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 .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mathews et al (20240064536) in view of AKTAS et al (20200067792).
Regarding claim 1, Mathews et al discloses, a method comprising (abstract, fig. 1-5):
obtaining, by an edge computing device, first probe data associated with operation of one or more components of a cellular network (¶ 0034-0035, 0063-0064, the device edge controller causing a technician or an autonomous device to be dispatched to service the one of the edge devices or a device associated with the one of the edge devices. For example, the device edge controller may determine that the problem with the edge device or the device associated with the edge device cannot be addressed by the device edge controller. In such situations, the device edge controller may cause a technician or an autonomous device to be dispatched to service the edge device or the device associated with the edge device. In this way, the device edge controller conserves computing resources, networking resources, and/or the like that would otherwise have been consumed in failing to identify problems or potential issues associated with the edge device or the device associated with the edge device, troubleshooting the edge device or the device associated with the edge device based on the problems or the potential issues, and/or the like, fig. 1-5);
providing the first probe data to a first model configured to determine first summary data based on the first probe data (¶ 0052-0053, 0063, the device edge may process data from an edge device, with a machine learning model, to generate edge analytic data associated with the edge device. For example, the device edge may receive the machine learning model from the device edge controller, as described above in connection with FIG. 1A. The machine learning model may include a machine learning model that processes data from an edge device to generate edge analytic data associated with the edge device. The device edge may train the machine learning model to generate the edge analytic data associated with the edge device. In some implementations, rather than training the machine learning model, the device edge may obtain the machine learning model from another system or device (e.g., the device edge controller) that trained the machine learning model. In this case, the device edge may provide the other system or device with updated training, validation, and/or test datasets to retrain the machine learning model in order to update the machine learning model. In some implementations, the machine learning model may include a clustering model, as described above);
determining that the first summary data has been requested by a first application (¶ 0055-0057, 0063, the device edge may process the edge analytic data, with the machine learning model, to detect a problem or to predict an issue. For example, the device edge may analyze the edge analytic data, and may detect the problem or predict the issue based on analyzing the edge analytic data. In some implementations, the device edge may process the edge analytic data, with the machine learning model, to detect the problem or to predict the issue with the edge device. The machine learning model may include a clustering model, as described above. In some implementations, the device edge may detect the problem or predict the issue with one or more devices of the core network and/or the RAN based on processing the edge analytic data with the machine learning model); and
providing the first summary data to a central server responsive to determining that the first summary data has been requested by the first application (¶ 0060, 0101-0102, the device edge may generate an alarm based on the problem or the issue, and may provide the alarm (e.g., via a text message, an email, a telephone call, a control system, and/or the like) to a party responsible for handling problems or issues associated with the edge device or one or more devices of the core network and/or the RAN. In this way, the device edge conserves computing resources, networking resources, and/or the like that would otherwise have been consumed in failing to identify problems or potential issues associated with the edge device or the devices of the core network and/or the RAN, handling network outages caused by the problems or the potential issues, and/or the like).
Mathews et al does not specifically disclose receiving a discovery reference signal on the basis of the second assistance data and performing measurement using the received the DRS and wherein the second assistance data further includes indication information indication whether the DRS is used for positioning or for discovering.
In the same field of endeavor, AKTAS et al discloses, northbound application (¶ 0055-0057, TSF 400 receives KPI violations from Network Monitoring Function 350 on interface, which is a simple API such as the REST API. TSF receives routing tables and network map from Controller on interface, which is the ‘Northbound’ API provided by the Controller. This is the same API that all controller Applications uses.).
AKTAS et al also discloses, one or more network probes implemented at a plurality of network interfaces at edges of the data network to measure key performance indicators (KPI), and (3) an SDN controller controlling the data network, the non-transitory computer storage medium comprising: (a) computer readable program code implementing a target selection function, the target selection function: (1) receiving from at least one network probe in the one or more network probes, one or more KPI threshold violations; (2) receiving a data network topology and traffic routing information from the SDN controller; (3) correlating the one or more KPI threshold violations received in (1) and the traffic routing information received in (2); (4) determining a subset of network switches within the plurality of network switches on which in-band telemetry is to be initiated (¶ 0065)
Therefore, before the effective filing date of the claim invention, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the device of Mathews et al by specifically adding feature in order to enhance system performance to probe request can proactively generate ping and trace-route packets, and send them via a northbound application to network locations to diagnose problems as taught by AKTAS et al.
