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 filed 02/25/2026 have been fully considered but they are not persuasive.
The examiner respectfully disagrees with the argument that prior art of record Rondeau et. al (US 2022/0386217), hereinafter Rondeau, does not teach: A SD-WAN application executing on a client device receiving “historic or predictive data relating to connectivity or usage of the client device” or “a SD-WAN Application which Determines Predicted Traffic corresponding to an Application of the Client Device based on the Historic or Predictive Data” The “routing device” of Rondeau on an aircraft is the client device executing an SD-WAN application. The Routing device of Rondeau attempts to use predictive data of the aircraft’s flight path to request different resources from the network: “the routing device 120 defines, before the movement, so-called theoretical routing rules, based on the parameter or parameters representative of the performance of the communication means estimated over the itinerary in the step S110 and on the requirements of each application associated with these parameters, e.g., their requirements in terms of bit rate” (Rondeau ¶ 0062). The SD-WAN application receives the predictive data from the aircraft’s routing device where that data corresponds to predicted traffic of an application of the routing device: “the routing device 120 defines, before the movement, so-called theoretical routing rules, based on the parameter or parameters representative of the performance of the communication means estimated over the itinerary in the step S110 and on the requirements of each application associated with these parameters, e.g., their requirements in terms of bit rate, of latency, of tolerance to packet losses” (Rondeau ¶ 0062). The aircraft’s routing device is analogous to the client device of the applications specification described in ¶ 0010: “In some embodiments, detecting the update condition includes determining, based on the one or more historic or predictive data, a predicted change from a first location of the client device using a first connection type supported by the SD-WAN application to a second location of the client device using a second connection type supported by the SD-WAN application.”
The examiner respectfully disagrees with the argument that the prior art of record fail to teach or suggest “a SD-WAN Application which Updates a Configuration of the SD-WAN Application, where the Selection or Establishment is Based on the Predicted Application Traffic.” While the examiner agrees with the argument presented that prior art of record Kolar et al. (US 2021/0160148), hereinafter Kolar, does not teach the above concept, prior art of record Henry et al. (US 2019/0124541) hereinafter Henry does teach this as it reports a predictive updating of an SD-WAN application based on projected throughput. Thus, while the previous rejection utilizing Kolar is withdraw, a new rejection is presented below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3-4, 6-11, and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Rondeau et al. (US 2022/0386217), hereinafter Rondeau in view of Henry et al. (US 2019/0124541) hereinafter Henry.
Regarding Claim 1, Rondeau teaches: A method comprising: receiving, by a software-defined wide area network (SD-WAN) application executing on a client device, one or more of historic or predictive data relating to connectivity or usage of the client device: “obtain, for each communication means, information obtained at instants prior to the instant t and necessary to the estimation of at least one parameter representative of the performance at the instant t of the communication means” (Rondeau ¶ 0032) where the data collected prior to instant t is understood to be historic data and “According to a particular embodiment, the routing device is an SD-WAN routing device” (Rondeau ¶ 0024); determining, by the SD-WAN application and based on the one or more historic or predictive data relating to the connectivity or usage of the client device, predicted traffic corresponding to an application of the client device: “compare, for each communication means, the parameter representative of the performance of the communication means estimated at the instant t with its value estimated before the movement” (Rondeau ¶ 0034) and “the routing device 120 defines, before the movement, so-called theoretical routing rules, based on the parameter or parameters representative of the performance of the communication means estimated over the itinerary in the step S110 and on the requirements of each application associated with these parameters, e.g., their requirements in terms of bit rate” (Rondeau ¶ 0062).
