DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is in response to the communication filed on 10/10/2024. Claims 1-20 are pending in this application.
Priority
This application claims priority of CN202210375838.3, filed on 04/11/2022. The assignee of record is ZTE Corporation. The listed inventor(s) is/are: Wang et al.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 10/10/2024 and 11/06/2025 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS(s) is/are being considered by the examiner.
Claim Objections
Claim 4 is objected to because of the following informalities:
In Claim 4, line 10, “one of the plurality of plurality of pieces” will read as “one of the plurality of pieces.”
Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3, 7-8, 12-13, 16-17 and 19-20 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Liu et al. (US 20200067828 A1, published 02/27/2020; hereinafter Liu).
For Claim 1, Liu teaches an audio and video cloud network (Liu, para. [0004] “… Disclosed herein are implementations of methods, apparatuses, and systems for real-time video communications …”) comprising:
a control hub, comprising a dispatching center (Liu teaches an edge node selector in a control node of a SDN determining terminal/client access and edge node selection) and a routing center (Liu teaches a routing controller in the control node of the SDN determining optimal routing paths; FIG. 2, FIG. 8, FIG. 9; para. [0116] “… FIG. 9 is a diagram of another example apparatus for real-time video communications according to implementations of this disclosure. For example, the apparatus can be a control node 900 of an SDN, and the SDN can be the SDN 200 in FIG. 2. The control node 900 can be the control node 814 in FIG. 8, or the control node 202 in FIG. 2 …”; para. [0120] “… The routing controller 906 can be used to determine cascade network topologies and propagate the same to the service nodes. In some implementations, based on the optimal paths associated with an edge node connected to data-sending terminals determined by the probe controller 904, the routing controller 906 can determine a cascade network topology by synthesizing the optimal paths with the edge node set as a sender …”; para. [0122] “… The edge node selector 910 can be used to select edge nodes for terminals. For example, the edge node selector 910 can receive statistics about parameters (e.g., a data transmission quota, a load status, and a health status) from candidate service nodes of a terminal …”); and
a plurality of media nodes (Liu exemplifies the service nodes in FIG. 2; para. [00420 “… In FIG. 2, the SDN 200 includes two types of nodes: service nodes and control nodes. The service nodes (e.g., service nodes 204-218) are used for receiving, forwarding, and delivering multimedia data from and to different user terminals …”; para. [0044] “… The service nodes can be further divided into two types: edge service nodes (or ‘edge nodes’ for simplicity) and router service nodes (or ‘router nodes’ for simplicity). An edge node is directly connected to an end-user terminal (or ‘terminal’ for simplicity), such as terminals 220-226 …”), comprising a transit node (Liu, FIG. 2; para. [0045] “… A router node is not directly connected to any terminal. The router note participates in forwarding data, such as the service nodes, 204,208, and 212-216 …”) and an edge node (Liu exemplifies edge nodes 206, 210 and 218 in FIG. 2),
wherein the transit node is in communicative connection with the routing center, and the edge node is in communicative connection with the transit node and the dispatching center (Liu, FIG. 2; para. [0042] “… the service nodes and the control nodes can be interconnected with each other. For example, for an SDN having N nodes, there can be least Nx(N-1) direct connections among them. That is, any two nodes in the SDN can be directly connected. …”; para. [0043] “… The solid lines with double arrows can represent bidirectional interconnections between the service nodes … The dash lines with double arrows can represent bidirectional connections between the service nodes and the control nodes …”).
For Claim 3, Liu teaches a method for accessing an audio and video cloud network (Liu, para. [0004] “… Disclosed herein are implementations of methods, apparatuses, and systems for real-time video communications …”), applied to a dispatching center (Liu exemplifies an edge node selector in a control node of a SDN determining terminal/client access and edge node selection in FIG. 9) in the audio and video cloud network further comprising a plurality of edge nodes (Liu exemplifies edge nodes edge nodes 206, 210 and 218 in communicative connection with the control node in FIG. 2; para. [0044] “… An edge node is directly connected to an end-user terminal (or ‘terminal’ for simplicity), such as terminals 220-226 …”) each being in communicative connection with the dispatching center, and having a respective one of a plurality of pieces of node information (Liu teaches each edge node having node information such as their transmission capacities, load status, etc.; FIG. 2; para. [0048] “… Parameters indicative of transmission capacities can be determined between the service nodes and can be transmitted to the control nodes for determining optimal data-transmission paths …”; para. [0049] “… the path metric can include a load status of a service node and a transmission metric (e.g., for a unidirectional or bidirectional transmission) between the service node and another service node …”), the method comprising:
acquiring a client access request reported by a client and each of the plurality of pieces of node information (Liu teaches receiving a multimedia data transmission request from a terminal/client and the node information of the candidate edge nodes; FIG. 2; para. [0058] “… For example, in FIG. 2, when the terminal 226 connects to the SDN 200 and initiates a multimedia data transmission to the terminal 220, service nodes 212-218 can be its candidate edge nodes and they can have different strengths in the consideration factors. The service node 212 can have the lowest connection latency to the terminal 226. The service node 214 can have been used as the edge node of the terminal 226 in a prior optimal path. The service node 216 can have the lightest current transmission load among the service nodes 212-218. The service node 218 can have the smallest geographic distance to the terminal 226 …”);
generating access dispatching information according to the client access request and each of the plurality of pieces of node information, wherein the access dispatching information comprises target edge node information (Liu teaches selecting candidate service node as the edge node to connect the requesting terminal/client based on the multimedia data transmission request and the node information of the candidate service nodes ; FIG. 2; para. [0057] “… the control nodes can select a service node as an edge node for connecting a terminal to the SDN. The connection between the terminal and the edge node is a non-SDN connection (e.g., an Internet connection). By receiving the path metrics, the control nodes can have real-time performance statistics of the SDN. The edge node can be selected from one or more candidate edge nodes based on at least one consideration factor of a prior optimal path that is associated with the terminal (e.g., the edge node used by the terminal in the prior optimal path can be a candidate edge node for the current selection), a rule of a network operator associated with the SDN (e.g., a rule that requires all terminals to be connected to a specific service node), a geographical location of the terminal, a geographical location of a candidate service node, and a path metric associated with the candidate edge node …”); and
sending the access dispatching information to the client and a target edge node corresponding to the target edge node information, to allow the client to access the target edge node, wherein the target edge node is selected from the plurality of edge nodes (Liu teaches configuring the edge node that is selected from the service nodes to authenticate the terminal/client, and also implying sending information to the terminal/client about which edge node is selected for connection; FIG. 2; para. [0059] “… after an edge node is selected for a terminal, the terminal can be authenticated before being connected to the SDN. For example, the edge node can be configured to authenticate the terminal. The authentication can be based on at least one of a permission of connecting the terminal to the SDN, a time limit of connecting the terminal to the SDN, a permission to send data by the terminal, and a permission to receive data by the terminal …”).
