data and/or the telemetry data remain useful in determining the resource allocation: and the resource allocation is to be based at least in part upon the measurement data and/or the telemetry data as classified into the data classifications.
Pillalamarri ‘779 teaches, the measurement data and/or the telemetry data are to be classified into data classifications based upon how long the measurement data and/or the telemetry data remain useful in determining the resource allocation([0028], [0029] and Fig. 3, a data classifier 303 that categorizes incoming data based on temporal relevance ate and urgency): and the resource allocation is to be based at least in part upon the measurement data and/or the telemetry data as classified into the data classifications([0028], [0029] and Figs. 3-4, allocating processing and storage resources (e.g. local fog node resources vs remote cloud resources) strictly based on the results of the aforementioned urgency/age classifications).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the communication system of Lee ‘127, by incorporating the teaching of Pillalamarri ‘779, since such modification would enable to form proximity-based fog networks which leads to hierarchical network management and strengthens security and privacy protections, as suggested by Pillalamarri ‘779(abstract).
Regarding claim 64, the combination of Lee ‘127 and Pillalamarri ‘779 teaches all of the claim limitations above, Lee ‘127 further teaches, wherein the set of NANs includes a set of radio access network functions (RANFs) of a next generation (NG) RAN architecture ([0030]-[0037], and Figs. 1-6, RAN functions comprising Distributed Unit(DU 151), Central unit CP 152and CU-UP a53), the edge compute node operates a RAN intelligent controller (RIC) of an 0-RAN Alliance (0- RAN) framework([0030]-[0037] and Figs. 1-6, O-RAN including RAN Intelligent controller (RIC)), and the set of edge apps include one or more non-RT RIC apps (xApps) or one or more non-RT RIC applications (rApps) ([0030]-[0037] and Figs. 1-6, non-RT RIC xApps 1-3).
Claims 55-58 and 66-69 are rejected under 35 U.S.C. 103 as being unpatentable over Lee ‘127 and Pillalamarri ‘779 as applied to claims above, and further in view of Subramanian et al(US 2020/0104184 A1).
Regarding claims 55 and 66, the combination of Lee ‘127 and Pillalamarri ‘779 does not explicitly teach, wherein the processor circuitry is configured to: receive, via the NIC, a policy from a orchestration function wherein the information included in the policy includes, a set of key performance measurements (KPMs), key performance indicators (KPIs), service level agreement (SLA) requirements, or quality of service (QoS) requirements related to related to one or more of accessibility, availability, latency, reliability, user experienced data rates, area traffic capacity, integrity, utilization, retainability, mobility, energy efficiency, or quality of service; and determine the resource allocation according to information included in the policy.
Subramanian ‘184 teaches, wherein the processor circuitry is configured to:
receive, via the NIC, a policy from a orchestration function ([0012]-[0015] and Fig. 1, the pod resource manager receiving scheduling orchestration request as a form of workload request) wherein the information included in the policy includes
a set of key performance measurements (KPMs),
key performance indicators (KPIs),
service level agreement (SLA) requirements, or
quality of service (QoS) requirements ([0012]- [0015] and Fig.1, the edge system’s manager receiving requirements and performance criteria as part of workload requests/policies which includes quality of service requirements and service level agreement (SLA) requirements, notice the claim limitation is written in alternative form thus examiner is required to show only one of the alternative claim limitations)
related to related to one or more of accessibility, availability, latency, reliability, user experienced data rates, area traffic capacity, integrity, utilization, retainability, mobility, energy efficiency, or quality of service([0012]-[0015], [0064] and Fig.1, quality of service requirements and service level agreement (SLA) requirements including availability and maximum latency requirements, notice the claim limitation is written in alternative form thus examiner is required to show only one of the alternative claim limitations) ; and
determine the resource allocation according to information included in the policy ([0012]-[0015], [0064] and Fig.1. the resource allocations by the pod manager being performed based on the provided requirements(policy) such as SAL/QoS).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the communication system of Lee ‘127, by incorporating the teaching of Subramanian ‘184, since such modification would provide artificial intelligence (AI) and machine learning (ML) techniques that can be used in connection with a neural network to accelerate determinations of suggested computing resource allocations based on hundreds to thousands (or more) of telemetry data in order to suggest a computing resource allocation, as suggested by Subramanian ‘184(abstract).
