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 Amendment
The Amendment filed December 2, 2025 has been entered. Claims 1-6 and 9-22 are pending in the application. Applicant has submitted amendments to the claims along with other remarks. Applicant’s amendments regarding 112(b) have not overcome the rejection. Applicant’s amendments regarding 112(a) have overcome the rejection. Claims 1-6 and 9-22 are still rejected by prior art references, refer to the following rejection for details.
Response to Arguments
Applicant’s arguments and amendments, see pp. 9-11 of the response, filed December 2, 2025, with respect to the rejection(s) of claim(s) 1-6 and 9-22 under §§ 102, 103 have been fully considered and are persuasive. However, upon further consideration for the amendments, a new ground(s) of rejection is made in view of new reference, please see the rejection for details.
Regarding the § 112(b) rejection, Applicant has amended claims 1, 12, and 18 to include the term “time-insensitive network domain functions.” The specification describes “time-insensitive network domain functions” at [0030] to at least include “e.g., non-real time microservice applications, or rApps.” The claim terms “non-real time” and “near real-time” were previously rejected as indefinite and by extension the term “time-insensitive network domain functions” is also considered indefinite.
Information Disclosure Statement
The information disclosure statement filed December 2, 2025 fails to comply with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 because some of the documents (e.g., NPL entitled “O-RAN: Towards an Open and Smart RAN) are illegible (example below) where date of document cannot be read. It has been placed in the application file, but the information referred to therein has not been considered as to the merits. Applicant is advised that the date of any re-submission of any item of information contained in this information disclosure statement or the submission of any missing element(s) will be the date of submission for purposes of determining compliance with the requirements based on the time of filing the statement, including all certification requirements for statements under 37 CFR 1.97(e). See MPEP § 609.05(a).
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Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1, 12, and 18 are rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention
Claims 1, 12, and 18 to include the term “time-insensitive network domain functions.” The specification describes “time-insensitive network domain functions” at [0030] to at least include “e.g., non-real time microservice applications, or rApps.” The claim terms “non-real time” and “near real-time” were previously rejected as indefinite and by extension the term “time-insensitive network domain functions” is also considered indefinite.
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, 5, 10-12, 14, 18, and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2017/0111423 (hereinafter “Cui”) in view of U.S. Publication No. 2021/0337420 (hereinafter “Lo”)
Regarding claim 1, Cui teaches: A system, comprising: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, ([0011] According to one aspect disclosed herein, a CHC includes a wireless network interface, a wired network interface, a CH device connection interface, a processor, and a memory) the operations comprising: obtaining a service requirement (requirement of best connection available links based on performance measurement) of a network-enabled user device ([0011] the CHC can obtain a wireline performance measurement for the wireline communications link and a wireless performance measurement for the wireless communications link, can compare the wireline performance measurement and the wireless performance measurement, and can select either the wireline communications link or the wireless communications link based upon the comparison.); receiving, at a network edge (CHC) and via a standard interface, first network information of a wireline network accessible by the network-enabled user device (Abstract - the CHC can obtain a wireline performance measurement for the wireline communications link, obtain a wireless performance measurement for the wireline communications link); receiving, at the network edge and via the standard interface, second network information of a wireless network accessible by the network-enabled user device (Abstract - the CHC can obtain a wireline performance measurement for the wireline communications link, obtain a wireless performance measurement for the wireline communications link); receiving, at the network edge, supporting information from a first microservice of a first plurality of microservices, wherein the first microservice is remote from the network edge; sending the first network information and the second network information to a second microservice of a second plurality of microservices at the network edge, wherein the second microservice is configured to select at least one of the wireline network or the wireless network according to the first network information, the second network information and the supporting information to obtain a network selection ([0009] Concepts and technologies disclosed herein are directed to real-time video delivery for connected home (“CH”) applications. Real-time can be measured in milliseconds/microseconds and is the responsiveness used to support the low timing threshold for applications such as video streaming. According to one aspect disclosed herein, a new capability is provided for a CH controller (“CHC”) to detect wireline network and wireless network availability and to dynamically select the best access technology to utilize for delivery of a video stream captured by a CH video camera under the control of the CHC. For example, the wireline network can be selected by default. However, when a wireline communications link to the wireline network is compromised, such as in the event of a robbery in which the link is severed or otherwise rendered inoperable, the CHC can detect the failure and can select a wireless communications link to the wireless network as a backup.); and establishing a network connection to the network-enabled user device according to the network selection, wherein a service according to the service requirement is supported via the network connection ([0057] Returning to operation 206, if the CHC 106 determines that both the wireline communications link 146 and the wireless communications link 140 are available, the method 200 proceeds to operation 216, where the CHC 106 obtains performance measurements for both the wireline communications link 146 and the wireless communications link 140. The performance measurements can provide insight into the overall performance of a corresponding network such that the CHC 106 can select the best network for providing the video stream 174 to the user device 110. The performance measurements can include speed, bandwidth, throughput, and latency.).
Cui does not explicitly teach: training a machine learning model to obtain a trained model according to time- insensitive network domain functions; the supporting information is obtained via time-insensitive network domain functions, and at least a portion of the supporting information is obtained by the trained model; identifying a network policy; and the network selection is further based on the network policy.
However, in the same field of endeavor, Lo teaches: training a machine learning model to obtain a trained model according to time- insensitive network domain functions ([0073-75], [0090-0111]); the supporting information is obtained via time-insensitive network domain functions ([0113]), and at least a portion of the supporting information ([0102]) is obtained by the trained model ([0078-79]); identifying a network policy ([0271] At operation 2001, the near-RT RIC receives a policy from the non-RT RIC over A1 interface or from the SMO entity over O1 interface.); and the network selection is further based on the network policy ([0276] The near-RT RIC monitors the measurement values, and decides to intervene to update/modify RAN behavior as deemed necessary by the policy).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Cui to include the feature of model based network selection and a combination of Cui with Lo renders the claim prima facie obvious within the described scope of the prior art and any indicated differences within the level of one of ordinary skill in the art (e.g., telecommunications engineer) according to a combination of known prior art elements with known methods to yield predictable results. MPEP 2143(I)(A) (e.g., providing machine learning based network selection).
