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 .
DETAILED ACTION
a. Claims 1-20 in the present application, filed on or after March 16, 2013, are being examined under the first inventor to file provisions of the AIA .
b. This is a first action on the merits based on Applicant’s claims submitted on 08/06/2024.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 10, 12-14, and 16 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Sethi et al. US Pub 2020/0136928 (hereinafter “Sethi”).
Regarding claim 1
Sethi discloses a device (i.e. “information handling system (IHS)” [0011]) comprising:
a processing system including a processor (“The information handling system may include one or more processing resources such as a central processing unit (CPU) or hardware or software control logic” [0011]); and
a memory (“The information handling system may include random access memory (RAM), ROM, and/or other types of nonvolatile memory.” [0011]) that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising:
obtaining historic failure data indicative of a plurality of past failures associated with a wireless communications network (“To illustrate, the machine learning may use historical data gathered from other client systems to identify a subset of the other client systems with the same (or similar) components, having the same (or similar) network topology, having the same (or similar) diagnostic information, and having the same (or similar) issues.” [0018]);
obtaining historic remediation data indicative of a plurality of past remediation attempts associated with the plurality of past failures (“The machine learning may be trained based on issues encountered by other client systems along with methods and tools that the technicians used to resolve the issues. Each recommendation of the machine learning may include a description of the issues, the solution that was used, and the name of the technician who resolved the issues in the past. This information may enable the technician troubleshooting the client system to discuss the issues with another technician who previously resolved a similar problem for a similarly configured client system.” [0019]);
obtaining current failure data indicative of a current failure associated with the wireless communications network (“The console may include a machine learning algorithm to predict one or more recommendations to resolve the issues associated with the client system. For example, the machine learning algorithm may use decision tree learning, association rule learning, artificial neural networks, deep learning, inductive logic programming, support vector machines, clustering, Bayesian networks, representation learning, similarity and metric learning, sparse dictionary learning, rule-based machine learning, learning classifier systems, or the like. For example, the machine learning may use the system configuration information (e.g., components, network topology, and the like) associated with the client system, the attributes associated with each component of the client system, the diagnostic information (e.g., gathered by the agents) associated with the client system, the issues experienced by the client system, or any combination thereof to predict one or more recommended solutions.” [0018]);
applying the historic failure data, the historic remediation data, and the current failure data to a generative artificial intelligence (AI) process (“For example, the machine learning algorithm may use decision tree learning, association rule learning, artificial neural networks, deep learning, inductive logic programming, support vector machines, clustering, Bayesian networks, representation learning, similarity and metric learning, sparse dictionary learning, rule-based machine learning, learning classifier systems, or the like.” [0018]), wherein the generative AI process outputs a proposed remediation solution to the current failure, and wherein the proposed remediation solution differs from each of the plurality of past remediation attempts (“After identifying the subset of other client systems, the machine learning may analyze the corresponding solutions used to resolve the issues with the subset of other client systems and predict which of the corresponding solutions are most likely to resolve the issues of the client system. If the machine learning presents more than one recommended solution, the recommended solutions may be ranked based on a confidence percentage, e.g., the first solution is 95% likely to solve the issues, the second solution is 90% likely to solve the issues, and so on.” [0018]); and
running one or more simulations of application of the proposed remediation solution to the current failure, wherein the running of the one or more simulations provides a simulation result (“The console 102 may include a machine learning (module) 134 to predict one or more recommendations 136 to resolve the issues associated with the client system 104(P). For example, the machine learning 134 may use the gathered data 118 and a database 148 that includes previously solved problems and the associated solutions to make the recommendations 136. The gathered data 118 may include system configuration information (e.g., components 108, network topology, and the like) associated with the client system 104(P), the attributes 112 associated with each of the components 108 of the client system 102, and diagnostic information (e.g., logs, events, such as restarts, and the like) associated with the client system 102 to predict the recommendations 136 (e.g., recommended solutions). The machine learning 134 may use the database 148 and the historical data 120 gathered from each the client systems 104(1) to 104(P) over a period of time to identify a subset of the data 120 that indicates other client systems 104 having components similar to the components 108, having the same (or similar) network topology, having the same (or similar) diagnostic information, and having similar (or the same) problems. After identifying the subset of the data 120, the machine learning 134 may analyze the similar problems 138 (e.g., associated with the client systems 104) and predict which of corresponding solutions 140 are most likely to resolve the issues of the client system 104(P). If the machine learning 134 presents multiple recommendations 136, each of the recommendations 136 may include a rank or a confidence percentage, e.g., the first solution 140(1) is 95% likely to solve the issues, the second solution is 90% likely to solve the issues, and so on.” [0027]).