Regarding Claim 1, Henry teaches: updating, by the SD-WAN application, and responsive to determining the predicted traffic, a configuration of the SD-WAN application, wherein the configuration of the SD-WAN application comprises a selection or establishment of one or more virtual tunnels among a plurality of available virtual tunnels over one or more network connections, the selection or the establishment being based on the predicted application traffic: “In various implementations, the policy includes scheduling UE traffic across a particular link (e.g., WLAN link, cellular network link)—e.g., “load balancing” and/or “path control.” For example, in various implementations, the policy is based on obtained parameters indicating how traffic should be treated by RANs 102. Examples of the parameters include but are not limited to: quality of service (QoS) (e.g., upstream, downstream, bandwidth, traffic remarking, policing, shaping, buffering, prioritizing, etc.), RSSI (received signal strength indicator), estimated throughput” (Henry 0043) and “In various implementation, the policy is predictive, meaning it is based at least in part on previously received encapsulated traffic” (Henry ¶ 0029); and transmitting, by the SD-WAN application, and via the one or more selected virtual tunnels, application traffic corresponding to the application using the updated configuration: “UE data traffic flows (e.g. WLAN data traffic and cellular network data traffic) are controlled based on the determined policy. In various implementations, the traffic controller 203 controls the UE data traffic based on the determined policy. For example, if the policy indicates that the WLAN link is being over-utilized, the traffic controller 203 can route more UE traffic along the cellular network link. For example, if the policy indicates that OTT (over the top) applications (e.g., FaceTime) have a high priority, the traffic controller 203 can instruct OTT applications running on the UE 201 to utilize the UE radio having the higher throughput” (Henry ¶ 0044).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the filed application to combine the disclosures of Rondeau with the disclosures of Henry to achieve the predictable result of improving network traffic flow strategies for UE control. According to Henry: “Previous systems disclose a network topology wherein the controller is part of a traffic path associated with a single network. Consequently, the controller has limited visibility into overall network traffic flows, limiting its effectiveness in controlling the UE. Greater visibility into overall network traffic flows would allow the controller to implement a more effective strategy of UE control” (Henry ¶ 0003).
Regarding Claim 3, Rondeau teaches: The method of claim 1, further comprising determining, based on the one or more historic or predictive data, a predicted change from a first location of the client device using a first connection type supported by the SD-WAN application to a second location of the client device using a second connection type supported by the SD-WAN application: “According to one configuration, the routing device 120 is an element of a network of SD-WAN type. It has available to it a plurality of communication means COMj, j∈{1, 2, . . . N}, where N is a positive integer greater than or equal to 2, to route each data stream to its destination. For example, the routing device 120 can route the data streams by using 4G communication means, 5G communication means, LEO (“Low Earth Orbit”), GEO (“Geostationary Orbit”) or even MEO (“Medium Earth Orbit”) satellites. The routing device 120 is therefore configured to dynamically select, for each data stream, during a flight or more generally during a movement of the vehicle, the most relevant routing path out of the paths offered by the different communication means available and thus enhance the experience of the user and the performance levels of the applications” (Rondeau ¶ 0053).
Regarding Claim 4, Rondeau teaches: The method of claim 3, wherein the first connection type is a Wi-Fi connection type, and wherein the second connection type is a cellular connection type: “For example, the routing device 120 can route the data streams by using 4G communication means, 5G communication means, LEO (“Low Earth Orbit”), GEO (“Geostationary Orbit”) or even MEO (“Medium Earth Orbit”) satellites” (Rondeau ¶ 0053).
Regarding Claim 6, Rondeau teaches: The method of claim 5, wherein updating the configuration comprises establishing a first virtual tunnel and a second virtual tunnel for the predicted traffic, wherein the first and second virtual tunnel are established over a same network connection: “For example, for each portion of itinerary, a portion for example corresponding to a segment as defined in FIG. 3, the application data streams transporting voice are routed by using the communication means offering the lowest latency or jitter. The application data streams transporting video are routed using the communication means offering the highest bit rate” (Rondeau ¶ 0063).