Regarding claim 5, Cui teaches: wherein the establishing the network connection to the network-enabled user device comprises designing and orchestrating a plurality of network elements to obtain a configuration of network infrastructure (routing data is considered a configuration of network infrastructure) ([0056] From operation 204, the method 200 proceeds to operation 206. At operation 206, if the CHC 106 determines that only the wireline communications link 146 is available, the CHC 106 selects the wireline communications link 146 for video delivery at operation 208. If, however, the CHC 106 determines that only the wireless communications link 140 is available, the CHC 106 selects the wireless communications link 140 for video delivery at operation 210. The method 200 proceeds from 208 or 210 to operation 212, where the CHC 106 receives a video stream (e.g., the video stream 174) from the CH video camera 114 and delivers the video stream 174 to the selected communications link—that is, either the wireless communications link 140 or the wireline communications link 146—for delivery to the user device 110. From operation 212, the method 200 proceeds to operation 214, where the method 200 ends.).
Regarding claim 10, Cui teaches: wherein the wireless network comprises at least one of a wireless link, a radio access network of a mobile cellular service, a satellite communications link, a microwave link, a free-space optical link, or a wired link ([0031] The CHC 106 is illustrated as being capable of operating on and in communication with a wireless access network 138 via a wireless communications link 140. The wireless access network 138 can include one or more radio access networks (“RANs”).).
Regarding claim 11, Cui teaches: wherein the wireline network comprises a passive optical network ([0033] The CHC 106 also is illustrated as being capable of operating on and in communication with a wireline network 144 via a wireline communications link 146. The wireline network 144 can be or can include one or more packet-switched networks. The wireline communications link 146 can be or can include any wireline cabling, some examples of which include coaxial cable and fiber optic cable.).
Regarding claim 12, Cui teaches: A method, comprising: identifying, by a processing system comprising a processor, a service requirement of a user device ([0011] the CHC can obtain a wireline performance measurement for the wireline communications link and a wireless performance measurement for the wireless communications link, can compare the wireline performance measurement and the wireless performance measurement, and can select either the wireline communications link or the wireless communications link based upon the comparison.); receiving, by the processing system ([0011] According to one aspect disclosed herein, a CHC includes a wireless network interface, a wired network interface, a CH device connection interface, a processor, and a memory), at a network edge and via a standard interface, first network information of a wireline network accessible by the user device (Abstract - the CHC can obtain a wireline performance measurement for the wireline communications link, obtain a wireless performance measurement for the wireline communications link); receiving, by the processing system, at the network edge and via the standard interface, second network information of a wireless network accessible by the user device (Abstract - the CHC can obtain a wireline performance measurement for the wireline communications link, obtain a wireless performance measurement for the wireline communications link); receiving, by the processing system, at the network edge, supporting information from a first microservice of a first plurality of microservices, wherein the first microservice is remote from the network edge forwarding, by the processing system, the first network information and the second network information to a second microservice of a second plurality of microservices at the network edge, wherein the second microservice is configured to select at least one of the wireline network or the wireless network according to the first network information, the second network information and the supporting information to obtain a network selection ([0009] Concepts and technologies disclosed herein are directed to real-time video delivery for connected home (“CH”) applications. Real-time can be measured in milliseconds/microseconds and is the responsiveness used to support the low timing threshold for applications such as video streaming. According to one aspect disclosed herein, a new capability is provided for a CH controller (“CHC”) to detect wireline network and wireless network availability and to dynamically select the best access technology to utilize for delivery of a video stream captured by a CH video camera under the control of the CHC. For example, the wireline network can be selected by default. However, when a wireline communications link to the wireline network is compromised, such as in the event of a robbery in which the link is severed or otherwise rendered inoperable, the CHC can detect the failure and can select a wireless communications link to the wireless network as a backup.); and coordinating a network connection to the user device according to the network selection, wherein a service according to the service requirement is supported via the network connection ([0057] Returning to operation 206, if the CHC 106 determines that both the wireline communications link 146 and the wireless communications link 140 are available, the method 200 proceeds to operation 216, where the CHC 106 obtains performance measurements for both the wireline communications link 146 and the wireless communications link 140. The performance measurements can provide insight into the overall performance of a corresponding network such that the CHC 106 can select the best network for providing the video stream 174 to the user device 110. The performance measurements can include speed, bandwidth, throughput, and latency.).
Cui does not explicitly teach: training a machine learning model to obtain a trained model according to time- insensitive network domain functions; the supporting information is obtained via time-insensitive network domain functions, and at least a portion of the supporting information is obtained by the trained model; identifying a network policy; and the network selection is further based on the network policy.
However, in the same field of endeavor, Lo teaches: training a machine learning model to obtain a trained model according to time- insensitive network domain functions ([0073-75], [0090-0111]); the supporting information is obtained via time-insensitive network domain functions ([0113]), and at least a portion of the supporting information ([0102]) is obtained by the trained model ([0078-79]); identifying a network policy ([0271] At operation 2001, the near-RT RIC receives a policy from the non-RT RIC over A1 interface or from the SMO entity over O1 interface.); and the network selection is further based on the network policy ([0276] The near-RT RIC monitors the measurement values, and decides to intervene to update/modify RAN behavior as deemed necessary by the policy).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Cui to include the feature of model based network selection and a combination of Cui with Lo renders the claim prima facie obvious within the described scope of the prior art and any indicated differences within the level of one of ordinary skill in the art (e.g., telecommunications engineer) according to a combination of known prior art elements with known methods to yield predictable results. MPEP 2143(I)(A) (e.g., providing machine learning based network selection).