Regarding claim 10
Sethi previously discloses the device of claim 1,
Sethi further discloses wherein each of the plurality of past failures comprises a hardware failure, a firmware failure, a software failure, or a combination thereof (“Each component may have multiple attributes, such as, for example, which port on one component is connected to which port on another component, configuration information, error messages, currently installed firmware version, currently installed software version, hardware version, installation logs, error logs, and the like… If the network link stops functioning, causing network traffic to be re-routed across other network links, then such an event may cause the one or more agents to gather data associated with each component of the client system.” [0013]).
Regarding claim 12
Sethi previously discloses the device of claim 1,
Sethi further discloses wherein each of the plurality of past remediation attempts results in a respective complete remediation, a respective partial remediation, or a respective totally incomplete remediation (“The machine learning may be trained based on issues encountered by other client systems along with methods and tools that the technicians used to resolve the issues. Each recommendation of the machine learning may include a description of the issues, the solution that was used, and the name of the technician who resolved the issues in the past. This information may enable the technician troubleshooting the client system to discuss the issues with another technician who previously resolved a similar problem for a similarly configured client system.” [0019]).
Regarding claim 13
Sethi previously discloses the device of claim 1,
Sethi further discloses wherein each of the plurality of past remediation attempts comprises: a respective hardware modification; a respective firmware modification; a respective software modification; or any combination thereof (“Each component may have multiple attributes, such as, for example, which port on one component is connected to which port on another component, configuration information, error messages, currently installed firmware version, currently installed software version, hardware version, installation logs, error logs, and the like… If the network link stops functioning, causing network traffic to be re-routed across other network links, then such an event may cause the one or more agents to gather data associated with each component of the client system.” [0013]).
Regarding claim 14
Sethi previously discloses the device of claim 1,
Sethi further discloses wherein the current failure comprises: a hardware failure; a firmware failure; a software failure; or a combination thereof (“Each component may have multiple attributes, such as, for example, which port on one component is connected to which port on another component, configuration information, error messages, currently installed firmware version, currently installed software version, hardware version, installation logs, error logs, and the like… If the network link stops functioning, causing network traffic to be re-routed across other network links, then such an event may cause the one or more agents to gather data associated with each component of the client system.” [0013]).
Regarding claim 16
Sethi previously discloses the device of claim 1,
Sethi further discloses wherein: the generative AI process comprises a machine leaning (ML) process (“The console may include a machine learning algorithm to predict one or more recommendations to resolve the issues associated with the client system. For example, the machine learning algorithm may use decision tree learning, association rule learning, artificial neural networks, deep learning, inductive logic programming, support vector machines, clustering, Bayesian networks, representation learning, similarity and metric learning, sparse dictionary learning, rule-based machine learning, learning classifier systems, or the like… To illustrate, the machine learning may use historical data gathered from other client systems to identify a subset of the other client systems with the same (or similar) components, having the same (or similar) network topology, having the same (or similar) diagnostic information, and having the same (or similar) issues. After identifying the subset of other client systems, the machine learning may analyze the corresponding solutions used to resolve the issues with the subset of other client systems and predict which of the corresponding solutions are most likely to resolve the issues of the client system. If the machine learning presents more than one recommended solution, the recommended solutions may be ranked based on a confidence percentage, e.g., the first solution is 95% likely to solve the issues, the second solution is 90% likely to solve the issues, and so on.” [0018]).
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 of this title, 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.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claims 2-6, 9, 11, 15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sethi et al. US Pub 2020/0136928 (hereinafter “Sethi”), in view of Priest US Pub 2022/0342394 (hereinafter “Priest”).