Regarding claim 2, Mathews et al and AKTAS et al disclose in claim 1, further,, AKTAS et al disclose, further comprising: providing, by the edge computing device, second probe data to a second model configured to determine second summary data based on the second probe data (¶ 0028-0031, 0065); determining that the second summary data has been requested by a second northbound application (¶ 0028-0031, 0065); and providing the second summary data to the central server responsive to determining that the second summary data has been requested by the second northbound application (¶ 0028-0031, 0065 one or more network probes implemented at a plurality of network interfaces at edges of the data network to measure key performance indicators (KPI), and (3) an SDN controller controlling the data network, the non-transitory computer storage medium comprising: (a) computer readable program code implementing a target selection function, the target selection function: (1) receiving from at least one network probe in the one or more network probes, one or more KPI threshold violations; (2) receiving a data network topology and traffic routing information from the SDN controller; (3) correlating the one or more KPI threshold violations received in (1) and the traffic routing information received in (2); (4) determining a subset of network switches within the plurality of network switches on which in-band telemetry is to be initiated (¶ 0065)
Therefore, before the effective filing date of the claim invention, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the device of Mathews et al by specifically adding feature in order to enhance system performance to probe request can proactively generate ping and trace-route packets, and send them via a northbound application to network locations to diagnose problems as taught by AKTAS et al.
Regarding claim 3, Mathews et al and AKTAS et al disclose in claim 1, further, Mathews et al disclose, wherein the first model comprises a trained machine learning model configured to classify whether operations of the one or more components are abnormal (¶ 0003-0005, configured to receive data from an edge device associated with a RAN, and process the data, with a machine learning model, to generate edge analytic data associated with the edge device. The one or more processors may be configured to process the edge analytic data, with the machine learning model, to detect a problem or to predict an issue associated with the edge device or a RAN device associated with the edge device, and provide particular edge analytic data associated with the problem or the issue to a cloud-computing system. The one or more processors may be configured to perform one or more actions based on the problem or the issue associated with the edge device or the RAN device).
Regarding claim 4, Mathews et al and AKTAS et al disclose in claim 1, further,, Mathews et al disclose, providing an information request from the first application to a trained machine learning model configured to determine whether the first summary data is related to the information request; and obtaining output from the trained machine learning model based on the information request, wherein the output from the trained machine learning model indicates that the information request is to be fulfilled by providing a plurality of summary data comprising the first summary data (¶ 0052-0053, 0060, 0101-0102, the device edge may generate an alarm based on the problem or the issue, and may provide the alarm (e.g., via a text message, an email, a telephone call, a control system, and/or the like) to a party responsible for handling problems or issues associated with the edge device or one or more devices of the core network and/or the RAN. In this way, the device edge conserves computing resources, networking resources, and/or the like that would otherwise have been consumed in failing to identify problems or potential issues associated with the edge device or the devices of the core network and/or the RAN, handling network outages caused by the problems or the potential issues, and/or the like).
Mathews et al does not specifically disclose receiving a discovery reference signal on the basis of the second assistance data and performing measurement using the received the DRS and wherein the second assistance data further includes indication information indication whether the DRS is used for positioning or for discovering.
In the same field of endeavor, AKTAS et al discloses, northbound application (¶ 0055-0057, TSF 400 receives KPI violations from Network Monitoring Function 350 on interface, which is a simple API such as the REST API. TSF receives routing tables and network map from Controller on interface, which is the ‘Northbound’ API provided by the Controller. This is the same API that all controller Applications uses.).
AKTAS et al also discloses, one or more network probes implemented at a plurality of network interfaces at edges of the data network to measure key performance indicators (KPI), and (3) an SDN controller controlling the data network, the non-transitory computer storage medium comprising: (a) computer readable program code implementing a target selection function, the target selection function: (1) receiving from at least one network probe in the one or more network probes, one or more KPI threshold violations; (2) receiving a data network topology and traffic routing information from the SDN controller; (3) correlating the one or more KPI threshold violations received in (1) and the traffic routing information received in (2); (4) determining a subset of network switches within the plurality of network switches on which in-band telemetry is to be initiated (¶ 0065). Therefore, before the effective filing date of the claim invention, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the device of Mathews et al by specifically adding feature in order to enhance system performance to probe request can proactively generate ping and trace-route packets, and send them via a northbound application to network locations to diagnose problems as taught by AKTAS et al.