Regarding claim 14, Cui teaches: wherein the wireline network and the wireless network comprise access networks ([0011] According to one aspect disclosed herein, a CHC includes a wireless network interface, a wired network interface, a CH device connection interface, a processor, and a memory. The wireless network interface connects the CHC to a wireless access network. The wired network interface connects the CHC to a wired access network.).
Regarding claim 18, Cui teaches: A non-transitory, machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, the operations comprising ([0011] According to one aspect disclosed herein, a CHC includes a wireless network interface, a wired network interface, a CH device connection interface, a processor, and a memory): determining a service requirement of a user device ([0011] the CHC can obtain a wireline performance measurement for the wireline communications link and a wireless performance measurement for the wireless communications link, can compare the wireline performance measurement and the wireless performance measurement, and can select either the wireline communications link or the wireless communications link based upon the comparison.); obtaining at a network edge, via a standard interface, first network information of a wireline network and second network information of a wireless network (Abstract - the CHC can obtain a wireline performance measurement for the wireline communications link, obtain a wireless performance measurement for the wireline communications link); providing, at the network edge, the first network information and the second network information to a first microservice configured to select at least one of the wireline network or the wireless network according to the first network information and the second network information to obtain a network selection, wherein the network selection is further based on supporting information received from a second microservice in communication with the first microservice, the supporting information obtained in non-real-time ([0022] Moreover, the CH sensor devices 112 can be used to trigger video capture by the CH video camera 114. Those skilled in the art will appreciated the applicability of the CH sensor devices 112 to various aspects of CH services, and for this reason, additional details in this regard are not provided. [0009] Concepts and technologies disclosed herein are directed to real-time video delivery for connected home (“CH”) applications. Real-time can be measured in milliseconds/microseconds and is the responsiveness used to support the low timing threshold for applications such as video streaming. According to one aspect disclosed herein, a new capability is provided for a CH controller (“CHC”) to detect wireline network and wireless network availability and to dynamically select the best access technology to utilize for delivery of a video stream captured by a CH video camera under the control of the CHC. For example, the wireline network can be selected by default. However, when a wireline communications link to the wireline network is compromised, such as in the event of a robbery in which the link is severed or otherwise rendered inoperable, the CHC can detect the failure and can select a wireless communications link to the wireless network as a backup.); and coordinating a network connection to the user device according to the network selection, wherein a service according to the service requirement is supported via the network connection ([0057] Returning to operation 206, if the CHC 106 determines that both the wireline communications link 146 and the wireless communications link 140 are available, the method 200 proceeds to operation 216, where the CHC 106 obtains performance measurements for both the wireline communications link 146 and the wireless communications link 140. The performance measurements can provide insight into the overall performance of a corresponding network such that the CHC 106 can select the best network for providing the video stream 174 to the user device 110. The performance measurements can include speed, bandwidth, throughput, and latency.).
Cui does not explicitly teach: training a machine learning model to obtain a trained model according to time- insensitive network domain functions; the supporting information is obtained via time-insensitive network domain functions, and at least a portion of the supporting information is obtained by the trained model; identifying a network policy; and the network selection is further based on the network policy.
However, in the same field of endeavor, Lo teaches: training a machine learning model to obtain a trained model according to time- insensitive network domain functions ([0073-75], [0090-0111]); the supporting information is obtained via time-insensitive network domain functions ([0113]), and at least a portion of the supporting information ([0102]) is obtained by the trained model ([0078-79]); identifying a network policy ([0271] At operation 2001, the near-RT RIC receives a policy from the non-RT RIC over A1 interface or from the SMO entity over O1 interface.); and the network selection is further based on the network policy ([0276] The near-RT RIC monitors the measurement values, and decides to intervene to update/modify RAN behavior as deemed necessary by the policy).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Cui to include the feature of model based network selection and a combination of Cui with Lo renders the claim prima facie obvious within the described scope of the prior art and any indicated differences within the level of one of ordinary skill in the art (e.g., telecommunications engineer) according to a combination of known prior art elements with known methods to yield predictable results. MPEP 2143(I)(A) (e.g., providing machine learning based network selection).
Regarding claim 20, Cui teaches: wherein the wireline network and the wireless network comprise access networks ([0011] According to one aspect disclosed herein, a CHC includes a wireless network interface, a wired network interface, a CH device connection interface, a processor, and a memory. The wireless network interface connects the CHC to a wireless access network. The wired network interface connects the CHC to a wired access network.).
Regarding claim 21, Lo teaches: wherein the network policy comprises maintaining a threshold level of the service requirement ([0271] The policy is targeted at achieving optimal beam management procedures that includes but are not limited to beam tracking, beam failure recovery. [0288] Rsrp-ThresholdCSI_RS).
Claims 2 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Cui in view of Lo and further in view of U.S. Publication No. 2008/0317045 (hereinafter “Chen”).
Regarding claim 2, Cui teaches performing operations based on a service requirement. Cui does not teach: categorizing the service requirement according to one of a metaverse service category or a non-metaverse service category to obtain a service categorization.
Chen discloses a method for providing differentiated service based on a classification or categorization of the service. Chen teaches: categorizing the service requirement according to one of a metaverse service category or a non-metaverse service category to obtain a service categorization ([0004] Providing DS-TE (Differentiated service-Traffic Engineering) services in an IP network using MPLS (Multi-Protocol Label Switching) technology is attracting more and more attention. It solves the problem of differentiating different service types in a network in an extensible way, and makes specific data get better treatment than a "Best Effort" service stream gets, and allows coexistence of delay-sensitive services and general IP services. Its main principle is that a router uses a group of well-defined structure blocks to classify service streams; individual level of service streams are differentiated through networks and services according to QoS required by the classified service streams. Thus, the DS-TE allows to perform different PHB (Per Hop Behavior) according to relative service priorities allocated to service streams when data packets are forwarded. Data packets sharing the same forwarding processing share the same level of services.).