Regarding claims 56 and 67, the combination of Lee ‘127 and Pillalamarri ‘779 does not explicitly teach, wherein the processor circuitry is configured to operate one or more machine learning models to: determine the resource allocation; and at least one of:
correlate individual data items of the telemetry data with one or more other data items of the telemetry data; correlate individual data items of the measurement data with one or more other data items of the measurement data;
correlate individual data items of the measurement data with the individual data items of the telemetry data; correlate service management data with the telemetry data or the measurement data; correlate data items of the service management data related to the received measurement data with resource allocations previously generated for the edge app; correlate one or more data items of the service management data with one or more resource requirements of the of edge app; correlate the one or more data items of the service management data with one or more resource requirements of a corresponding network slice in which the edge app is to operate; correlate platform resource slices of the edge compute node with one or more network slices; predict or inferring data to compensate missing data service management data; or
predict a reliability of individual components of the edge compute node based at least on the telemetry data.
Subramanian ‘184 teaches, wherein the processor circuitry is configured to operate one or more machine learning models to: determine the resource allocation (0012]- [0015], [0064] and Fig.1, the pod resource manager may provide the request to an accelerator to run the workload request through an Artificial Intelligence (AI) model and so that the AI model suggests a resource configuration); and at least one of:
correlate individual data items of the telemetry data with one or more other data items of the telemetry data([0012]- [0015] and Fig.1, collecting plurality of telemetry data from edge clusters, and the accelerator( AI) maintains boundedness metric for each workload and correlates telemetry data with each other (e.g. CPU vs Memory VS network usage));
correlate individual data items of the measurement data with one or more other data items of the measurement data ([0012]- [0015] and Fig.1, the reinforcement learning model uses the KPI of each workload as a measured output and compares the output KPI with the previous run of the same workload);
correlate individual data items of the measurement data with the individual data items of the telemetry data([0012]- [0015] and Fig.1, the model learns relation between telemetry (resource usage pattern) and measurements( performance results), thus correlating these data types for deciding the next resource allocation) ;
correlate service management data with the telemetry data or the measurement data; correlate data items of the service management data related to the received measurement data with resource allocations previously generated for the edge app;
correlate one or more data items of the service management data with one or more resource requirements of the of edge app;
correlate the one or more data items of the service management data with one or more resource requirements of a corresponding network slice in which the edge app is to operate;
correlate platform resource slices of the edge compute node with one or more network slices; predict or inferring data to compensate missing data service management data; or
predict a reliability of individual components of the edge compute node based at least on the telemetry data (notice, the claim limitation is written in alternative form thus examiner is required to show only one of the alternative claim limitations).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the communication system of Lee ‘127, by incorporating the teaching of Subramanian ‘184, since such modification would provide artificial intelligence (AI) and machine learning (ML) techniques that can be used in connection with a neural network to accelerate determinations of suggested computing resource allocations based on hundreds to thousands (or more) of telemetry data in order to suggest a computing resource allocation, as suggested by Subramanian ‘184(abstract).
Regarding claims 57 and 68, the combination of Lee ‘127, Pillalamarri ‘779 and Subramanian ‘184 teaches all of the claim limitations Subramanian ‘184 further teaches, wherein the resource allocation indicates to move the corresponding edge app from being operated by a first processing element of the edge compute node to be operated by a second processing element of the edge compute node( [0012]- [0015] and Fig.1, a single workload getting multiple resource configurations at different stages which indicates that workload being moved from one hardware to another) and
to determine the resource allocation, the processor circuitry is configured to:
determine adjustments to hardware, software, or network resources allocated to the edge app according to a run-time priority level assigned to the edge app ([0012]- [0015], [0031] and Fig.1, pod manager 510 may prioritize which requests for resource configuration are provided to accelerator 520 based on associated workloads having the lowest required end-to-end latency, a repeat performance of a workload whose prior execution failed an SLA requirement, or other factors).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the communication system of Lee ‘127, by incorporating the teaching of Subramanian ‘184, since such modification would provide artificial intelligence (AI) and machine learning (ML) techniques that can be used in connection with a neural network to accelerate determinations of suggested computing resource allocations based on hundreds to thousands (or more) of telemetry data in order to suggest a computing resource allocation, as suggested by Subramanian ‘184(abstract).
Regarding claims 58 and 69, the combination of Lee ‘127 and Pillalamarri ‘779 does not explicitly teach, wherein the resource allocation indicates to: dynamically increase or decrease power levels or frequency levels of a processing element operating the corresponding edge app; dynamically adjust last level cache (LLC), memory bandwidth, or interface bandwidth allocated to the corresponding edge app; scale up one or more of hardware, software, or resources for the corresponding edge app; or scale down one or more of hardware, software, or resources for the corresponding edge app.