Regarding claims 5, Mathews et al and AKTAS et al disclose in claim 1, further,, Mathews et al disclose, further comprising: providing performance data indicative of an accuracy of operation of the first model to a control module of the central server; and obtaining one or more updates to the first model from the control module of the central server responsive to providing the performance data to the control module of the central server (¶ 0003-0005, 0028, 0056, configured to receive data from an edge device associated with a RAN, and process the data, with a machine learning model, to generate edge analytic data associated with the edge device. The one or more processors may be configured to process the edge analytic data, with the machine learning model, to detect a problem or to predict an issue associated with the edge device or a RAN device associated with the edge device, and provide particular edge analytic data associated with the problem or the issue to a cloud-computing system. The one or more processors may be configured to perform one or more actions based on the problem or the issue associated with the edge device or the RAN device. The set of instructions, when executed by one or more processors of the device, may cause the device to provide particular edge analytic data associated with the problem or the issue to a cloud-computing system, and delete particular edge analytic data unassociated with the problem or the issue. The set of instructions, when executed by one or more processors of the device, may cause the device to perform one or more actions based on the problem or the issue associated with the edge device or the RAN device.).
Regarding claim 6, Mathews et al and AKTAS et al disclose in claim 1, further,, AKTAS et al disclose, wherein the edge computing device obtains the first probe data from a distributed unit of the cellular network, proximate the edge computing device, wherein the distributed unit is associated with a plurality of radio units (¶ 0028-0031, 0065 one or more network probes implemented at a plurality of network interfaces at edges of the data network to measure key performance indicators (KPI), and (3) an SDN controller controlling the data network, the non-transitory computer storage medium comprising: (a) computer readable program code implementing a target selection function, the target selection function: (1) receiving from at least one network probe in the one or more network probes, one or more KPI threshold violations; (2) receiving a data network topology and traffic routing information from the SDN controller; (3) correlating the one or more KPI threshold violations received in (1) and the traffic routing information received in (2); (4) determining a subset of network switches within the plurality of network switches on which in-band telemetry is to be initiated (¶ 0065). Therefore, before the effective filing date of the claim invention, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the device of Mathews et al by specifically adding feature in order to enhance system performance to probe request can proactively generate ping and trace-route packets, and send them via a northbound application to network locations to diagnose problems as taught by AKTAS et al.
Regarding claim 7, Mathews et al and AKTAS et al disclose in claim 1, further,, AKTAS et al disclose, wherein the edge computing device obtains the first probe data from a central unit of the cellular network, proximate the edge computing device, wherein the central unit is associated with a plurality of distributed units (¶ 0028-0031, 0065 one or more network probes implemented at a plurality of network interfaces at edges of the data network to measure key performance indicators (KPI), and (3) an SDN controller controlling the data network, the non-transitory computer storage medium comprising: (a) computer readable program code implementing a target selection function, the target selection function: (1) receiving from at least one network probe in the one or more network probes, one or more KPI threshold violations; (2) receiving a data network topology and traffic routing information from the SDN controller; (3) correlating the one or more KPI threshold violations received in (1) and the traffic routing information received in (2); (4) determining a subset of network switches within the plurality of network switches on which in-band telemetry is to be initiated (¶ 0065). Therefore, before the effective filing date of the claim invention, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the device of Mathews et al by specifically adding feature in order to enhance system performance to probe request can proactively generate ping and trace-route packets, and send them via a northbound application to network locations to diagnose problems as taught by AKTAS et al.
Regarding claim 8, Mathews et al and AKTAS et al disclose in claim 1, further,, Mathews et al disclose, wherein the first model is further configured to generate a plurality of tags in association with the first summary data, and wherein determining that the first summary data has been requested by the first northbound application comprises determining that a tag associated with the first northbound application is associated with the first summary data (¶ 0003-0005, configured to receive data from an edge device associated with a RAN, and process the data, with a machine learning model, to generate edge analytic data associated with the edge device. The one or more processors may be configured to process the edge analytic data, with the machine learning model, to detect a problem or to predict an issue associated with the edge device or a RAN device associated with the edge device, and provide particular edge analytic data associated with the problem or the issue to a cloud-computing system. The one or more processors may be configured to perform one or more actions based on the problem or the issue associated with the edge device or the RAN device).
