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 .
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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, 4, 6, 9, 11, 14, 16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Umapathy et al. (2021/0007023) hereinafter Umapathy in view of Hammons, Roger (2012/0155330), hereinafter Hammons and Tan et al. (2016/0162783), hereinafter Tan.
Re. Claim 16, Umapathy teaches an electronic device in a cloud computing environment, the electronic device to implement functions to support a node managing configuration of devices in a radio access network, the electronic device to optimize radio access network operations (¶0044 - Some cloud service categories that the cloud 144 may provide include, for example… Network as a Service (NaaS), which is a cloud service category involving transport connectivity and related network capabilities… Function as a Service (FaaS) to the applications running in the edge devices (e.g., smartphones or IoT) to accelerate their workloads and applications), wherein the node operates as a base station of the radio access network (¶0005 - In a computer network, a node may be a modem, bridge, switch or host computer. In a cellular telecommunications network, a node may be the base station controller, home location register or GPRS support nodes), the electronic device comprising: a non-transitory machine-readable storage medium having stored therein a optimization coordinator; and a processor coupled to the non-transitory machine-readable storage medium (Fig. 8), the processor to execute the optimization coordinator (Fig. 8, 852, 860, & ¶0155 - The instructions 888 provided via the memory circuitry 854 and/or the storage circuitry 858 of FIG. 8 are embodied as one or more non-transitory computer readable storage media (see e.g., NTCRSM 860) including program code, a computer program product or data to create the computer program, with the computer program or data, to direct the processor circuitry 858 of node 850 to perform electronic operations in the node 850), to determine a topology of the radio access network (Fig. 1, & ¶0019 - FIG. 1 illustrates the different layers of communication occurring within the environment 100, starting from endpoint sensors or things layer 110 (e.g., operating in an Internet of Things (IoT) network topology) comprising one or more IoT devices 111 (also referred to as edge endpoints 110 or the like)) for the node based on the neighbor nodes discovered (¶0039 - Usually, the reporting criteria is a measurement result of a serving cell and/or neighboring cell(s) in comparison with a threshold parameter, where the measurement results may or may not be combined with hysteresis parameters and/or one or more positive or negative offsets).
Yet, Umapathy does not expressly teach how to initilize at least one optimization function that evaluates local configuration of the node and the neighbor nodes that affect the node for a predicted network metric for the node, to determine current network metric and how to exchange optimization information and network metric information with neighbor nodes in the topology, to determine an updated local configuration for the node based on a negotiated optimization with the neighbor nodes, and to update an optimization function based on collected updated network metric information of the node executing the updated local configuration.
However, Hammons explicitly teaches how to initialize at least one optimization function that evaluates local configuration of the node and the neighbor nodes that affect the node for a predicted network metric for the node to determine current network metric (¶0048 - This is accomplished by synthesizing incoming information from neighboring nodes and determining which nodes will provide the best connections based on specific utilities each node is attempting to optimize), and how to exchange optimization information for the node and the current network metric with neighbor nodes in the topology to obtain a negotiated optimization for the topology (¶0048 - Neighboring nodes then exchange messages to request, accept, or reject directional connections between them based on their preferred optimizations. The overall directional network topology is achieved by consensus driven by local preferences. Examiner interprets these exchange messages to include information used to optimize the topology as well as containing network metrics), and how to determine an updated local configuration for the node based on the negotiated optimization with the neighbor nodes (¶0048 - The overall directional network topology is achieved by consensus driven by local preferences), and to update the optimization function based on updated network metric of the node executing the updated local configuration (¶0049 - [A] node goes through a cyclic process of surveying its surroundings, making new connections and breaking old connections based on updated information, then beginning the process again by surveying its surroundings).
However, Umapathy and Hammons does not expressly teach discovering the neighbor nodes in the radio access network using any one or more of geographic neighbor relations, radiation patterns, automatic neighbor relations, or information from a network planning tool, construct[ing] a coordination graph having the neighbor nodes at edges of the coordination graph, initializing an optimization function… [wherein] a predicted network metric is related to antenna configuration for the node based on the coordination graph and find[ing] a globally optimal joint antenna configuration with the neighbor nodes.