Thus, the combination of Cui and Lo with Chen each disclose optimizing network traffic based on a service requirement. A person of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the categorization Chen could have been substituted for the service requirement of Cui because both perform the function of optimizing network traffic according to a service requirement. Furthermore, a person of ordinary skill in the art would have been able to carry out the substitution. Finally, the substitution achieves the predictable result of categorizing the service requirement and optimizing network utilization using the methods known in Chen.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute the categorization techniques in Chen for the service requirement in Cui according to known methods to yield the predictable result of optimization of network traffic according to categorization of a service requirement. Chen and Cui are silent on whether the service requirement is specifically related to metaverse or non-metaverse service categories; however, these elements are referred to generically in name only and specific technical details of their categorization is not recited. Generic categorization is taught by Chen (See also, PAN-OS non-patent literature).
Regarding claim 15, Cui teaches performing operations based on a service requirement. Cui does not teach: categorizing, by the processing system, the service requirement according to one of a metaverse service category or a non-metaverse service category to obtain a service categorization.
Chen discloses a method for providing differentiated service based on a classification or categorization of the service. Chen teaches: categorizing, by the processing system, the service requirement according to one of a metaverse service category or a non-metaverse service category to obtain a service categorization ([0004] Providing DS-TE (Differentiated service-Traffic Engineering) services in an IP network using MPLS (Multi-Protocol Label Switching) technology is attracting more and more attention. It solves the problem of differentiating different service types in a network in an extensible way, and makes specific data get better treatment than a "Best Effort" service stream gets, and allows coexistence of delay-sensitive services and general IP services. Its main principle is that a router uses a group of well-defined structure blocks to classify service streams; individual level of service streams are differentiated through networks and services according to QoS required by the classified service streams. Thus, the DS-TE allows to perform different PHB (Per Hop Behavior) according to relative service priorities allocated to service streams when data packets are forwarded. Data packets sharing the same forwarding processing share the same level of services.).
Thus, the combination of Cui and Lo with Chen each disclose optimizing network traffic based on a service requirement. A person of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the categorization Chen could have been substituted for the service requirement of Cui because both perform the function of optimizing network traffic according to a service requirement. Furthermore, a person of ordinary skill in the art would have been able to carry out the substitution. Finally, the substitution achieves the predictable result of categorizing the service requirement and optimizing network utilization using the methods known in Chen.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute the categorization techniques in Chen for the service requirement in Cui according to known methods to yield the predictable result of optimization of network traffic according to categorization of a service requirement. Chen and Cui are silent on whether the service requirement is specifically related to metaverse or non-metaverse service categories; however, these elements are referred to generically in name only and specific technical details of their categorization is not recited. Generic categorization is taught by Chen (See also, PAN-OS non-patent literature).
Claims 3, 13, 16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over in view of Lo and Chen and further in view of U.S. Publication No. 2020/0296023 (hereinafter “Kumar”)
Regarding claim 3, the combination of Cui, Lo, and Chen teaches performing operations based on a service requirement and optimizing network traffic flow. Although inherent, the combination does not specifically teach: wherein the network selection is further based on the service categorization.
However, in the same field of endeavor, Kumar teaches: wherein the network selection is further based on the service categorization ([0031] In the example of FIG. 1, SD-WAN system 2 comprises a software defined network (SDN) and network functions virtualization (NFV) architecture. SDN controller device 14 may provide a high-level controller for configuring and managing the routing and switching infrastructure of SD-WAN system 2. MN orchestrator device 13 may provide a high-level orchestrator for configuring and managing virtualization of network services into service nodes 10 of data center 9. In some instances, SDN controller 14 manages deployment of virtual machines (VMs) within the operating environment of data center. For example, SDN controller 14 may interact with provider edge (PE) router 8 to specify service chain information, described in more detail below. For example, the service chain information provided by SDN controller 14 may specify any combination and ordering of services provided by service nodes 10, traffic engineering information for tunneling or otherwise transporting packet flows along service paths, rate limits, Type of Service (TOS) markings or packet classifiers that specify criteria for matching packet flows to a particular service chain. Further example details of an SDN controller are described in PCT International Patent Application PCT/US13/44378, filed Jun. 5, 2013, the entire content of which is incorporated herein by reference. [0033] As described herein, elements within SD-WAN system 2, such as SD-WAN appliance 18, perform application data monitoring using various application quality of experience (QoE) metric functions, such as real-time performance monitoring (RPM) or two-way active measurement protocol (TWAMP), for example. That is, RPM and TWAMP may be used within SD-WAN system 2 to measure both one-way and two-way or round-trip metrics of network performance, such as path connectivity, path delay, packet jitter, packet loss, packet re-ordering, and the like, e.g., on a per-subscriber basis between network devices, also referred to as hosts or endpoints. In general, a QoE measurement architecture includes network devices that each support the used protocol and perform specific roles to start data sessions and exchange test packets for the data sessions. In the example network architecture illustrated in FIG. 1, SD-WAN appliance 18 is configured to perform the QoE metric predictions. SD-WAN appliance 18 allows for load sharing across connections and adjusts traffic flows based on network conditions to improve performance. [0034] SD-WAN appliance 18, which performs the traffic monitoring functions described herein, also determines QoE metrics, such as service level agreement (SLA) metrics that include round-trip time (RTT), jitter, and packet loss, which were influenced by applications' real-time parameters like packet size, queues and burst of packets to determine the best path. However, different applications have different packet sizes in their data flows. Furthermore, different applications have different traffic patterns, some of which may be inconsistent with different levels of burst and bandwidth usage during the normal execution of the application. This can lead to false positives regarding a link's ability to handle the application traffic if a large number of packets are unexpectedly received for the application. As described below with respect to FIGS. 2-4, the techniques described herein show how SD-WAN appliance 18 can implement a machine learning algorithm to determine historical traffic patterns for various applications and adjust the probing frequency and other probing parameters based on the received application traffic.).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Cui to include the feature of selecting the network based on the categorization and a combination of Cui, Lo, and Chen with Kumar renders the claim prima facie obvious within the described scope of the prior art and any indicated differences within the level of one of ordinary skill in the art (e.g., telecommunications engineer) according to a combination of known prior art elements with known methods to yield predictable results. MPEP 2143(I)(A) (e.g., selecting the network based on the categorization).