Regarding Claim 7, Rondeau teaches: The method of claim 6, wherein transmitting the application traffic using the updated configuration comprises load balancing the application traffic between the first virtual tunnel and the second virtual tunnel: “For example, these routing rules indicate, for a first portion of itinerary, e.g., the first segment of FIG. 3, that the application data streams associated with instant messaging applications or with photo and video sharing applications are routed by using, preferably, the first communication means COM1, the data streams associated with applications hosting videos are routed, preferably, by using the first communication means COM2 . . . The theoretical routing rules are adapted dynamically according to the variations along the itinerary of the parameter or parameters representative of the performance of the communication means” (Rondeau ¶ 0064).
Regarding Claim 8, Rondeau teaches: The method of claim 1, wherein the historic data comprises at least one of resource usage history data of one or more applications of the client device: “according to a particular embodiment, the at least one parameter representative of the performance of the communication means belongs to the set of parameters comprising a bit rate, a latency, a packet loss ratio, a jitter” (Rondeau ¶ 0023) and “obtain, for each communication means, information obtained at instants prior to the instant t and necessary to the estimation of at least one parameter representative of the performance at the instant t of the communication means” (Rondeau ¶ 0032), historic location data of locations of the client device, or historic connection data of connections used by the SD-WAN application.
Regarding Claim 9, Rondeau teaches: The method of claim 1, wherein the predictive data comprises at least one of calendar data for a user of the client device, communication data for the user of the client device: “estimating, by using a machine learning method, along the planned itinerary, for each communication means, the parameter representative of the performance of the communication means based on the information obtained; defining theoretical routing rules based on the estimated parameter representative of the performance of the communication means and application requirements” (Rondeau ¶ 0010-0011), or data received via an application program interface (API) for predictive usage of one or more resources of the client device.
Regarding Claim 10, Rondeau teaches: The method of claim 9, further comprising determining, by the SD-WAN application, based on the calendar data or the communication data, to establish a session for the client device at a second time subsequent to a current time: “obtaining information relating to the movement, the information comprising at least one planned itinerary for the movement and, for each communication means, information necessary to the estimation of at least one parameter representative of the performance of the communication means; estimating, by using a machine learning method, along the planned itinerary, for each communication means, the parameter representative of the performance of the communication means based on the information obtained; defining theoretical routing rules based on the estimated parameter representative of the performance of the communication means and application requirements” (Rondeau ¶ 0009-0011); determining, by the SD-WAN application, a predicted session modality for the session to be established: “The routing device 120 is therefore configured to dynamically select, for each data stream, during a flight or more generally during a movement of the vehicle, the most relevant routing path out of the paths offered by the different communication means available and thus enhance the experience of the user and the performance levels of the applications” (Rondeau ¶ 0053).
Rondeau does not teach: determining, by the SD-WAN application, a quality of service (QoS) value for the session, wherein updating the configuration of the SD-WAN application comprises establishing the session based on the predicted session modality and the QoS value prior to the second time.
Regarding Claim 10, Henry teaches: determining, by the SD-WAN application, a quality of service (QoS) value for the session, wherein updating the configuration of the SD-WAN application comprises establishing the session based on the predicted session modality and the QoS value prior to the second time: “In various implementations, the policy includes scheduling UE traffic across a particular link (e.g., WLAN link, cellular network link)—e.g., “load balancing” and/or “path control.” For example, in various implementations, the policy is based on obtained parameters indicating how traffic should be treated by RANs 102. Examples of the parameters include but are not limited to: quality of service (QoS) (e.g., upstream, downstream, bandwidth, traffic remarking, policing, shaping, buffering, prioritizing, etc.), RSSI (received signal strength indicator), estimated throughput” (Henry 0043) and “In various implementation, the policy is predictive, meaning it is based at least in part on previously received encapsulated traffic” (Henry ¶ 0029).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the filed application to combine the disclosures of Rondeau with the disclosures of Henry to achieve the predictable result of improving network traffic flow strategies for UE control. According to Henry: “Previous systems disclose a network topology wherein the controller is part of a traffic path associated with a single network. Consequently, the controller has limited visibility into overall network traffic flows, limiting its effectiveness in controlling the UE. Greater visibility into overall network traffic flows would allow the controller to implement a more effective strategy of UE control” (Henry ¶ 0003).