Regarding claim 9, Mathews et al and AKTAS et al disclose in claim 1, further,, Mathews et al disclose, wherein determining that the first summary data has been requested by the first northbound application comprises determining that the first data belongs to a first topic, and determining that the first topic is associated with the first northbound application (¶ 0052-0053, 0060, 0101-0102, the device edge may generate an alarm based on the problem or the issue, and may provide the alarm (e.g., via a text message, an email, a telephone call, a control system, and/or the like) to a party responsible for handling problems or issues associated with the edge device or one or more devices of the core network and/or the RAN. In this way, the device edge conserves computing resources, networking resources, and/or the like that would otherwise have been consumed in failing to identify problems or potential issues associated with the edge device or the devices of the core network and/or the RAN, handling network outages caused by the problems or the potential issues, and/or the like).
Mathews et al does not specifically disclose receiving a discovery reference signal on the basis of the second assistance data and performing measurement using the received the DRS and wherein the second assistance data further includes indication information indication whether the DRS is used for positioning or for discovering. In the same field of endeavor, AKTAS et al discloses, northbound application (¶ 0055-0057, TSF 400 receives KPI violations from Network Monitoring Function 350 on interface, which is a simple API such as the REST API. TSF receives routing tables and network map from Controller on interface, which is the ‘Northbound’ API provided by the Controller. This is the same API that all controller Applications uses.).
AKTAS et al also discloses, one or more network probes implemented at a plurality of network interfaces at edges of the data network to measure key performance indicators (KPI), and (3) an SDN controller controlling the data network, the non-transitory computer storage medium comprising: (a) computer readable program code implementing a target selection function, the target selection function: (1) receiving from at least one network probe in the one or more network probes, one or more KPI threshold violations; (2) receiving a data network topology and traffic routing information from the SDN controller; (3) correlating the one or more KPI threshold violations received in (1) and the traffic routing information received in (2); (4) determining a subset of network switches within the plurality of network switches on which in-band telemetry is to be initiated (¶ 0065). Therefore, before the effective filing date of the claim invention, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the device of Mathews et al by specifically adding feature in order to enhance system performance to probe request can proactively generate ping and trace-route packets, and send them via a northbound application to network locations to diagnose problems as taught by AKTAS et al.
Regarding claim 10, Mathews et al and AKTAS et al disclose in claim 1, further,, Mathews et al disclose, further comprising performing a corrective action based on the first summary data, wherein the corrective action comprises one or more of: adjusting power output of one or more radios of the cellular network; adjusting antenna directionality of one or more antennas of the cellular network; adjusting radio resource allocation; adjusting processing device resource allocation; or adjusting one or more network slices (¶ 0003-0005, configured to receive data from an edge device associated with a RAN, and process the data, with a machine learning model, to generate edge analytic data associated with the edge device. The one or more processors may be configured to process the edge analytic data, with the machine learning model, to detect a problem or to predict an issue associated with the edge device or a RAN device associated with the edge device, and provide particular edge analytic data associated with the problem or the issue to a cloud-computing system. The one or more processors may be configured to perform one or more actions based on the problem or the issue associated with the edge device or the RAN device. The set of instructions, when executed by one or more processors of the device, may cause the device to provide particular edge analytic data associated with the problem or the issue to a cloud-computing system, and delete particular edge analytic data unassociated with the problem or the issue. The set of instructions, when executed by one or more processors of the device, may cause the device to perform one or more actions based on the problem or the issue associated with the edge device or the RAN device.).