Regarding claim 13, Cui teaches performing operations based on a service requirement. Cui does not teach: categorizing the service requirement according to one of a metaverse service category or a non-metaverse service category to obtain a service categorization.
Chen discloses a method for providing differentiated service based on a classification or categorization of the service. Chen teaches: categorizing the service requirement according to one of a metaverse service category or a non-metaverse service category to obtain a service categorization ([0004] Providing DS-TE (Differentiated service-Traffic Engineering) services in an IP network using MPLS (Multi-Protocol Label Switching) technology is attracting more and more attention. It solves the problem of differentiating different service types in a network in an extensible way, and makes specific data get better treatment than a "Best Effort" service stream gets, and allows coexistence of delay-sensitive services and general IP services. Its main principle is that a router uses a group of well-defined structure blocks to classify service streams; individual level of service streams are differentiated through networks and services according to QoS required by the classified service streams. Thus, the DS-TE allows to perform different PHB (Per Hop Behavior) according to relative service priorities allocated to service streams when data packets are forwarded. Data packets sharing the same forwarding processing share the same level of services.).
Thus, the combination of Cui and Lo and Chen each disclose optimizing network traffic based on a service requirement. A person of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the categorization Chen could have been substituted for the service requirement of Cui because both perform the function of optimizing network traffic according to a service requirement. Furthermore, a person of ordinary skill in the art would have been able to carry out the substitution. Finally, the substitution achieves the predictable result of categorizing the service requirement and optimizing network utilization using the methods known in Chen.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute the categorization techniques in Chen for the service requirement in Cui according to known methods to yield the predictable result of optimization of network traffic according to categorization of a service requirement. Chen and Cui are silent on whether the service requirement is specifically related to metaverse or non-metaverse service categories; however, these elements are referred to generically in name only and specific technical details of their categorization is not recited. Generic categorization is taught by Chen (See also, PAN-OS non-patent literature).
The combination of Cui, Lo, and Chen teaches performing operations based on a service requirement and optimizing network traffic flow. Although inherent, the combination does not specifically teach: wherein the network selection is further based on the service categorization.
However, in the same field of endeavor, Kumar teaches: wherein the network selection is further based on the service categorization ([0031] In the example of FIG. 1, SD-WAN system 2 comprises a software defined network (SDN) and network functions virtualization (NFV) architecture. SDN controller device 14 may provide a high-level controller for configuring and managing the routing and switching infrastructure of SD-WAN system 2. MN orchestrator device 13 may provide a high-level orchestrator for configuring and managing virtualization of network services into service nodes 10 of data center 9. In some instances, SDN controller 14 manages deployment of virtual machines (VMs) within the operating environment of data center. For example, SDN controller 14 may interact with provider edge (PE) router 8 to specify service chain information, described in more detail below. For example, the service chain information provided by SDN controller 14 may specify any combination and ordering of services provided by service nodes 10, traffic engineering information for tunneling or otherwise transporting packet flows along service paths, rate limits, Type of Service (TOS) markings or packet classifiers that specify criteria for matching packet flows to a particular service chain. Further example details of an SDN controller are described in PCT International Patent Application PCT/US13/44378, filed Jun. 5, 2013, the entire content of which is incorporated herein by reference. [0033] As described herein, elements within SD-WAN system 2, such as SD-WAN appliance 18, perform application data monitoring using various application quality of experience (QoE) metric functions, such as real-time performance monitoring (RPM) or two-way active measurement protocol (TWAMP), for example. That is, RPM and TWAMP may be used within SD-WAN system 2 to measure both one-way and two-way or round-trip metrics of network performance, such as path connectivity, path delay, packet jitter, packet loss, packet re-ordering, and the like, e.g., on a per-subscriber basis between network devices, also referred to as hosts or endpoints. In general, a QoE measurement architecture includes network devices that each support the used protocol and perform specific roles to start data sessions and exchange test packets for the data sessions. In the example network architecture illustrated in FIG. 1, SD-WAN appliance 18 is configured to perform the QoE metric predictions. SD-WAN appliance 18 allows for load sharing across connections and adjusts traffic flows based on network conditions to improve performance. [0034] SD-WAN appliance 18, which performs the traffic monitoring functions described herein, also determines QoE metrics, such as service level agreement (SLA) metrics that include round-trip time (RTT), jitter, and packet loss, which were influenced by applications' real-time parameters like packet size, queues and burst of packets to determine the best path. However, different applications have different packet sizes in their data flows. Furthermore, different applications have different traffic patterns, some of which may be inconsistent with different levels of burst and bandwidth usage during the normal execution of the application. This can lead to false positives regarding a link's ability to handle the application traffic if a large number of packets are unexpectedly received for the application. As described below with respect to FIGS. 2-4, the techniques described herein show how SD-WAN appliance 18 can implement a machine learning algorithm to determine historical traffic patterns for various applications and adjust the probing frequency and other probing parameters based on the received application traffic.).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Cui to include the feature of selecting the network based on the categorization and a combination of Cui and Chen with Kumar renders the claim prima facie obvious within the described scope of the prior art and any indicated differences within the level of one of ordinary skill in the art (e.g., telecommunications engineer) according to a combination of known prior art elements with known methods to yield predictable results. MPEP 2143(I)(A) (e.g., selecting the network based on the categorization).