Subramanian ‘184 teaches, wherein the resource allocation indicates to:
dynamically increase or decrease power levels or frequency levels of a processing element operating the corresponding edge app ([0012]- [0015], [0035] and table 1, changing the allocated power by changing the processor frequency, as the processor frequency is correlated to a power level);
dynamically adjust last level cache (LLC), memory bandwidth, or interface bandwidth allocated to the corresponding edge app; scale up one or more of hardware, software, or resources for the corresponding edge app ([0030], Table 1 and Fig. 1, dynamically adjusting network bandwidth and memory allocation); or
scale down one or more of hardware, software, or resources for the corresponding edge app([0030]-[0035] and Figs. 1, 5, resource allocations are flexible and can be reduced when the application run-time conditions allow).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the communication system of Lee ‘127, by incorporating the teaching of Subramanian ‘184, since such modification would provide artificial intelligence (AI) and machine learning (ML) techniques that can be used in connection with a neural network to accelerate determinations of suggested computing resource allocations based on hundreds to thousands (or more) of telemetry data in order to suggest a computing resource allocation, as suggested by Subramanian ‘184(abstract).
Claims 59 and 70 are rejected under 35 U.S.C. 103 as being unpatentable over Lee ‘127 and Pillalamarri ‘779 as applied to claims above, and further in view of Yang et al(US 2021/0258969 A1).
Regarding claims 59 and 70, the combination of Lee ‘127 and Pillalamarri ‘779 does not explicitly teach, wherein the processor circuitry is configured to: send, via the NIC, the resource allocation to a service management and orchestration framework for management of resources of multiple edge compute nodes.
Yang ‘969 teaches, wherein the processor circuitry is configured to:
send, via the NIC, the resource allocation to a service management and orchestration framework for management of resources of multiple edge compute nodes ([0034]- [0038] and Figs. 1-2, service management and orchestration device 218 for managing multiple resources of the end devices).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the communication system of Lee ‘127, by incorporating the teaching of Yang ‘969, since such modification would enable to satisfy the increasing complexity, densification, and demands of end device application services of a future generation network, as suggested by Yang ‘969([0001]).
Claims 60, 61, 71 and 72 are rejected under 35 U.S.C. 103 as being unpatentable over Lee ‘127 and Pillalamarri ‘779 as applied to claims above, and further in view of Sivaraj et al(US 2021/0377804 A1).
Regarding claims 60 and 71, the combination of Lee ‘127 and Pillalamarri ‘779 does not explicitly teach, wherein, to configure the at least one NAN, the processor circuitry is configured to: configure a real-time (RT) control loop operated by the at least one NAN; and configure a near-RT control loop operated by the edge compute node, wherein the near-RT control loop operates according to a first time scale, the RT control loop operates according to a second time scale, and the first time scale is larger than the second time scale.
Sivaraj ‘804 teaches, wherein, to configure the at least one NAN, the processor circuitry is configured to: configure a real-time (RT) control loop operated by the at least one NAN([0053]-[0055], real-time control loops called Loop 1, carried out by DUs and RUs, operate in timescales in the order of 1 ms); and configure a near-RT control loop operated by the edge compute node([0053]-[0055], near-real-time loops, typically done in CUs and near-RT RIC, range from 5 to 500 ms). wherein the near-RT control loop operates according to a first time scale([0053]-[0055], near-real-time loops, typically done in CUs and near-RT RIC, range from 5 to 500 ms), the RT control loop operates according to a second time scale, and the first time scale is larger than the second time scale([0053]-[0055], real-time control loops called Loop 1, carried out by DUs and RUs, operate in timescales in the order of 1 ms, which is near-RT 500ms is greater than RT 1ms).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the communication system of Lee ‘127, by incorporating the teaching of Sivaraj ‘804 since such modification would enable to asses inter-node communications relative performance of the radio access network node devices to adjust network traffic splits between the radio access network node devices, as suggested by Sivaraj ‘804(abstract).
Regarding claims 61 and 72, the combination of Lee ‘127, Pillalamarri ‘779 and Sivaraj ‘804 teaches all of the claim limitations Sivaraj ‘804 further teaches, wherein individual sets of the telemetry data are classified as belonging to a corresponding tier of a set of data tiers([0060], [0065], data being separated according granularity(tiers), individual sets of the measurement data are classified as belonging to a corresponding tier of a set of data tiers ([0060], [0065], classifying data based on its source and interface for receiving the data RAN KPIs are classified differently than ONAP KPIs) and each tier of the set of data tiers corresponds to a timescale of a control loop of a set of control loops[0060], [0065], (Near-RT vs Non-RT)., wherein the set of control loops includes the RT control loop and the near-RT control loop( [0060], [0065], mapping data granularity(tiers) directly to the specific control loops (Near-RT vs Non-RT)).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the communication system of Lee ‘127, by incorporating the teaching of Sivaraj ‘804 since such modification would enable to asses inter-node communications relative performance of the radio access network node devices to adjust network traffic splits between the radio access network node devices, as suggested by Sivaraj ‘804(abstract).
Allowable Subject Matter
Claims 62, 63 and 73 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/AWET HAILE/Primary Examiner, Art Unit 2474
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