Regarding claim 2
Sethi previously discloses the device of claim 1, wherein:
Sethi further discloses the proposed remediation solution comprises modification of firmware of the network component located at the site of the current failure (“The console 102 may automatically compare the most the most recent diagnostic information (e.g., gathered data 118) with previously gathered and uploaded diagnostic information (e.g., data 120) associated with the client system 102 and display comparison information 132. For example, a recent firmware upgrade of a particular of the components 108 may have introduced one or more issues. The console 102 may determine, based on the comparison 132 of the recently gathered diagnostic information (e.g., the gathered data 118) with previously gathered diagnostic information (e.g., the data 120), that the firmware version of the particular component has changed and highlight this change in the graphical representation 124. The comparison 132 may enable the technician to identify particular attributes of one or more components 108 that have changed recently and may be causing the issues.” [0026]).
Sethi does not specifically teach that the proposed remediation solution comprises construction of a new hardware element configured to interface with a network component located at a site of the current failure, modification of an existing hardware element configured to interface with the network component located at the site of the current failure, modification of firmware of the network component located at the site of the current failure, modification of software of the network component located at the site of the current failure, or any combination thereof.
In an analogous art, Priest discloses the proposed remediation solution comprises construction of a new hardware element configured to interface with a network component located at a site of the current failure (“The equipment can be spare parts, replacement parts, removed parts, and the like.” [0035]), modification of an existing hardware element configured to interface with the network component located at the site of the current failure (“The plurality of operations can include any of inspecting and monitoring a component of the cell tower, performing repair, and installing components of the cell tower.” [0007]).
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Sethi’s method for providing, using machine learning, assistance to technical support, to include Priest’s tethered robot system for cell sites and towers, in order to inspect, install, reconfigure, and repair cellular equipment (Priest [0006]). Thus, a person of ordinary skill would have appreciated the ability to incorporate Priest’s tethered robot system for cell sites and towers into Sethi’s method for providing, using machine learning, assistance to technical support since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 3
Sethi, as modified by Priest, previously discloses the device of claim 2,
Sethi further discloses wherein: in a case that the simulation result indicates at least a partial remediation of the current failure (“Each solution of the one or more solutions may correspond to a previously resolved problem that is similar to the particular problem. The server may display, the one or more solutions. Each solution of the one or more solutions may have an associated confidence level. For example, the confidence level may be determined based on: (i) a similarity of the particular problem of the client system to the previously resolved problem, (ii) a similarity of the plurality of components of the client system to a second plurality of components included in a second client system associated with the previously resolved problem, (iii) a similarity of a network topology of the client system to a second network topology of the second client system associated with the previously resolved problem, or any combination thereof.” [0017]), the operations further comprise facilitating an implementation of the proposed remediation solution (“If the machine learning presents more than one recommended solution, the recommended solutions may be ranked based on a confidence percentage, e.g., the first solution is 95% likely to solve the issues, the second solution is 90% likely to solve the issues, and so on.” [0018]).
Regarding claim 4
Sethi, as modified by Priest, previously discloses the device of claim 3,
Priest further discloses wherein the implementation of the proposed remediation solution comprises: configuring an unmanned aerial vehicle (UAV) (“The UAV 50 may be referred to as a drone or the like. The UAV 50 may be a commercially available UAV platform that has been modified to carry specific electronic components.” [0085]; Fig. 14) with a mechanism to apply the proposed remediation solution (“In other embodiments, the components are flown up to the robot 100 via a UAV 50 (refer to FIG. 14 described below)” [0082]).
Regarding claim 5
Sethi, as modified by Priest, previously discloses the device of claim 4,
Priest further discloses wherein the implementation of the proposed remediation solution further comprises: dispatching the UAV (“FIG. 15 is a schematic diagram of another exemplary robot system 90 configured for inspecting, installing, reconfiguring, and repairing cellular equipment 14 at a cell site 12 in accordance with the present disclosure. In some embodiments, the robot system 90 further includes a UAV 50. In some of these embodiments, the controller 200 is configured to control both the UAV 50 and the robot 100 and to coordinate movements there between.” [0087]), after configuration, to the site of the current failure (“In some embodiments, the UAV 50 is configured to transport the robot 100, such as to a top of the cell tower 12. In some of these embodiments, the UAV 50 includes a tether 56 configured to connect to the robot 100 for lifting the robot 100. In some embodiments, the UAV 50 includes magnets 55 mounted to a base 54 thereof. The magnets 55 are configured to secure the robot 100 to the UAV 50.” [0088]).