Regarding Claim 11, Rondeau teaches: A device comprising: one or more processors: “According to the hardware architecture example represented in FIG. 4, the routing device 120 then comprises, linked by a communication bus 1200: a processor or CPU (Central Processing Unit) 1201; a random access memory RAM 1202; a Read Only Memory ROM 1203; a storage unit 1204 such as a hard disc or such as a storage medium reader, e.g., an SD (Secure Digital) card reader, at least one communication interface 1205 allowing the routing device 120 to send or receive information” (Rondeau ¶ 0078) configured to: receive, by a software-defined wide area network (SD-WAN) application executing on the device, one or more of historic or predictive data relating to connectivity or usage of the device: “obtain, for each communication means, information obtained at instants prior to the instant t and necessary to the estimation of at least one parameter representative of the performance at the instant t of the communication means” (Rondeau ¶ 0032) where the data collected prior to instant t is understood to be historic data and “According to a particular embodiment, the routing device is an SD-WAN routing device” (Rondeau ¶ 0024); determine, by the SD-WAN application and based on the one or more historic or predictive data relating to the connectivity or usage of the client device, predicted traffic corresponding to an application of the client device: “compare, for each communication means, the parameter representative of the performance of the communication means estimated at the instant t with its value estimated before the movement” (Rondeau ¶ 0034) and “the routing device 120 defines, before the movement, so-called theoretical routing rules, based on the parameter or parameters representative of the performance of the communication means estimated over the itinerary in the step S110 and on the requirements of each application associated with these parameters, e.g., their requirements in terms of bit rate” (Rondeau ¶ 0062).
Rondeau does not teach: update, by the SD-WAN application, and responsive to detecting the update condition, a configuration of the SD-WAN application, wherein the configuration of the SD-WAN application comprises a selection of one or more virtual tunnels among a plurality of available virtual tunnels over one or more network connections; and transmit, by the SD-WAN application, and via the one or more selected virtual tunnels, application traffic using the updated configuration.
Regarding Claim 11, Henry teaches: update, by the SD-WAN application, and responsive to detecting the update condition, a configuration of the SD-WAN application, wherein the configuration of the SD-WAN application comprises a selection of one or more virtual tunnels among a plurality of available virtual tunnels over one or more network connections: “In various implementations, the policy includes scheduling UE traffic across a particular link (e.g., WLAN link, cellular network link)—e.g., “load balancing” and/or “path control.” For example, in various implementations, the policy is based on obtained parameters indicating how traffic should be treated by RANs 102. Examples of the parameters include but are not limited to: quality of service (QoS) (e.g., upstream, downstream, bandwidth, traffic remarking, policing, shaping, buffering, prioritizing, etc.), RSSI (received signal strength indicator), estimated throughput” (Henry 0043) and “In various implementation, the policy is predictive, meaning it is based at least in part on previously received encapsulated traffic” (Henry ¶ 0029); and transmit, by the SD-WAN application, and via the one or more selected virtual tunnels, application traffic using the updated configuration: “UE data traffic flows (e.g. WLAN data traffic and cellular network data traffic) are controlled based on the determined policy. In various implementations, the traffic controller 203 controls the UE data traffic based on the determined policy. For example, if the policy indicates that the WLAN link is being over-utilized, the traffic controller 203 can route more UE traffic along the cellular network link. For example, if the policy indicates that OTT (over the top) applications (e.g., FaceTime) have a high priority, the traffic controller 203 can instruct OTT applications running on the UE 201 to utilize the UE radio having the higher throughput” (Henry ¶ 0044).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the filed application to combine the disclosures of Rondeau with the disclosures of Henry to achieve the predictable result of improving network traffic flow strategies for UE control. According to Henry: “Previous systems disclose a network topology wherein the controller is part of a traffic path associated with a single network. Consequently, the controller has limited visibility into overall network traffic flows, limiting its effectiveness in controlling the UE. Greater visibility into overall network traffic flows would allow the controller to implement a more effective strategy of UE control” (Henry ¶ 0003).