Yet, Tan explicitly teaches discovering the neighbor nodes in the radio access network using any one or more of geographic neighbor relations (¶0242 - Embodiment algorithms may address this issue by implementing the following steps. Construct an interaction graph GU of up-tilt/power-up candidate cell nodes with edges between them if they are neighbors or have significantly interacting common neighbors. Additionally, Examiner interprets that only one of the claimed features needs to be mapped because of the presence of “Or”), radiation patterns, automatic neighbor relations, OR information from a network planning tool, construct[ing] a coordination graph having the neighbor nodes at edges of the coordination graph (¶0242 - Embodiment algorithms may address this issue by implementing the following steps. Construct an interaction graph GU of up-tilt/power-up candidate cell nodes with edges between them if they are neighbors or have significantly interacting common neighbors), wherein to construct the coordination graph comprises: constructing a graph structure using neighbor relations in which each antenna is represented as a vertex (¶0113, ¶0242, Fig. 7, Items 702, 704, 706, and 708 are neighboring antenna that construct a graph in relation to one another to map a path through an area, each antenna is seen as a corner or vertex in the mapping (see arrows present in the mapping)) and initializing a payoff function as an optimization function at each edge, wherein the payoff function is a function for incentivizing the node to optimize performance based on coordination with the neighbor nodes (¶0054 - Simulated annealing based (SA-based) optimization algorithms may be used to evaluate global solutions for multi-cell wireless networks. As used herein, the term “global solution” refers to a set of local solutions for two or more wireless network coverage areas in a wireless network. Each “local solution” specifies one or more wireless configuration parameters for a particular wireless network coverage area); initializing an optimization function… [wherein] a predicted network metric is related to antenna configuration for the node based on the coordination graph (Fig. 6, ¶0096 - Aspects of this disclosure provide techniques for dynamically adjusting cell-specific radio frequency (RF) configuration parameters (e.g., electrical antenna tilt, reference symbol (RS) pilot power, etc.) to optimize an objective function. In one embodiment, RF parameters of a single cell are adjusted to maximize a per-cell performance metric. In another embodiment, RF parameters for two or more cells are jointly adjusted to maximize a network performance metric), wherein the payoff function is initialized using a random value or using an estimated value (¶0113 - Process 600 is not aware of the particular starting point 702. Iterating through the base incremental adjustment phase 605 will attain a first intermediate network performance level 704. The biased random adjustment phase will then kick in to perform chaotic jumps to identify the desired optimum network performance level 708, possibly through one or more second intermediate network performance levels 706.) and find[ing] a globally optimal joint antenna configuration with the neighbor nodes (¶0096 - Aspects of this disclosure provide techniques for dynamically adjusting cell-specific radio frequency (RF) configuration parameters (e.g., electrical antenna tilt, reference symbol (RS) pilot power, etc.) to optimize an objective function. In one embodiment, RF parameters of a single cell are adjusted to maximize a per-cell performance metric. In another embodiment, RF parameters for two or more cells are jointly adjusted to maximize a network performance metric. & ¶0155 - Embodiments of this disclosure may provide techniques for avoiding local optimization to obtain globally optimal, or near globally optimal, solutions).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to add the teaching of Hammons and Tan to the teaching of Umapathy. The motivation for such would be to optimize the means of obtaining a globally optimal antenna configuration as Hammons provides a means by which neighboring nodes share information with one another to determine an updated configuration and update the function based on the updated local configuration (Hammons, ¶0048-0049) and Tan provides a means of creating a coordination graph based off neighboring nodes and their related antenna configuration (Tan, ¶0096). All of the claimed elements were known in the prior art and one skilled in the art could have combined the elements, as claimed by known methods, and the combination would have yielded predictable results to one having ordinary skill in the art at the time of invention.
Re. Claim 1, Claim 1 is the method claim disclosed by Claim 16 and is thus rejected under the same standard.
Re. Claim 6, Claim 6 discloses the method and device claimed in Claim 1 and this is rejected under the same standard as Claims 1 and 16.
Re. Claim 11, Claim 11 discloses the method and electronic device claimed in Claim 1 and this is rejected under the same standard as Claims 1 and 16.