Regarding claim 6
Sethi, as modified by Priest, previously discloses the device of claim 5,
Priest further discloses wherein the implementation of the proposed remediation solution further comprises: instructing the UAV to carry out, while at the site of the current failure, remediation of the current failure (“FIG. 15 is a schematic diagram of another exemplary robot system 90 configured for inspecting, installing, reconfiguring, and repairing cellular equipment 14 at a cell site 12 in accordance with the present disclosure. In some embodiments, the robot system 90 further includes a UAV 50. In some of these embodiments, the controller 200 is configured to control both the UAV 50 and the robot 100 and to coordinate movements there between.” [0087]).
Regarding claim 9
Sethi previously discloses the device of claim 1,
Sethi does not specifically teach wherein the wireless communications network comprises: an eNodeB, a gNodeB, a fourth-generation (4G) cellular communications base station; a fifth-generation (5G) cellular communications base station; a subsequent generation cellular communications base station; or any combination thereof.
In an analogous art, Priest discloses wherein the wireless communications network comprises: an eNodeB, a gNodeB, a fourth-generation (4G) cellular communications base station (“components of the cell tower, such as cables, radios, antennas, and the like” [0037]); a fifth-generation (5G) cellular communications base station; a subsequent generation cellular communications base station; or any combination thereof (“Any number of suitable wireless data communication protocols, techniques, or methodologies can be supported by the wireless interfaces 186, including, without limitation: RF; IrDA (infrared); Bluetooth; ZigBee (and other variants of the IEEE 802.15 protocol); IEEE 802.11 (any variation); IEEE 802.16 (WiMAX or any other variation); Direct Sequence Spread Spectrum; Frequency Hopping Spread Spectrum; Long Term Evolution (LTE); cellular/wireless/cordless telecommunication protocols (e.g. 3G/4G, etc.); wireless home network communication protocols; paging network protocols; magnetic induction; satellite data communication protocols; wireless hospital or health care facility network protocols such as those operating in the WMTS bands; GPRS; proprietary wireless data communication protocols such as variants of Wireless USB; and any other protocols for wireless communication.” [0050]).
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Sethi’s method for providing, using machine learning, assistance to technical support, to include Priest’s tethered robot system for cell sites and towers, in order to inspect, install, reconfigure, and repair cellular equipment (Priest [0006]). Thus, a person of ordinary skill would have appreciated the ability to incorporate Priest’s tethered robot system for cell sites and towers into Sethi’s method for providing, using machine learning, assistance to technical support since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 11
Sethi previously discloses the device of claim 10,
Sethi further discloses wherein each of the plurality of past failures is associated with a respective router (“The representative client system 104(P) may include a set of (one or more) components 108(1) to 108(N), such as, for example, network routers, network switches, servers, computers (e.g., laptops, desktops, workstations, and the like), storage devices, and the like.” [0022])
In an analogous art, Priest discloses wherein each of the plurality of past failures is associated with a respective RAN node, a respective cellular base station (“the present disclosure relates to robot systems configured to operate on a cell tower to inspect, install, reconfigure, and repair cellular equipment.” [0002]).
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Sethi’s method for providing, using machine learning, assistance to technical support, to include Priest’s tethered robot system for cell sites and towers, in order to inspect, install, reconfigure, and repair cellular equipment (Priest [0006]). Thus, a person of ordinary skill would have appreciated the ability to incorporate Priest’s tethered robot system for cell sites and towers into Sethi’s method for providing, using machine learning, assistance to technical support since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 15
The device of claim 14, wherein the current failure is associated with a respective RAN node, a respective cellular base station, a respective router, or any combination thereof.
The scope and subject matter of apparatus claim 15 are similar to the apparatus as claimed in claim 11. Therefore apparatus claim 15 corresponds to apparatus claim 11 and is rejected for the same reasons of obviousness as used in claim 11 rejection above.