Regarding Claim 13, Rondeau teaches: The device of claim 11, further comprsing determining, based on the one or more historic or predictive data, a predicted change from a first location of the device using a first connection type supported by the SD-WAN application to a second location of the device using a second connection type supported by the SD-WAN application : “According to one configuration, the routing device 120 is an element of a network of SD-WAN type. It has available to it a plurality of communication means COMj, j∈{1, 2, . . . N}, where N is a positive integer greater than or equal to 2, to route each data stream to its destination. For example, the routing device 120 can route the data streams by using 4G communication means, 5G communication means, LEO (“Low Earth Orbit”), GEO (“Geostationary Orbit”) or even MEO (“Medium Earth Orbit”) satellites. The routing device 120 is therefore configured to dynamically select, for each data stream, during a flight or more generally during a movement of the vehicle, the most relevant routing path out of the paths offered by the different communication means available and thus enhance the experience of the user and the performance levels of the applications” (Rondeau ¶ 0053).
Regarding Claim 14, Rondeau teaches: The device of claim 13, wherein the first connection type is a Wi-Fi connection type, and wherein the second connection type is a cellular connection type: “For example, the routing device 120 can route the data streams by using 4G communication means, 5G communication means, LEO (“Low Earth Orbit”), GEO (“Geostationary Orbit”) or even MEO (“Medium Earth Orbit”) satellites” (Rondeau ¶ 0053).
Regarding Claim 15, Rondeau teaches: The device of claim 11, wherein updating the configuration comprises establishing the one or more selected tunnels for the application traffic: “estimate, by using a machine learning method, along the planned itinerary, for each communication means, at least one parameter representative of the performance of the communication means based on the information obtained” (Rondeau ¶ 0028), where it would be readily apparent that the “communication means” refers to a tunnel in the context of an SD-WAN.
Regarding Claim 16, Rondeau teaches: The device of claim 15, wherein updating the configuration comprises establishing a first virtual tunnel and a second virtual tunnel, of the one or more selected tunnels, for the predicted application traffic, wherein the first virtual tunnel and the second virtual tunnel are established over a same network connection: “For example, for each portion of itinerary, a portion for example corresponding to a segment as defined in FIG. 3, the application data streams transporting voice are routed by using the communication means offering the lowest latency or jitter. The application data streams transporting video are routed using the communication means offering the highest bit rate” (Rondeau ¶ 0063), and wherein transmitting the application traffic using the updated configuration comprises load balancing the application traffic between the first virtual tunnel and the second virtual tunnel: “For example, these routing rules indicate, for a first portion of itinerary, e.g., the first segment of FIG. 3, that the application data streams associated with instant messaging applications or with photo and video sharing applications are routed by using, preferably, the first communication means COM1, the data streams associated with applications hosting videos are routed, preferably, by using the first communication means COM2 . . . The theoretical routing rules are adapted dynamically according to the variations along the itinerary of the parameter or parameters representative of the performance of the communication means” (Rondeau ¶ 0064).
Regarding Claim 17, Rondeau teaches: The device of claim 11, wherein the historic data comprises at least one of resource usage history data of one or more applications of the device: “according to a particular embodiment, the at least one parameter representative of the performance of the communication means belongs to the set of parameters comprising a bit rate, a latency, a packet loss ratio, a jitter” (Rondeau ¶ 0023) and “obtain, for each communication means, information obtained at instants prior to the instant t and necessary to the estimation of at least one parameter representative of the performance at the instant t of the communication means” (Rondeau ¶ 0032), historic location data of locations of the device, or historic connection data of connections used by the SD-WAN application.