For Claim 7, Liu teaches a routing and forwarding method for an audio and video cloud network (Liu, para. [0004] “… Disclosed herein are implementations of methods, apparatuses, and systems for real-time video communications …”), applied to a routing center (Liu exemplifies a routing controller in the control node of the SDN determining optimal routing paths in FIG. 2) in the audio and video cloud network further comprising a plurality of transit nodes (Liu exemplifies the service nodes in FIG. 2; para. [0044] “… The service nodes can be further divided into two types: edge service nodes (or ‘edge nodes’ for simplicity) and router service nodes (or ‘router nodes’ for simplicity). An edge node is directly connected to an end-user terminal (or ‘terminal’ for simplicity), such as terminals 220-226 …”; para. [0045] “… A router node is not directly connected to any terminal. The router note participates in forwarding data, such as the service nodes, 204,208, and 212-216 … a service node can switch between roles of an edge node and a router node in different time, or function as both at the same time …”) each being in communicative connection with the routing center (Liu, FIG. 2; para. [0042] “… the service nodes and the control nodes can be interconnected with each other. For example, for an SDN having N nodes, there can be least Nx(N-1) direct connections among them. That is, any two nodes in the SDN can be directly connected. …”; para. [0043] “… The solid lines with double arrows can represent bidirectional interconnections between the service nodes … The dash lines with double arrows can represent bidirectional connections between the service nodes and the control nodes …”), the routing and forwarding method comprising:
acquiring a client access request from a client (Liu teaches receiving a multimedia data transmission request), starting transit node information (Liu exemplifies service node 210 connecting with a terminal/client in FIG. 2, the service node comprises the corresponding node information such as their transmission capacities, load status, etc.), and link quality information (Liu teaches path metrics between two connected service nodes) between any two connected transit nodes among the plurality of transit nodes, wherein the starting transit node information is node information of the transit node corresponding to the client (Liu, FIG. 2; para. [0048] “… Parameters indicative of transmission capacities can be determined between the service nodes and can be transmitted to the control nodes for determining optimal data-transmission paths (referred to as ‘optimal paths’ for simplicity) between the service nodes. The parameters can be referred to as ‘path metrics’ hereinafter … the transmission metric can include any number of any combination of a latency, a packet-loss ratio, a network traffic load, and a transmission quota”; para. [0049] “… the path metric can include a load status of a service node and a transmission metric (e.g., for a unidirectional or bidirectional transmission) between the service node and another service node …”; para. [0056] “… When the service node 210 receives user data (e.g., multimedia data) from the terminal 224, the path data can be attached to packet headers of the user data …”);
parsing the client access request to obtain service requirement information and target transit node information (Liu teaches selecting candidate service node as the edge node to connect the receiving terminal/client based on the multimedia data transmission request and the node information of the candidate service nodes, for example, the data is requested to be transmitted to from terminal/client 224 to terminal/client 220, the candidate service node 206 is selected as the edge node (i.e. target transit node) to connect to the receiving terminal/client 220; FIG. 2; para. [0057] “… the control nodes can select a service node as an edge node for connecting a terminal to the SDN. The connection between the terminal and the edge node is a non-SDN connection (e.g., an Internet connection). By receiving the path metrics, the control nodes can have real-time performance statistics of the SDN. The edge node can be selected from one or more candidate edge nodes based on at least one consideration factor of a prior optimal path that is associated with the terminal (e.g., the edge node used by the terminal in the prior optimal path can be a candidate edge node for the current selection), a rule of a network operator associated with the SDN (e.g., a rule that requires all terminals to be connected to a specific service node), a geographical location of the terminal, a geographical location of a candidate service node, and a path metric associated with the candidate edge node …”); and
generating routing and forwarding path information according to the service requirement information, the starting transit node information, the target transit node information, and the link quality information, and sending the routing and forwarding path information to the plurality of transit nodes (Liu teaches determining optimal paths between service nodes based on the path metrics, and generating the optimal path when receiving the request to send the data to a receiving terminal/client, the optimal path comprising the node information for the service nodes along the optimal path; FIG. 2; para. [0056] “… For data transmission from the terminal 224 to the terminal 220, the optimal path can be determined as the service nodes 210-208-206 … When the service node 210 receives user data (e.g., multimedia data) from the terminal 224, the path data can be attached to packet headers of the user data. The user data can be transmitted to the service node 208 that serves as a router node. The service node 208 can obtain the path data from the packet headers of the user data, and extract the optimal path 210-208-206 by processing the path data (e.g., by reading from the routing table). Based on the optimal path, the service node 208 can forward the user data to a next service node of the optimal path, in which the packet headers of the user data also include the path data. The next service node can repeat the same operation to obtain the optimal path and forward the user data until the edge node of the terminal 224 (should be the terminal 220) is reached, which is the service node 206 in this example. The service node 206 can deliver the user data to the terminal 224 …”).