Regarding claim 17
Sethi discloses adding the sensor data (i.e. “the gathered data 118” in Fig. 1) to a database (i.e. “database 148” in Fig. 1), wherein the database also includes: historic failure data that characterizes a plurality of past failures associated with the wireless communications network, and historic remediation data that characterizes a plurality of past remediation attempts associated with the plurality of past failures (“For example, the machine learning 134 may use the gathered data 118 and a database 148 that includes previously solved problems and the associated solutions to make the recommendations 136. The gathered data 118 may include system configuration information (e.g., components 108, network topology, and the like) associated with the client system 104(P), the attributes 112 associated with each of the components 108 of the client system 102, and diagnostic information (e.g., logs, events, such as restarts, and the like) associated with the client system 102 to predict the recommendations 136 (e.g., recommended solutions). The machine learning 134 may use the database 148 and the historical data 120 gathered from each the client systems 104(1) to 104(P) over a period of time to identify a subset of the data 120 that indicates other client systems 104 having components similar to the components 108, having the same (or similar) network topology, having the same (or similar) diagnostic information, and having similar (or the same) problems. After identifying the subset of the data 120, the machine learning 134 may analyze the similar problems 138 (e.g., associated with the client systems 104) and predict which of corresponding solutions 140 are most likely to resolve the issues of the client system 104(P). If the machine learning 134 presents multiple recommendations 136, each of the recommendations 136 may include a rank or a confidence percentage, e.g., the first solution 140(1) is 95% likely to solve the issues, the second solution is 90% likely to solve the issues, and so on.” [0027]); and
applying the sensor data, the historic failure data, and the historic remediation data to a generative artificial intelligence (AI) process, wherein the generative AI process provides a proposed remediation solution to the current failure, and wherein the proposed remediation solution is distinct from each of the plurality of past remediation attempts (“To illustrate, the machine learning may use historical data gathered from other client systems to identify a subset of the other client systems with the same (or similar) components, having the same (or similar) network topology, having the same (or similar) diagnostic information, and having the same (or similar) issues. After identifying the subset of other client systems, the machine learning may analyze the corresponding solutions used to resolve the issues with the subset of other client systems and predict which of the corresponding solutions are most likely to resolve the issues of the client system. If the machine learning presents more than one recommended solution, the recommended solutions may be ranked based on a confidence percentage, e.g., the first solution is 95% likely to solve the issues, the second solution is 90% likely to solve the issues, and so on.” [0018]).
In an analogous art, Priest discloses 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:
obtaining current failure data that identifies an occurrence of a current failure associated with a wireless communications network (“The plurality of operations can include any of inspecting and monitoring a component of the cell tower, performing repair, and installing components of the cell tower.” [0007]), wherein the current failure data comprises a location of the current failure (i.e. specific cell tower);
responsive to the obtaining of the current failure data, dispatching an unmanned aerial vehicle (UAV) to the location of the current failure (“In other embodiments, the components are flown up to the robot 100 via a UAV 50 (refer to FIG. 14 described below)” [0082]);
obtaining sensor data from the UAV (“The UAV 50 may be referred to as a drone or the like. The UAV 50 may be a commercially available UAV platform that has been modified to carry specific electronic components.” [0085]), wherein the sensor data comprises one or more photos, one or more videos, or a combination thereof (“The UAV 50 also includes an image sensor 53, such as a camera, which is used to take still photographs, video, and the like. Specifically, the image sensor 53 is used to provide a real-time display on a screen for control of the UAV 50.” [0085]);
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Sethi’s method for providing, using machine learning, assistance to technical support, to include Priest’s tethered robot system for cell sites and towers, in order to inspect, install, reconfigure, and repair cellular equipment (Priest [0006]). Thus, a person of ordinary skill would have appreciated the ability to incorporate Priest’s tethered robot system for cell sites and towers into Sethi’s method for providing, using machine learning, assistance to technical support since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 18
Sethi, as modified by Priest, previously discloses the non-transitory machine-readable medium of claim 17, wherein the operations further comprise:
Sethi further discloses running one or more simulations of application of the proposed remediation solution to the current failure, wherein the running of the one or more simulations provides a simulation result, and wherein the simulation result comprises success or failure (“If the machine learning presents more than one recommended solution, the recommended solutions may be ranked based on a confidence percentage, e.g., the first solution is 95% likely to solve the issues, the second solution is 90% likely to solve the issues, and so on.” [0018]); and
outputting the simulation result to a graphical user interface (GUI) (“The console 102 may provide a user interface (UI) 122, such as a graphical user interface (GUI), in which various information associated with one of the client systems 104 may be displayed.” [0024]), a hardcopy printer (“provide various outputs to the user, and may include a keyboard, a touchpad, a mouse, a printer, audio input/output devices, and so forth.” [0047]), or any combination thereof.