Regarding Claim 18, Rondeau teaches: The device of claim 11, wherein the predictive data comprises at least one of calendar data for a user of the device, communication data for the user of the device: “estimating, by using a machine learning method, along the planned itinerary, for each communication means, the parameter representative of the performance of the communication means based on the information obtained; defining theoretical routing rules based on the estimated parameter representative of the performance of the communication means and application requirements” (Rondeau ¶ 0010-0011), or data received via an application program interface (API) for predictive usage of one or more resources of the device.
Regarding Claim 19, Rondeau teaches: The device of claim 18, further comprising determining, by the SD-WAN application, based on the calendar data or the communication data, to establish a session for the client device at a second time subsequent to a current time: “obtaining information relating to the movement, the information comprising at least one planned itinerary for the movement and, for each communication means, information necessary to the estimation of at least one parameter representative of the performance of the communication means; estimating, by using a machine learning method, along the planned itinerary, for each communication means, the parameter representative of the performance of the communication means based on the information obtained; defining theoretical routing rules based on the estimated parameter representative of the performance of the communication means and application requirements” (Rondeau ¶ 0009-0011); determining, by the SD-WAN application, a predicted session modality for the session to be established: “The routing device 120 is therefore configured to dynamically select, for each data stream, during a flight or more generally during a movement of the vehicle, the most relevant routing path out of the paths offered by the different communication means available and thus enhance the experience of the user and the performance levels of the applications” (Rondeau ¶ 0053).
Rondeau does not teach: determining, by the SD-WAN application, a quality of service (QoS) value for the session, wherein updating the configuration of the SD-WAN application comprises establishing the session based on the predicted session modality and the QoS value prior to the second time.
Regarding Claim 19, Henry teaches: determining, by the SD-WAN application, a quality of service (QoS) value for the session, wherein updating the configuration of the SD-WAN application comprises establishing the session based on the predicted session modality and the QoS value prior to the second time: “In various implementations, the policy includes scheduling UE traffic across a particular link (e.g., WLAN link, cellular network link)—e.g., “load balancing” and/or “path control.” For example, in various implementations, the policy is based on obtained parameters indicating how traffic should be treated by RANs 102. Examples of the parameters include but are not limited to: quality of service (QoS) (e.g., upstream, downstream, bandwidth, traffic remarking, policing, shaping, buffering, prioritizing, etc.), RSSI (received signal strength indicator), estimated throughput” (Henry 0043) and “In various implementation, the policy is predictive, meaning it is based at least in part on previously received encapsulated traffic” (Henry ¶ 0029).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the filed application to combine the disclosures of Rondeau with the disclosures of Henry to achieve the predictable result of improving network traffic flow strategies for UE control. According to Henry: “Previous systems disclose a network topology wherein the controller is part of a traffic path associated with a single network. Consequently, the controller has limited visibility into overall network traffic flows, limiting its effectiveness in controlling the UE. Greater visibility into overall network traffic flows would allow the controller to implement a more effective strategy of UE control” (Henry ¶ 0003).
Regarding Claim 20, Rondeau teaches: A non-transitory computer readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to: “According to the hardware architecture example represented in FIG. 4, the routing device 120 then comprises, linked by a communication bus 1200: a processor or CPU (Central Processing Unit) 1201; a random access memory RAM 1202; a Read Only Memory ROM 1203; a storage unit 1204 such as a hard disc or such as a storage medium reader, e.g., an SD (Secure Digital) card reader, at least one communication interface 1205 allowing the routing device 120 to send or receive information” (Rondeau ¶ 0078) receive, by a software-defined wide area network (SD-WAN) application executing on a client device, one or more of historic or predictive data relating to connectivity or usage of the client device: “obtain, for each communication means, information obtained at instants prior to the instant t and necessary to the estimation of at least one parameter representative of the performance at the instant t of the communication means” (Rondeau ¶ 0032) where the data collected prior to instant t is understood to be historic data and “According to a particular embodiment, the routing device is an SD-WAN routing device” (Rondeau ¶ 0024); determine, by the SD-WAN application, and based on the one or more historic or predictive data relating to the connectivity or usage of the client device, an update condition for the SD-WAN application, predicted traffic corresponding to an application of the client device: “compare, for each communication means, the parameter representative of the performance of the communication means estimated at the instant t with its value estimated before the movement” (Rondeau ¶ 0034).