Regarding claim 16, the combination of Cui, Lo, and Chen teaches performing operations based on a service requirement and optimizing network traffic flow. Although inherent, the combination does not specifically teach: wherein the network selection is further based on the service categorization.
However, in the same field of endeavor, Kumar teaches: wherein the network selection is further based on the service categorization ([0031] In the example of FIG. 1, SD-WAN system 2 comprises a software defined network (SDN) and network functions virtualization (NFV) architecture. SDN controller device 14 may provide a high-level controller for configuring and managing the routing and switching infrastructure of SD-WAN system 2. MN orchestrator device 13 may provide a high-level orchestrator for configuring and managing virtualization of network services into service nodes 10 of data center 9. In some instances, SDN controller 14 manages deployment of virtual machines (VMs) within the operating environment of data center. For example, SDN controller 14 may interact with provider edge (PE) router 8 to specify service chain information, described in more detail below. For example, the service chain information provided by SDN controller 14 may specify any combination and ordering of services provided by service nodes 10, traffic engineering information for tunneling or otherwise transporting packet flows along service paths, rate limits, Type of Service (TOS) markings or packet classifiers that specify criteria for matching packet flows to a particular service chain. Further example details of an SDN controller are described in PCT International Patent Application PCT/US13/44378, filed Jun. 5, 2013, the entire content of which is incorporated herein by reference. [0033] As described herein, elements within SD-WAN system 2, such as SD-WAN appliance 18, perform application data monitoring using various application quality of experience (QoE) metric functions, such as real-time performance monitoring (RPM) or two-way active measurement protocol (TWAMP), for example. That is, RPM and TWAMP may be used within SD-WAN system 2 to measure both one-way and two-way or round-trip metrics of network performance, such as path connectivity, path delay, packet jitter, packet loss, packet re-ordering, and the like, e.g., on a per-subscriber basis between network devices, also referred to as hosts or endpoints. In general, a QoE measurement architecture includes network devices that each support the used protocol and perform specific roles to start data sessions and exchange test packets for the data sessions. In the example network architecture illustrated in FIG. 1, SD-WAN appliance 18 is configured to perform the QoE metric predictions. SD-WAN appliance 18 allows for load sharing across connections and adjusts traffic flows based on network conditions to improve performance. [0034] SD-WAN appliance 18, which performs the traffic monitoring functions described herein, also determines QoE metrics, such as service level agreement (SLA) metrics that include round-trip time (RTT), jitter, and packet loss, which were influenced by applications' real-time parameters like packet size, queues and burst of packets to determine the best path. However, different applications have different packet sizes in their data flows. Furthermore, different applications have different traffic patterns, some of which may be inconsistent with different levels of burst and bandwidth usage during the normal execution of the application. This can lead to false positives regarding a link's ability to handle the application traffic if a large number of packets are unexpectedly received for the application. As described below with respect to FIGS. 2-4, the techniques described herein show how SD-WAN appliance 18 can implement a machine learning algorithm to determine historical traffic patterns for various applications and adjust the probing frequency and other probing parameters based on the received application traffic.).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Cui to include the feature of selecting the network based on the categorization and a combination of Cui, Lo, and Chen with Kumar renders the claim prima facie obvious within the described scope of the prior art and any indicated differences within the level of one of ordinary skill in the art (e.g., telecommunications engineer) according to a combination of known prior art elements with known methods to yield predictable results. MPEP 2143(I)(A) (e.g., selecting the network based on the categorization).
Regarding claim 19, Cui teaches performing operations based on a service requirement. Cui does not teach: categorizing the service requirement according to one of a metaverse service category or a non-metaverse service category to obtain a service categorization.
Chen discloses a method for providing differentiated service based on a classification or categorization of the service. Chen teaches: categorizing the service requirement according to one of a metaverse service category or a non-metaverse service category to obtain a service categorization ([0004] Providing DS-TE (Differentiated service-Traffic Engineering) services in an IP network using MPLS (Multi-Protocol Label Switching) technology is attracting more and more attention. It solves the problem of differentiating different service types in a network in an extensible way, and makes specific data get better treatment than a "Best Effort" service stream gets, and allows coexistence of delay-sensitive services and general IP services. Its main principle is that a router uses a group of well-defined structure blocks to classify service streams; individual level of service streams are differentiated through networks and services according to QoS required by the classified service streams. Thus, the DS-TE allows to perform different PHB (Per Hop Behavior) according to relative service priorities allocated to service streams when data packets are forwarded. Data packets sharing the same forwarding processing share the same level of services.).
Thus, the combination of Cui and Lo and Chen each disclose optimizing network traffic based on a service requirement. A person of ordinary skill in the art before the effective filing date of the claimed invention would have recognized that the categorization Chen could have been substituted for the service requirement of Cui because both perform the function of optimizing network traffic according to a service requirement. Furthermore, a person of ordinary skill in the art would have been able to carry out the substitution. Finally, the substitution achieves the predictable result of categorizing the service requirement and optimizing network utilization using the methods known in Chen.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute the categorization techniques in Chen for the service requirement in Cui according to known methods to yield the predictable result of optimization of network traffic according to categorization of a service requirement. Chen and Cui are silent on whether the service requirement is specifically related to metaverse or non-metaverse service categories; however, these elements are referred to generically in name only and specific technical details of their categorization is not recited. Generic categorization is taught by Chen (See also, PAN-OS non-patent literature).
The combination of Cui, Lo, and Chen teaches performing operations based on a service requirement and optimizing network traffic flow. Although inherent, the combination does not specifically teach: wherein the network selection is further based on the service categorization.