Regarding claim 19
Sethi discloses adding, by the processing system (i.e. “information handling system (IHS)” [0011]), the image data (i.e. “the gathered data 118” in Fig. 1) to a database (i.e. “database 148” in Fig. 1), wherein the database also includes: historic failure data that characterizes a plurality of past failures associated with the cellular communications network, and historic remediation data that characterizes a plurality of past remediation attempts associated with the plurality of past failures (“For example, the machine learning 134 may use the gathered data 118 and a database 148 that includes previously solved problems and the associated solutions to make the recommendations 136. The gathered data 118 may include system configuration information (e.g., components 108, network topology, and the like) associated with the client system 104(P), the attributes 112 associated with each of the components 108 of the client system 102, and diagnostic information (e.g., logs, events, such as restarts, and the like) associated with the client system 102 to predict the recommendations 136 (e.g., recommended solutions). The machine learning 134 may use the database 148 and the historical data 120 gathered from each the client systems 104(1) to 104(P) over a period of time to identify a subset of the data 120 that indicates other client systems 104 having components similar to the components 108, having the same (or similar) network topology, having the same (or similar) diagnostic information, and having similar (or the same) problems. After identifying the subset of the data 120, the machine learning 134 may analyze the similar problems 138 (e.g., associated with the client systems 104) and predict which of corresponding solutions 140 are most likely to resolve the issues of the client system 104(P). If the machine learning 134 presents multiple recommendations 136, each of the recommendations 136 may include a rank or a confidence percentage, e.g., the first solution 140(1) is 95% likely to solve the issues, the second solution is 90% likely to solve the issues, and so on.” [0027]);
applying, by the processing system, the image data, the historic failure data, and the historic remediation data to a generative artificial intelligence (AI) process, wherein the generative AI process provides a proposed remediation solution to the second failure, and wherein the proposed remediation solution is distinct from each of the plurality of past remediation attempt s(“To illustrate, the machine learning may use historical data gathered from other client systems to identify a subset of the other client systems with the same (or similar) components, having the same (or similar) network topology, having the same (or similar) diagnostic information, and having the same (or similar) issues. After identifying the subset of other client systems, the machine learning may analyze the corresponding solutions used to resolve the issues with the subset of other client systems and predict which of the corresponding solutions are most likely to resolve the issues of the client system. If the machine learning presents more than one recommended solution, the recommended solutions may be ranked based on a confidence percentage, e.g., the first solution is 95% likely to solve the issues, the second solution is 90% likely to solve the issues, and so on.” [0018]);
In an analogous art, Priest discloses a method (“a method including the steps of positioning a robot on a cell tower to perform a task chosen from a plurality of operations to the cell tower; capturing data associated with components being audited based on the task being performed; and processing the data collected to verify whether the component being audited is in a predetermined condition. The plurality of operations include any of inspecting and monitoring a component of the cell tower, performing repair, and installing components of the cell tower.” [0008]) comprising:
dispatching, by a processing system comprising a processor (“a processor coupled to the wireless interfaces; and memory storing instructions that, when executed, cause the processor to: process commands to position the robot on the cell tower to perform a task chosen from a plurality of operations to the cell tower; process commands to capture data associated with components being audited based on the task being performed; and process the data collected to verify whether the component being audited is in a predetermined condition” [0007]), a first unmanned aerial vehicle (UAV) (“FIG. 15 is a schematic diagram of another exemplary robot system 90 configured for inspecting, installing, reconfiguring, and repairing cellular equipment 14 at a cell site 12 in accordance with the present disclosure. In some embodiments, the robot system 90 further includes a UAV 50. In some of these embodiments, the controller 200 is configured to control both the UAV 50 and the robot 100 and to coordinate movements there between.” [0087]) to a first location (e.g. “cell site 12”) of a first failure associated with a cellular communications network (“The plurality of operations can include any of inspecting and monitoring a component of the cell tower, performing repair, and installing components of the cell tower.” [0007]), wherein the cellular communications network comprises a plurality of base stations (i.e. “cell sites”), and wherein the first location (i.e. “cell site 12”) corresponds to a first one of the plurality of base stations (i.e. “cell sites”);
receiving, by the processing system, image data from the first UAV, wherein the image data comprises one or more photos of the first one of the base stations, one or more videos of the first one of the base stations, or a combination thereof (“The UAV 50 also includes an image sensor 53, such as a camera, which is used to take still photographs, video, and the like. Specifically, the image sensor 53 is used to provide a real-time display on a screen for control of the UAV 50.” [0085]);