For Claim 8, Liu teaches the routing and forwarding method of claim 7, wherein generating the routing and forwarding path information according to the service requirement information, the starting transit node information, the target transit node information, and the link quality information comprises:
selecting a path planning policy from a plurality of path planning policies according to the service requirement information (Liu teaches selecting latency related path planning policy; FIG. 2; para. [0054] “… for any two of the service nodes, K asymmetric optimal paths can be determined, in which K is a positive integer. The K optimal paths can have different latency values. In some implementations, the K optimal paths can be ordered by the latency values (e.g., in an ascending order), which can serve to each other as alternative paths. For example, when the lowest-latency path of the K optimal paths used for data transmission becomes unavailable (e.g., disconnected or a latency thereof sharply increases), the second-lowest-latency path can be switched to continue the data transmission …”); and
selecting a plurality of routing and forwarding paths according to the path planning policy and the link quality information, and generating the routing and forwarding path information corresponding to the plurality of routing and forwarding paths (Liu teaches determining optimal paths between the service nodes, and the optimal paths having information of the patch metrics between the service nodes; FIG. 2; para. [0052] “… The determined path metrics can be reported to the control nodes. For example, whenever a transmission metric (e.g., an asymmetric transmission metric) is determined by a service node between the same and another service node, the service node can instantly transmit the path metric to the control nodes. Also, the load status of a service node can be repetitively monitored and transmitted to the control node (e.g., together with the transmission metric). Based on the path metrics, the control nodes can determine optimal paths between the service nodes …”), wherein a starting node of each of the plurality of routing and forwarding paths is determined by the starting transit node information, and an end node of each the plurality of routing and forwarding paths is determined by the target transit node information (Liu teaches the optimal paths having information of starting edge nodes and ending edge nodes that are connected to the terminals/clients; FIG. 2; para. [0056] “… For data transmission from the terminal 224 to the terminal 220, the optimal path can be determined as the service nodes 210-208-206 …”).
For Claim 12, the claim is substantially similar to claim 3 and therefore is rejected for the same reasoning set forth above. Additionally, Liu teaches a controller, comprising: a memory, a processor, and a computer program stored in the memory and executable by the processor which, when executed by the processor, causes the processor to perform the method for accessing an audio and video cloud network of claim 3 (Liu, FIG. 1; para. [0028] “… The memory 110 can include any transitory or non-transitory device capable of storing codes and data that can be accessed by the processor (e.g., via a bus) …”; para. [0032] “… The apparatuses 102 and 104 can be used as control nodes of the SDN …”).
For Claim 13, the claim is substantially similar to claim 3 and therefore is rejected for the same reasoning set forth above. Additionally, Liu teaches a non-transitory computer-readable storage medium, storing a computer-executable instruction which, when executed by a computer, causes the computer to perform the method for accessing an audio and video cloud network of claim 3 (Liu, FIG. 1; para. [0028] “… The memory 110 can include any transitory or non-transitory device capable of storing codes and data that can be accessed by the processor (e.g., via a bus) …”; para. [0032] “… The apparatuses 102 and 104 can be used as control nodes of the SDN …”).
For Claim 16, the claim is substantially similar to claim 7 and therefore is rejected for the same reasoning set forth above. Additionally, Liu teaches a controller, comprising: a memory, a processor, and a computer program stored in the memory and executable by the processor which, when executed by the processor, causes the processor to perform the routing and forwarding method for an audio and video cloud network of claim 7 (Liu, FIG. 1; para. [0028] “… The memory 110 can include any transitory or non-transitory device capable of storing codes and data that can be accessed by the processor (e.g., via a bus) …”; para. [0032] “… The apparatuses 102 and 104 can be used as control nodes of the SDN …”).
For Claim 17, the claim is substantially similar to claim 7 and therefore is rejected for the same reasoning set forth above. Additionally, Liu teaches a non-transitory computer-readable storage medium, storing a computer-executable instruction which, when executed by a computer, causes the computer to perform the routing and forwarding method for an audio and video cloud network of claim 7 (Liu, FIG. 1; para. [0028] “… The memory 110 can include any transitory or non-transitory device capable of storing codes and data that can be accessed by the processor (e.g., via a bus) …”; para. [0032] “… The apparatuses 102 and 104 can be used as control nodes of the SDN …”).
For Claim 19, Liu teaches the audio and video cloud network of claim 1, wherein the transit node is one of a plurality of transit nodes (Liu exemplifies the service nodes in FIG. 2), and the routing center is configured to collect link quality information between any two connected transit nodes among the plurality of transit nodes, to calculate and plan dynamically an optimal path policy (Liu, FIG. 2; para. [0050] “… The path metrics can be determined between any two service nodes using an active mode, a passive mode, or a combination thereof. In the active mode, any two directly-connected service nodes of the SDN can mutually send and receive test data packets (e.g., dummy data packets with stuffing data), such as using a routing protocol. By performing the calculation using the received test data packets, the transmission metric (e.g., a latency, or a packet-loss ratio) can be determined …”; para. [0052] “… The determined path metrics can be reported to the control nodes … Based on the path metrics, the control nodes can determine optimal paths between the service nodes …”).