Regarding claim 11, Mathews et al and AKTAS et al disclose in claim 1, further, Mathews et al disclose, one or more non-transitory, computer-readable storage media having computer-readable instructions thereon which, when executed by one or more processing devices (¶ 0005), cause the one or more processing devices to perform operations comprising:
obtaining first probe data associated with operation of one or more components of a cellular network (¶ 0034-0035, 0063-0064, the device edge controller causing a technician or an autonomous device to be dispatched to service the one of the edge devices or a device associated with the one of the edge devices. For example, the device edge controller may determine that the problem with the edge device or the device associated with the edge device cannot be addressed by the device edge controller. In such situations, the device edge controller may cause a technician or an autonomous device to be dispatched to service the edge device or the device associated with the edge device. In this way, the device edge controller conserves computing resources, networking resources, and/or the like that would otherwise have been consumed in failing to identify problems or potential issues associated with the edge device or the device associated with the edge device, troubleshooting the edge device or the device associated with the edge device based on the problems or the potential issues, and/or the like, fig. 1-5);
providing the first probe data to a first model configured to determine first summary data based on the first probe data (¶ 0052-0053, 0063, the device edge may process data from an edge device, with a machine learning model, to generate edge analytic data associated with the edge device. For example, the device edge may receive the machine learning model from the device edge controller, as described above in connection with FIG. 1A. The machine learning model may include a machine learning model that processes data from an edge device to generate edge analytic data associated with the edge device. The device edge may train the machine learning model to generate the edge analytic data associated with the edge device. In some implementations, rather than training the machine learning model, the device edge may obtain the machine learning model from another system or device (e.g., the device edge controller) that trained the machine learning model. In this case, the device edge may provide the other system or device with updated training, validation, and/or test datasets to retrain the machine learning model in order to update the machine learning model. In some implementations, the machine learning model may include a clustering model, as described above);
determining that the first summary data has been requested by a first application (¶ 0055-0057, 0063, the device edge may process the edge analytic data, with the machine learning model, to detect a problem or to predict an issue. For example, the device edge may analyze the edge analytic data, and may detect the problem or predict the issue based on analyzing the edge analytic data. In some implementations, the device edge may process the edge analytic data, with the machine learning model, to detect the problem or to predict the issue with the edge device. The machine learning model may include a clustering model, as described above. In some implementations, the device edge may detect the problem or predict the issue with one or more devices of the core network and/or the RAN based on processing the edge analytic data with the machine learning model); and
providing the first summary data to a central server responsive to determining that the first summary data has been requested by the first application (¶ 0060, 0101-0102, the device edge may generate an alarm based on the problem or the issue, and may provide the alarm e.g., via a text message, an email, a telephone call, a control system, and/or the like to a party responsible for handling problems or issues associated with the edge device or one or more devices of the core network and/or the RAN. In this way, the device edge conserves computing resources, networking resources, and/or the like that would otherwise have been consumed in failing to identify problems or potential issues associated with the edge device or the devices of the core network and/or the RAN, handling network outages caused by the problems or the potential issues, and/or the like).
Mathews et al does not specifically disclose receiving a discovery reference signal on the basis of the second assistance data and performing measurement using the received the DRS and wherein the second assistance data further includes indication information indication whether the DRS is used for positioning or for discovering. In the same field of endeavor, AKTAS et al discloses, northbound application (¶ 0055-0057, TSF 400 receives KPI violations from Network Monitoring Function 350 on interface, which is a simple API such as the REST API. TSF receives routing tables and network map from Controller on interface, which is the ‘Northbound’ API provided by the Controller. This is the same API that all controller Applications uses.).
AKTAS et al also discloses, one or more network probes implemented at a plurality of network interfaces at edges of the data network to measure key performance indicators (KPI), and (3) an SDN controller controlling the data network, the non-transitory computer storage medium comprising: (a) computer readable program code implementing a target selection function, the target selection function: (1) receiving from at least one network probe in the one or more network probes, one or more KPI threshold violations; (2) receiving a data network topology and traffic routing information from the SDN controller; (3) correlating the one or more KPI threshold violations received in (1) and the traffic routing information received in (2); (4) determining a subset of network switches within the plurality of network switches on which in-band telemetry is to be initiated (¶ 0065). Therefore, before the effective filing date of the claim invention, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the device of Mathews et al by specifically adding feature in order to enhance system performance to probe request can proactively generate ping and trace-route packets, and send them via a northbound application to network locations to diagnose problems as taught by AKTAS et al.