obtaining, by the processing system, current failure data that identifies an occurrence of a second failure associated with a second one of the plurality of base stations (“Due to the geographic coverage nature of wireless service, there are hundreds of thousands of cell towers in the United States. For example, in 2014, it was estimated that there were more than 310,000 cell towers in the United States.” [0003]), wherein the current failure data comprises a second location of the second failure, wherein the second location corresponds to the second one of the plurality of base stations (“The plurality of operations can include any of inspecting and monitoring a component of the cell tower, performing repair, and installing components of the cell tower.” [0007]); and wherein the second location is a different location than the first location (i.e. another cell site different from cell site 12); and
dispatching, by the processing system, a second UAV (“The UAV 50 may be referred to as a drone or the like. The UAV 50 may be a commercially available UAV platform that has been modified to carry specific electronic components” [0085]) to the second location of the second failure (i.e. another cell site different from cell site 12), wherein the second UAV is configured to implement the proposed remediation solution (“In some embodiments, the UAV 50 is configured to transport the robot 100, such as to a top of the cell tower 12. In some of these embodiments, the UAV 50 includes a tether 56 configured to connect to the robot 100 for lifting the robot 100. In some embodiments, the UAV 50 includes magnets 55 mounted to a base 54 thereof. The magnets 55 are configured to secure the robot 100 to the UAV 50.” [0088]).
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Sethi’s method for providing, using machine learning, assistance to technical support, to include Priest’s tethered robot system for cell sites and towers, in order to inspect, install, reconfigure, and repair cellular equipment (Priest [0006]). Thus, a person of ordinary skill would have appreciated the ability to incorporate Priest’s tethered robot system for cell sites and towers into Sethi’s method for providing, using machine learning, assistance to technical support since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claim 20
Sethi, as modified by Priest, previously discloses the method of claim 19, wherein:
Priest further discloses the first UAV is a same UAV as the second UAV (“The UAV 50 may be referred to as a drone or the like. The UAV 50 may be a commercially available UAV platform that has been modified to carry specific electronic components” [0085]);
the cellular communications network comprises: an eNodeB, a gNodeB, a fourth- generation (4G) cellular communications base station; a fifth-generation (5G) cellular communications base station; a subsequent generation cellular communications base station (“components of the cell tower, such as cables, radios, antennas, and the like” [0037]); or any combination thereof (“Any number of suitable wireless data communication protocols, techniques, or methodologies can be supported by the wireless interfaces 186, including, without limitation: RF; IrDA (infrared); Bluetooth; ZigBee (and other variants of the IEEE 802.15 protocol); IEEE 802.11 (any variation); IEEE 802.16 (WiMAX or any other variation); Direct Sequence Spread Spectrum; Frequency Hopping Spread Spectrum; Long Term Evolution (LTE); cellular/wireless/cordless telecommunication protocols (e.g. 3G/4G, etc.); wireless home network communication protocols; paging network protocols; magnetic induction; satellite data communication protocols; wireless hospital or health care facility network protocols such as those operating in the WMTS bands; GPRS; proprietary wireless data communication protocols such as variants of Wireless USB; and any other protocols for wireless communication.” [0050]); and
the proposed remediation solution comprises construction of a new hardware element configured to interface with a network component located at the second location (“The plurality of operations can include any of inspecting and monitoring a component of the cell tower, performing repair, and installing components of the cell tower.” [0007]), modification of an existing hardware element configured to interface with the network component located at the second location (“The equipment can be spare parts, replacement parts, removed parts, and the like.” [0035]),
Sethi further discloses the proposed remediation solution comprises modification of firmware of the network component located at the second location (“The console 102 may automatically compare the most the most recent diagnostic information (e.g., gathered data 118) with previously gathered and uploaded diagnostic information (e.g., data 120) associated with the client system 102 and display comparison information 132. For example, a recent firmware upgrade of a particular of the components 108 may have introduced one or more issues. The console 102 may determine, based on the comparison 132 of the recently gathered diagnostic information (e.g., the gathered data 118) with previously gathered diagnostic information (e.g., the data 120), that the firmware version of the particular component has changed and highlight this change in the graphical representation 124. The comparison 132 may enable the technician to identify particular attributes of one or more components 108 that have changed recently and may be causing the issues.” [0026]).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Sethi, in view of Priest, and further in view of Ghosh et al. US Pub 2020/0097921 (hereinafter “Ghosh”).