Rondeau does not teach: update, by the SD-WAN application, and responsive to determining the predicted traffic, a configuration of the SD-WAN application, wherein the configuration of the SD-WAN application comprises a selection or establishment of one or more virtual tunnels among a plurality of available virtual tunnels over one or more network connections, the selection or the establishment being based on the predicted application traffic; and transmit, by the SD-WAN application, and via the one or more selected virtual tunnels, application traffic corresponding to the application using the updated configuration.
Regarding Claim 20, Henry teaches: update, by the SD-WAN application, and responsive to determining the predicted traffic, a configuration of the SD-WAN application, wherein the configuration of the SD-WAN application comprises a selection or establishment of one or more virtual tunnels among a plurality of available virtual tunnels over one or more network connections, the selection or the establishment being based on the predicted application traffic: “In various implementations, the policy includes scheduling UE traffic across a particular link (e.g., WLAN link, cellular network link)—e.g., “load balancing” and/or “path control.” For example, in various implementations, the policy is based on obtained parameters indicating how traffic should be treated by RANs 102. Examples of the parameters include but are not limited to: quality of service (QoS) (e.g., upstream, downstream, bandwidth, traffic remarking, policing, shaping, buffering, prioritizing, etc.), RSSI (received signal strength indicator), estimated throughput” (Henry 0043) and “In various implementation, the policy is predictive, meaning it is based at least in part on previously received encapsulated traffic” (Henry ¶ 0029); and transmit, by the SD-WAN application, and via the one or more selected virtual tunnels, application traffic corresponding to the application using the updated configuration: “UE data traffic flows (e.g. WLAN data traffic and cellular network data traffic) are controlled based on the determined policy. In various implementations, the traffic controller 203 controls the UE data traffic based on the determined policy. For example, if the policy indicates that the WLAN link is being over-utilized, the traffic controller 203 can route more UE traffic along the cellular network link. For example, if the policy indicates that OTT (over the top) applications (e.g., FaceTime) have a high priority, the traffic controller 203 can instruct OTT applications running on the UE 201 to utilize the UE radio having the higher throughput” (Henry ¶ 0044).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the filed application to combine the disclosures of Rondeau with the disclosures of Henry to achieve the predictable result of improving network traffic flow strategies for UE control. According to Henry: “Previous systems disclose a network topology wherein the controller is part of a traffic path associated with a single network. Consequently, the controller has limited visibility into overall network traffic flows, limiting its effectiveness in controlling the UE. Greater visibility into overall network traffic flows would allow the controller to implement a more effective strategy of UE control” (Henry ¶ 0003).
Claims 2, 5, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Rondeau and Henry in further view of Kolar et al. (US 2021/0160148), hereinafter Kolar.
Regarding Claim 2, Rondeau and Henry teach: The method of claim 1.
Rondeau and Henry do not teach: updating the configuration comprises switching a designation of a network connection, on which the one or more selected virtual tunnels are established, from a primary connection to a back-up connection.