Regarding claim 12, Mathews et al and AKTAS et al disclose in claim 1, further,, Mathews et al disclose, wherein the first model comprises a trained machine learning model configured to classify whether operations of the one or more components are abnormal (¶ 0003-0005, 0028, 0056, configured to receive data from an edge device associated with a RAN, and process the data, with a machine learning model, to generate edge analytic data associated with the edge device. The one or more processors may be configured to process the edge analytic data, with the machine learning model, to detect a problem or to predict an issue associated with the edge device or a RAN device associated with the edge device, and provide particular edge analytic data associated with the problem or the issue to a cloud-computing system. The one or more processors may be configured to perform one or more actions based on the problem or the issue associated with the edge device or the RAN device. The set of instructions, when executed by one or more processors of the device, may cause the device to provide particular edge analytic data associated with the problem or the issue to a cloud-computing system, and delete particular edge analytic data unassociated with the problem or the issue. The set of instructions, when executed by one or more processors of the device, may cause the device to perform one or more actions based on the problem or the issue associated with the edge device or the RAN device.).
Regarding claim 13, Mathews et al and AKTAS et al disclose in claim 1, further,, Mathews et al disclose, wherein determining that the first summary data has been requested by a first northbound application comprises: providing an information request from the first northbound application to a trained machine learning model; and obtaining output from the trained machine learning model based on the information request, wherein the output from the trained machine learning model indicates that the information request may be fulfilled by providing a plurality of summary data comprising the first summary data (¶ 0028-0031, 0065 one or more network probes implemented at a plurality of network interfaces at edges of the data network to measure key performance indicators (KPI), and (3) an SDN controller controlling the data network, the non-transitory computer storage medium comprising: (a) computer readable program code implementing a target selection function, the target selection function: (1) receiving from at least one network probe in the one or more network probes, one or more KPI threshold violations; (2) receiving a data network topology and traffic routing information from the SDN controller; (3) correlating the one or more KPI threshold violations received in (1) and the traffic routing information received in (2); (4) determining a subset of network switches within the plurality of network switches on which in-band telemetry is to be initiated (¶ 0065). Therefore, before the effective filing date of the claim invention, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the device of Mathews et al by specifically adding feature in order to enhance system performance to probe request can proactively generate ping and trace-route packets, and send them via a northbound application to network locations to diagnose problems as taught by AKTAS et al.
Regarding claim 14, Mathews et al and AKTAS et al disclose in claim 1, further,, Mathews et al disclose, providing performance data indicative of an accuracy of operation of the first model to a control module of the central server; and obtaining one or more updates to the first model from the control module of the central server responsive to providing the performance data to the control module of the central server (¶ 0028-0031, 0065 one or more network probes implemented at a plurality of network interfaces at edges of the data network to measure key performance indicators (KPI), and (3) an SDN controller controlling the data network, the non-transitory computer storage medium comprising: (a) computer readable program code implementing a target selection function, the target selection function: (1) receiving from at least one network probe in the one or more network probes, one or more KPI threshold violations; (2) receiving a data network topology and traffic routing information from the SDN controller; (3) correlating the one or more KPI threshold violations received in (1) and the traffic routing information received in (2); (4) determining a subset of network switches within the plurality of network switches on which in-band telemetry is to be initiated (¶ 0065). Therefore, before the effective filing date of the claim invention, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the device of Mathews et al by specifically adding feature in order to enhance system performance to probe request can proactively generate ping and trace-route packets, and send them via a northbound application to network locations to diagnose problems as taught by AKTAS et al.
Regarding claim 15, Mathews et al and AKTAS et al disclose in claim 1, further,, Mathews et al disclose, wherein the one or more processing devices comprise an edge computing device, and wherein the edge computing device obtains the first probe data from a central unit of the cellular network, proximate the edge computing device, wherein the central unit is associated with a plurality of distributed units (¶ 0003-0005, 0028, 0056, configured to receive data from an edge device associated with a RAN, and process the data, with a machine learning model, to generate edge analytic data associated with the edge device. The one or more processors may be configured to process the edge analytic data, with the machine learning model, to detect a problem or to predict an issue associated with the edge device or a RAN device associated with the edge device, and provide particular edge analytic data associated with the problem or the issue to a cloud-computing system. The one or more processors may be configured to perform one or more actions based on the problem or the issue associated with the edge device or the RAN device. The set of instructions, when executed by one or more processors of the device, may cause the device to provide particular edge analytic data associated with the problem or the issue to a cloud-computing system, and delete particular edge analytic data unassociated with the problem or the issue. The set of instructions, when executed by one or more processors of the device, may cause the device to perform one or more actions based on the problem or the issue associated with the edge device or the RAN device.).