Regarding claim 7
Sethi, as modified by Priest, previously discloses the device of claim 3,
Sethi and Priest do not specifically teach wherein the at least partial remediation comprises: remediation within a threshold amount of a total remediation.
In an analogous art, Ghosh discloses wherein the at least partial remediation comprises: remediation within a threshold amount of a total remediation (“At 1812, the computing device may determine whether a probability of success of the one or more repair actions is below a threshold probability. For instance, in some cases, if the likelihood of success is low, a repair action may not be provided in response to the repair request. According, if the probability of success does not exceed the threshold, the process may go to block 1814; alternatively, if the probability of success does exceed the threshold the process may go to block 1816.” [0228]).
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Sethi’s method for providing, using machine learning, assistance to technical support, as modified by Priest, to include Ghosh’s machine learning model trained to predict individual levels of the repair hierarchy, in order to determine at least one repair action based on the received repair request (Ghosh [Abstract]). Thus, a person of ordinary skill would have appreciated the ability to incorporate Ghosh’s machine learning model trained to predict individual levels of the repair hierarchy into Sethi’s method for providing, using machine learning, assistance to technical support since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Sethi, in view of Priest, and further in view of McReynolds et al. US Patent 11750479 (hereinafter “McReynolds”).
Regarding claim 8
Sethi, as modified by Priest, previously discloses the device of claim 3,
Sethi and Priest do not specifically teach wherein the at least partial remediation comprises: a total remediation.
In an analogous art, McReynolds discloses wherein the at least partial remediation comprises: a total remediation (“To remediate the ticket, the support engineer may assist in the implementation of remediation processes from mitigations 212. Mitigations 212 may be implemented with a data structure that provides remediation processes for attempts to resolve tickets. Mitigations 212 may rank order the potential remediation processes based on pain mitigation efficiency score (PMES). The PMES may take into account both a quantity of time to complete a potential remediation process as well as the likely reduction in pain level provided by the potential remediation process. PMES for potential remediation processes may be based on previously performed remediation processes.” Col. 7, lines 50-62 and furthermore “At operation 308, remediation of the customer encountered issues is documented based on changes in customer pain level during each attempted remediation and timeliness of each completed remediation.” Col. 13, lines 8-12).
Before the effective filling date of the claimed invention, it would have been obvious to one of ordinary skill in the art to modify Sethi’s method for providing, using machine learning, assistance to technical support, as modified by Priest, to include McReynolds’ method for managing customer encountered issue resolution, in order to identify whether performed remediation processes for resolving the customer encountered issues were successful, and to what extent they were successful (McReynolds [Abstract]). Thus, a person of ordinary skill would have appreciated the ability to incorporate McReynolds’ method for managing customer encountered issue resolution into Sethi’s method for providing, using machine learning, assistance to technical support since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHUONG M NGUYEN whose telephone number is (571)272-8184. The examiner can normally be reached M-F 10:00am - 6:30pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Derrick Ferris can be reached at 571-272-3123. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CHUONG M NGUYEN/Primary Examiner, Art Unit 2411
Prosecution Insights