However, in the same field of endeavor, Kumar teaches: wherein the network selection is further based on the service categorization ([0031] In the example of FIG. 1, SD-WAN system 2 comprises a software defined network (SDN) and network functions virtualization (NFV) architecture. SDN controller device 14 may provide a high-level controller for configuring and managing the routing and switching infrastructure of SD-WAN system 2. MN orchestrator device 13 may provide a high-level orchestrator for configuring and managing virtualization of network services into service nodes 10 of data center 9. In some instances, SDN controller 14 manages deployment of virtual machines (VMs) within the operating environment of data center. For example, SDN controller 14 may interact with provider edge (PE) router 8 to specify service chain information, described in more detail below. For example, the service chain information provided by SDN controller 14 may specify any combination and ordering of services provided by service nodes 10, traffic engineering information for tunneling or otherwise transporting packet flows along service paths, rate limits, Type of Service (TOS) markings or packet classifiers that specify criteria for matching packet flows to a particular service chain. Further example details of an SDN controller are described in PCT International Patent Application PCT/US13/44378, filed Jun. 5, 2013, the entire content of which is incorporated herein by reference. [0033] As described herein, elements within SD-WAN system 2, such as SD-WAN appliance 18, perform application data monitoring using various application quality of experience (QoE) metric functions, such as real-time performance monitoring (RPM) or two-way active measurement protocol (TWAMP), for example. That is, RPM and TWAMP may be used within SD-WAN system 2 to measure both one-way and two-way or round-trip metrics of network performance, such as path connectivity, path delay, packet jitter, packet loss, packet re-ordering, and the like, e.g., on a per-subscriber basis between network devices, also referred to as hosts or endpoints. In general, a QoE measurement architecture includes network devices that each support the used protocol and perform specific roles to start data sessions and exchange test packets for the data sessions. In the example network architecture illustrated in FIG. 1, SD-WAN appliance 18 is configured to perform the QoE metric predictions. SD-WAN appliance 18 allows for load sharing across connections and adjusts traffic flows based on network conditions to improve performance. [0034] SD-WAN appliance 18, which performs the traffic monitoring functions described herein, also determines QoE metrics, such as service level agreement (SLA) metrics that include round-trip time (RTT), jitter, and packet loss, which were influenced by applications' real-time parameters like packet size, queues and burst of packets to determine the best path. However, different applications have different packet sizes in their data flows. Furthermore, different applications have different traffic patterns, some of which may be inconsistent with different levels of burst and bandwidth usage during the normal execution of the application. This can lead to false positives regarding a link's ability to handle the application traffic if a large number of packets are unexpectedly received for the application. As described below with respect to FIGS. 2-4, the techniques described herein show how SD-WAN appliance 18 can implement a machine learning algorithm to determine historical traffic patterns for various applications and adjust the probing frequency and other probing parameters based on the received application traffic.).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Cui to include the feature of selecting the network based on the categorization and a combination of Cui and Chen with Kumar renders the claim prima facie obvious within the described scope of the prior art and any indicated differences within the level of one of ordinary skill in the art (e.g., telecommunications engineer) according to a combination of known prior art elements with known methods to yield predictable results. MPEP 2143(I)(A) (e.g., selecting the network based on the categorization).
Claims 4, 17, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Cui in view of Lo and Chen and further in view of U.S. Publication No. 2024/0070234 (hereinafter “Wells”)
Regarding claim 4, the combination of Cui, Lo, and Chen teaches performing operations based on a service requirement and optimizing network traffic flow. The combination does not specifically teach: wherein responsive to the service categorization comprising the metaverse service category, the operations further comprising: determining a data set pertaining to the network-enabled user device, the data set identifying a plurality of virtual items occurring with an instance of a metaverse environment of a metaverse service; and mapping an item of the plurality of virtual items to the network-enabled user device, wherein the network selection is further based on the mapping.
However, in the same field of endeavor, Wells teaches: wherein responsive to the service categorization comprising the metaverse service category, the operations further comprising: determining a data set pertaining to the network-enabled user device, the data set identifying a plurality of virtual items occurring with an instance of a metaverse environment of a metaverse service; and mapping an item of the plurality of virtual items to the network-enabled user device, wherein the network selection is further based on the mapping ([0095], [0096], [0099], [0101], [0102] At step 901, a node (not shown) may request to import an item into, e.g., system 800 (described hereinabove with respect to FIG. 8), which may be a BES (not shown). At step 902, the system may determine what type of asset is being put into the system. At this point, the system may determine whether the item is, for example, a metaverse object, a non-metaverse digital object, or a physical object.).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Cui, Lo, and Chen to include the feature of mapping a virtual item to a network-enabled user device and a combination of Cui and Chen with Wells renders the claim prima facie obvious within the described scope of the prior art and any indicated differences within the level of one of ordinary skill in the art (e.g., telecommunications engineer) according to a combination of known prior art elements with known methods to yield predictable results. MPEP 2143(I)(A) (e.g., mapping a virtual item to a network-enabled user device).
Regarding claim 17, the combination of Cui, Lo, and Chen teaches performing operations based on a service requirement and optimizing network traffic flow. The combination does not specifically teach: wherein responsive to the service categorization comprising the metaverse service category, the operations further comprising: determining a data set pertaining to the network-enabled user device, the data set identifying a plurality of virtual items occurring with an instance of a metaverse environment of a metaverse service; and mapping an item of the plurality of virtual items to the network-enabled user device, wherein the network selection is further based on the mapping.
However, in the same field of endeavor, Wells teaches: wherein responsive to the service categorization comprising the metaverse service category, the operations further comprising: determining a data set pertaining to the network-enabled user device, the data set identifying a plurality of virtual items occurring with an instance of a metaverse environment of a metaverse service; and mapping an item of the plurality of virtual items to the network-enabled user device, wherein the network selection is further based on the mapping ([0095], [0096], [0099], [0101], [0102] At step 901, a node (not shown) may request to import an item into, e.g., system 800 (described hereinabove with respect to FIG. 8), which may be a BES (not shown). At step 902, the system may determine what type of asset is being put into the system. At this point, the system may determine whether the item is, for example, a metaverse object, a non-metaverse digital object, or a physical object.).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Cui, Lo, and Chen to include the feature of mapping a virtual item to a network-enabled user device and a combination of Cui and Chen with Wells renders the claim prima facie obvious within the described scope of the prior art and any indicated differences within the level of one of ordinary skill in the art (e.g., telecommunications engineer) according to a combination of known prior art elements with known methods to yield predictable results. MPEP 2143(I)(A) (e.g., mapping a virtual item to a network-enabled user device).