Regarding Claim 2, Kolar teaches: updating the configuration comprises switching a designation of a network connection, on which the one or more selected virtual tunnels are established, from a primary connection to a back-up connection: “the service may predict a failure of the first tunnel, based on the assessment of the telemetry data regarding the first tunnel by the machine learning model, as described in greater detail above. In further embodiments, the prediction may relate to any form of failure or other rare event in the network, such as link flapping, radio failures, route processor failures, or the like. At step 925, as detailed above, the service may proactively reroute at least a subset of traffic on the first tunnel onto a second tunnel in the SD-WAN, in advance of the predicted failure of the first tunnel. For example, the service may reroute critical traffic from the first tunnel to the second tunnel, to ensure that the traffic is not disrupted when the predicted failure of the first tunnel occurs. Procedure 900 then ends at step 930” (Kolar ¶ 0130-0131).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the disclosure of Rondeau and Henry with Kolar for the purpose of anticipating tunnel failure and improving network efficiency. According to Kolar: “With the recent evolution of machine learning, predictive failure detection and proactive routing in an SD-WAN now becomes possible through the use of machine learning techniques” (Kolar ¶ 0004).
Regarding Claim 5, Rondeau and Henry teach: The method of claim 1.
Rondeau and Henry do not teach: updating the configuration comprises establishing the one or more selected tunnels for the application traffic.
Regarding Claim 5, Kolar teaches: updating the configuration comprises establishing the one or more selected tunnels for the application traffic: “the service may predict a failure of the first tunnel, based on the assessment of the telemetry data regarding the first tunnel by the machine learning model, as described in greater detail above. In further embodiments, the prediction may relate to any form of failure or other rare event in the network, such as link flapping, radio failures, route processor failures, or the like. At step 925, as detailed above, the service may proactively reroute at least a subset of traffic on the first tunnel onto a second tunnel in the SD-WAN, in advance of the predicted failure of the first tunnel. For example, the service may reroute critical traffic from the first tunnel to the second tunnel, to ensure that the traffic is not disrupted when the predicted failure of the first tunnel occurs. Procedure 900 then ends at step 930” (Kolar ¶ 0130-0131).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the disclosure of Rondeau and Henry with Kolar for the purpose of anticipating tunnel failure and improving network efficiency. According to Kolar: “With the recent evolution of machine learning, predictive failure detection and proactive routing in an SD-WAN now becomes possible through the use of machine learning techniques” (Kolar ¶ 0004).
Regarding Claim 12, Rondeau and Henry teach: The device of claim 11.
Rondeau and Henry do not teach: updating the configuration comprises switching a designation of a network connection, on which the one or more selected virtual tunnels are established, from a primary connection to a back-up connection.
Regarding Claim 12, Kolar teaches: updating the configuration comprises switching a designation of a network connection, on which the one or more selected virtual tunnels are established, from a primary connection to a back-up connection: “the service may predict a failure of the first tunnel, based on the assessment of the telemetry data regarding the first tunnel by the machine learning model, as described in greater detail above. In further embodiments, the prediction may relate to any form of failure or other rare event in the network, such as link flapping, radio failures, route processor failures, or the like. At step 925, as detailed above, the service may proactively reroute at least a subset of traffic on the first tunnel onto a second tunnel in the SD-WAN, in advance of the predicted failure of the first tunnel. For example, the service may reroute critical traffic from the first tunnel to the second tunnel, to ensure that the traffic is not disrupted when the predicted failure of the first tunnel occurs. Procedure 900 then ends at step 930” (Kolar ¶ 0130-0131).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the disclosure of Rondeau and Henry with Kolar for the purpose of anticipating tunnel failure and improving network efficiency. According to Kolar: “With the recent evolution of machine learning, predictive failure detection and proactive routing in an SD-WAN now becomes possible through the use of machine learning techniques” (Kolar ¶ 0004).
Conclusion
THIS ACTION IS MADE FINAL. 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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/BRADLEY D LYTLE JR./Examiner, Art Unit 2473 /B.D.L./Examiner, Art Unit 2473
/KWANG B YAO/Supervisory Patent Examiner, Art Unit 2473
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