For Claim 20, Liu teaches the audio and video cloud network of claim 1, wherein the edge node is one of a plurality of edge nodes (Liu exemplifies the service node 210 as the edge node for the terminal/client 224 in FIG. 2), and the dispatching center is configured to acquire a client access request reported by a client and node information of the edge nodes, to analyze the client access request and the node information, and to select a target edge node to allow the client to access the target edge node from the plurality of edge nodes according to a nearest-node access mode or a best-node access mode (Liu teaches receiving a multimedia data transmission request from a terminal/client and the node information of the candidate edge nodes, and selecting a candidate service node as the edge node to connect the requesting terminal/client according to different consideration factors; FIG. 2; para. [0058] “… For example, in FIG. 2, when the terminal 226 connects to the SDN 200 and initiates a multimedia data transmission to the terminal 220, service nodes 212-218 can be its candidate edge nodes and they can have different strengths in the consideration factors. The service node 212 can have the lowest connection latency to the terminal 226. The service node 214 can have been used as the edge node of the terminal 226 in a prior optimal path. The service node 216 can have the lightest current transmission load among the service nodes 212-218. The service node 218 can have the smallest geographic distance to the terminal 226 … each consideration factor can also be assigned with a weight to represent its priority level. Under each consideration factor, the control nodes can assign a score to a candidate edge node to represent its strength in that consideration factor … Based on the total scores of the candidate edge nodes, the control nodes can select the edge node based on a rule (e.g., selecting the candidate edge node that receives the highest or lowest total score) …”).
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 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 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 2, 6, 11 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (US 20200067828 A1, published 02/27/2020; hereinafter Liu), in view of Wang et al. (US 20240031427 A1, priority dated 04/14/2021; hereinafter Wang).
For Claim 2, Liu teaches the audio and video cloud network of claim 1 wherein the audio and video cloud network is in communicative connection with a telecommunication operator network (Liu teaches centralized SDN controllers and distributed service nodes in different layers that comprises physical layer; FIG. 10; para. [0123] “… The channel 1000 can be seen as a set of service nodes that serve the same channel. The service nodes of the channel 1000 can be divided into different layers. The layers can be divided based on at least one of geographic locations of the service nodes, ISP's the service nodes connecting to, topologies of a local network of the service nodes, and autonomous systems (AS's) the service nodes belonging to …”), and …
Liu does not explicitly teach, but Wang teaches a network architecture of the audio and video cloud network matches a network architecture of the telecommunication operator network (Wang teaches aligning the MEC architecture with the physical telecom architecture for deploying the MEC architecture; FIG. 4, FIG. 5; para. [0008] “… The technical solution of the present application is to provide a CNI (i.e. cloud-network integration) oriented MEC architecture. An access network (AN) side of the architecture is provided with a plurality of edge computing nodes, a physical channel of the AN side is split into a plurality of subchannels with each of the subchannels supporting a media access control (MAC) access mode, and a software defined network (SDN) controller is arranged in the architecture and is configured to allocate resources of the subchannels at a physical layer and protocols at an MAC layer and control offloading at the edge computing nodes or a cloud center by a terminal user. …”; para. [0033] “… FIG. 4 shows a schematic diagram of a distributed node architecture based on software-defined MEC. The architecture includes multiple MEC nodes achieving different functions and roles, including, e.g., a common node (CNode), regional node (RANode), a super node (SNode), and a certificate authority (CA) node …”; para. [0036] “… SNode: SNode indicates a super intelligent node positioned in the MEC data center between the access network and the convergence network. Each SNode is responsible for managing a certain number of CNodes and RANodes allocated to the SNode by the SDN controller. The SNode is responsible for managing remote installation of a VM on the CNodes/RANodes, joining/exiting of nodes (seamless extension), node configuration, user management, etc. …”; para. [0039] “… an adaptive resource allocation strategy based on deep reinforcement learning is adopted. Specifically, for the adaptive resource allocation algorithm, a CNI oriented fine-grained edge architecture is abstracted into a system model shown in FIG. 5. The model is set to be a multi-level network consisting of m users client{1, 2, …, m}, n edge computing nodes CNode{1, 2 , …, n}, an SDN controller, an SNode and a central cloud. …”).
Wang and Liu are analogous art because they are both related to cloud network systems.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the cloud network integration techniques of Wang with the system of Liu to facilitate fine-grained control of resource allocations to support the MEC with physical hardware of terminal devices and the connection capability of wireless channels (Wang, para. [0006]).