Regarding claim 16, Mathews et al and AKTAS et al disclose in claim 1, further,, Mathews et al disclose, a system comprising memory and a processing device coupled to the memory (¶ 0005), wherein the processing device is configured to:
obtain first probe data associated with operation of one or more components of a cellular network (¶ 0034-0035, 0063-0064, the device edge controller causing a technician or an autonomous device to be dispatched to service the one of the edge devices or a device associated with the one of the edge devices. For example, the device edge controller may determine that the problem with the edge device or the device associated with the edge device cannot be addressed by the device edge controller. In such situations, the device edge controller may cause a technician or an autonomous device to be dispatched to service the edge device or the device associated with the edge device. In this way, the device edge controller conserves computing resources, networking resources, and/or the like that would otherwise have been consumed in failing to identify problems or potential issues associated with the edge device or the device associated with the edge device, troubleshooting the edge device or the device associated with the edge device based on the problems or the potential issues, and/or the like, fig. 1-5);
provide the first probe data to a first model configured to determine first summary data based on the first probe data (¶ 0052-0053, 0063, the device edge may process data from an edge device, with a machine learning model, to generate edge analytic data associated with the edge device. For example, the device edge may receive the machine learning model from the device edge controller, as described above in connection with FIG. 1A. The machine learning model may include a machine learning model that processes data from an edge device to generate edge analytic data associated with the edge device. The device edge may train the machine learning model to generate the edge analytic data associated with the edge device. In some implementations, rather than training the machine learning model, the device edge may obtain the machine learning model from another system or device (e.g., the device edge controller) that trained the machine learning model. In this case, the device edge may provide the other system or device with updated training, validation, and/or test datasets to retrain the machine learning model in order to update the machine learning model. In some implementations, the machine learning model may include a clustering model, as described above);
determine that the first summary data has been requested by a first application (¶ 0055-0057, 0063, the device edge may process the edge analytic data, with the machine learning model, to detect a problem or to predict an issue. For example, the device edge may analyze the edge analytic data, and may detect the problem or predict the issue based on analyzing the edge analytic data. In some implementations, the device edge may process the edge analytic data, with the machine learning model, to detect the problem or to predict the issue with the edge device. The machine learning model may include a clustering model, as described above. In some implementations, the device edge may detect the problem or predict the issue with one or more devices of the core network and/or the RAN based on processing the edge analytic data with the machine learning model); and
provide the first summary data to a central server responsive to determining that the first summary data has been requested by the first application (¶ 0060, 0101-0102, the device edge may generate an alarm based on the problem or the issue, and may provide the alarm (e.g., via a text message, an email, a telephone call, a control system, and/or the like) to a party responsible for handling problems or issues associated with the edge device or one or more devices of the core network and/or the RAN. In this way, the device edge conserves computing resources, networking resources, and/or the like that would otherwise have been consumed in failing to identify problems or potential issues associated with the edge device or the devices of the core network and/or the RAN, handling network outages caused by the problems or the potential issues, and/or the like).
Mathews et al does not specifically disclose receiving a discovery reference signal on the basis of the second assistance data and performing measurement using the received the DRS and wherein the second assistance data further includes indication information indication whether the DRS is used for positioning or for discovering.
In the same field of endeavor, AKTAS et al discloses, northbound application (¶ 0055-0057, TSF 400 receives KPI violations from Network Monitoring Function 350 on interface, which is a simple API such as the REST API. TSF receives routing tables and network map from Controller on interface, which is the ‘Northbound’ API provided by the Controller. This is the same API that all controller Applications uses.).
AKTAS et al also discloses, one or more network probes implemented at a plurality of network interfaces at edges of the data network to measure key performance indicators (KPI), and (3) an SDN controller controlling the data network, the non-transitory computer storage medium comprising: (a) computer readable program code implementing a target selection function, the target selection function: (1) receiving from at least one network probe in the one or more network probes, one or more KPI threshold violations; (2) receiving a data network topology and traffic routing information from the SDN controller; (3) correlating the one or more KPI threshold violations received in (1) and the traffic routing information received in (2); (4) determining a subset of network switches within the plurality of network switches on which in-band telemetry is to be initiated (¶ 0065)
Therefore, before the effective filing date of the claim invention, it would have been obvious to one of ordinary skill in the art at the time the invention was made to modify the device of Mathews et al by specifically adding feature in order to enhance system performance to probe request can proactively generate ping and trace-route packets, and send them via a northbound application to network locations to diagnose problems as taught by AKTAS et al.