Additionally, Umapathy teaches an electronic device functioning as a part of a node in communication with a radio access network, the electronic device to implement a plurality of virtual machines that support network function virtualization (NFV) (¶0041 - Network Functions Virtualization (NFV) may be utilized to virtualize any or all of the above-described network node functions via executable instructions stored in one or more computer-readable storage mediums), the plurality of virtual machines to execute a method to optimize radio access network operations (¶0041 - NFV systems can be used to execute virtual or reconfigurable implementations of one or more CN 142 components or functions).
Re. Claims 4, 9, 14, and 19, Umapathy, Hammons, and Tan teach Claims 1, 6, 11, and 16.
However, neither Umapathy nor Tan expressly teach implementing the updated local configuration; and collecting updated network metric information for the node implementing the update local configuration.
Yet, Hammons explicitly teaches implementing the updated local configuration; and collecting updated network metric information for the node implementing the update local configuration (¶0049 - a node goes through a cyclic process of surveying its surroundings, making new connections and breaking old connections based on updated information, then beginning the process again by surveying its surroundings).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to add the teaching of Hammons to the teaching of Umapathy and Tan. The motivation for such would be to optimize connections between nodes in the coordination graph as Hammons provides a means of updating the node based on the local configuration and collecting updated information for the nodes (Hammons, ¶0049). All of the claimed elements were known in the prior art and one skilled in the art could have combined the elements, as claimed by known methods, and the combination would have yielded predictable results to one having ordinary skill in the art at the time of invention.
Claims 5, 10, 15, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Umapathy in view of Hammons, Tan, and Gupta et al. (2020/0382968), hereinafter Gupta.
Re. Claims 5, 10, 15, and 20, Umapathy, Hammons, and Tan teach Claims 1, 6, 11, and 16.
However, the combination of Umapathy, Hammons, and Tan does not expressly teach transferring pretrained optimization functions to another node in another radio access network.
Yet, Gupta explicitly teaches transferring pretrained optimization functions to another node in another radio access network (¶0015 - The network automation platform can send trained ML models to a devices of a radio access network (RAN)).
Therefore, it would have been obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to add the teaching of Gupta to the teaching of Umapathy, Hammons, and Tan. The motivation for such would be to further improve inter-RAN communication as Gupta provides a means for transferring pretrained functions to another node in a RAN (Gupta, ¶0015). All of the claimed elements were known in the prior art and one skilled in the art could have combined the elements, as claimed by known methods, and the combination would have yielded predictable results to one having ordinary skill in the art at the time of invention.
Response to Arguments
Applicant's arguments filed 07/24/2026 have been fully considered but they are not persuasive. Applicant has provided new amendments in light of the previous rejection in order to argue, as seen on Pg. 10 of the remarks, that reference Tam does not expressly teach the newly amended limitations. To this end, Examiner has produced Tam Fig. 7, and ¶0113 (Process 600 is not aware of the particular starting point 702. Iterating through the base incremental adjustment phase 605 will attain a first intermediate network performance level 704. The biased random adjustment phase will then kick in to perform chaotic jumps to identify the desired optimum network performance level 708, possibly through one or more second intermediate network performance levels 706) which he interprets as disclosing a graph of antenna locations comprised as such that the antennae reside on the vertices and that the payoff function starts at a random value. Further, Examiner finds that Tam ¶0054 (Simulated annealing based (SA-based) optimization algorithms may be used to evaluate global solutions for multi-cell wireless networks. As used herein, the term “global solution” refers to a set of local solutions for two or more wireless network coverage areas in a wireless network. Each “local solution” specifies one or more wireless configuration parameters for a particular wireless network coverage area) discloses an optimization function similar to that claimed as a “payoff function” in the present application. Under this interpretation, Examiner has amended the current rejection accordingly and finds the arguments provided against the previous Non-Final rejection to be non-persuasive. As such, Examiner upholds the previously presented rejection under 35 U.S.C. § 103 for independent claims 1, 6, 11, and 16 and all claims depending therein.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Akdeniz et al. (2023/0189319) - ¶0002-0034
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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/N.J.S./Examiner, Art Unit 2475
/HASHIM S BHATTI/Primary Examiner, Art Unit 2475
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