For Claim 6, Liu teaches the method of claim 3, wherein the audio and video cloud network is in communicative connection with a telecommunication operator network (Liu teaches centralized SDN controllers and distributed service nodes in different layers that comprises physical layer; FIG. 10; para. [0123] “… The channel 1000 can be seen as a set of service nodes that serve the same channel. The service nodes of the channel 1000 can be divided into different layers. The layers can be divided based on at least one of geographic locations of the service nodes, ISP's the service nodes connecting to, topologies of a local network of the service nodes, and autonomous systems (AS's) the service nodes belonging to …”), and …
Liu does not explicitly teach, but Wang teaches a network architecture of the audio and video cloud network matches a network architecture of the telecommunication operator network (Wang teaches aligning the MEC architecture with the physical telecom architecture for deploying the MEC architecture; FIG. 4, FIG. 5; para. [0008] “… The technical solution of the present application is to provide a CNI (i.e. cloud-network integration) oriented MEC architecture. An access network (AN) side of the architecture is provided with a plurality of edge computing nodes, a physical channel of the AN side is split into a plurality of subchannels with each of the subchannels supporting a media access control (MAC) access mode, and a software defined network (SDN) controller is arranged in the architecture and is configured to allocate resources of the subchannels at a physical layer and protocols at an MAC layer and control offloading at the edge computing nodes or a cloud center by a terminal user. …”; para. [0033] “… FIG. 4 shows a schematic diagram of a distributed node architecture based on software-defined MEC. The architecture includes multiple MEC nodes achieving different functions and roles, including, e.g., a common node (CNode), regional node (RANode), a super node (SNode), and a certificate authority (CA) node …”; para. [0036] “… SNode: SNode indicates a super intelligent node positioned in the MEC data center between the access network and the convergence network. Each SNode is responsible for managing a certain number of CNodes and RANodes allocated to the SNode by the SDN controller. The SNode is responsible for managing remote installation of a VM on the CNodes/RANodes, joining/exiting of nodes (seamless extension), node configuration, user management, etc. …”; para. [0039] “… an adaptive resource allocation strategy based on deep reinforcement learning is adopted. Specifically, for the adaptive resource allocation algorithm, a CNI oriented fine-grained edge architecture is abstracted into a system model shown in FIG. 5. The model is set to be a multi-level network consisting of m users client{1, 2, …, m}, n edge computing nodes CNode{1, 2 , …, n}, an SDN controller, an SNode and a central cloud. …”); and
after generating the access dispatching information according to the client access request and each of the plurality of pieces of node information, the method further comprises:
notifying the telecommunication operator network of the access dispatching information, to allow the telecommunication operator network to adjust a Quality of Service (QOS) level of a link corresponding to an access dispatching path configured for the client to access the target edge node (Wang, para. [0027] “… Under this background, the protocol stack is further provided with a software defined network (SDN) controller, which runs above the physical layer/MAC layer slicing, and is responsible for the allocation of the subchannels at the physical layer and protocols at the MAC layer. By timely adjusting the subchannel resources of the physical layer and an access protocol of the MAC layer, the SDN controller aims to carry out cooperative optimization on the resources of the physical layer and the MAC layer by using the diversity of underlying channels, so that the resource utilization rate is improved to the maximum extent …”).
Wang and Liu are analogous art because they are both related to cloud network systems.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the cloud network integration techniques of Wang with the system of Liu to facilitate fine-grained control of resource allocations to support the MEC with physical hardware of terminal devices and the connection capability of wireless channels (Wang, para. [0006]).
For Claim 11, Liu teaches the routing and forwarding method of claim 8, wherein the audio and video cloud network is in communicative connection with a telecommunication operator network (Liu teaches centralized SDN controllers and distributed service nodes in different layers that comprises physical layer; FIG. 10; para. [0123] “… The channel 1000 can be seen as a set of service nodes that serve the same channel. The service nodes of the channel 1000 can be divided into different layers. The layers can be divided based on at least one of geographic locations of the service nodes, ISP's the service nodes connecting to, topologies of a local network of the service nodes, and autonomous systems (AS's) the service nodes belonging to …”), and …
Liu does not explicitly teach, but Wang teaches a network architecture of the audio and video cloud network matches a network architecture of the telecommunication operator network (Wang teaches aligning the MEC architecture with the physical telecom architecture for deploying the MEC architecture; FIG. 4, FIG. 5; para. [0008] “… The technical solution of the present application is to provide a CNI (i.e. cloud-network integration) oriented MEC architecture. An access network (AN) side of the architecture is provided with a plurality of edge computing nodes, a physical channel of the AN side is split into a plurality of subchannels with each of the subchannels supporting a media access control (MAC) access mode, and a software defined network (SDN) controller is arranged in the architecture and is configured to allocate resources of the subchannels at a physical layer and protocols at an MAC layer and control offloading at the edge computing nodes or a cloud center by a terminal user. …”; para. [0033] “… FIG. 4 shows a schematic diagram of a distributed node architecture based on software-defined MEC. The architecture includes multiple MEC nodes achieving different functions and roles, including, e.g., a common node (CNode), regional node (RANode), a super node (SNode), and a certificate authority (CA) node …”; para. [0036] “… SNode: SNode indicates a super intelligent node positioned in the MEC data center between the access network and the convergence network. Each SNode is responsible for managing a certain number of CNodes and RANodes allocated to the SNode by the SDN controller. The SNode is responsible for managing remote installation of a VM on the CNodes/RANodes, joining/exiting of nodes (seamless extension), node configuration, user management, etc. …”; para. [0039] “… an adaptive resource allocation strategy based on deep reinforcement learning is adopted. Specifically, for the adaptive resource allocation algorithm, a CNI oriented fine-grained edge architecture is abstracted into a system model shown in FIG. 5. The model is set to be a multi-level network consisting of m users client{1, 2, …, m}, n edge computing nodes CNode{1, 2 , …, n}, an SDN controller, an SNode and a central cloud. …”); and
after generating the routing and forwarding path information according to the service requirement information, the starting transit node information, the target transit node information, and the link quality information, the method further comprises:
notifying the telecommunication operator network of the routing and forwarding path information, to allow the telecommunication operator network to adjust a QoS level of a link corresponding to the routing and forwarding path (Wang, para. [0027] “… Under this background, the protocol stack is further provided with a software defined network (SDN) controller, which runs above the physical layer/MAC layer slicing, and is responsible for the allocation of the subchannels at the physical layer and protocols at the MAC layer. By timely adjusting the subchannel resources of the physical layer and an access protocol of the MAC layer, the SDN controller aims to carry out cooperative optimization on the resources of the physical layer and the MAC layer by using the diversity of underlying channels, so that the resource utilization rate is improved to the maximum extent …”).