Regarding claim 22, the combination of Cui, Lo, and Chen teaches performing operations based on a service requirement and optimizing network traffic flow. The combination does not specifically teach: wherein responsive to the service categorization comprising the metaverse service category, the operations further comprising: determining a data set pertaining to the network-enabled user device, the data set identifying a plurality of virtual items occurring with an instance of a metaverse environment of a metaverse service; and mapping an item of the plurality of virtual items to the network-enabled user device, wherein the network selection is further based on the mapping.
However, in the same field of endeavor, Wells teaches: wherein responsive to the service categorization comprising the metaverse service category, the operations further comprising: determining a data set pertaining to the network-enabled user device, the data set identifying a plurality of virtual items occurring with an instance of a metaverse environment of a metaverse service; and mapping an item of the plurality of virtual items to the network-enabled user device, wherein the network selection is further based on the mapping ([0095], [0096], [0099], [0101], [0102] At step 901, a node (not shown) may request to import an item into, e.g., system 800 (described hereinabove with respect to FIG. 8), which may be a BES (not shown). At step 902, the system may determine what type of asset is being put into the system. At this point, the system may determine whether the item is, for example, a metaverse object, a non-metaverse digital object, or a physical object.).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Cui, Lo, and Chen to include the feature of mapping a virtual item to a network-enabled user device and a combination of Cui and Chen with Wells renders the claim prima facie obvious within the described scope of the prior art and any indicated differences within the level of one of ordinary skill in the art (e.g., telecommunications engineer) according to a combination of known prior art elements with known methods to yield predictable results. MPEP 2143(I)(A) (e.g., mapping a virtual item to a network-enabled user device).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Cui in view of Lo and Chen and further in view of U.S. Publication No. 2024/0184980 (hereinafter “Luthra”)
Regarding claim 6, the combination of Cui, Lo, and Chen teaches performing operations based on a service requirement and optimizing network traffic flow. The combination does not specifically teach: wherein the plurality of network elements comprises virtual network elements of a cloud service.
However, in the same field of endeavor, Luthra teaches: wherein the plurality of network elements comprises virtual network elements of a cloud service ([0040] A fronthaul network is coincident with the backhaul network, but subtly different. In a cloud RAN (C-RAN) the backhaul data is decoded from the fronthaul network at centralized controllers, from where the backhaul data is then transferred to the CN. The fronthaul portion of a C-RAN includes the intermediate links between the centralized radio controllers and the radio heads (or masts) at the edge of a cellular network. Event sources from 5G transport networks 114 are events occurring in the 5G transport networks 114. In a non-limiting example, one or more incidents occurring within radio controllers or network switches of 5G transport networks 114.).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Cui and Chen to include the feature of cloud services and a combination of Cui, Lo, and Chen with Luthra renders the claim prima facie obvious within the described scope of the prior art and any indicated differences within the level of one of ordinary skill in the art (e.g., telecommunications engineer) according to a combination of known prior art elements with known methods to yield predictable results. MPEP 2143(I)(A) (e.g., cloud services).
Claims 7 are rejected under 35 U.S.C. 103 as being unpatentable over Cui in view of Lo and Chen and further in view of U.S. Publication No. 2019/0246298 (hereinafter “Roman”)
Regarding claim 7, the combination of Cui, Lo, and Chen teaches performing operations based on a service requirement and optimizing network traffic flow. The combination does not specifically teach: wherein the supporting information is obtained via time-insensitive network domain functions, and wherein the operations further comprise: training a machine learning model to obtain a trained model according to the time-insensitive network domain functions; and obtaining by the trained model, at least a portion of the supporting information.
However, in the same field of endeavor, Roman teaches: wherein the supporting information is obtained via time-insensitive network domain functions, and wherein the operations further comprise: training a machine learning model to obtain a trained model according to the time-insensitive network domain functions; and obtaining by the trained model, at least a portion of the supporting information ([0012] The training of the machine learning model may correspond to a supervised learning as the machine learning is done with labeled training data that may consist of a set of training examples corresponding to the respective test results obtained. Each training example may correspond to a pair consisting of a certain input object (input parameter) as well as a desired output parameter that is also called supervisory signal, namely a parameter indicating the quality of the service (output parameter). The supervised learning algorithm used by the machine learning model analyzes the training data and generates an inferred mathematical model, for instance a function, which can be used for mapping new examples that go beyond the training examples.).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Cui, Lo, and Chen to include the feature of training a machine learning model to provide supporting information and a combination of Cui and Chen with Roman renders the claim prima facie obvious within the described scope of the prior art and any indicated differences within the level of one of ordinary skill in the art (e.g., telecommunications engineer) according to a combination of known prior art elements with known methods to yield predictable results. MPEP 2143(I)(A) (e.g., training a machine learning model to provide supporting information).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Publication No. 2022/0183093 (Sevindik) related to methods and apparatus for utilizing dual radio access technologies in wireless systems
U.S. Publication No. 2024/0267792 (Zhu) related to dynamic traffic management for multi-access management services
Non-Patent Literature (Rony et al.) "Joint Access-Backhaul Perspective on Mobility Management in 5G Networks" (Year: 2017)
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/JAB/ Examiner, Art Unit 2643
/JINSONG HU/ Supervisory Patent Examiner, Art Unit 2643