Regarding claim 17, Mathews et al and AKTAS et al disclose in claim 1, further,, Mathews et al disclose, wherein the first model comprises a trained machine learning model (¶ 0003-0005, 0028, 0056, configured to receive data from an edge device associated with a RAN, and process the data, with a machine learning model, to generate edge analytic data associated with the edge device. The one or more processors may be configured to process the edge analytic data, with the machine learning model, to detect a problem or to predict an issue associated with the edge device or a RAN device associated with the edge device, and provide particular edge analytic data associated with the problem or the issue to a cloud-computing system. The one or more processors may be configured to perform one or more actions based on the problem or the issue associated with the edge device or the RAN device. The set of instructions, when executed by one or more processors of the device, may cause the device to provide particular edge analytic data associated with the problem or the issue to a cloud-computing system, and delete particular edge analytic data unassociated with the problem or the issue. The set of instructions, when executed by one or more processors of the device, may cause the device to perform one or more actions based on the problem or the issue associated with the edge device or the RAN device.).
Regarding claim 18, Mathews et al and AKTAS et al disclose in claim 1, further, Mathews et al disclose, wherein determining that the first summary data has been requested by a first northbound application comprises: providing an information request from the first northbound application to a trained machine learning model; and obtaining output from the trained machine learning model based on the information request, wherein the output from the trained machine learning model indicates that the information request may be fulfilled by providing a plurality of summary data comprising the first summary data (¶ 0003-0005, 0028, 0056, configured to receive data from an edge device associated with a RAN, and process the data, with a machine learning model, to generate edge analytic data associated with the edge device. The one or more processors may be configured to process the edge analytic data, with the machine learning model, to detect a problem or to predict an issue associated with the edge device or a RAN device associated with the edge device, and provide particular edge analytic data associated with the problem or the issue to a cloud-computing system. The one or more processors may be configured to perform one or more actions based on the problem or the issue associated with the edge device or the RAN device. The set of instructions, when executed by one or more processors of the device, may cause the device to provide particular edge analytic data associated with the problem or the issue to a cloud-computing system, and delete particular edge analytic data unassociated with the problem or the issue. The set of instructions, when executed by one or more processors of the device, may cause the device to perform one or more actions based on the problem or the issue associated with the edge device or the RAN device.).
Regarding claim 19, Mathews et al and AKTAS et al disclose in claim 1, further,, Mathews et al disclose, wherein the processing device is further configured to: provide performance data indicative of an accuracy of operation of the first model to a control module of the central server; and obtain one or more updates to the first model from the control module of the central server responsive to providing the performance data to the control module of the central server (¶ 0003-0005, 0028, 0056, configured to receive data from an edge device associated with a RAN, and process the data, with a machine learning model, to generate edge analytic data associated with the edge device. The one or more processors may be configured to process the edge analytic data, with the machine learning model, to detect a problem or to predict an issue associated with the edge device or a RAN device associated with the edge device, and provide particular edge analytic data associated with the problem or the issue to a cloud-computing system).
Regarding claim 20, Mathews et al and AKTAS et al disclose in claim 1, further,, Mathews et al disclose, wherein the processing device comprise an edge computing device, and wherein the edge computing device obtains the first probe data from a central unit of the cellular network, proximate the edge computing device, wherein the central unit is associated with a plurality of distributed units (¶ 0003-0005, 0028, 0056, configured to receive data from an edge device associated with a RAN, and process the data, with a machine learning model, to generate edge analytic data associated with the edge device. The one or more processors may be configured to process the edge analytic data, with the machine learning model, to detect a problem or to predict an issue associated with the edge device or a RAN device associated with the edge device, and provide particular edge analytic data associated with the problem or the issue to a cloud-computing system. The one or more processors may be configured to perform one or more actions based on the problem or the issue associated with the edge device or the RAN device. The set of instructions, when executed by one or more processors of the device, may cause the device to provide particular edge analytic data associated with the problem or the issue to a cloud-computing system, and delete particular edge analytic data unassociated with the problem or the issue. The set of instructions, when executed by one or more processors of the device, may cause the device to perform one or more actions based on the problem or the issue associated with the edge device or the RAN device.).
Conclusion
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/KHAWAR IQBAL/Primary Examiner, Art Unit 2643
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