Wang and Liu are analogous art because they are both related to cloud network systems.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the cloud network integration techniques of Wang with the system of Liu to facilitate fine-grained control of resource allocations to support the MEC with physical hardware of terminal devices and the connection capability of wireless channels (Wang, para. [0006]).
For Claim 14, the claim is substantially similar to claim 2 and therefore is rejected for the same reasoning set forth above.
For Claim 15, the claim is substantially similar to claim 2 and therefore is rejected for the same reasoning set forth above.
Claim Rejections - 35 USC § 103
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (US 20200067828 A1, published 02/27/2020; hereinafter Liu), in view of Drai et al. (US 20090172167 A1, published 07/02/2009; hereinafter Drai).
For Claim 4, Liu teaches the method of claim 3, wherein … and each of the plurality of pieces of the node information comprises a respective one of a plurality of pieces of node location information (Liu, FIG. 2; para. [0057] “… the control nodes can select a service node as an edge node for connecting a terminal to the SDN. The connection between the terminal and the edge node is a non-SDN connection (e.g., an Internet connection). By receiving the path metrics, the control nodes can have real-time performance statistics of the SDN. The edge node can be selected from one or more candidate edge nodes based on at least one consideration factor of a prior optimal path that is associated with the terminal (e.g., the edge node used by the terminal in the prior optimal path can be a candidate edge node for the current selection), a rule of a network operator associated with the SDN (e.g., a rule that requires all terminals to be connected to a specific service node), a geographical location of the terminal, a geographical location of a candidate service node, and a path metric associated with the candidate edge node …”); and …
generating the access dispatching information according to target edge node information corresponding to the target edge node (Liu teaches selecting candidate service node as the edge node to connect the requesting terminal/client; FIG. 2; para. [0057] “… the control nodes can select a service node as an edge node for connecting a terminal to the SDN. The connection between the terminal and the edge node is a non-SDN connection (e.g., an Internet connection). By receiving the path metrics, the control nodes can have real-time performance statistics of the SDN. The edge node can be selected from one or more candidate edge nodes based on at least one consideration factor of a prior optimal path that is associated with the terminal (e.g., the edge node used by the terminal in the prior optimal path can be a candidate edge node for the current selection), a rule of a network operator associated with the SDN (e.g., a rule that requires all terminals to be connected to a specific service node), a geographical location of the terminal, a geographical location of a candidate service node, and a path metric associated with the candidate edge node …”)
Liu does not explicitly teach, but Drai teaches the client access request comprises geographical location information for the client (Drai, para. [0032] “… Obtaining information regarding the end user, the information including a geographical location and a desired content related activity …”),
generating the access dispatching information according to the client access request and each of the plurality of pieces of node information comprises:
acquiring each of the plurality of pieces of node location information and the geographical location information, and calculating a respective one of a plurality of physical distances between each of the plurality of edge nodes and the client, wherein the respective one of the plurality of physical distances is calculated according to the geographical location information and the respective one of the plurality of plurality of pieces of node location information (Drai, para. [0050] “… Optionally the system further comprises a plurality of CDN edges from a plurality of CDN vendors, wherein the CDN center further determines whether to distribute the content through one of the plurality of the CDN edges or through one or more of the customer edges, or a combination thereof …”; para. [0063] “… According to some embodiments for resource sharing for content distribution, such sharing may optionally and preferably be performed between customers (i.e. customer edges), between a customer and a CDN, and between different geographical locations for a customer with multiple edges and the CDN. …”);
selecting at least one edge node each corresponding to the respective physical distance less than or equal to a preset distance as the target edge node (Drai, para. [0039] “… Optionally, the at least one routing target is further selected according to one or more of a low distance or a low latency separation from the distribution center by the end user …”; Examiner notes that Drai’s teaching of selecting the routing target based on proximity required distinguishing which nodes are close enough to serve the user effectively).
Drai and Liu are analogous art because they are both related to content distribution network.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the selecting edge node based on proximity techniques of Drai with the system of Liu to provide more efficient and cost-effective content distribution to the end users (Drai, para. [0004]).
Claim Rejections - 35 USC § 103
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (US 20200067828 A1, published 02/27/2020; hereinafter Liu), in view of McCoy et al. (US 20210390642 A1, published 12/16/2021; hereinafter McCoy).
For Claim 5, Liu teaches the method of claim 3, wherein each of a first edge node set of the plurality of the edge nodes to which the client had been accessed has a first node information set of the plurality of pieces of node information (Liu, FIG. 2; para. [0057] “… the control nodes can select a service node as an edge node for connecting a terminal to the SDN. The connection between the terminal and the edge node is a non-SDN connection (e.g., an Internet connection). By receiving the path metrics, the control nodes can have real-time performance statistics of the SDN. The edge node can be selected from one or more candidate edge nodes based on at least one consideration factor of a prior optimal path that is associated with the terminal (e.g., the edge node used by the terminal in the prior optimal path can be a candidate edge node for the current selection), a rule of a network operator associated with the SDN (e.g., a rule that requires all terminals to be connected to a specific service node), a geographical location of the terminal, a geographical location of a candidate service node, and a path metric associated with the candidate edge node …”), and …; and …
generating the access dispatching information according to the target edge node information corresponding to the target edge node (Liu teaches selecting candidate service node as the edge node to connect the requesting terminal/client; FIG. 2; para. [0057] “… the control nodes can select a service node as an edge node for connecting a terminal to the SDN. The connection between the terminal and the edge node is a non-SDN connection (e.g., an Internet connection). By receiving the path metrics, the control nodes can have real-time performance statistics of the SDN. The edge node can be selected from one or more candidate edge nodes based on at least one consideration factor of a prior optimal path that is associated with the terminal (e.g., the edge node used by the terminal in the prior optimal path can be a candidate edge node for the current selection), a rule of a network operator associated with the SDN (e.g., a rule that requires all terminals to be connected to a specific service node), a geographical location of the terminal, a geographical location of a candidate service node, and a path metric associated with the candidate edge node …”).
Liu does not explicitly teach, but McCoy teaches each of the first node information set comprises a respective one of a plurality of pieces of historical quality information (McCoy teaches the candidate nodes having information such as past performance or past behavior; FIG. 2; para. [0028] “… at 210 the system may select one of the candidate nodes to provide each service element using any suitable selection process, such by random selection, by ranking the nodes based on past performance, or by other prioritization methods (such as by selecting a node that has not provided a service element within a particular time period, or by continuing to use a node that provided an immediately previous service element) …”);
generating the access dispatching information according to the client access request and each of the plurality of pieces of node information comprises:
acquiring each of the plurality of pieces of historical quality information (McCoy teaches the candidate nodes having information such as past performance or past behavior; FIG. 2; para. [0028] “… at 210 the system may select one of the candidate nodes to provide each service element using any suitable selection process, such by random selection, by ranking the nodes based on past performance, or by other prioritization methods (such as by selecting a node that has not provided a service element within a particular time period, or by continuing to use a node that provided an immediately previous service element) …”) or a plurality of real-time link connection statuses each indicating a connection status of a link between the client and a respective one of the plurality of edge nodes;
generating a client access black-and-white list according to each of the plurality of pieces of historical quality information or the plurality of real-time link connection statuses, wherein the client access black-and-white list comprises at least one edge node, and each of the at least one edge node is set with a respective one of a plurality of access priorities (McCoy teaches ranking and prioritizing the candidate nodes based on information such as past performance or past behavior; FIG. 2; para. [0028] “… at 210 the system may select one of the candidate nodes to provide each service element using any suitable selection process, such by random selection, by ranking the nodes based on past performance, or by other prioritization methods (such as by selecting a node that has not provided a service element within a particular time period, or by continuing to use a node that provided an immediately previous service element) …”);
selecting a second edge node set of the plurality of edge nodes of which the respective one of the access priorities satisfies a preset priority from the at least one edge node in the client access black-and-white list as the target edge node (McCoy teaches selecting a node using the logic of ranking and prioritizing the candidate nodes based on information such as past performance or past behavior; FIG. 2; para. [0028] “… at 210 the system may select one of the candidate nodes to provide each service element using any suitable selection process, such by random selection, by ranking the nodes based on past performance, or by other prioritization methods (such as by selecting a node that has not provided a service element within a particular time period, or by continuing to use a node that provided an immediately previous service element) …”).
McCoy and Liu are analogous art because they are both related to content distribution network.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the ranking or prioritizing the candidate nodes techniques of McCoy with the system of Liu to improve the performance of edge servers for content delivery (McCoy, para. [0003]).
Claim Rejections - 35 USC § 103
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (US 20200067828 A1, published 02/27/2020; hereinafter Liu), in view of Aukia et al. (US 6594268 B1, published 07/15/2003; hereinafter Aukia), and in further view of Odiaka (US 6829347 B1, published 12/07/2004; hereinafter Odiaka).
For Claim 9, Liu teaches the routing and forwarding method of claim 8.
Liu does not explicitly teach, but Aukia teaches wherein the plurality of path planning policies comprise quality-first path planning policy (Aukia teaches classifying packet flows and then determining routing packet flows based on QoS information or delays, etc., steps 801-803 in FIG. 8, col. 16, l. 62 – col. 17, l. 26), … and capacity-first path planning policy (Aukia teaches classifying packet flows (i.e. the service requirement) and then reserving bandwidth for routing of packet flows, steps 804 and 805 in FIG. 8, col. 17, ll. 27-44), and selecting the path planning policy from the plurality of path planning policies according to the service requirement information comprises one of:
selecting the quality-first path planning policy in response to the service requirement information being Quality of Service (QOS)-first requirement information;
selecting the cost-first path planning policy in response to the service requirement information being service cost-first requirement information; or
selecting the capacity-first path planning policy in response to the service requirement information being service capacity-first requirement information (Aukia teaches classifying packet flows (i.e. the service requirement) and then reserving bandwidth for routing of packet flows, steps 804 and 805 in FIG. 8, col. 17, ll. 27-44 “… At step 804 the packet flows are then classified as multiplexable (packet flows that may be mixed with other flow traffic on one or more links) or non-multiplexable (packet flows that are desirably assigned a dedicated link and dedicated capacity) based on comparison of the corresponding effective bandwidth ed0 values with a multiplexing threshold … ”).
Aukia and Liu are analogous art because they are both related to packet communication networks.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the classified routing strategies techniques of Aukia with the system of Liu to facilitate guaranteed bandwidth and minimum transport delay for customers (Aukia, col. 1, ll. 11-28).
Liu-Aukia does not explicitly teach, but Odiaka teaches path planning policies also comprise cost-first path planning policy (Odiaka, col. 8, ll. 52-64 “… The selection of an appropriate profile enables constraint based routing to be implemented in a cost-based routing engine, for example, a routing engine based on the Dijkstra and/or Yen K-Shortest path algorithms for determining possible routes for a trail. Such routing engines return a number of possible routes which have been computed for selection by the user, and are usually returned to the user in a least-cost order …”)
Odiaka and Liu-Aukia are analogous art because they are both related to packet communication networks.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the cost based routing strategies techniques of Odiaka with the system of Liu-Aukia to facilitate the policy based routing (Odiaka, col. 2, ll. 7-16).
Claim Rejections - 35 USC § 103
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (US 20200067828 A1, published 02/27/2020; hereinafter Liu), in view of Sze et al. (US 20210067907 A1, published 03/04/2021; hereinafter Sze).
For Claim 10, Liu teaches the routing and forwarding method of claim 8. Liu does not explicitly teach, but Sze teaches further comprising:
performing data slicing through a transit node in the plurality of transit nodes to obtain a plurality of data slicings (Sze teaches dividing data packets for transmission; FIG. 7; para. [0137] “… Referring to FIG. 7, the transmitter has identified multiple possible laborer devices, each with a single WAN interface available. The transmitter's WAN Discovery Module detects each available WAN and passes the addressing information to the Buffer Management and Transport Controller (BMTC), allowing the BMTC to access them as a virtual WAN interface. The transmitter's Router module divides the data packets and directs each to the appropriate device, for retransmission on the laborer's WAN interface …”);
selecting a first routing and forwarding path to transmit a first number of the data slicings and selecting a second routing and forwarding path to transmit a second number of the data slicings (Sze teaches directing divided packets to different paths; FIG. 7; para. [0137]), wherein transmission quality of the first routing and forwarding path is higher than transmission quality of the second routing and forwarding path, the first number is greater than the second number (Sze teaches better performance paths transmitting more data; para. [0102] “… Also, during transmission, if the network latency increases beyond a configured tolerance, it is also possible that the RF interface is sending more data than the network is capable of delivering or data is backing up inside the RF interface/network. In this circumstance the Buffer Management and Transport controller may decrease the amount of data the RF interface/network is allowed to transmit. When the latency returns to normal, the Transport control may allow the RF interface to carry more data …”), and the transmission quality is determined by the link quality information (Sze teaches the transmission quality being determined based on bandwidth, latency and lost data; para. [0147] “… When encoding starts, the mobile unit first verifies the available bandwidth on each connection. The connection test measures available bandwidth, transmit latency (one way network delay from the mobile unit to the receiver), and lost data. Based on these parameters, a connection suitability and ideal rate is established …”); and
performing splicing processing on the received data slicings through the target transit node (Sze, FIG. 6, FIG. 7; para. [0136] “… The streams are directed to the receiver where the transport buffers are reassembled …”).
Sze and Liu are analogous art because they are both related to computing communication networks.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the dividing data packets for transmission techniques of Sze with the system of Liu to reduce data transmission failure (Sze, para. [0006]).
Claim Rejections - 35 USC § 103
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (US 20200067828 A1, published 02/27/2020; hereinafter Liu), in view of Smith (US 20220255978 A1, published 08/11/2022; hereinafter Smith).
For Claim 18, Liu teaches the audio and video cloud network of claim 1. Liu does not explicitly teach, but Smith teaches wherein protocol conversion is supported in the audio and video cloud network to unify an external protocol into a private protocol adopted within the audio and video cloud network (Smith, FIG. 2; para. [0030] “… Continuing with FIG. 2, video content 201 is communicated from the video source 204, to network 110, to the stream converter 280, back to network 110, and finally to the user device 260. The stream converter 280 converts the stream content from a first streaming protocol to a second streaming protocol without transcoding media content. Conversion of a video stream may be necessary when a user device does not have a media application that is compatible with a particular streaming protocol. In this case, the stream converter 280 may be used to allow the video source 204 to stream media content to user device 260 by converting the video stream from a first protocol to a second protocol …”).
Smith and Liu are analogous art because they are both related to computing communication networks.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the protocol conversion techniques of Smith with the system of Liu to support video streaming across protocols (Smith, para. [0030]).
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is listed below, thank you:
i. You et al. (US 20210273987 A1) teaches In a method for selecting a mobile edge computing (MEC) node that performed by an edge cloud gateway, a first HyperText Transfer Protocol (HTTP) service request forwarded by the UPF is received by processing circuitry of the edge cloud gateway. A destination address of the first HTTP service request is an edge-application virtual Internet Protocol address (VIP). A corresponding MEC processing server is determined according to the first HTTP service request and a preset offloading policy, and the first HTTP service request is offloaded to the corresponding MEC processing server. The edge cloud gateway is disposed in a system for selecting a MEC node, such that the system includes at least a user plane function (UPF) and the edge cloud gateway (Abstract).
ii. Li et al. (US 20230291808 A1) teaches that A data processing method, which relates to the technical field of cloud applications. The method, performed by a target edge node, includes: receiving, based on a cloud application client switching from a first type of network to a second type of network, a first connection request forwarded by a stream proxy in a transit edge node associated with the second type of network, and establishing a first communication connection between the cloud application client and the target edge node associated with the first type of network based on the first connection request, and transmitting, based on the first communication connection, application data in the cloud application between the cloud application client and the target edge node through the stream proxy (Abstract).
Conclusion
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/Z.D./Examiner, Art Unit 2444
/SCOTT B CHRISTENSEN/Primary Examiner, Art Unit 2444